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Author SHA1 Message Date
Willem Jiang af6e48ccaa fix(i18n): add Chinese translations for account settings page (#2712)
The account settings page had all user-facing strings (profile labels,
  password form placeholders, validation messages, button text) hardcoded
  in English. Replace them with i18n translation keys so the page renders
  correctly when the locale is set to Chinese.

 Fixed #2710
2026-05-04 11:15:16 +08:00
Willem Jiang b10eb7bafc feat(github): Added container push workflow (#2709)
* feat(github):Added container push workflow

* Apply suggestions from code review

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
2026-05-04 11:14:34 +08:00
YuJitang d02f762ab0 feat: refine token usage display modes (#2329)
* feat: refine token usage display modes

* docs: clarify token usage accounting semantics

* fix: avoid duplicate subtask debug keys

* style: format token usage tests

* chore: address token attribution review feedback

* Update test_token_usage_middleware.py

* Update test_token_usage_middleware.py

* chore: simplify token attribution fallback

* fix token usage metadata follow-up handling

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-05-04 09:56:16 +08:00
Willem Jiang 82e7936d36 fix(docker): set UTF-8 locale to prevent ASCII encoding errors in minimal containers (#2707)
* fix(docker): set UTF-8 locale to prevent ASCII encoding errors in minimal containers

* Potential fix for pull request finding

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
2026-05-04 09:41:10 +08:00
Nan Gao 222a7773cb fix(frontend): avoid misleading error message when agent api is disable (#2697) (#2698) 2026-05-04 09:38:05 +08:00
Nan Gao f80ac961ec fix(harness): restore legacy skills path fallback (#2694) (#2696)
* fix(harness): restore legacy skills path fallback (#2694)

* fix(format): make format

* Potential fix for pull request finding

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
2026-05-03 23:40:59 +08:00
wanxsb 44ab21fc44 feat(community): add Serper web search provider (#2630)
* feat(community): add Serper web search provider

Add a new community search provider backed by the Serper Google Search
API (https://serper.dev). Serper returns real-time Google results via a
simple JSON API and requires only an API key — no extra Python package.

Changes:
- backend/packages/harness/deerflow/community/serper/__init__.py
- backend/packages/harness/deerflow/community/serper/tools.py
  Implements web_search_tool using httpx (already a project dependency).
  API key is read from config.yaml `api_key` field or SERPER_API_KEY env var.
  Follows the same interface / output shape as the existing ddg_search provider.
  Exposes max_results parameter (default 5) with config override logic.
- backend/tests/test_serper_tools.py
  Unit tests covering API key resolution, config overrides, HTTP errors,
  empty results, and parameter passing.
- config.example.yaml: add commented-out Serper example alongside other providers
- .env.example: add SERPER_API_KEY placeholder

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* Fix the lint error

* Fix the lint error

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-05-02 16:22:35 +08:00
Hinotobi e543bbf5d6 [security] fix(upload): reject symlinked upload destinations (#2623)
* fix: reject symlinked upload destinations

* test: harden upload destination checks

* fix: address PR feedback for #2623

* test: cover safe upload re-uploads

* fix: preserve upload limit checks after rebase

* fix(upload): stream safe HTTP upload writes
2026-05-02 15:19:28 +08:00
Xinmin Zeng ca3332f8bf fix(gateway): return ISO 8601 timestamps from threads endpoints (#2599)
* fix(gateway): return ISO 8601 timestamps from threads endpoints (#2594)

ThreadResponse documents created_at / updated_at as ISO timestamps,
matching the LangGraph Platform schema (langgraph_sdk.schema.Thread
exposes them as datetime, JSON-encoded as ISO 8601). The gateway
threads router was instead emitting str(time.time()) — unix-second
floats — breaking frontend new Date() parsing and producing a mixed
ISO/unix wire format that also corrupted the search sort order.

Centralize timestamp generation in deerflow.utils.time:
- now_iso()       — datetime.now(UTC).isoformat()
- coerce_iso(x)   — heals legacy unix-timestamp strings on read so the
                    store converges to ISO without a one-shot migration

threads.py: replace 6 time.time() call sites with now_iso(); wrap all
read paths and Phase-2 checkpoint metadata with coerce_iso(); _store_upsert
opportunistically heals legacy created_at on update; drop unused time import.

thread_runs.py: reuse now_iso() instead of a private duplicate _now_iso(),
preventing future drift between the two timestamp call sites.

Tests: 9 unit tests for the helper; 5 integration tests pinning the ISO
contract for create/get/patch/search and the legacy-healing path on the
internal store upsert. Full suite: 2144 passed, 15 skipped, 0 failed.

Closes #2594

* fix(gateway): coerce checkpoint metadata timestamps to ISO on read

After the merge with main, three additional read paths in ``threads.py``
were still emitting raw ``str(metadata.get("created_at", ""))`` —
``get_thread_state``, ``update_thread_state``, and ``get_thread_history``.

Same root cause as #2594: when the checkpoint metadata's ``created_at``
is a unix-second float (legacy data, or a checkpoint written by an older
Gateway version), ``str(float)`` produces ``"1777252410.411327"`` and the
frontend's ``new Date(...)`` returns ``Invalid Date``. The fix on the
``/threads/{id}`` GET path was already in place; these three sibling
endpoints needed the same treatment.

All four call sites now flow through ``coerce_iso``, so:
- legacy float metadata heals to ISO on the way out,
- ISO metadata passes through unchanged,
- ``datetime`` instances (which the new ``coerce_iso`` branch handles
  explicitly) emit with the ``T`` separator instead of falling through
  to the space-separated ``str(datetime)`` form.

Coverage added for the two endpoints not already pinned by the merge:
- ``test_get_thread_state_returns_iso_for_legacy_checkpoint_metadata``
- ``test_get_thread_history_returns_iso_for_legacy_checkpoint_metadata``

Both pre-seed a checkpoint whose metadata carries the literal float
from the issue body and assert the wire format is ISO.
2026-05-02 15:16:16 +08:00
Willem Jiang bb8b234d85 chroe(2585): keep polishing the code of codex token usage (#2689) 2026-05-02 15:04:11 +08:00
KiteEater 17447fccbe fix(runtime): make rollback restore checkpoint supersede newer checkpoints (#2582)
* Restore rollback checkpoints with fresh ids

* Tighten rollback checkpoint tests and imports

* Update test_run_worker_rollback.py

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-05-02 11:25:45 +08:00
KiteEater 866d1ca409 Populate Codex usage metadata for token accounting (#2585) 2026-05-02 11:16:03 +08:00
greatmengqi 8ba01dfd83 refactor: thread app_config through lead and subagent task path (#2666)
* refactor: thread app config through lead prompt

* fix: honor explicit app config across runtime paths

* style: format subagent executor tests

* fix: thread resolved app config and guard subagents-only fallback

Address two PR review findings:

1. _create_summarization_middleware passed the original (possibly None)
   app_config into create_chat_model, forcing the model factory back to
   ambient get_app_config() and risking config drift between the
   middleware's resolved view and the model's view. Pass the resolved
   AppConfig instance through end-to-end.

2. get_available_subagent_names accepted Any-typed config and forwarded
   it to is_host_bash_allowed, which reads ``.sandbox``. A
   SubagentsAppConfig (also accepted upstream as a sum-type input) has
   no ``.sandbox`` attribute and would be silently treated as "no
   sandbox configured", incorrectly disabling the bash subagent. Guard
   on hasattr and fall back to ambient lookup otherwise.

Adds regression tests for both paths.

* chore: simplify hasattr guard and tighten regression tests

- Collapse if/else into ternary in get_available_subagent_names; hasattr(None, ...) is False so the explicit None check was redundant.
- Drop comments that narrate the change rather than explain non-obvious WHY (test names already convey intent).
- Replace stringly-typed sentinel "no-arg" in regression test with direct args tuple comparison.

---------

Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
2026-05-02 06:37:49 +08:00
Willem Jiang 189b82405c fix(sandbox): pass no_change_timeout to exec_command to prevent 120s premature termination (#2685)
* fix(sandbox): pass no_change_timeout to exec_command to prevent 120s premature termination

  The agent_sandbox library's shell API defaults no_change_timeout to 120
  seconds. When AioSandbox.execute_command() called exec_command() without
  this parameter, commands producing no output for 120s would return with
  NO_CHANGE_TIMEOUT status even though the script was still running.

  Pass no_change_timeout=600 to all exec_command calls (matching the
  client-level HTTP timeout) so long-running commands are not cut short.

  Fixes #2668

* test(sandbox): add assertions for no_change_timeout in execute_command and list_dir

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/2f37bc72-0826-4443-a6ba-e5b78c22fb5a

Co-authored-by: WillemJiang <219644+WillemJiang@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
2026-05-01 22:27:02 +08:00
Nan Gao 487c1d939f fix(subagents): use model override for tools and middleware (#2641)
* fix(subagents): use model override for tools and middleware

* fix(config): resolve effective subagent model

* fix(subagents): defer app config loading

* fix(subagents): fully defer config.yaml load in executor __init__

The previous attempt only relocated the explicit get_app_config() call,
but left resolve_subagent_model_name(...) running eagerly in __init__.
That helper has its own internal get_app_config() fallback, which still
fired when both app_config and parent_model were None and
config.model == "inherit" — exactly the path unit tests hit, breaking
21 tests in CI with FileNotFoundError: config.yaml.

Skip the eager resolve in __init__ when it would require loading the
config file, and defer to _create_agent (which already has the
app_config or get_app_config() fallback).
2026-05-01 22:21:10 +08:00
Nan Gao c09c334544 fix(harness): resolve runtime paths from project root (#2642)
* fix(harness): resolve runtime paths from project root

* docs(config): update

* fix(config): address runtime path review feedback

* test(config): fix skills path e2e root

* test(config): cover legacy config fallback when project root lacks config files

Verifies that when DEER_FLOW_PROJECT_ROOT is unset and cwd has no
config.yaml/extensions_config.json, AppConfig and ExtensionsConfig fall back
to the legacy backend/repo-root candidates — the backward-compat path
requested in PR #2642 review.

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-05-01 22:19:50 +08:00
KiteEater 8939ccaed2 fix(uploads): enforce streaming upload limits in gateway (#2589)
* fix: enforce gateway upload limits

* fix: acquire sandbox before upload writes

* Fix upload limit config wiring

* Sanitize upload size error filenames

* test: call upload routes unwrapped

* fix: guard upload limits endpoint

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-05-01 20:19:30 +08:00
JerryLee 83938cf35a fix(subagents): propagate user context across threaded execution (#2676) 2026-05-01 16:27:18 +08:00
yangzheli 78633c69ac fix(agents): propagate agent_name into ToolRuntime.context for setup_agent (#2679)
* fix(agents): propagate agent_name into ToolRuntime.context for setup_agent (#2677)

When creating a custom agent via the web UI, SOUL.md was always written
to the global base_dir/SOUL.md instead of agents/<name>/SOUL.md.

Root cause: the bootstrap flow sends agent_name via body.context, but
two layers were broken:

1. services.py only forwarded body.context keys into config["configurable"];
   config["context"] was never populated.
2. worker.py constructed the parent Runtime with a hard-coded
   {thread_id, run_id} context, ignoring config["context"] entirely.

After the langgraph >= 1.1.9 bump (#98a5b34f), ToolRuntime.context no
longer falls back to configurable, so setup_agent's
runtime.context.get("agent_name") returned None and the tool's silent
agent_name=None -> base_dir fallback kicked in, overwriting the global
SOUL.md.

Fix:
- services.py: extract merge_run_context_overrides() and write the
  whitelisted context keys into both configurable (legacy readers) and
  context (langgraph 1.1+ ToolRuntime consumers).
- worker.py: extract _build_runtime_context() and merge config["context"]
  into the Runtime's context (without letting callers override
  thread_id/run_id).

The base_dir fallback in setup_agent_tool.py is left in place because
the IM /bootstrap channel command depends on it. That code path can
be tightened in a follow-up.

Adds regression tests covering both helpers.

* Apply suggestions from code review

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
2026-05-01 16:00:11 +08:00
greatmengqi 8b61c94e1d fix: keep lead agent graph factory signature compatible (#2678)
Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
2026-05-01 15:43:28 +08:00
Xun 1ad1420e31 refactor(skills): Unified skill storage capability (#2613) 2026-05-01 13:23:26 +08:00
He Wang eba3b9e18d fix(config): unify log_level from config.yaml across Gateway and debug entry points (#2601)
Centralize log level parsing in `logging_level_from_config()` and
application in `apply_logging_level()` within `deerflow.config.app_config`.

- Gateway lifespan applies configured log level on startup
- `debug.py` uses shared helpers instead of local duplicates
- `apply_logging_level()` targets only `deerflow`/`app` logger hierarchies
  so third-party library verbosity is not affected; root handler levels
  are only lowered (never raised) to allow configured loggers through
  without suppressing third-party output; root logger level is not modified
- Config field description updated to clarify scope
- Tests save/restore global logging state to avoid test pollution

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
2026-04-30 22:27:14 +08:00
Willem Jiang c0da278269 fix(memory): replace short-lived asyncio.run() with persistent event loop (#2627)
* fix(memory): replace short-lived asyncio.run() with persistent event loop to prevent zombie httpx connections

  The memory updater used asyncio.run() inside daemon threads, creating
  and destroying short-lived event loops on every update. Langchain
  providers (e.g. langchain-anthropic) cache httpx AsyncClient instances
  globally via @lru_cache, so SSL connections created on a loop that is
  subsequently destroyed become zombie connections in the shared pool.
  When the main agent's lead run later reuses one of these connections,
  httpx/anyio triggers RuntimeError: Event loop is closed during
  connection cleanup.

  Replace the ThreadPoolExecutor + asyncio.run() pattern with a
  _MemoryLoopRunner that maintains a single persistent event loop in a
  daemon thread for the process lifetime. Since the loop never closes,
  connections bound to it never become invalid. The _run_async_update_sync
  function now submits coroutines to this persistent loop via
  run_coroutine_threadsafe instead of creating throwaway loops.

* update the code to address the review comments

* Fix the review comments of 2615

 P1 — user_id forwarded through sync path: Added user_id parameter to _prepare_update_prompt, _finalize_update, and _do_update_memory_sync, and forwarded it to get_memory_data(agent_name, user_id=user_id) and
  save(..., user_id=user_id). The update_memory() entry point now passes user_id through both the executor.submit path and the direct call path. Added TestUserIdForwarding with two regression tests (sync + async)
   verifying get_memory_data and save receive the correct user_id.

  P2 — aupdate_memory() delegates to sync: Replaced the model.ainvoke() call with asyncio.to_thread(self._do_update_memory_sync, ...). This eliminates the unsafe async provider client path entirely — all memory
  updater entry points now use the isolated sync model.invoke() path. Updated the test from asserting ainvoke is awaited to asserting invoke is called and ainvoke is not.

  Nit — duplicate comment removed: Removed the duplicated # Matches sentences... comment on line 230.

* Chore(test): update the code of test_memory_updater

---------

Co-authored-by: rayhpeng <rayhpeng@gmail.com>
2026-04-30 17:59:57 +08:00
KiteEater 7dea1666ce fix: avoid temporary event loops in async subagent execution (#2414)
* fix: avoid temporary event loops in async subagent execution

* Rename isolated subagent loop globals

* Harden isolated subagent loop shutdown and logging

* Sort subagent executor imports

* Format subagent executor

* Remove isolated loop pool from subagent executor

* Format subagent executor cleanup

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-04-30 15:29:17 +08:00
Chincherry93 88d47f677f fix(nginx): add catch-all /api/ location for auth routes (#2657)
The recent refactor (7bf618de) removed the langgraph upstream and added
individual /api/* prefix locations for models, memory, mcp, skills,
agents, threads, and sandboxes. However, /api/v1/auth/* routes (login,
register, setup-status, etc.) were not covered by any explicit location
block, causing them to fall through to the frontend catch-all.

In Docker production builds, Next.js rewrites are baked at build time
with http://127.0.0.1:8001 (the gateway is unreachable from the
frontend container's localhost), resulting in ECONNREFUSED errors for
all auth operations.

Adding a catch-all `location /api/` block after the more specific
prefix/regex locations ensures all gateway API routes are properly
proxied. nginx's location matching priority means the more specific
locations above still take precedence for /api/langgraph/, /api/models,
/api/memory, /api/mcp, /api/skills, /api/agents, /api/threads/*, and
/api/sandboxes.

Co-authored-by: Chincherry93 <Chincherry93@users.noreply.github.com>
2026-04-30 15:21:22 +08:00
greatmengqi 38714b6ceb refactor: thread app_config through middleware factories (#2652)
* refactor: thread app_config through middleware factories

Continues the incremental config-refactor sequence (#2611 root, #2612 lead
path) one layer deeper into the middleware factories. Two ambient lookups
inside _build_runtime_middlewares are eliminated and the LLMErrorHandling
band-aid removed:

- _build_runtime_middlewares / build_lead_runtime_middlewares /
  build_subagent_runtime_middlewares now require app_config: AppConfig.
- get_guardrails_config() inside the factory is replaced with
  app_config.guardrails (semantically identical — same default-factory
  GuardrailsConfig — verified by direct equality check).
- LLMErrorHandlingMiddleware.__init__ now requires app_config and reads
  circuit_breaker fields directly. The class-level
  circuit_failure_threshold / circuit_recovery_timeout_sec defaults are
  removed along with the try/except (FileNotFoundError, RuntimeError):
  pass band-aid — the let-it-crash invariant the rest of the refactor
  enforces.

Caller chain (already-resolved app_config sources):
- _build_middlewares in lead_agent/agent.py: reorder so
  resolved_app_config = app_config or get_app_config() is computed BEFORE
  build_lead_runtime_middlewares is called, then passed as kwarg.
- SubagentExecutor: optional app_config parameter (mirrors the lead-agent
  pattern); _create_agent does the same `or get_app_config()` fallback at
  agent-build time, so task_tool callers don't need to plumb app_config
  through yet (typed-context plumbing for tool runtimes is a separate
  refactor).

Tests:
- test_llm_error_handling_middleware: _make_app_config helper using
  AppConfig(sandbox=SandboxConfig(use="test")) — same minimal-config
  pattern conftest already uses. Three direct LLMErrorHandlingMiddleware()
  calls each followed by post-construction circuit_breaker mutation fold
  cleanly into _build_middleware(circuit_failure_threshold=...,
  circuit_recovery_timeout_sec=...).

Verification:
- tests/test_llm_error_handling_middleware.py — 14 passed
- tests/test_subagent_executor.py — 28 passed
- tests/test_tool_error_handling_middleware.py — 6 passed
- tests/test_task_tool_core_logic.py — 18 passed (verifies task_tool
  unchanged behavior)
- Full suite: 2697 passed, 3 skipped. The single intermittent failure in
  tests/test_client_e2e.py::test_tool_call_produces_events is pre-existing
  LLM flakiness (the test asserts the model decided to call a tool;
  reproduces 1/3 on unchanged main as well).

* fix: address middleware app config review comments

* fix: satisfy app config annotation lint

* test: cover explicit app config middleware wiring

---------

Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
2026-04-30 12:41:09 +08:00
Hinotobi 74081a85a6 [security] fix(sandbox): bind local Docker ports to loopback (#2633)
* fix(sandbox): bind local Docker ports to loopback

* fix(sandbox): preserve IPv6 loopback Docker binds

* fix(sandbox): log Docker bind host selection
2026-04-30 11:40:28 +08:00
Jsonz 24a5a00679 fix: avoid duplicate call to extractReasoningContentFromMessage (#2661)
In convertToSteps(), the extractReasoningContentFromMessage function was
called twice for the same message - once to check if reasoning exists and
again to assign it to the step object. Reuse the already-extracted value
from the local variable instead.
2026-04-30 11:33:49 +08:00
He Wang 08afdcb907 feat(channels): add DingTalk channel integration (#2628)
* feat(channels): add DingTalk channel integration

Add a new DingTalk messaging channel using the dingtalk-stream SDK
with Stream Push (WebSocket), requiring no public IP. Supports both
plain sampleMarkdown replies and optional AI Card streaming for a
typewriter effect when card_template_id is configured.

- Add DingTalkChannel implementation with token management, message
  routing, allowed_users filtering, and markdown adaptation
- Register dingtalk in channel service registry and capability map
- Propagate inbound metadata to outbound messages in ChannelManager
  for DingTalk sender context (sender_staff_id, conversation_type)
- Add dingtalk-stream dependency to pyproject.toml
- Add configuration examples in config.example.yaml and .env.example
- Update all README translations with setup instructions
- Add comprehensive test suite (test_dingtalk_channel.py) and
  metadata propagation test in test_channels.py
- Update backend CLAUDE.md to document DingTalk channel

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(channels): address PR review feedback for DingTalk integration

- Replace runtime mutation of CHANNEL_CAPABILITIES with a
  `supports_streaming` property on the Channel base class, overridden
  by DingTalkChannel, FeishuChannel, and WeComChannel
- Store stream client reference and attempt graceful disconnect in
  stop(); guard _on_chatbot_message with _running check to prevent
  post-stop message processing
- Use msg.chat_id as the primary routing key in send/send_file via
  a shared _resolve_routing helper, with metadata as fallback
- Fix process() return type annotation from tuple[str, str] to
  tuple[int, str] to match AckMessage.STATUS_OK
- Protect _incoming_messages with threading.Lock for cross-thread
  safety between the Stream Push thread and the asyncio loop
- Re-add Docker Compose URL guidance removed during DingTalk setup
  docs addition in README.md
- Fix incomplete sentence in README_zh.md (missing verb "启用")

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(docs): restore plain paragraph format for Docker Compose note

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(channels): fix isinstance TypeError and add file size guard in DingTalk channel

Use tuple syntax for isinstance() type check to avoid runtime TypeError
with PEP 604 union types. Add upload size limit (20MB) before reading
files into memory. Narrow exception handlers to specific types.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(channels): propagate markdown fallback errors and validate access token response

- Re-raise exceptions in _send_markdown_fallback to prevent partial
  deliveries (files sent without accompanying text)
- Validate _get_access_token response: reject non-dict bodies, empty
  tokens, and coerce invalid expireIn to a safe default
- Add tests for both fixes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(channels): validate upload response and broaden send_file exception handling

- Validate _upload_media JSON response: handle JSONDecodeError and
  non-dict payloads gracefully by returning None
- Broaden send_file exception tuple to include TypeError and
  AttributeError for unexpected JSON shapes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(channels): fix streaming race on channel registration and slim outbound metadata

- Register channel in service before calling start() to avoid race
  where background receiver publishes inbound before registration,
  causing manager to fall back to static CHANNEL_CAPABILITIES
- Strip known-large metadata keys (raw_message, ref_msg) from outbound
  messages to prevent memory bloat from propagated inbound payloads

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* Update service.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update CLAUDE.md

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
2026-04-30 11:25:33 +08:00
sunsine 0691c4dda3 fix(security): allow disabling API docs in production via GATEWAY_ENABLE_DOCS (#2651)
* fix(security): allow disabling API docs in production via GATEWAY_ENABLE_DOCS

Expose /docs, /redoc, and /openapi.json only when GATEWAY_ENABLE_DOCS=true
(default). Setting GATEWAY_ENABLE_DOCS=false disables all three endpoints,
preventing unauthorized API surface discovery in production deployments.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* test(security): add unit tests and docs for GATEWAY_ENABLE_DOCS

Add 7 tests covering default behavior, env var parsing (case-insensitive,
fail-closed), endpoint visibility, and health endpoint independence.
Update CONFIGURATION.md and CLAUDE.md with the new toggle.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* style(security): apply ruff formatting to gateway app.py

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-04-30 10:58:32 +08:00
yangzheli f7b10d42e4 fix(frontend): create thread on first submit in new-agent page (#2656)
The new-agent page pre-generates a thread UUID and passed it directly
to useThreadStream, which made the LangGraph SDK POST to
/threads/{uuid}/runs/stream against a thread the backend had never
created. After PR #2566 introduced multi-tenant owner checks on the
runs endpoints, that request now 404s with "Thread not found".

Pass threadId: undefined to useThreadStream so the SDK takes the
create-then-run path. The pre-generated UUID is still forwarded via
SubmitOptions.threadId in sendMessage, so the new thread is created
with that exact id and onCreated rebinds the hook to it.

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
2026-04-30 06:41:54 +08:00
Willem Jiang 4a9f1d547b Merge pull request #2566 from bytedance/release/2.0-rc
introduces a complete authentication/authorization system, SQL persistence layer, run event history, user data isolation, and extensive documentation in both English and Chinese. The core additions are:

Auth system: JWT-based auth with local email/password provider, CSRF protection, rate limiting
Persistence layer: SQLAlchemy 2.0 async ORM with SQLite backend (users, threads, runs, events, feedback)
User isolation: Per-user data scoping via contextvars sentinel pattern
Frontend: Login/setup pages, AuthProvider, CSRF-aware fetcher
Documentation: Comprehensive EN/ZH docs for harness, application, tutorials
2026-04-28 21:56:35 +08:00
Willem Jiang 11afd32459 Fix the log Injection error of skills.py 2026-04-28 21:42:38 +08:00
Willem Jiang 64f4dc1639 fixed the CI build errors 2026-04-28 19:01:36 +08:00
Willem Jiang 844ad8e528 Merge branch 'main' into release/2.0-rc 2026-04-28 15:44:02 +08:00
pyp0327 395c14357b chore(adpator):Adapt MindIE engine model and improve testing and fixes (#2523)
* feat(models): 适配 MindIE引擎的模型

* test: add unit tests for MindIEChatModel adapter and fix PR review comments

* chore: update uv.lock with pytest-asyncio

* build: add pytest-asyncio to test dependencies

* fix: address PR review comments (lazy import, cache clients, safe newline escape, strict xml regex)

* fix(mindie): preserve string args without JSON quotes in XML tool call serialization

* fix(mindie): preserve string args without JSON quotes in XML tool call serialization

* test_mindie_provider:format

* Potential fix for pull request finding

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

* fix(mindie): prevent nested tool_call params from leaking into outer args

* fixed by escaping XML entities in _fix_messages and unescaping during parse, with regression tests added.

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
2026-04-28 15:09:31 +08:00
greatmengqi e82940c03d refactor: thread release config through lead path (#2612)
Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
2026-04-28 14:53:18 +08:00
DanielWalnut 6bd88fe14c fix(sandbox): block host bash traversal escapes (#2560)
* fix(sandbox): block host bash traversal escapes

Fixes #2535

* fix(sandbox): harden local bash path guards

* fix(sandbox): avoid bash cd argument false positives

* Fix the lint error

Add function to resolve and validate user data path.

* Fix the lint error

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-04-28 12:18:41 +08:00
DanielWalnut 39c5da94f3 fix(sandbox): prevent local custom mount symlink escapes (#2558)
* fix(sandbox): prevent local custom mount symlink escapes

Fixes #2506

* fix(sandbox): harden custom mount symlink handling

* fix(sandbox): format internal symlink directory listings
2026-04-28 11:59:46 +08:00
DanielWalnut 707ed328dd fix(skills): scan skill archives before install (#2561)
* fix(skills): scan skill archives before install

Fixes #2536

* fix(skills): scan archive support files before install

* style(skills): format archive installer

* fix(skills): address archive install review comments
2026-04-28 11:56:11 +08:00
DanielWalnut f7dfb88a30 fix(aio-sandbox): redact env values in container logs (#2562)
* fix(aio-sandbox): redact env values in container logs

Fixes #2534

* fix(aio-sandbox): address env log review comments
2026-04-28 11:47:56 +08:00
Willem Jiang 69649d8aae Fix the issues when reviewing 2566 persistant part (#2604)
* Fix the code review command of journal & event store P0,P1 issues

* Fix the code review command of journal & event store P2 issues

* Apply suggestions from code review

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update backend/packages/harness/deerflow/runtime/journal.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Refactor logger debug message formatting

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
2026-04-28 11:44:40 +08:00
Willem Jiang 4e4e4f92a0 fix(security): harden auth system and fix run journal logic bug (#2593)
* fix(security): harden auth system and fix run journal logic bug

  - Fix inverted condition in RunJournal.on_chat_model_start that prevented
    first human message capture (not messages → messages)
  - Pre-hash passwords with SHA-256 before bcrypt to avoid silent 72-byte
    truncation vulnerability
  - Move load_dotenv() from module scope into get_auth_config() to prevent
    import-time os.environ mutation breaking test isolation
  - Return generic ‘Invalid token’ instead of exposing specific error
    variants (expired, malformed, invalid_signature) to clients
  - Make @require_auth independently enforce 401 instead of silently
    passing through when AuthMiddleware is absent
  - Rate-limit /setup-status endpoint with per-IP cooldown to mitigate
    initialization-state information leak
  - Document in-process rate limiter limitation for multi-worker deployments

* fix(security): return 429+Retry-After on setup-status rate limit, bound cooldown dict

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/070d0be8-99a5-46c8-85bb-6b81b5284021

Co-authored-by: WillemJiang <219644+WillemJiang@users.noreply.github.com>

* fix(security): add versioned password hashes with auto-migration on login

  The SHA-256 pre-hash change silently broke verification for any existing
  bcrypt-only password hashes. Introduce a <N>$ prefix scheme so hashes
  are self-describing:

  - v2 (current): bcrypt(b64(sha256(password))) with $ prefix
  - v1 (legacy): plain bcrypt, prefixed $ or bare (no prefix)

  verify_password auto-detects the version and falls back to v1 for older
  hashes. LocalAuthProvider.authenticate() now rehashes legacy hashes to v2
  on successful login via needs_rehash(), so existing users upgrade
  transparently without a dedicated migration step.

* fix(auth): harden verify_password, best-effort rehash, update require_auth docstring, downgrade journal logging

- password.py: wrap bcrypt.checkpw in try/except → return False for malformed/corrupt hashes instead of crashing
- local_provider.py: wrap auto-rehash update_user() in try/except so transient DB errors don't fail valid logins
- authz.py: update require_auth docstring to reflect independent 401 enforcement
- journal.py: downgrade on_chat_model_start from INFO to DEBUG, log only metadata (batch_count, message_counts) instead of full serialized/messages content

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/48c5cf31-a4ab-418a-982a-6343c37bb299

Co-authored-by: WillemJiang <219644+WillemJiang@users.noreply.github.com>

* fix(auth): address code review - narrow ValueError catch, add rehash warning log, rename num_batches

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/48c5cf31-a4ab-418a-982a-6343c37bb299

Co-authored-by: WillemJiang <219644+WillemJiang@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
2026-04-28 11:34:07 +08:00
DanielWalnut af8c0cfb78 fix(harness): constrain view_image to thread data paths (#2557)
* fix(harness): constrain view_image to thread data paths

Fixes #2530

* fix(harness): address view_image review findings

* style(harness): format view_image changes

* fix(harness): address view_image review comments
2026-04-28 11:13:17 +08:00
greatmengqi b8bc4826d8 refactor: root release config in gateway runtime (#2611)
Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
2026-04-28 00:13:04 +08:00
yangzheli 748429ef0d fix(frontend): add missing mock routes for runs-list, models, and suggestions (#2578)
* fix(frontend): add missing mock routes for runs-list, models, and suggestions

* Potential fix for pull request finding

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
2026-04-26 23:29:59 +08:00
JeffJiang ed9ebfac4d fix: enforce 'request' parameter requirement in require_auth decorator 2026-04-26 22:49:27 +08:00
JeffJiang da174dfd4d feat: implement process-local internal authentication for Gateway and enhance CSRF handling 2026-04-26 22:20:57 +08:00
Willem Jiang 897dae5475 fix the lint error of backend 2026-04-26 22:16:04 +08:00
Willem Jiang eba6c0eab2 fix unit tests of test_upload_files and test_shutdown 2026-04-26 22:10:54 +08:00
Willem Jiang 60754f0c50 fix the unit tests error of agent provider 2026-04-26 22:10:54 +08:00
Willem Jiang ac18b9c424 Apply the code reviewer suggestion of abstractmethod 2026-04-26 22:10:54 +08:00
JeffJiang 35ef8b7c13 feat: add default database configuration for AppConfig and update example config 2026-04-26 20:46:45 +08:00
JeffJiang 7bf618de67 Refactor DeerFlow to use Gateway's LangGraph-compatible API
- Updated documentation and comments to reflect the transition from LangGraph Server to Gateway.
- Changed default URLs in ChannelManager and tests to point to Gateway.
- Removed references to LangGraph Server in deployment scripts and configurations.
- Updated Nginx configuration to route API traffic to Gateway.
- Adjusted frontend configurations to utilize Gateway's API.
- Removed LangGraph service from Docker Compose files, consolidating services under Gateway.
- Added regression tests to ensure Gateway integration works as expected.

Co-authored-by: Copilot <copilot@github.com>
2026-04-26 20:38:34 +08:00
Willem Jiang 653b7ae17a Apply the code reviewer suggestion of abstractmethod 2026-04-26 20:31:14 +08:00
Willem Jiang 16aedf459a Potential fix for pull request finding 'Unused import'
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
2026-04-26 20:29:27 +08:00
yangzheli c5d57b4533 fix: resolve make dev and test-e2e errors (#2570) 2026-04-26 17:27:32 +08:00
Willem Jiang e4ff444a71 Fixed the warning message of uv 2026-04-26 17:02:04 +08:00
Willem Jiang 64a43bc448 fix the lint error by updating the .prettierignore 2026-04-26 16:19:36 +08:00
Willem Jiang 3f88045b98 try to fix the frontend e2e test errors 2026-04-26 15:48:57 +08:00
Willem Jiang 9eca429a29 fix the lint errors in the frontend 2026-04-26 15:37:16 +08:00
Willem Jiang 28381e1383 fix the lint errors in frontend 2026-04-26 15:11:22 +08:00
Willem Jiang 829e82a9af fix the lint error in backend 2026-04-26 15:09:25 +08:00
JeffJiang 3b71e2d377 feat: add request parameter to generate_suggestions endpoint for enhanced context
Co-authored-by: Copilot <copilot@github.com>
2026-04-26 12:36:05 +08:00
JeffJiang 98a5b34f76 fix: resolve merge conflict in pnpm-lock.yaml and clean up better-auth dependencies 2026-04-26 12:31:52 +08:00
JeffJiang db5ad86381 feat: enhance chat history loading with new hooks and UI components (#2338)
* Refactor API fetch calls to use a unified fetch function; enhance chat history loading with new hooks and UI components

- Replaced `fetchWithAuth` with a generic `fetch` function across various API modules for consistency.
- Updated `useThreadStream` and `useThreadHistory` hooks to manage chat history loading, including loading states and pagination.
- Introduced `LoadMoreHistoryIndicator` component for better user experience when loading more chat history.
- Enhanced message handling in `MessageList` to accommodate new loading states and history management.
- Added support for run messages in the thread context, improving the overall message handling logic.
- Updated translations for loading indicators in English and Chinese.

* Fix test assertions for run ordering in RunManager tests

- Updated assertions in `test_list_by_thread` to reflect correct ordering of runs.
- Modified `test_list_by_thread_is_stable_when_timestamps_tie` to ensure stable ordering when timestamps are tied.
2026-04-26 11:20:17 +08:00
rayhpeng 2e05f380c4 feat(persistence): per-user filesystem isolation, run-scoped APIs, and state/history simplification (#2153)
* feat(persistence): add unified persistence layer with event store, token tracking, and feedback (#1930)

* feat(persistence): add SQLAlchemy 2.0 async ORM scaffold

Introduce a unified database configuration (DatabaseConfig) that
controls both the LangGraph checkpointer and the DeerFlow application
persistence layer from a single `database:` config section.

New modules:
- deerflow.config.database_config — Pydantic config with memory/sqlite/postgres backends
- deerflow.persistence — async engine lifecycle, DeclarativeBase with to_dict mixin, Alembic skeleton
- deerflow.runtime.runs.store — RunStore ABC + MemoryRunStore implementation

Gateway integration initializes/tears down the persistence engine in
the existing langgraph_runtime() context manager. Legacy checkpointer
config is preserved for backward compatibility.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add RunEventStore ABC + MemoryRunEventStore

Phase 2-A prerequisite for event storage: adds the unified run event
stream interface (RunEventStore) with an in-memory implementation,
RunEventsConfig, gateway integration, and comprehensive tests (27 cases).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add ORM models, repositories, DB/JSONL event stores, RunJournal, and API endpoints

Phase 2-B: run persistence + event storage + token tracking.

- ORM models: RunRow (with token fields), ThreadMetaRow, RunEventRow
- RunRepository implements RunStore ABC via SQLAlchemy ORM
- ThreadMetaRepository with owner access control
- DbRunEventStore with trace content truncation and cursor pagination
- JsonlRunEventStore with per-run files and seq recovery from disk
- RunJournal (BaseCallbackHandler) captures LLM/tool/lifecycle events,
  accumulates token usage by caller type, buffers and flushes to store
- RunManager now accepts optional RunStore for persistent backing
- Worker creates RunJournal, writes human_message, injects callbacks
- Gateway deps use factory functions (RunRepository when DB available)
- New endpoints: messages, run messages, run events, token-usage
- ThreadCreateRequest gains assistant_id field
- 92 tests pass (33 new), zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add user feedback + follow-up run association

Phase 2-C: feedback and follow-up tracking.

- FeedbackRow ORM model (rating +1/-1, optional message_id, comment)
- FeedbackRepository with CRUD, list_by_run/thread, aggregate stats
- Feedback API endpoints: create, list, stats, delete
- follow_up_to_run_id in RunCreateRequest (explicit or auto-detected
  from latest successful run on the thread)
- Worker writes follow_up_to_run_id into human_message event metadata
- Gateway deps: feedback_repo factory + getter
- 17 new tests (14 FeedbackRepository + 3 follow-up association)
- 109 total tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test+config: comprehensive Phase 2 test coverage + deprecate checkpointer config

- config.example.yaml: deprecate standalone checkpointer section, activate
  unified database:sqlite as default (drives both checkpointer + app data)
- New: test_thread_meta_repo.py (14 tests) — full ThreadMetaRepository coverage
  including check_access owner logic, list_by_owner pagination
- Extended test_run_repository.py (+4 tests) — completion preserves fields,
  list ordering desc, limit, owner_none returns all
- Extended test_run_journal.py (+8 tests) — on_chain_error, track_tokens=false,
  middleware no ai_message, unknown caller tokens, convenience fields,
  tool_error, non-summarization custom event
- Extended test_run_event_store.py (+7 tests) — DB batch seq continuity,
  make_run_event_store factory (memory/db/jsonl/fallback/unknown)
- Extended test_phase2b_integration.py (+4 tests) — create_or_reject persists,
  follow-up metadata, summarization in history, full DB-backed lifecycle
- Fixed DB integration test to use proper fake objects (not MagicMock)
  for JSON-serializable metadata
- 157 total Phase 2 tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* config: move default sqlite_dir to .deer-flow/data

Keep SQLite databases alongside other DeerFlow-managed data
(threads, memory) under the .deer-flow/ directory instead of a
top-level ./data folder.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): remove UTFJSON, use engine-level json_serializer + datetime.now()

- Replace custom UTFJSON type with standard sqlalchemy.JSON in all ORM
  models. Add json_serializer=json.dumps(ensure_ascii=False) to all
  create_async_engine calls so non-ASCII text (Chinese etc.) is stored
  as-is in both SQLite and Postgres.
- Change ORM datetime defaults from datetime.now(UTC) to datetime.now(),
  remove UTC imports.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): simplify deps.py with getter factory + inline repos

- Replace 6 identical getter functions with _require() factory.
- Inline 3 _make_*_repo() factories into langgraph_runtime(), call
  get_session_factory() once instead of 3 times.
- Add thread_meta upsert in start_run (services.py).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(docker): add UV_EXTRAS build arg for optional dependencies

Support installing optional dependency groups (e.g. postgres) at
Docker build time via UV_EXTRAS build arg:
  UV_EXTRAS=postgres docker compose build

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(journal): fix flush, token tracking, and consolidate tests

RunJournal fixes:
- _flush_sync: retain events in buffer when no event loop instead of
  dropping them; worker's finally block flushes via async flush().
- on_llm_end: add tool_calls filter and caller=="lead_agent" guard for
  ai_message events; mark message IDs for dedup with record_llm_usage.
- worker.py: persist completion data (tokens, message count) to RunStore
  in finally block.

Model factory:
- Auto-inject stream_usage=True for BaseChatOpenAI subclasses with
  custom api_base, so usage_metadata is populated in streaming responses.

Test consolidation:
- Delete test_phase2b_integration.py (redundant with existing tests).
- Move DB-backed lifecycle test into test_run_journal.py.
- Add tests for stream_usage injection in test_model_factory.py.
- Clean up executor/task_tool dead journal references.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): widen content type to str|dict in all store backends

Allow event content to be a dict (for structured OpenAI-format messages)
in addition to plain strings. Dict values are JSON-serialized for the DB
backend and deserialized on read; memory and JSONL backends handle dicts
natively. Trace truncation now serializes dicts to JSON before measuring.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(events): use metadata flag instead of heuristic for dict content detection

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(converters): add LangChain-to-OpenAI message format converters

Pure functions langchain_to_openai_message, langchain_to_openai_completion,
langchain_messages_to_openai, and _infer_finish_reason for converting
LangChain BaseMessage objects to OpenAI Chat Completions format, used by
RunJournal for event storage. 15 unit tests added.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(converters): handle empty list content as null, clean up test

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): human_message content uses OpenAI user message format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): ai_message uses OpenAI format, add ai_tool_call message event

- ai_message content now uses {"role": "assistant", "content": "..."} format
- New ai_tool_call message event emitted when lead_agent LLM responds with tool_calls
- ai_tool_call uses langchain_to_openai_message converter for consistent format
- Both events include finish_reason in metadata ("stop" or "tool_calls")

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add tool_result message event with OpenAI tool message format

Cache tool_call_id from on_tool_start keyed by run_id as fallback for on_tool_end,
then emit a tool_result message event (role=tool, tool_call_id, content) after each
successful tool completion.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): summary content uses OpenAI system message format

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): replace llm_start/llm_end with llm_request/llm_response in OpenAI format

Add on_chat_model_start to capture structured prompt messages as llm_request events.
Replace llm_end trace events with llm_response using OpenAI Chat Completions format.
Track llm_call_index to pair request/response events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add record_middleware method for middleware trace events

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test(events): add full run sequence integration test for OpenAI content format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): align message events with checkpoint format and add middleware tag injection

- Message events (ai_message, ai_tool_call, tool_result, human_message) now use
  BaseMessage.model_dump() format, matching LangGraph checkpoint values.messages
- on_tool_end extracts tool_call_id/name/status from ToolMessage objects
- on_tool_error now emits tool_result message events with error status
- record_middleware uses middleware:{tag} event_type and middleware category
- Summarization custom events use middleware:summarize category
- TitleMiddleware injects middleware:title tag via get_config() inheritance
- SummarizationMiddleware model bound with middleware:summarize tag
- Worker writes human_message using HumanMessage.model_dump()

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): switch search endpoint to threads_meta table and sync title

- POST /api/threads/search now queries threads_meta table directly,
  removing the two-phase Store + Checkpointer scan approach
- Add ThreadMetaRepository.search() with metadata/status filters
- Add ThreadMetaRepository.update_display_name() for title sync
- Worker syncs checkpoint title to threads_meta.display_name on run completion
- Map display_name to values.title in search response for API compatibility

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): history endpoint reads messages from event store

- POST /api/threads/{thread_id}/history now combines two data sources:
  checkpointer for checkpoint_id, metadata, title, thread_data;
  event store for messages (complete history, not truncated by summarization)
- Strip internal LangGraph metadata keys from response
- Remove full channel_values serialization in favor of selective fields

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: remove duplicate optional-dependencies header in pyproject.toml

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(middleware): pass tagged config to TitleMiddleware ainvoke call

Without the config, the middleware:title tag was not injected,
causing the LLM response to be recorded as a lead_agent ai_message
in run_events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: resolve merge conflict in .env.example

Keep both DATABASE_URL (from persistence-scaffold) and WECOM
credentials (from main) after the merge.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address review feedback on PR #1851

- Fix naive datetime.now() → datetime.now(UTC) in all ORM models
- Fix seq race condition in DbRunEventStore.put() with FOR UPDATE
  and UNIQUE(thread_id, seq) constraint
- Encapsulate _store access in RunManager.update_run_completion()
- Deduplicate _store.put() logic in RunManager via _persist_to_store()
- Add update_run_completion to RunStore ABC + MemoryRunStore
- Wire follow_up_to_run_id through the full create path
- Add error recovery to RunJournal._flush_sync() lost-event scenario
- Add migration note for search_threads breaking change
- Fix test_checkpointer_none_fix mock to set database=None

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore: update uv.lock

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address 22 review comments from CodeQL, Copilot, and Code Quality

Bug fixes:
- Sanitize log params to prevent log injection (CodeQL)
- Reset threads_meta.status to idle/error when run completes
- Attach messages only to latest checkpoint in /history response
- Write threads_meta on POST /threads so new threads appear in search

Lint fixes:
- Remove unused imports (journal.py, migrations/env.py, test_converters.py)
- Convert lambda to named function (engine.py, Ruff E731)
- Remove unused logger definitions in repos (Ruff F841)
- Add logging to JSONL decode errors and empty except blocks
- Separate assert side-effects in tests (CodeQL)
- Remove unused local variables in tests (Ruff F841)
- Fix max_trace_content truncation to use byte length, not char length

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style: apply ruff format to persistence and runtime files

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Potential fix for pull request finding 'Statement has no effect'

Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>

* refactor(runtime): introduce RunContext to reduce run_agent parameter bloat

Extract checkpointer, store, event_store, run_events_config, thread_meta_repo,
and follow_up_to_run_id into a frozen RunContext dataclass. Add get_run_context()
in deps.py to build the base context from app.state singletons. start_run() uses
dataclasses.replace() to enrich per-run fields before passing ctx to run_agent.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): move sanitize_log_param to app/gateway/utils.py

Extract the log-injection sanitizer from routers/threads.py into a shared
utils module and rename to sanitize_log_param (public API). Eliminates the
reverse service → router import in services.py.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* perf: use SQL aggregation for feedback stats and thread token usage

Replace Python-side counting in FeedbackRepository.aggregate_by_run with
a single SELECT COUNT/SUM query. Add RunStore.aggregate_tokens_by_thread
abstract method with SQL GROUP BY implementation in RunRepository and
Python fallback in MemoryRunStore. Simplify the thread_token_usage
endpoint to delegate to the new method, eliminating the limit=10000
truncation risk.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: annotate DbRunEventStore.put() as low-frequency path

Add docstring clarifying that put() opens a per-call transaction with
FOR UPDATE and should only be used for infrequent writes (currently
just the initial human_message event). High-throughput callers should
use put_batch() instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(threads): fall back to Store search when ThreadMetaRepository is unavailable

When database.backend=memory (default) or no SQL session factory is
configured, search_threads now queries the LangGraph Store instead of
returning 503. Returns empty list if neither Store nor repo is available.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): introduce ThreadMetaStore ABC for backend-agnostic thread metadata

Add ThreadMetaStore abstract base class with create/get/search/update/delete
interface. ThreadMetaRepository (SQL) now inherits from it. New
MemoryThreadMetaStore wraps LangGraph BaseStore for memory-mode deployments.

deps.py now always provides a non-None thread_meta_repo, eliminating all
`if thread_meta_repo is not None` guards in services.py, worker.py, and
routers/threads.py. search_threads no longer needs a Store fallback branch.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(history): read messages from checkpointer instead of RunEventStore

The /history endpoint now reads messages directly from the
checkpointer's channel_values (the authoritative source) instead of
querying RunEventStore.list_messages(). The RunEventStore API is
preserved for other consumers.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address new Copilot review comments

- feedback.py: validate thread_id/run_id before deleting feedback
- jsonl.py: add path traversal protection with ID validation
- run_repo.py: parse `before` to datetime for PostgreSQL compat
- thread_meta_repo.py: fix pagination when metadata filter is active
- database_config.py: use resolve_path for sqlite_dir consistency

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Implement skill self-evolution and skill_manage flow (#1874)

* chore: ignore .worktrees directory

* Add skill_manage self-evolution flow

* Fix CI regressions for skill_manage

* Address PR review feedback for skill evolution

* fix(skill-evolution): preserve history on delete

* fix(skill-evolution): tighten scanner fallbacks

* docs: add skill_manage e2e evidence screenshot

* fix(skill-manage): avoid blocking fs ops in session runtime

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

* fix(config): resolve sqlite_dir relative to CWD, not Paths.base_dir

resolve_path() resolves relative to Paths.base_dir (.deer-flow),
which double-nested the path to .deer-flow/.deer-flow/data/app.db.
Use Path.resolve() (CWD-relative) instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Feature/feishu receive file (#1608)

* feat(feishu): add channel file materialization hook for inbound messages

- Introduce Channel.receive_file(msg, thread_id) as a base method for file materialization; default is no-op.
- Implement FeishuChannel.receive_file to download files/images from Feishu messages, save to sandbox, and inject virtual paths into msg.text.
- Update ChannelManager to call receive_file for any channel if msg.files is present, enabling downstream model access to user-uploaded files.
- No impact on Slack/Telegram or other channels (they inherit the default no-op).

* style(backend): format code with ruff for lint compliance

- Auto-formatted packages/harness/deerflow/agents/factory.py and tests/test_create_deerflow_agent.py using `ruff format`
- Ensured both files conform to project linting standards
- Fixes CI lint check failures caused by code style issues

* fix(feishu): handle file write operation asynchronously to prevent blocking

* fix(feishu): rename GetMessageResourceRequest to _GetMessageResourceRequest and remove redundant code

* test(feishu): add tests for receive_file method and placeholder replacement

* fix(manager): remove unnecessary type casting for channel retrieval

* fix(feishu): update logging messages to reflect resource handling instead of image

* fix(feishu): sanitize filename by replacing invalid characters in file uploads

* fix(feishu): improve filename sanitization and reorder image key handling in message processing

* fix(feishu): add thread lock to prevent filename conflicts during file downloads

* fix(test): correct bad merge in test_feishu_parser.py

* chore: run ruff and apply formatting cleanup
fix(feishu): preserve rich-text attachment order and improve fallback filename handling

* fix(docker): restore gateway env vars and fix langgraph empty arg issue (#1915)

Two production docker-compose.yaml bugs prevent `make up` from working:

1. Gateway missing DEER_FLOW_CONFIG_PATH and DEER_FLOW_EXTENSIONS_CONFIG_PATH
   environment overrides. Added in fb2d99f (#1836) but accidentally reverted
   by ca2fb95 (#1847). Without them, gateway reads host paths from .env via
   env_file, causing FileNotFoundError inside the container.

2. Langgraph command fails when LANGGRAPH_ALLOW_BLOCKING is unset (default).
   Empty $${allow_blocking} inserts a bare space between flags, causing
   ' --no-reload' to be parsed as unexpected extra argument. Fix by building
   args string first and conditionally appending --allow-blocking.

Co-authored-by: cooper <cooperfu@tencent.com>

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities (#1904)

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities

Fix `<button>` inside `<a>` invalid HTML in artifact components and add
missing `noopener,noreferrer` to `window.open` calls to prevent reverse
tabnabbing.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(frontend): address Copilot review on tabnabbing and double-tab-open

Remove redundant parent onClick on web_fetch ChainOfThoughtStep to
prevent opening two tabs on link click, and explicitly null out
window.opener after window.open() for defensive tabnabbing hardening.

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* refactor(persistence): organize entities into per-entity directories

Restructure the persistence layer from horizontal "models/ + repositories/"
split into vertical entity-aligned directories. Each entity (thread_meta,
run, feedback) now owns its ORM model, abstract interface (where applicable),
and concrete implementations under a single directory with an aggregating
__init__.py for one-line imports.

Layout:
  persistence/thread_meta/{base,model,sql,memory}.py
  persistence/run/{model,sql}.py
  persistence/feedback/{model,sql}.py

models/__init__.py is kept as a facade so Alembic autogenerate continues to
discover all ORM tables via Base.metadata. RunEventRow remains under
models/run_event.py because its storage implementation lives in
runtime/events/store/db.py and has no matching repository directory.

The repositories/ directory is removed entirely. All call sites in
gateway/deps.py and tests are updated to import from the new entity
packages, e.g.:

    from deerflow.persistence.thread_meta import ThreadMetaRepository
    from deerflow.persistence.run import RunRepository
    from deerflow.persistence.feedback import FeedbackRepository

Full test suite passes (1690 passed, 14 skipped).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(gateway): sync thread rename and delete through ThreadMetaStore

The POST /threads/{id}/state endpoint previously synced title changes
only to the LangGraph Store via _store_upsert. In sqlite mode the search
endpoint reads from the ThreadMetaRepository SQL table, so renames never
appeared in /threads/search until the next agent run completed (worker.py
syncs title from checkpoint to thread_meta in its finally block).

Likewise the DELETE /threads/{id} endpoint cleaned up the filesystem,
Store, and checkpointer but left the threads_meta row orphaned in sqlite,
so deleted threads kept appearing in /threads/search.

Fix both endpoints by routing through the ThreadMetaStore abstraction
which already has the correct sqlite/memory implementations wired up by
deps.py. The rename path now calls update_display_name() and the delete
path calls delete() — both work uniformly across backends.

Verified end-to-end with curl in gateway mode against sqlite backend.
Existing test suite (1690 passed) and focused router/repo tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): route all thread metadata access through ThreadMetaStore

Following the rename/delete bug fix in PR1, migrate the remaining direct
LangGraph Store reads/writes in the threads router and services to the
ThreadMetaStore abstraction so that the sqlite and memory backends behave
identically and the legacy dual-write paths can be removed.

Migrated endpoints (threads.py):
- create_thread: idempotency check + write now use thread_meta_repo.get/create
  instead of dual-writing the LangGraph Store and the SQL row.
- get_thread: reads from thread_meta_repo.get; the checkpoint-only fallback
  for legacy threads is preserved.
- patch_thread: replaced _store_get/_store_put with thread_meta_repo.update_metadata.
- delete_thread_data: dropped the legacy store.adelete; thread_meta_repo.delete
  already covers it.

Removed dead code (services.py):
- _upsert_thread_in_store — redundant with the immediately following
  thread_meta_repo.create() call.
- _sync_thread_title_after_run — worker.py's finally block already syncs
  the title via thread_meta_repo.update_display_name() after each run.

Removed dead code (threads.py):
- _store_get / _store_put / _store_upsert helpers (no remaining callers).
- THREADS_NS constant.
- get_store import (router no longer touches the LangGraph Store directly).

New abstract method:
- ThreadMetaStore.update_metadata(thread_id, metadata) merges metadata into
  the thread's metadata field. Implemented in both ThreadMetaRepository (SQL,
  read-modify-write inside one session) and MemoryThreadMetaStore. Three new
  unit tests cover merge / empty / nonexistent behaviour.

Net change: -134 lines. Full test suite: 1693 passed, 14 skipped.
Verified end-to-end with curl in gateway mode against sqlite backend
(create / patch / get / rename / search / delete).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: JilongSun <965640067@qq.com>
Co-authored-by: jie <49781832+stan-fu@users.noreply.github.com>
Co-authored-by: cooper <cooperfu@tencent.com>
Co-authored-by: yangzheli <43645580+yangzheli@users.noreply.github.com>

* feat(auth): release-validation pass for 2.0-rc — 12 blockers + simplify follow-ups (#2008)

* feat(auth): introduce backend auth module

Port RFC-001 authentication core from PR #1728:
- JWT token handling (create_access_token, decode_token, TokenPayload)
- Password hashing (bcrypt) with verify_password
- SQLite UserRepository with base interface
- Provider Factory pattern (LocalAuthProvider)
- CLI reset_admin tool
- Auth-specific errors (AuthErrorCode, TokenError, AuthErrorResponse)

Deps:
- bcrypt>=4.0.0
- pyjwt>=2.9.0
- email-validator>=2.0.0
- backend/uv.toml pins public PyPI index

Tests: 12 pure unit tests (test_auth_config.py, test_auth_errors.py).

Scope note: authz.py, test_auth.py, and test_auth_type_system.py are
deferred to commit 2 because they depend on middleware and deps wiring
that is not yet in place. Commit 1 stays "pure new files only" as the
spec mandates.

* feat(auth): wire auth end-to-end (middleware + frontend replacement)

Backend:
- Port auth_middleware, csrf_middleware, langgraph_auth, routers/auth
- Port authz decorator (owner_filter_key defaults to 'owner_id')
- Merge app.py: register AuthMiddleware + CSRFMiddleware + CORS, add
  _ensure_admin_user lifespan hook, _migrate_orphaned_threads helper,
  register auth router
- Merge deps.py: add get_local_provider, get_current_user_from_request,
  get_optional_user_from_request; keep get_current_user as thin str|None
  adapter for feedback router
- langgraph.json: add auth path pointing to langgraph_auth.py:auth
- Rename metadata['user_id'] -> metadata['owner_id'] in langgraph_auth
  (both metadata write and LangGraph filter dict) + test fixtures

Frontend:
- Delete better-auth library and api catch-all route
- Remove better-auth npm dependency and env vars (BETTER_AUTH_SECRET,
  BETTER_AUTH_GITHUB_*) from env.js
- Port frontend/src/core/auth/* (AuthProvider, gateway-config,
  proxy-policy, server-side getServerSideUser, types)
- Port frontend/src/core/api/fetcher.ts
- Port (auth)/layout, (auth)/login, (auth)/setup pages
- Rewrite workspace/layout.tsx as server component that calls
  getServerSideUser and wraps in AuthProvider
- Port workspace/workspace-content.tsx for the client-side sidebar logic

Tests:
- Port 5 auth test files (test_auth, test_auth_middleware,
  test_auth_type_system, test_ensure_admin, test_langgraph_auth)
- 176 auth tests PASS

After this commit: login/logout/registration flow works, but persistence
layer does not yet filter by owner_id. Commit 4 closes that gap.

* feat(auth): account settings page + i18n

- Port account-settings-page.tsx (change password, change email, logout)
- Wire into settings-dialog.tsx as new "account" section with UserIcon,
  rendered first in the section list
- Add i18n keys:
  - en-US/zh-CN: settings.sections.account ("Account" / "账号")
  - en-US/zh-CN: button.logout ("Log out" / "退出登录")
  - types.ts: matching type declarations

* feat(auth): enforce owner_id across 2.0-rc persistence layer

Add request-scoped contextvar-based owner filtering to threads_meta,
runs, run_events, and feedback repositories. Router code is unchanged
— isolation is enforced at the storage layer so that any caller that
forgets to pass owner_id still gets filtered results, and new routes
cannot accidentally leak data.

Core infrastructure
-------------------
- deerflow/runtime/user_context.py (new):
  - ContextVar[CurrentUser | None] with default None
  - runtime_checkable CurrentUser Protocol (structural subtype with .id)
  - set/reset/get/require helpers
  - AUTO sentinel + resolve_owner_id(value, method_name) for sentinel
    three-state resolution: AUTO reads contextvar, explicit str
    overrides, explicit None bypasses the filter (for migration/CLI)

Repository changes
------------------
- ThreadMetaRepository: create/get/search/update_*/delete gain
  owner_id=AUTO kwarg; read paths filter by owner, writes stamp it,
  mutations check ownership before applying
- RunRepository: put/get/list_by_thread/delete gain owner_id=AUTO kwarg
- FeedbackRepository: create/get/list_by_run/list_by_thread/delete
  gain owner_id=AUTO kwarg
- DbRunEventStore: list_messages/list_events/list_messages_by_run/
  count_messages/delete_by_thread/delete_by_run gain owner_id=AUTO
  kwarg. Write paths (put/put_batch) read contextvar softly: when a
  request-scoped user is available, owner_id is stamped; background
  worker writes without a user context pass None which is valid
  (orphan row to be bound by migration)

Schema
------
- persistence/models/run_event.py: RunEventRow.owner_id = Mapped[
  str | None] = mapped_column(String(64), nullable=True, index=True)
- No alembic migration needed: 2.0 ships fresh, Base.metadata.create_all
  picks up the new column automatically

Middleware
----------
- auth_middleware.py: after cookie check, call get_optional_user_from_
  request to load the real User, stamp it into request.state.user AND
  the contextvar via set_current_user, reset in a try/finally. Public
  paths and unauthenticated requests continue without contextvar, and
  @require_auth handles the strict 401 path

Test infrastructure
-------------------
- tests/conftest.py: @pytest.fixture(autouse=True) _auto_user_context
  sets a default SimpleNamespace(id="test-user-autouse") on every test
  unless marked @pytest.mark.no_auto_user. Keeps existing 20+
  persistence tests passing without modification
- pyproject.toml [tool.pytest.ini_options]: register no_auto_user
  marker so pytest does not emit warnings for opt-out tests
- tests/test_user_context.py: 6 tests covering three-state semantics,
  Protocol duck typing, and require/optional APIs
- tests/test_thread_meta_repo.py: one test updated to pass owner_id=
  None explicitly where it was previously relying on the old default

Test results
------------
- test_user_context.py: 6 passed
- test_auth*.py + test_langgraph_auth.py + test_ensure_admin.py: 127
- test_run_event_store / test_run_repository / test_thread_meta_repo
  / test_feedback: 92 passed
- Full backend suite: 1905 passed, 2 failed (both @requires_llm flaky
  integration tests unrelated to auth), 1 skipped

* feat(auth): extend orphan migration to 2.0-rc persistence tables

_ensure_admin_user now runs a three-step pipeline on every boot:

  Step 1 (fatal):     admin user exists / is created / password is reset
  Step 2 (non-fatal): LangGraph store orphan threads → admin
  Step 3 (non-fatal): SQL persistence tables → admin
    - threads_meta
    - runs
    - run_events
    - feedback

Each step is idempotent. The fatal/non-fatal split mirrors PR #1728's
original philosophy: admin creation failure blocks startup (the system
is unusable without an admin), whereas migration failures log a warning
and let the service proceed (a partial migration is recoverable; a
missing admin is not).

Key helpers
-----------
- _iter_store_items(store, namespace, *, page_size=500):
  async generator that cursor-paginates across LangGraph store pages.
  Fixes PR #1728's hardcoded limit=1000 bug that would silently lose
  orphans beyond the first page.

- _migrate_orphaned_threads(store, admin_user_id):
  Rewritten to use _iter_store_items. Returns the migrated count so the
  caller can log it; raises only on unhandled exceptions.

- _migrate_orphan_sql_tables(admin_user_id):
  Imports the 4 ORM models lazily, grabs the shared session factory,
  runs one UPDATE per table in a single transaction, commits once.
  No-op when no persistence backend is configured (in-memory dev).

Tests: test_ensure_admin.py (8 passed)

* test(auth): port AUTH test plan docs + lint/format pass

- Port backend/docs/AUTH_TEST_PLAN.md and AUTH_UPGRADE.md from PR #1728
- Rename metadata.user_id → metadata.owner_id in AUTH_TEST_PLAN.md
  (4 occurrences from the original PR doc)
- ruff auto-fix UP037 in sentinel type annotations: drop quotes around
  "str | None | _AutoSentinel" now that from __future__ import
  annotations makes them implicit string forms
- ruff format: 2 files (app/gateway/app.py, runtime/user_context.py)

Note on test coverage additions:
- conftest.py autouse fixture was already added in commit 4 (had to
  be co-located with the repository changes to keep pre-existing
  persistence tests passing)
- cross-user isolation E2E tests (test_owner_isolation.py) deferred
  — enforcement is already proven by the 98-test repository suite
  via the autouse fixture + explicit _AUTO sentinel exercises
- New test cases (TC-API-17..20, TC-ATK-13, TC-MIG-01..07) listed
  in AUTH_TEST_PLAN.md are deferred to a follow-up PR — they are
  manual-QA test cases rather than pytest code, and the spec-level
  coverage is already met by test_user_context.py + the 98-test
  repository suite.

Final test results:
- Auth suite (test_auth*, test_langgraph_auth, test_ensure_admin,
  test_user_context): 186 passed
- Persistence suite (test_run_event_store, test_run_repository,
  test_thread_meta_repo, test_feedback): 98 passed
- Lint: ruff check + ruff format both clean

* test(auth): add cross-user isolation test suite

10 tests exercising the storage-layer owner filter by manually
switching the user_context contextvar between two users. Verifies
the safety invariant:

  After a repository write with owner_id=A, a subsequent read with
  owner_id=B must not return the row, and vice versa.

Covers all 4 tables that own user-scoped data:

TC-API-17  threads_meta  — read, search, update, delete cross-user
TC-API-18  runs          — get, list_by_thread, delete cross-user
TC-API-19  run_events    — list_messages, list_events, count_messages,
                           delete_by_thread (CRITICAL: raw conversation
                           content leak vector)
TC-API-20  feedback      — get, list_by_run, delete cross-user

Plus two meta-tests verifying the sentinel pattern itself:
- AUTO + unset contextvar raises RuntimeError
- explicit owner_id=None bypasses the filter (migration escape hatch)

Architecture note
-----------------
These tests bypass the HTTP layer by design. The full chain
(cookie → middleware → contextvar → repository) is covered piecewise:

- test_auth_middleware.py: middleware sets contextvar from cookies
- test_owner_isolation.py: repositories enforce isolation when
  contextvar is set to different users

Together they prove the end-to-end safety property without the
ceremony of spinning up a full TestClient + in-memory DB for every
router endpoint.

Tests pass: 231 (full auth + persistence + isolation suite)
Lint: clean

* refactor(auth): migrate user repository to SQLAlchemy ORM

Move the users table into the shared persistence engine so auth
matches the pattern of threads_meta, runs, run_events, and feedback —
one engine, one session factory, one schema init codepath.

New files
---------
- persistence/user/__init__.py, persistence/user/model.py: UserRow
  ORM class with partial unique index on (oauth_provider, oauth_id)
- Registered in persistence/models/__init__.py so
  Base.metadata.create_all() picks it up

Modified
--------
- auth/repositories/sqlite.py: rewritten as async SQLAlchemy,
  identical constructor pattern to the other four repositories
  (def __init__(self, session_factory) + self._sf = session_factory)
- auth/config.py: drop users_db_path field — storage is configured
  through config.database like every other table
- deps.py/get_local_provider: construct SQLiteUserRepository with
  the shared session factory, fail fast if engine is not initialised
- tests/test_auth.py: rewrite test_sqlite_round_trip_new_fields to
  use the shared engine (init_engine + close_engine in a tempdir)
- tests/test_auth_type_system.py: add per-test autouse fixture that
  spins up a scratch engine and resets deps._cached_* singletons

* refactor(auth): remove SQL orphan migration (unused in supported scenarios)

The _migrate_orphan_sql_tables helper existed to bind NULL owner_id
rows in threads_meta, runs, run_events, and feedback to the admin on
first boot. But in every supported upgrade path, it's a no-op:

  1. Fresh install: create_all builds fresh tables, no legacy rows
  2. No-auth → with-auth (no existing persistence DB): persistence
     tables are created fresh by create_all, no legacy rows
  3. No-auth → with-auth (has existing persistence DB from #1930):
     NOT a supported upgrade path — "有 DB 到有 DB" schema evolution
     is out of scope; users wipe DB or run manual ALTER

So the SQL orphan migration never has anything to do in the
supported matrix. Delete the function, simplify _ensure_admin_user
from a 3-step pipeline to a 2-step one (admin creation + LangGraph
store orphan migration only).

LangGraph store orphan migration stays: it serves the real
"no-auth → with-auth" upgrade path where a user's existing LangGraph
thread metadata has no owner_id field and needs to be stamped with
the newly-created admin's id.

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): write initial admin password to 0600 file instead of logs

CodeQL py/clear-text-logging-sensitive-data flagged 3 call sites that
logged the auto-generated admin password to stdout via logger.info().
Production log aggregators (ELK/Splunk/etc) would have captured those
cleartext secrets. Replace with a shared helper that writes to
.deer-flow/admin_initial_credentials.txt with mode 0600, and log only
the path.

New file
--------
- app/gateway/auth/credential_file.py: write_initial_credentials()
  helper. Takes email, password, and a "initial"/"reset" label.
  Creates .deer-flow/ if missing, writes a header comment plus the
  email+password, chmods 0o600, returns the absolute Path.

Modified
--------
- app/gateway/app.py: both _ensure_admin_user paths (fresh creation
  + needs_setup password reset) now write to file and log the path
- app/gateway/auth/reset_admin.py: rewritten to use the shared ORM
  repo (SQLiteUserRepository with session_factory) and the
  credential_file helper. The previous implementation was broken
  after the earlier ORM refactor — it still imported _get_users_conn
  and constructed SQLiteUserRepository() without a session factory.

No tests changed — the three password-log sites are all exercised
via existing test_ensure_admin.py which checks that startup
succeeds, not that a specific string appears in logs.

CodeQL alerts 272, 283, 284: all resolved.

* security(auth): strict JWT validation in middleware (fix junk cookie bypass)

AUTH_TEST_PLAN test 7.5.8 expects junk cookies to be rejected with
401. The previous middleware behaviour was "presence-only": check
that some access_token cookie exists, then pass through. In
combination with my Task-12 decision to skip @require_auth
decorators on routes, this created a gap where a request with any
cookie-shaped string (e.g. access_token=not-a-jwt) would bypass
authentication on routes that do not touch the repository
(/api/models, /api/mcp/config, /api/memory, /api/skills, …).

Fix: middleware now calls get_current_user_from_request() strictly
and catches the resulting HTTPException to render a 401 with the
proper fine-grained error code (token_invalid, token_expired,
user_not_found, …). On success it stamps request.state.user and
the contextvar so repository-layer owner filters work downstream.

The 4 old "_with_cookie_passes" tests in test_auth_middleware.py
were written for the presence-only behaviour; they asserted that
a junk cookie would make the handler return 200. They are renamed
to "_with_junk_cookie_rejected" and their assertions flipped to
401. The negative path (no cookie → 401 not_authenticated)
is unchanged.

Verified:
  no cookie       → 401 not_authenticated
  junk cookie     → 401 token_invalid     (the fixed bug)
  expired cookie  → 401 token_expired

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): wire @require_permission(owner_check=True) on isolation routes

Apply the require_permission decorator to all 28 routes that take a
{thread_id} path parameter. Combined with the strict middleware
(previous commit), this gives the double-layer protection that
AUTH_TEST_PLAN test 7.5.9 documents:

  Layer 1 (AuthMiddleware): cookie + JWT validation, rejects junk
                            cookies and stamps request.state.user
  Layer 2 (@require_permission with owner_check=True): per-resource
                            ownership verification via
                            ThreadMetaStore.check_access — returns
                            404 if a different user owns the thread

The decorator's owner_check branch is rewritten to use the SQL
thread_meta_repo (the 2.0-rc persistence layer) instead of the
LangGraph store path that PR #1728 used (_store_get / get_store
in routers/threads.py). The inject_record convenience is dropped
— no caller in 2.0 needs the LangGraph blob, and the SQL repo has
a different shape.

Routes decorated (28 total):
- threads.py: delete, patch, get, get-state, post-state, post-history
- thread_runs.py: post-runs, post-runs-stream, post-runs-wait,
  list_runs, get_run, cancel_run, join_run, stream_existing_run,
  list_thread_messages, list_run_messages, list_run_events,
  thread_token_usage
- feedback.py: create, list, stats, delete
- uploads.py: upload (added Request param), list, delete
- artifacts.py: get_artifact
- suggestions.py: generate (renamed body parameter to avoid
  conflict with FastAPI Request)

Test fixes:
- test_suggestions_router.py: bypass the decorator via __wrapped__
  (the unit tests cover parsing logic, not auth — no point spinning
  up a thread_meta_repo just to test JSON unwrapping)
- test_auth_middleware.py 4 fake-cookie tests: already updated in
  the previous commit (745bf432)

Tests: 293 passed (auth + persistence + isolation + suggestions)
Lint: clean

* security(auth): defense-in-depth fixes from release validation pass

Eight findings caught while running the AUTH_TEST_PLAN end-to-end against
the deployed sg_dev stack. Each is a pre-condition for shipping
release/2.0-rc that the previous PRs missed.

Backend hardening
- routers/auth.py: rate limiter X-Real-IP now requires AUTH_TRUSTED_PROXIES
  whitelist (CIDR/IP allowlist). Without nginx in front, the previous code
  honored arbitrary X-Real-IP, letting an attacker rotate the header to
  fully bypass the per-IP login lockout.
- routers/auth.py: 36-entry common-password blocklist via Pydantic
  field_validator on RegisterRequest + ChangePasswordRequest. The shared
  _validate_strong_password helper keeps the constraint in one place.
- routers/threads.py: ThreadCreateRequest + ThreadPatchRequest strip
  server-reserved metadata keys (owner_id, user_id) via Pydantic
  field_validator so a forged value can never round-trip back to other
  clients reading the same thread. The actual ownership invariant stays
  on the threads_meta row; this closes the metadata-blob echo gap.
- authz.py + thread_meta/sql.py: require_permission gains a require_existing
  flag plumbed through check_access(require_existing=True). Destructive
  routes (DELETE/PATCH/state-update/runs/feedback) now treat a missing
  thread_meta row as 404 instead of "untracked legacy thread, allow",
  closing the cross-user delete-idempotence gap where any user could
  successfully DELETE another user's deleted thread.
- repositories/sqlite.py + base.py: update_user raises UserNotFoundError
  on a vanished row instead of silently returning the input. Concurrent
  delete during password reset can no longer look like a successful update.
- runtime/user_context.py: resolve_owner_id() coerces User.id (UUID) to
  str at the contextvar boundary so SQLAlchemy String(64) columns can
  bind it. The whole 2.0-rc isolation pipeline was previously broken
  end-to-end (POST /api/threads → 500 "type 'UUID' is not supported").
- persistence/engine.py: SQLAlchemy listener enables PRAGMA journal_mode=WAL,
  synchronous=NORMAL, foreign_keys=ON on every new SQLite connection.
  TC-UPG-06 in the test plan expects WAL; previous code shipped with the
  default 'delete' journal.
- auth_middleware.py: stamp request.state.auth = AuthContext(...) so
  @require_permission's short-circuit fires; previously every isolation
  request did a duplicate JWT decode + users SELECT. Also unifies the
  401 payload through AuthErrorResponse(...).model_dump().
- app.py: _ensure_admin_user restructure removes the noqa F821 scoping
  bug where 'password' was referenced outside the branch that defined it.
  New _announce_credentials helper absorbs the duplicate log block in
  the fresh-admin and reset-admin branches.

* fix(frontend+nginx): rollout CSRF on every state-changing client path

The frontend was 100% broken in gateway-pro mode for any user trying to
open a specific chat thread. Three cumulative bugs each silently
masked the next.

LangGraph SDK CSRF gap (api-client.ts)
- The Client constructor took only apiUrl, no defaultHeaders, no fetch
  interceptor. The SDK's internal fetch never sent X-CSRF-Token, so
  every state-changing /api/langgraph-compat/* call (runs/stream,
  threads/search, threads/{tid}/history, ...) hit CSRFMiddleware and
  got 403 before reaching the auth check. UI symptom: empty thread page
  with no error message; the SPA's hooks swallowed the rejection.
- Fix: pass an onRequest hook that injects X-CSRF-Token from the
  csrf_token cookie per request. Reading the cookie per call (not at
  construction time) handles login / logout / password-change cookie
  rotation transparently. The SDK's prepareFetchOptions calls
  onRequest for both regular requests AND streaming/SSE/reconnect, so
  the same hook covers runs.stream and runs.joinStream.

Raw fetch CSRF gap (7 files)
- Audit: 11 frontend fetch sites, only 2 included CSRF (login/setup +
  account-settings change-password). The other 7 routed through raw
  fetch() with no header — suggestions, memory, agents, mcp, skills,
  uploads, and the local thread cleanup hook all 403'd silently.
- Fix: enhance fetcher.ts:fetchWithAuth to auto-inject X-CSRF-Token on
  POST/PUT/DELETE/PATCH from a single shared readCsrfCookie() helper.
  Convert all 7 raw fetch() callers to fetchWithAuth so the contract
  is centrally enforced. api-client.ts and fetcher.ts share
  readCsrfCookie + STATE_CHANGING_METHODS to avoid drift.

nginx routing + buffering (nginx.local.conf)
- The auth feature shipped without updating the nginx config: per-API
  explicit location blocks but no /api/v1/auth/, /api/feedback, /api/runs.
  The frontend's client-side fetches to /api/v1/auth/login/local 404'd
  from the Next.js side because nginx routed /api/* to the frontend.
- Fix: add catch-all `location /api/` that proxies to the gateway.
  nginx longest-prefix matching keeps the explicit blocks (/api/models,
  /api/threads regex, /api/langgraph/, ...) winning for their paths.
- Fix: disable proxy_buffering + proxy_request_buffering for the
  frontend `location /` block. Without it, nginx tries to spool large
  Next.js chunks into /var/lib/nginx/proxy (root-owned) and fails with
  Permission denied → ERR_INCOMPLETE_CHUNKED_ENCODING → ChunkLoadError.

* test(auth): release-validation test infra and new coverage

Test fixtures and unit tests added during the validation pass.

Router test helpers (NEW: tests/_router_auth_helpers.py)
- make_authed_test_app(): builds a FastAPI test app with a stub
  middleware that stamps request.state.user + request.state.auth and a
  permissive thread_meta_repo mock. TestClient-based router tests
  (test_artifacts_router, test_threads_router) use it instead of bare
  FastAPI() so the new @require_permission(owner_check=True) decorators
  short-circuit cleanly.
- call_unwrapped(): walks the __wrapped__ chain to invoke the underlying
  handler without going through the authz wrappers. Direct-call tests
  (test_uploads_router) use it. Typed with ParamSpec so the wrapped
  signature flows through.

Backend test additions
- test_auth.py: 7 tests for the new _get_client_ip trust model (no
  proxy / trusted proxy / untrusted peer / XFF rejection / invalid
  CIDR / no client). 5 tests for the password blocklist (literal,
  case-insensitive, strong password accepted, change-password binding,
  short-password length-check still fires before blocklist).
  test_update_user_raises_when_row_concurrently_deleted: closes a
  shipped-without-coverage gap on the new UserNotFoundError contract.
- test_thread_meta_repo.py: 4 tests for check_access(require_existing=True)
  — strict missing-row denial, strict owner match, strict owner mismatch,
  strict null-owner still allowed (shared rows survive the tightening).
- test_ensure_admin.py: 3 tests for _migrate_orphaned_threads /
  _iter_store_items pagination, covering the TC-UPG-02 upgrade story
  end-to-end via mock store. Closes the gap where the cursor pagination
  was untested even though the previous PR rewrote it.
- test_threads_router.py: 5 tests for _strip_reserved_metadata
  (owner_id removal, user_id removal, safe-keys passthrough, empty
  input, both-stripped).
- test_auth_type_system.py: replace "password123" fixtures with
  Tr0ub4dor3a / AnotherStr0ngPwd! so the new password blocklist
  doesn't reject the test data.

* docs(auth): refresh TC-DOCKER-05 + document Docker validation gap

- AUTH_TEST_PLAN.md TC-DOCKER-05: the previous expectation
  ("admin password visible in docker logs") was stale after the simplify
  pass that moved credentials to a 0600 file. The grep "Password:" check
  would have silently failed and given a false sense of coverage. New
  expectation matches the actual file-based path: 0600 file in
  DEER_FLOW_HOME, log shows the path (not the secret), reverse-grep
  asserts no leaked password in container logs.
- NEW: docs/AUTH_TEST_DOCKER_GAP.md documents the only un-executed
  block in the test plan (TC-DOCKER-01..06). Reason: sg_dev validation
  host has no Docker daemon installed. The doc maps each Docker case
  to an already-validated bare-metal equivalent (TC-1.1, TC-REENT-01,
  TC-API-02 etc.) so the gap is auditable, and includes pre-flight
  reproduction steps for whoever has Docker available.

---------

Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>

* refactor(persistence): unify SQLite to single deerflow.db and move checkpointer to runtime

Merge checkpoints.db and app.db into a single deerflow.db file (WAL mode
handles concurrent access safely). Move checkpointer module from
agents/checkpointer to runtime/checkpointer to better reflect its role
as a runtime infrastructure concern.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): rename owner_id to user_id and thread_meta_repo to thread_store

Rename owner_id to user_id across all persistence models, repositories,
stores, routers, and tests for clearer semantics. Rename thread_meta_repo
to thread_store for consistency with run_store/run_event_store naming.
Add ThreadMetaStore return type annotation to get_thread_store().

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): unify ThreadMetaStore interface with user isolation and factory

Add user_id parameter to all ThreadMetaStore abstract methods. Implement
owner isolation in MemoryThreadMetaStore with _get_owned_record helper.
Add check_access to base class and memory implementation. Add
make_thread_store factory to simplify deps.py initialization. Add
memory-backend isolation tests.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(feedback): add UNIQUE(thread_id, run_id, user_id) constraint

Add UNIQUE constraint to FeedbackRow to enforce one feedback per user per run,
enabling upsert behavior in Task 2. Update tests to use distinct user_ids for
multiple feedback records per run, and pass user_id=None to list_by_run for
admin-style queries that bypass user isolation.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(feedback): add upsert() method with UNIQUE enforcement

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): add delete_by_run() and list_by_thread_grouped()

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): add PUT upsert and DELETE-by-run endpoints

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): enrich messages endpoint with per-run feedback data

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): add frontend feedback API client

Adds upsertFeedback and deleteFeedback API functions backed by
fetchWithAuth, targeting the /api/threads/{id}/runs/{id}/feedback
endpoint.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): wire feedback data into message rendering for history echo

Adds useThreadFeedback hook that fetches run-level feedback from the
messages API and builds a runId->FeedbackData map. MessageList now calls
this hook and passes feedback and runId to each MessageListItem so
previously-submitted thumbs are pre-filled when revisiting a thread.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(feedback): correct run_id mapping for feedback echo

The feedbackMap was keyed by run_id but looked up by LangGraph message ID.
Fixed by tracking AI message ordinal index to correlate event store
run_ids with LangGraph SDK messages.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(feedback): use real threadId and refresh after stream

- Pass threadId prop to MessageListItem instead of reading "new" from URL params
- Invalidate thread-feedback query on stream finish so buttons appear immediately
- Show feedback buttons always visible, copy button on hover only

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style(feedback): group copy and feedback buttons together on the left

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style(feedback): always show toolbar buttons without hover

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): stream hang when run_events.backend=db

DbRunEventStore._user_id_from_context() returned user.id without
coercing it to str. User.id is a Pydantic UUID, and aiosqlite cannot
bind a raw UUID object to a VARCHAR column, so the INSERT for the
initial human_message event silently rolled back and raised out of
the worker task. Because that put() sat outside the worker's try
block, the finally-clause that publishes end-of-stream never ran
and the SSE stream hung forever.

jsonl mode was unaffected because json.dumps(default=str) coerces
UUID objects transparently.

Fixes:
- db.py: coerce user.id to str at the context-read boundary (matches
  what resolve_user_id already does for the other repositories)
- worker.py: move RunJournal init + human_message put inside the try
  block so any failure flows through the finally/publish_end path
  instead of hanging the subscriber

Defense-in-depth:
- engine.py: add PRAGMA busy_timeout=5000 so checkpointer and event
  store wait for each other on the shared deerflow.db file instead
  of failing immediately under write-lock contention
- journal.py: skip fire-and-forget _flush_sync when a previous flush
  task is still in flight, to avoid piling up concurrent put_batch
  writes on the same SQLAlchemy engine during streaming; flush() now
  waits for pending tasks before draining the buffer
- database_config.py: doc-only update clarifying WAL + busy_timeout
  keep the unified deerflow.db safe for both workloads

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore(persistence): drop redundant busy_timeout PRAGMA

Python's sqlite3 driver defaults to a 5-second busy timeout via the
``timeout`` kwarg of ``sqlite3.connect``, and aiosqlite + SQLAlchemy's
aiosqlite dialect inherit that default. Setting ``PRAGMA busy_timeout=5000``
explicitly was a no-op — verified by reading back the PRAGMA on a fresh
connection (it already reports 5000ms without our PRAGMA).

Concurrent stress test (50 checkpoint writes + 20 event batches + 50
thread_meta updates on the same deerflow.db) still completes with zero
errors and 200/200 rows after removing the explicit PRAGMA.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(journal): unwrap Command tool results in on_tool_end

Tools that update graph state (e.g. ``present_files``) return
``Command(update={'messages': [ToolMessage(...)], 'artifacts': [...]})``.
LangGraph later unwraps the inner ``ToolMessage`` into checkpoint state,
but ``RunJournal.on_tool_end`` was receiving the ``Command`` object
directly via the LangChain callback chain and storing
``str(Command(update={...}))`` as the tool_result content.

This produced a visible divergence between the event-store and the
checkpoint for any thread that used a Command-returning tool, blocking
the event-store-backed history fix in the follow-up commit. Concrete
example from thread ``6d30913e-dcd4-41c8-8941-f66c716cf359`` (seq=48):
checkpoint had ``'Successfully presented files'`` while event_store
stored the full Command repr.

The fix detects ``Command`` in ``on_tool_end``, extracts the first
``ToolMessage`` from ``update['messages']``, and lets the existing
ToolMessage branch handle the ``model_dump()`` path. Legacy rows still
containing the Command repr are separately cleaned up by the history
helper in the follow-up commit.

Tests:
- ``test_tool_end_unwraps_command_with_inner_tool_message`` — unit test
  of the unwrap branch with a constructed Command
- ``test_tool_invoke_end_to_end_unwraps_command`` — end-to-end via
  ``CallbackManager`` + ``tool.invoke`` to exercise the real LangChain
  dispatch path that production uses, matching the repro shape from
  ``present_files``
- Counter-proof: temporarily reverted the patch, both tests failed with
  the exact ``Command(update={...})`` repr that was stored in the
  production SQLite row at seq=48, confirming LangChain does pass the
  ``Command`` through callbacks (the unwrap is load-bearing)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(threads): load history messages from event store, immune to summarize

``get_thread_history`` and ``get_thread_state`` in Gateway mode read
messages from ``checkpoint.channel_values["messages"]``. After
SummarizationMiddleware runs mid-run, that list is rewritten in-place:
pre-summarize messages are dropped and a synthetic summary-as-human
message takes position 0. The frontend then renders a chat history that
starts with ``"Here is a summary of the conversation to date:..."``
instead of the user's original query, and all earlier turns are gone.

The event store (``RunEventStore``) is append-only and never rewritten,
so it retains the full transcript. This commit adds a helper
``_get_event_store_messages`` that loads the event store's message
stream and overrides ``values["messages"]`` in both endpoints; the
checkpoint fallback kicks in only when the event store is unavailable.

Behavior contract of the helper:

- **Full pagination.** ``list_messages`` returns the newest ``limit``
  records when no cursor is given, so a fixed limit silently drops
  older messages on long threads. The helper sizes the read from
  ``count_messages()`` and pages forward with ``after_seq`` cursors.
- **Copy-on-read.** Each content dict is copied before ``id`` is
  patched so the live store object (``MemoryRunEventStore`` returns
  references) is never mutated.
- **Stable ids.** Messages with ``id=None`` (human + tool_result,
  which don't receive an id until checkpoint persistence) get a
  deterministic ``uuid5(NAMESPACE_URL, f"{thread_id}:{seq}")`` so
  React keys stay stable across requests. AI messages keep their
  LLM-assigned ``lc_run--*`` ids.
- **Legacy ``Command`` repr sanitization.** Rows captured before the
  ``journal.py`` ``on_tool_end`` fix (previous commit) stored
  ``str(Command(update={'messages': [ToolMessage(content='X', ...)]}))``
  as the tool_result content. ``_sanitize_legacy_command_repr``
  regex-extracts the inner text so old threads render cleanly.
- **Inline feedback.** When loading the stream, the helper also pulls
  ``feedback_repo.list_by_thread_grouped`` and attaches ``run_id`` to
  every message plus ``feedback`` to the final ``ai_message`` of each
  run. This removes the frontend's need to fetch a second endpoint
  and positional-index-map its way back to the right run. When the
  feedback subsystem is unavailable, the ``feedback`` field is left
  absent entirely so the frontend hides the button rather than
  rendering it over a broken write path.
- **User context.** ``DbRunEventStore`` is user-scoped by default via
  ``resolve_user_id(AUTO)``. The helper relies on the ``@require_permission``
  decorator having populated the user contextvar on both callers; the
  docstring documents this dependency explicitly so nobody wires it
  into a CLI or migration script without passing ``user_id=None``.

Real data verification against thread
``6d30913e-dcd4-41c8-8941-f66c716cf359``: checkpoint showed 12 messages
(summarize-corrupted), event store had 16. The original human message
``"最新伊美局势"`` was preserved as seq=1 in the event store and
correctly restored to position 0 in the helper output. Helper output
for AI messages was byte-identical to checkpoint for every overlapping
message; only tool_result ids differed (patched to uuid5) and the
legacy Command repr at seq=48 was sanitized.

Tests:
- ``test_thread_state_event_store.py`` — 18 tests covering
  ``_sanitize_legacy_command_repr`` (passthrough, single/double-quote
  extraction, unparseable fallback), helper happy path (all message
  types, stable uuid5, store non-mutation), multi-page pagination,
  summarize regression (recovers pre-summarize messages), feedback
  attachment (per-run, multi-run threads, repo failure graceful),
  and dependency failure fallback to ``None``.

Docs:
- ``docs/superpowers/plans/2026-04-10-event-store-history.md`` — the
  implementation plan this commit realizes, with Task 1 revised after
  the evaluation findings (pagination, copy-on-read, Command wrap
  already landed in journal.py, frontend feedback pagination in the
  follow-up commit, Standard-mode follow-up noted).
- ``docs/superpowers/specs/2026-04-11-runjournal-history-evaluation.md``
  — the Claude + second-opinion evaluation document that drove the
  plan revisions (pagination bug, dict-mutation bug, feedback hidden
  bug, Command bug).
- ``docs/superpowers/specs/2026-04-11-summarize-marker-design.md`` —
  design for a follow-up PR that visually marks summarize events in
  history, based on a verified ``adispatch_custom_event`` experiment
  (``trace=False`` middleware nodes can still forward the Pregel task
  config via explicit signature injection).

Scope: Gateway mode only (``make dev-pro``). Standard mode
(``make dev``) hits LangGraph Server directly and bypasses these
endpoints; the summarize symptom is still present there and is
tracked as a separate follow-up in the plan.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(feedback): inline feedback on history and drop positional mapping

The old ``useThreadFeedback`` hook loaded ``GET /api/threads/{id}/messages?limit=200``
and built two parallel lookup tables: ``runIdByAiIndex`` (an ordinal array of
run_ids for every ``ai_message``-typed event) and ``feedbackByRunId``. The render
loop in ``message-list.tsx`` walked the AI messages in order, incrementing
``aiMessageIndex`` on each non-human message, and used that ordinal to look up
the run_id and feedback.

This shape had three latent bugs we could observe on real threads:

1. **Fetch was capped at 200 messages.** Long or tool-heavy threads silently
   dropped earlier entries from the map, so feedback buttons could be missing
   on messages they should own.
2. **Ordinal mismatch.** The render loop counted every non-human message
   (including each intermediate ``ai_tool_call``), but ``runIdByAiIndex`` only
   pushed entries for ``event_type == "ai_message"``. A run with 3 tool_calls
   + 1 final AI message would push 1 entry while the render consumed 4
   positions, so buttons mapped to the wrong positions across multi-run
   threads.
3. **Two parallel data paths.** The ``/history`` render path and the
   ``/messages`` feedback-lookup path could drift in-between an
   ``invalidateQueries`` call and the next refetch, producing transient
   mismaps.

The previous commit moved the authoritative message source for history to
the event store and added ``run_id`` + ``feedback`` inline on each message
dict returned by ``_get_event_store_messages``. This commit aligns the
frontend with that contract:

- **Delete** ``useThreadFeedback``, ``ThreadFeedbackData``,
  ``runIdByAiIndex``, ``feedbackByRunId``, and ``fetchAllThreadMessages``.
- **Introduce** ``useThreadMessageEnrichment`` that fetches
  ``POST /history?limit=1`` once, indexes the returned messages by
  ``message.id`` into a ``Map<id, {run_id, feedback?}>``, and invalidates
  on stream completion (``onFinish`` in ``useThreadStream``). Keying by
  ``message.id`` is stable across runs, tool_call chains, and summarize.
- **Simplify** ``message-list.tsx`` to drop the ``aiMessageIndex``
  counter and read ``enrichment?.get(msg.id)`` at each render step.
- **Rewire** ``message-list-item.tsx`` so the feedback button renders
  when ``feedback !== undefined`` rather than when the message happens
  to be non-human. ``feedback`` is ``undefined`` for non-eligible
  messages (humans, non-final AI, tools), ``null`` for the final
  ai_message of an unrated run, and a ``FeedbackData`` object once
  rated — cleanly distinguishing "not eligible" from "eligible but
  unrated".

``/api/threads/{id}/messages`` is kept as a debug/export surface; no
frontend code calls it anymore but the backend router is untouched.

Validation:
- ``pnpm check`` clean (0 errors, 1 pre-existing unrelated warning)
- Live test on thread ``3d5dea4a`` after gateway restart confirmed the
  original user query is restored to position 0 and the feedback
  button behaves correctly on the final AI message.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(rebase): remove duplicate definitions and update stale module paths

Rebase left duplicate function blocks in worker.py (triple human_message
write causing 3x user messages in /history), deps.py, and prompt.py.
Also update checkpointer imports from the old deerflow.agents.checkpointer
path to deerflow.runtime.checkpointer, and clean up orphaned feedback
props in the frontend message components.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(rebase): restore FeedbackButtons component and enrichment lost during rebase

The FeedbackButtons component (defined inline in message-list-item.tsx)
was introduced in commit 95df8d13 but lost during rebase. The previous
rebase cleanup commit incorrectly removed the feedback/runId props and
enrichment hook as "orphaned code" instead of restoring the missing
component. This commit restores:

- FeedbackButtons component with thumbs up/down toggle and optimistic state
- FeedbackData/upsertFeedback/deleteFeedback imports
- feedback and runId props on MessageListItem
- useThreadMessageEnrichment hook and entry lookup in message-list.tsx

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(user-context): add DEFAULT_USER_ID and get_effective_user_id helper

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(paths): add user-aware path methods with optional user_id parameter

Add _validate_user_id(), user_dir(), user_memory_file(), user_agent_memory_file()
and optional keyword-only user_id parameter to all thread-related path methods.
When user_id is provided, paths resolve under users/{user_id}/threads/{thread_id}/;
when omitted, legacy layout is preserved for backward compatibility.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(memory): add user_id to MemoryStorage interface for per-user isolation

Thread user_id through MemoryStorage.load/reload/save abstract methods and
FileMemoryStorage, re-keying the in-memory cache from bare agent_name to a
(user_id, agent_name) tuple to prevent cross-user cache collisions.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(memory): thread user_id through memory updater layer

Add `user_id` keyword-only parameter to all public updater functions
(_save_memory_to_file, get_memory_data, reload_memory_data, import_memory_data,
clear_memory_data, create/delete/update_memory_fact) and regular keyword param
to MemoryUpdater.update_memory + update_memory_from_conversation, propagating
it to every storage load/save/reload call.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(memory): capture user_id at enqueue time for async-safe thread isolation

Add user_id field to ConversationContext and MemoryUpdateQueue.add() so the
user identity is stored explicitly at request time, before threading.Timer
fires on a different thread where ContextVar values do not propagate.
MemoryMiddleware.after_agent() now calls get_effective_user_id() at enqueue
time and passes the value through to updater.update_memory().

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(isolation): wire user_id through all Paths and memory callsites

Pass user_id=get_effective_user_id() at every callsite that invokes
Paths methods or memory functions, enabling per-user filesystem isolation
throughout the harness and app layers.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(migration): add idempotent script for per-user data migration

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: update CLAUDE.md and config docs for per-user isolation

* feat(events): add pagination to list_messages_by_run on all store backends

Replicates the existing before_seq/after_seq/limit cursor-pagination pattern
from list_messages onto list_messages_by_run across the abstract interface,
MemoryRunEventStore, JsonlRunEventStore, and DbRunEventStore.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(api): add GET /api/runs/{run_id}/messages with cursor pagination

New endpoint resolves thread_id from the run record and delegates to
RunEventStore.list_messages_by_run for cursor-based pagination.
Ownership is enforced implicitly via RunStore.get() user filtering.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(api): add GET /api/runs/{run_id}/feedback

Delegates to FeedbackRepository.list_by_run via the existing _resolve_run
helper; includes tests for success, 404, empty list, and 503 (no DB).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(api): retrofit cursor pagination onto GET /threads/{tid}/runs/{rid}/messages

Replace bare list[dict] response with {data: [...], has_more: bool} envelope,
forwarding limit/before_seq/after_seq query params to the event store.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: add run-level API endpoints to CLAUDE.md routers table

* refactor(threads): remove event-store message loader and feedback from state/history endpoints

State and history endpoints now return messages purely from the
checkpointer's channel_values. The _get_event_store_messages helper
(which loaded the full event-store transcript with feedback attached)
is removed along with its tests. Frontend will use the dedicated
GET /api/runs/{run_id}/messages and /feedback endpoints instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add unified persistence layer with event store, token tracking, and feedback (#1930)

* feat(persistence): add SQLAlchemy 2.0 async ORM scaffold

Introduce a unified database configuration (DatabaseConfig) that
controls both the LangGraph checkpointer and the DeerFlow application
persistence layer from a single `database:` config section.

New modules:
- deerflow.config.database_config — Pydantic config with memory/sqlite/postgres backends
- deerflow.persistence — async engine lifecycle, DeclarativeBase with to_dict mixin, Alembic skeleton
- deerflow.runtime.runs.store — RunStore ABC + MemoryRunStore implementation

Gateway integration initializes/tears down the persistence engine in
the existing langgraph_runtime() context manager. Legacy checkpointer
config is preserved for backward compatibility.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add RunEventStore ABC + MemoryRunEventStore

Phase 2-A prerequisite for event storage: adds the unified run event
stream interface (RunEventStore) with an in-memory implementation,
RunEventsConfig, gateway integration, and comprehensive tests (27 cases).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add ORM models, repositories, DB/JSONL event stores, RunJournal, and API endpoints

Phase 2-B: run persistence + event storage + token tracking.

- ORM models: RunRow (with token fields), ThreadMetaRow, RunEventRow
- RunRepository implements RunStore ABC via SQLAlchemy ORM
- ThreadMetaRepository with owner access control
- DbRunEventStore with trace content truncation and cursor pagination
- JsonlRunEventStore with per-run files and seq recovery from disk
- RunJournal (BaseCallbackHandler) captures LLM/tool/lifecycle events,
  accumulates token usage by caller type, buffers and flushes to store
- RunManager now accepts optional RunStore for persistent backing
- Worker creates RunJournal, writes human_message, injects callbacks
- Gateway deps use factory functions (RunRepository when DB available)
- New endpoints: messages, run messages, run events, token-usage
- ThreadCreateRequest gains assistant_id field
- 92 tests pass (33 new), zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add user feedback + follow-up run association

Phase 2-C: feedback and follow-up tracking.

- FeedbackRow ORM model (rating +1/-1, optional message_id, comment)
- FeedbackRepository with CRUD, list_by_run/thread, aggregate stats
- Feedback API endpoints: create, list, stats, delete
- follow_up_to_run_id in RunCreateRequest (explicit or auto-detected
  from latest successful run on the thread)
- Worker writes follow_up_to_run_id into human_message event metadata
- Gateway deps: feedback_repo factory + getter
- 17 new tests (14 FeedbackRepository + 3 follow-up association)
- 109 total tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test+config: comprehensive Phase 2 test coverage + deprecate checkpointer config

- config.example.yaml: deprecate standalone checkpointer section, activate
  unified database:sqlite as default (drives both checkpointer + app data)
- New: test_thread_meta_repo.py (14 tests) — full ThreadMetaRepository coverage
  including check_access owner logic, list_by_owner pagination
- Extended test_run_repository.py (+4 tests) — completion preserves fields,
  list ordering desc, limit, owner_none returns all
- Extended test_run_journal.py (+8 tests) — on_chain_error, track_tokens=false,
  middleware no ai_message, unknown caller tokens, convenience fields,
  tool_error, non-summarization custom event
- Extended test_run_event_store.py (+7 tests) — DB batch seq continuity,
  make_run_event_store factory (memory/db/jsonl/fallback/unknown)
- Extended test_phase2b_integration.py (+4 tests) — create_or_reject persists,
  follow-up metadata, summarization in history, full DB-backed lifecycle
- Fixed DB integration test to use proper fake objects (not MagicMock)
  for JSON-serializable metadata
- 157 total Phase 2 tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* config: move default sqlite_dir to .deer-flow/data

Keep SQLite databases alongside other DeerFlow-managed data
(threads, memory) under the .deer-flow/ directory instead of a
top-level ./data folder.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): remove UTFJSON, use engine-level json_serializer + datetime.now()

- Replace custom UTFJSON type with standard sqlalchemy.JSON in all ORM
  models. Add json_serializer=json.dumps(ensure_ascii=False) to all
  create_async_engine calls so non-ASCII text (Chinese etc.) is stored
  as-is in both SQLite and Postgres.
- Change ORM datetime defaults from datetime.now(UTC) to datetime.now(),
  remove UTC imports.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): simplify deps.py with getter factory + inline repos

- Replace 6 identical getter functions with _require() factory.
- Inline 3 _make_*_repo() factories into langgraph_runtime(), call
  get_session_factory() once instead of 3 times.
- Add thread_meta upsert in start_run (services.py).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(docker): add UV_EXTRAS build arg for optional dependencies

Support installing optional dependency groups (e.g. postgres) at
Docker build time via UV_EXTRAS build arg:
  UV_EXTRAS=postgres docker compose build

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(journal): fix flush, token tracking, and consolidate tests

RunJournal fixes:
- _flush_sync: retain events in buffer when no event loop instead of
  dropping them; worker's finally block flushes via async flush().
- on_llm_end: add tool_calls filter and caller=="lead_agent" guard for
  ai_message events; mark message IDs for dedup with record_llm_usage.
- worker.py: persist completion data (tokens, message count) to RunStore
  in finally block.

Model factory:
- Auto-inject stream_usage=True for BaseChatOpenAI subclasses with
  custom api_base, so usage_metadata is populated in streaming responses.

Test consolidation:
- Delete test_phase2b_integration.py (redundant with existing tests).
- Move DB-backed lifecycle test into test_run_journal.py.
- Add tests for stream_usage injection in test_model_factory.py.
- Clean up executor/task_tool dead journal references.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): widen content type to str|dict in all store backends

Allow event content to be a dict (for structured OpenAI-format messages)
in addition to plain strings. Dict values are JSON-serialized for the DB
backend and deserialized on read; memory and JSONL backends handle dicts
natively. Trace truncation now serializes dicts to JSON before measuring.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(events): use metadata flag instead of heuristic for dict content detection

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(converters): add LangChain-to-OpenAI message format converters

Pure functions langchain_to_openai_message, langchain_to_openai_completion,
langchain_messages_to_openai, and _infer_finish_reason for converting
LangChain BaseMessage objects to OpenAI Chat Completions format, used by
RunJournal for event storage. 15 unit tests added.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(converters): handle empty list content as null, clean up test

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): human_message content uses OpenAI user message format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): ai_message uses OpenAI format, add ai_tool_call message event

- ai_message content now uses {"role": "assistant", "content": "..."} format
- New ai_tool_call message event emitted when lead_agent LLM responds with tool_calls
- ai_tool_call uses langchain_to_openai_message converter for consistent format
- Both events include finish_reason in metadata ("stop" or "tool_calls")

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add tool_result message event with OpenAI tool message format

Cache tool_call_id from on_tool_start keyed by run_id as fallback for on_tool_end,
then emit a tool_result message event (role=tool, tool_call_id, content) after each
successful tool completion.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): summary content uses OpenAI system message format

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): replace llm_start/llm_end with llm_request/llm_response in OpenAI format

Add on_chat_model_start to capture structured prompt messages as llm_request events.
Replace llm_end trace events with llm_response using OpenAI Chat Completions format.
Track llm_call_index to pair request/response events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add record_middleware method for middleware trace events

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test(events): add full run sequence integration test for OpenAI content format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): align message events with checkpoint format and add middleware tag injection

- Message events (ai_message, ai_tool_call, tool_result, human_message) now use
  BaseMessage.model_dump() format, matching LangGraph checkpoint values.messages
- on_tool_end extracts tool_call_id/name/status from ToolMessage objects
- on_tool_error now emits tool_result message events with error status
- record_middleware uses middleware:{tag} event_type and middleware category
- Summarization custom events use middleware:summarize category
- TitleMiddleware injects middleware:title tag via get_config() inheritance
- SummarizationMiddleware model bound with middleware:summarize tag
- Worker writes human_message using HumanMessage.model_dump()

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): switch search endpoint to threads_meta table and sync title

- POST /api/threads/search now queries threads_meta table directly,
  removing the two-phase Store + Checkpointer scan approach
- Add ThreadMetaRepository.search() with metadata/status filters
- Add ThreadMetaRepository.update_display_name() for title sync
- Worker syncs checkpoint title to threads_meta.display_name on run completion
- Map display_name to values.title in search response for API compatibility

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): history endpoint reads messages from event store

- POST /api/threads/{thread_id}/history now combines two data sources:
  checkpointer for checkpoint_id, metadata, title, thread_data;
  event store for messages (complete history, not truncated by summarization)
- Strip internal LangGraph metadata keys from response
- Remove full channel_values serialization in favor of selective fields

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: remove duplicate optional-dependencies header in pyproject.toml

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(middleware): pass tagged config to TitleMiddleware ainvoke call

Without the config, the middleware:title tag was not injected,
causing the LLM response to be recorded as a lead_agent ai_message
in run_events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: resolve merge conflict in .env.example

Keep both DATABASE_URL (from persistence-scaffold) and WECOM
credentials (from main) after the merge.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address review feedback on PR #1851

- Fix naive datetime.now() → datetime.now(UTC) in all ORM models
- Fix seq race condition in DbRunEventStore.put() with FOR UPDATE
  and UNIQUE(thread_id, seq) constraint
- Encapsulate _store access in RunManager.update_run_completion()
- Deduplicate _store.put() logic in RunManager via _persist_to_store()
- Add update_run_completion to RunStore ABC + MemoryRunStore
- Wire follow_up_to_run_id through the full create path
- Add error recovery to RunJournal._flush_sync() lost-event scenario
- Add migration note for search_threads breaking change
- Fix test_checkpointer_none_fix mock to set database=None

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore: update uv.lock

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address 22 review comments from CodeQL, Copilot, and Code Quality

Bug fixes:
- Sanitize log params to prevent log injection (CodeQL)
- Reset threads_meta.status to idle/error when run completes
- Attach messages only to latest checkpoint in /history response
- Write threads_meta on POST /threads so new threads appear in search

Lint fixes:
- Remove unused imports (journal.py, migrations/env.py, test_converters.py)
- Convert lambda to named function (engine.py, Ruff E731)
- Remove unused logger definitions in repos (Ruff F841)
- Add logging to JSONL decode errors and empty except blocks
- Separate assert side-effects in tests (CodeQL)
- Remove unused local variables in tests (Ruff F841)
- Fix max_trace_content truncation to use byte length, not char length

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style: apply ruff format to persistence and runtime files

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Potential fix for pull request finding 'Statement has no effect'

Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>

* refactor(runtime): introduce RunContext to reduce run_agent parameter bloat

Extract checkpointer, store, event_store, run_events_config, thread_meta_repo,
and follow_up_to_run_id into a frozen RunContext dataclass. Add get_run_context()
in deps.py to build the base context from app.state singletons. start_run() uses
dataclasses.replace() to enrich per-run fields before passing ctx to run_agent.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): move sanitize_log_param to app/gateway/utils.py

Extract the log-injection sanitizer from routers/threads.py into a shared
utils module and rename to sanitize_log_param (public API). Eliminates the
reverse service → router import in services.py.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* perf: use SQL aggregation for feedback stats and thread token usage

Replace Python-side counting in FeedbackRepository.aggregate_by_run with
a single SELECT COUNT/SUM query. Add RunStore.aggregate_tokens_by_thread
abstract method with SQL GROUP BY implementation in RunRepository and
Python fallback in MemoryRunStore. Simplify the thread_token_usage
endpoint to delegate to the new method, eliminating the limit=10000
truncation risk.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: annotate DbRunEventStore.put() as low-frequency path

Add docstring clarifying that put() opens a per-call transaction with
FOR UPDATE and should only be used for infrequent writes (currently
just the initial human_message event). High-throughput callers should
use put_batch() instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(threads): fall back to Store search when ThreadMetaRepository is unavailable

When database.backend=memory (default) or no SQL session factory is
configured, search_threads now queries the LangGraph Store instead of
returning 503. Returns empty list if neither Store nor repo is available.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): introduce ThreadMetaStore ABC for backend-agnostic thread metadata

Add ThreadMetaStore abstract base class with create/get/search/update/delete
interface. ThreadMetaRepository (SQL) now inherits from it. New
MemoryThreadMetaStore wraps LangGraph BaseStore for memory-mode deployments.

deps.py now always provides a non-None thread_meta_repo, eliminating all
`if thread_meta_repo is not None` guards in services.py, worker.py, and
routers/threads.py. search_threads no longer needs a Store fallback branch.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(history): read messages from checkpointer instead of RunEventStore

The /history endpoint now reads messages directly from the
checkpointer's channel_values (the authoritative source) instead of
querying RunEventStore.list_messages(). The RunEventStore API is
preserved for other consumers.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address new Copilot review comments

- feedback.py: validate thread_id/run_id before deleting feedback
- jsonl.py: add path traversal protection with ID validation
- run_repo.py: parse `before` to datetime for PostgreSQL compat
- thread_meta_repo.py: fix pagination when metadata filter is active
- database_config.py: use resolve_path for sqlite_dir consistency

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Implement skill self-evolution and skill_manage flow (#1874)

* chore: ignore .worktrees directory

* Add skill_manage self-evolution flow

* Fix CI regressions for skill_manage

* Address PR review feedback for skill evolution

* fix(skill-evolution): preserve history on delete

* fix(skill-evolution): tighten scanner fallbacks

* docs: add skill_manage e2e evidence screenshot

* fix(skill-manage): avoid blocking fs ops in session runtime

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

* fix(config): resolve sqlite_dir relative to CWD, not Paths.base_dir

resolve_path() resolves relative to Paths.base_dir (.deer-flow),
which double-nested the path to .deer-flow/.deer-flow/data/app.db.
Use Path.resolve() (CWD-relative) instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Feature/feishu receive file (#1608)

* feat(feishu): add channel file materialization hook for inbound messages

- Introduce Channel.receive_file(msg, thread_id) as a base method for file materialization; default is no-op.
- Implement FeishuChannel.receive_file to download files/images from Feishu messages, save to sandbox, and inject virtual paths into msg.text.
- Update ChannelManager to call receive_file for any channel if msg.files is present, enabling downstream model access to user-uploaded files.
- No impact on Slack/Telegram or other channels (they inherit the default no-op).

* style(backend): format code with ruff for lint compliance

- Auto-formatted packages/harness/deerflow/agents/factory.py and tests/test_create_deerflow_agent.py using `ruff format`
- Ensured both files conform to project linting standards
- Fixes CI lint check failures caused by code style issues

* fix(feishu): handle file write operation asynchronously to prevent blocking

* fix(feishu): rename GetMessageResourceRequest to _GetMessageResourceRequest and remove redundant code

* test(feishu): add tests for receive_file method and placeholder replacement

* fix(manager): remove unnecessary type casting for channel retrieval

* fix(feishu): update logging messages to reflect resource handling instead of image

* fix(feishu): sanitize filename by replacing invalid characters in file uploads

* fix(feishu): improve filename sanitization and reorder image key handling in message processing

* fix(feishu): add thread lock to prevent filename conflicts during file downloads

* fix(test): correct bad merge in test_feishu_parser.py

* chore: run ruff and apply formatting cleanup
fix(feishu): preserve rich-text attachment order and improve fallback filename handling

* fix(docker): restore gateway env vars and fix langgraph empty arg issue (#1915)

Two production docker-compose.yaml bugs prevent `make up` from working:

1. Gateway missing DEER_FLOW_CONFIG_PATH and DEER_FLOW_EXTENSIONS_CONFIG_PATH
   environment overrides. Added in fb2d99f (#1836) but accidentally reverted
   by ca2fb95 (#1847). Without them, gateway reads host paths from .env via
   env_file, causing FileNotFoundError inside the container.

2. Langgraph command fails when LANGGRAPH_ALLOW_BLOCKING is unset (default).
   Empty $${allow_blocking} inserts a bare space between flags, causing
   ' --no-reload' to be parsed as unexpected extra argument. Fix by building
   args string first and conditionally appending --allow-blocking.

Co-authored-by: cooper <cooperfu@tencent.com>

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities (#1904)

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities

Fix `<button>` inside `<a>` invalid HTML in artifact components and add
missing `noopener,noreferrer` to `window.open` calls to prevent reverse
tabnabbing.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(frontend): address Copilot review on tabnabbing and double-tab-open

Remove redundant parent onClick on web_fetch ChainOfThoughtStep to
prevent opening two tabs on link click, and explicitly null out
window.opener after window.open() for defensive tabnabbing hardening.

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* refactor(persistence): organize entities into per-entity directories

Restructure the persistence layer from horizontal "models/ + repositories/"
split into vertical entity-aligned directories. Each entity (thread_meta,
run, feedback) now owns its ORM model, abstract interface (where applicable),
and concrete implementations under a single directory with an aggregating
__init__.py for one-line imports.

Layout:
  persistence/thread_meta/{base,model,sql,memory}.py
  persistence/run/{model,sql}.py
  persistence/feedback/{model,sql}.py

models/__init__.py is kept as a facade so Alembic autogenerate continues to
discover all ORM tables via Base.metadata. RunEventRow remains under
models/run_event.py because its storage implementation lives in
runtime/events/store/db.py and has no matching repository directory.

The repositories/ directory is removed entirely. All call sites in
gateway/deps.py and tests are updated to import from the new entity
packages, e.g.:

    from deerflow.persistence.thread_meta import ThreadMetaRepository
    from deerflow.persistence.run import RunRepository
    from deerflow.persistence.feedback import FeedbackRepository

Full test suite passes (1690 passed, 14 skipped).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(gateway): sync thread rename and delete through ThreadMetaStore

The POST /threads/{id}/state endpoint previously synced title changes
only to the LangGraph Store via _store_upsert. In sqlite mode the search
endpoint reads from the ThreadMetaRepository SQL table, so renames never
appeared in /threads/search until the next agent run completed (worker.py
syncs title from checkpoint to thread_meta in its finally block).

Likewise the DELETE /threads/{id} endpoint cleaned up the filesystem,
Store, and checkpointer but left the threads_meta row orphaned in sqlite,
so deleted threads kept appearing in /threads/search.

Fix both endpoints by routing through the ThreadMetaStore abstraction
which already has the correct sqlite/memory implementations wired up by
deps.py. The rename path now calls update_display_name() and the delete
path calls delete() — both work uniformly across backends.

Verified end-to-end with curl in gateway mode against sqlite backend.
Existing test suite (1690 passed) and focused router/repo tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): route all thread metadata access through ThreadMetaStore

Following the rename/delete bug fix in PR1, migrate the remaining direct
LangGraph Store reads/writes in the threads router and services to the
ThreadMetaStore abstraction so that the sqlite and memory backends behave
identically and the legacy dual-write paths can be removed.

Migrated endpoints (threads.py):
- create_thread: idempotency check + write now use thread_meta_repo.get/create
  instead of dual-writing the LangGraph Store and the SQL row.
- get_thread: reads from thread_meta_repo.get; the checkpoint-only fallback
  for legacy threads is preserved.
- patch_thread: replaced _store_get/_store_put with thread_meta_repo.update_metadata.
- delete_thread_data: dropped the legacy store.adelete; thread_meta_repo.delete
  already covers it.

Removed dead code (services.py):
- _upsert_thread_in_store — redundant with the immediately following
  thread_meta_repo.create() call.
- _sync_thread_title_after_run — worker.py's finally block already syncs
  the title via thread_meta_repo.update_display_name() after each run.

Removed dead code (threads.py):
- _store_get / _store_put / _store_upsert helpers (no remaining callers).
- THREADS_NS constant.
- get_store import (router no longer touches the LangGraph Store directly).

New abstract method:
- ThreadMetaStore.update_metadata(thread_id, metadata) merges metadata into
  the thread's metadata field. Implemented in both ThreadMetaRepository (SQL,
  read-modify-write inside one session) and MemoryThreadMetaStore. Three new
  unit tests cover merge / empty / nonexistent behaviour.

Net change: -134 lines. Full test suite: 1693 passed, 14 skipped.
Verified end-to-end with curl in gateway mode against sqlite backend
(create / patch / get / rename / search / delete).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: JilongSun <965640067@qq.com>
Co-authored-by: jie <49781832+stan-fu@users.noreply.github.com>
Co-authored-by: cooper <cooperfu@tencent.com>
Co-authored-by: yangzheli <43645580+yangzheli@users.noreply.github.com>

* feat(auth): release-validation pass for 2.0-rc — 12 blockers + simplify follow-ups (#2008)

* feat(auth): introduce backend auth module

Port RFC-001 authentication core from PR #1728:
- JWT token handling (create_access_token, decode_token, TokenPayload)
- Password hashing (bcrypt) with verify_password
- SQLite UserRepository with base interface
- Provider Factory pattern (LocalAuthProvider)
- CLI reset_admin tool
- Auth-specific errors (AuthErrorCode, TokenError, AuthErrorResponse)

Deps:
- bcrypt>=4.0.0
- pyjwt>=2.9.0
- email-validator>=2.0.0
- backend/uv.toml pins public PyPI index

Tests: 12 pure unit tests (test_auth_config.py, test_auth_errors.py).

Scope note: authz.py, test_auth.py, and test_auth_type_system.py are
deferred to commit 2 because they depend on middleware and deps wiring
that is not yet in place. Commit 1 stays "pure new files only" as the
spec mandates.

* feat(auth): wire auth end-to-end (middleware + frontend replacement)

Backend:
- Port auth_middleware, csrf_middleware, langgraph_auth, routers/auth
- Port authz decorator (owner_filter_key defaults to 'owner_id')
- Merge app.py: register AuthMiddleware + CSRFMiddleware + CORS, add
  _ensure_admin_user lifespan hook, _migrate_orphaned_threads helper,
  register auth router
- Merge deps.py: add get_local_provider, get_current_user_from_request,
  get_optional_user_from_request; keep get_current_user as thin str|None
  adapter for feedback router
- langgraph.json: add auth path pointing to langgraph_auth.py:auth
- Rename metadata['user_id'] -> metadata['owner_id'] in langgraph_auth
  (both metadata write and LangGraph filter dict) + test fixtures

Frontend:
- Delete better-auth library and api catch-all route
- Remove better-auth npm dependency and env vars (BETTER_AUTH_SECRET,
  BETTER_AUTH_GITHUB_*) from env.js
- Port frontend/src/core/auth/* (AuthProvider, gateway-config,
  proxy-policy, server-side getServerSideUser, types)
- Port frontend/src/core/api/fetcher.ts
- Port (auth)/layout, (auth)/login, (auth)/setup pages
- Rewrite workspace/layout.tsx as server component that calls
  getServerSideUser and wraps in AuthProvider
- Port workspace/workspace-content.tsx for the client-side sidebar logic

Tests:
- Port 5 auth test files (test_auth, test_auth_middleware,
  test_auth_type_system, test_ensure_admin, test_langgraph_auth)
- 176 auth tests PASS

After this commit: login/logout/registration flow works, but persistence
layer does not yet filter by owner_id. Commit 4 closes that gap.

* feat(auth): account settings page + i18n

- Port account-settings-page.tsx (change password, change email, logout)
- Wire into settings-dialog.tsx as new "account" section with UserIcon,
  rendered first in the section list
- Add i18n keys:
  - en-US/zh-CN: settings.sections.account ("Account" / "账号")
  - en-US/zh-CN: button.logout ("Log out" / "退出登录")
  - types.ts: matching type declarations

* feat(auth): enforce owner_id across 2.0-rc persistence layer

Add request-scoped contextvar-based owner filtering to threads_meta,
runs, run_events, and feedback repositories. Router code is unchanged
— isolation is enforced at the storage layer so that any caller that
forgets to pass owner_id still gets filtered results, and new routes
cannot accidentally leak data.

Core infrastructure
-------------------
- deerflow/runtime/user_context.py (new):
  - ContextVar[CurrentUser | None] with default None
  - runtime_checkable CurrentUser Protocol (structural subtype with .id)
  - set/reset/get/require helpers
  - AUTO sentinel + resolve_owner_id(value, method_name) for sentinel
    three-state resolution: AUTO reads contextvar, explicit str
    overrides, explicit None bypasses the filter (for migration/CLI)

Repository changes
------------------
- ThreadMetaRepository: create/get/search/update_*/delete gain
  owner_id=AUTO kwarg; read paths filter by owner, writes stamp it,
  mutations check ownership before applying
- RunRepository: put/get/list_by_thread/delete gain owner_id=AUTO kwarg
- FeedbackRepository: create/get/list_by_run/list_by_thread/delete
  gain owner_id=AUTO kwarg
- DbRunEventStore: list_messages/list_events/list_messages_by_run/
  count_messages/delete_by_thread/delete_by_run gain owner_id=AUTO
  kwarg. Write paths (put/put_batch) read contextvar softly: when a
  request-scoped user is available, owner_id is stamped; background
  worker writes without a user context pass None which is valid
  (orphan row to be bound by migration)

Schema
------
- persistence/models/run_event.py: RunEventRow.owner_id = Mapped[
  str | None] = mapped_column(String(64), nullable=True, index=True)
- No alembic migration needed: 2.0 ships fresh, Base.metadata.create_all
  picks up the new column automatically

Middleware
----------
- auth_middleware.py: after cookie check, call get_optional_user_from_
  request to load the real User, stamp it into request.state.user AND
  the contextvar via set_current_user, reset in a try/finally. Public
  paths and unauthenticated requests continue without contextvar, and
  @require_auth handles the strict 401 path

Test infrastructure
-------------------
- tests/conftest.py: @pytest.fixture(autouse=True) _auto_user_context
  sets a default SimpleNamespace(id="test-user-autouse") on every test
  unless marked @pytest.mark.no_auto_user. Keeps existing 20+
  persistence tests passing without modification
- pyproject.toml [tool.pytest.ini_options]: register no_auto_user
  marker so pytest does not emit warnings for opt-out tests
- tests/test_user_context.py: 6 tests covering three-state semantics,
  Protocol duck typing, and require/optional APIs
- tests/test_thread_meta_repo.py: one test updated to pass owner_id=
  None explicitly where it was previously relying on the old default

Test results
------------
- test_user_context.py: 6 passed
- test_auth*.py + test_langgraph_auth.py + test_ensure_admin.py: 127
- test_run_event_store / test_run_repository / test_thread_meta_repo
  / test_feedback: 92 passed
- Full backend suite: 1905 passed, 2 failed (both @requires_llm flaky
  integration tests unrelated to auth), 1 skipped

* feat(auth): extend orphan migration to 2.0-rc persistence tables

_ensure_admin_user now runs a three-step pipeline on every boot:

  Step 1 (fatal):     admin user exists / is created / password is reset
  Step 2 (non-fatal): LangGraph store orphan threads → admin
  Step 3 (non-fatal): SQL persistence tables → admin
    - threads_meta
    - runs
    - run_events
    - feedback

Each step is idempotent. The fatal/non-fatal split mirrors PR #1728's
original philosophy: admin creation failure blocks startup (the system
is unusable without an admin), whereas migration failures log a warning
and let the service proceed (a partial migration is recoverable; a
missing admin is not).

Key helpers
-----------
- _iter_store_items(store, namespace, *, page_size=500):
  async generator that cursor-paginates across LangGraph store pages.
  Fixes PR #1728's hardcoded limit=1000 bug that would silently lose
  orphans beyond the first page.

- _migrate_orphaned_threads(store, admin_user_id):
  Rewritten to use _iter_store_items. Returns the migrated count so the
  caller can log it; raises only on unhandled exceptions.

- _migrate_orphan_sql_tables(admin_user_id):
  Imports the 4 ORM models lazily, grabs the shared session factory,
  runs one UPDATE per table in a single transaction, commits once.
  No-op when no persistence backend is configured (in-memory dev).

Tests: test_ensure_admin.py (8 passed)

* test(auth): port AUTH test plan docs + lint/format pass

- Port backend/docs/AUTH_TEST_PLAN.md and AUTH_UPGRADE.md from PR #1728
- Rename metadata.user_id → metadata.owner_id in AUTH_TEST_PLAN.md
  (4 occurrences from the original PR doc)
- ruff auto-fix UP037 in sentinel type annotations: drop quotes around
  "str | None | _AutoSentinel" now that from __future__ import
  annotations makes them implicit string forms
- ruff format: 2 files (app/gateway/app.py, runtime/user_context.py)

Note on test coverage additions:
- conftest.py autouse fixture was already added in commit 4 (had to
  be co-located with the repository changes to keep pre-existing
  persistence tests passing)
- cross-user isolation E2E tests (test_owner_isolation.py) deferred
  — enforcement is already proven by the 98-test repository suite
  via the autouse fixture + explicit _AUTO sentinel exercises
- New test cases (TC-API-17..20, TC-ATK-13, TC-MIG-01..07) listed
  in AUTH_TEST_PLAN.md are deferred to a follow-up PR — they are
  manual-QA test cases rather than pytest code, and the spec-level
  coverage is already met by test_user_context.py + the 98-test
  repository suite.

Final test results:
- Auth suite (test_auth*, test_langgraph_auth, test_ensure_admin,
  test_user_context): 186 passed
- Persistence suite (test_run_event_store, test_run_repository,
  test_thread_meta_repo, test_feedback): 98 passed
- Lint: ruff check + ruff format both clean

* test(auth): add cross-user isolation test suite

10 tests exercising the storage-layer owner filter by manually
switching the user_context contextvar between two users. Verifies
the safety invariant:

  After a repository write with owner_id=A, a subsequent read with
  owner_id=B must not return the row, and vice versa.

Covers all 4 tables that own user-scoped data:

TC-API-17  threads_meta  — read, search, update, delete cross-user
TC-API-18  runs          — get, list_by_thread, delete cross-user
TC-API-19  run_events    — list_messages, list_events, count_messages,
                           delete_by_thread (CRITICAL: raw conversation
                           content leak vector)
TC-API-20  feedback      — get, list_by_run, delete cross-user

Plus two meta-tests verifying the sentinel pattern itself:
- AUTO + unset contextvar raises RuntimeError
- explicit owner_id=None bypasses the filter (migration escape hatch)

Architecture note
-----------------
These tests bypass the HTTP layer by design. The full chain
(cookie → middleware → contextvar → repository) is covered piecewise:

- test_auth_middleware.py: middleware sets contextvar from cookies
- test_owner_isolation.py: repositories enforce isolation when
  contextvar is set to different users

Together they prove the end-to-end safety property without the
ceremony of spinning up a full TestClient + in-memory DB for every
router endpoint.

Tests pass: 231 (full auth + persistence + isolation suite)
Lint: clean

* refactor(auth): migrate user repository to SQLAlchemy ORM

Move the users table into the shared persistence engine so auth
matches the pattern of threads_meta, runs, run_events, and feedback —
one engine, one session factory, one schema init codepath.

New files
---------
- persistence/user/__init__.py, persistence/user/model.py: UserRow
  ORM class with partial unique index on (oauth_provider, oauth_id)
- Registered in persistence/models/__init__.py so
  Base.metadata.create_all() picks it up

Modified
--------
- auth/repositories/sqlite.py: rewritten as async SQLAlchemy,
  identical constructor pattern to the other four repositories
  (def __init__(self, session_factory) + self._sf = session_factory)
- auth/config.py: drop users_db_path field — storage is configured
  through config.database like every other table
- deps.py/get_local_provider: construct SQLiteUserRepository with
  the shared session factory, fail fast if engine is not initialised
- tests/test_auth.py: rewrite test_sqlite_round_trip_new_fields to
  use the shared engine (init_engine + close_engine in a tempdir)
- tests/test_auth_type_system.py: add per-test autouse fixture that
  spins up a scratch engine and resets deps._cached_* singletons

* refactor(auth): remove SQL orphan migration (unused in supported scenarios)

The _migrate_orphan_sql_tables helper existed to bind NULL owner_id
rows in threads_meta, runs, run_events, and feedback to the admin on
first boot. But in every supported upgrade path, it's a no-op:

  1. Fresh install: create_all builds fresh tables, no legacy rows
  2. No-auth → with-auth (no existing persistence DB): persistence
     tables are created fresh by create_all, no legacy rows
  3. No-auth → with-auth (has existing persistence DB from #1930):
     NOT a supported upgrade path — "有 DB 到有 DB" schema evolution
     is out of scope; users wipe DB or run manual ALTER

So the SQL orphan migration never has anything to do in the
supported matrix. Delete the function, simplify _ensure_admin_user
from a 3-step pipeline to a 2-step one (admin creation + LangGraph
store orphan migration only).

LangGraph store orphan migration stays: it serves the real
"no-auth → with-auth" upgrade path where a user's existing LangGraph
thread metadata has no owner_id field and needs to be stamped with
the newly-created admin's id.

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): write initial admin password to 0600 file instead of logs

CodeQL py/clear-text-logging-sensitive-data flagged 3 call sites that
logged the auto-generated admin password to stdout via logger.info().
Production log aggregators (ELK/Splunk/etc) would have captured those
cleartext secrets. Replace with a shared helper that writes to
.deer-flow/admin_initial_credentials.txt with mode 0600, and log only
the path.

New file
--------
- app/gateway/auth/credential_file.py: write_initial_credentials()
  helper. Takes email, password, and a "initial"/"reset" label.
  Creates .deer-flow/ if missing, writes a header comment plus the
  email+password, chmods 0o600, returns the absolute Path.

Modified
--------
- app/gateway/app.py: both _ensure_admin_user paths (fresh creation
  + needs_setup password reset) now write to file and log the path
- app/gateway/auth/reset_admin.py: rewritten to use the shared ORM
  repo (SQLiteUserRepository with session_factory) and the
  credential_file helper. The previous implementation was broken
  after the earlier ORM refactor — it still imported _get_users_conn
  and constructed SQLiteUserRepository() without a session factory.

No tests changed — the three password-log sites are all exercised
via existing test_ensure_admin.py which checks that startup
succeeds, not that a specific string appears in logs.

CodeQL alerts 272, 283, 284: all resolved.

* security(auth): strict JWT validation in middleware (fix junk cookie bypass)

AUTH_TEST_PLAN test 7.5.8 expects junk cookies to be rejected with
401. The previous middleware behaviour was "presence-only": check
that some access_token cookie exists, then pass through. In
combination with my Task-12 decision to skip @require_auth
decorators on routes, this created a gap where a request with any
cookie-shaped string (e.g. access_token=not-a-jwt) would bypass
authentication on routes that do not touch the repository
(/api/models, /api/mcp/config, /api/memory, /api/skills, …).

Fix: middleware now calls get_current_user_from_request() strictly
and catches the resulting HTTPException to render a 401 with the
proper fine-grained error code (token_invalid, token_expired,
user_not_found, …). On success it stamps request.state.user and
the contextvar so repository-layer owner filters work downstream.

The 4 old "_with_cookie_passes" tests in test_auth_middleware.py
were written for the presence-only behaviour; they asserted that
a junk cookie would make the handler return 200. They are renamed
to "_with_junk_cookie_rejected" and their assertions flipped to
401. The negative path (no cookie → 401 not_authenticated)
is unchanged.

Verified:
  no cookie       → 401 not_authenticated
  junk cookie     → 401 token_invalid     (the fixed bug)
  expired cookie  → 401 token_expired

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): wire @require_permission(owner_check=True) on isolation routes

Apply the require_permission decorator to all 28 routes that take a
{thread_id} path parameter. Combined with the strict middleware
(previous commit), this gives the double-layer protection that
AUTH_TEST_PLAN test 7.5.9 documents:

  Layer 1 (AuthMiddleware): cookie + JWT validation, rejects junk
                            cookies and stamps request.state.user
  Layer 2 (@require_permission with owner_check=True): per-resource
                            ownership verification via
                            ThreadMetaStore.check_access — returns
                            404 if a different user owns the thread

The decorator's owner_check branch is rewritten to use the SQL
thread_meta_repo (the 2.0-rc persistence layer) instead of the
LangGraph store path that PR #1728 used (_store_get / get_store
in routers/threads.py). The inject_record convenience is dropped
— no caller in 2.0 needs the LangGraph blob, and the SQL repo has
a different shape.

Routes decorated (28 total):
- threads.py: delete, patch, get, get-state, post-state, post-history
- thread_runs.py: post-runs, post-runs-stream, post-runs-wait,
  list_runs, get_run, cancel_run, join_run, stream_existing_run,
  list_thread_messages, list_run_messages, list_run_events,
  thread_token_usage
- feedback.py: create, list, stats, delete
- uploads.py: upload (added Request param), list, delete
- artifacts.py: get_artifact
- suggestions.py: generate (renamed body parameter to avoid
  conflict with FastAPI Request)

Test fixes:
- test_suggestions_router.py: bypass the decorator via __wrapped__
  (the unit tests cover parsing logic, not auth — no point spinning
  up a thread_meta_repo just to test JSON unwrapping)
- test_auth_middleware.py 4 fake-cookie tests: already updated in
  the previous commit (745bf432)

Tests: 293 passed (auth + persistence + isolation + suggestions)
Lint: clean

* security(auth): defense-in-depth fixes from release validation pass

Eight findings caught while running the AUTH_TEST_PLAN end-to-end against
the deployed sg_dev stack. Each is a pre-condition for shipping
release/2.0-rc that the previous PRs missed.

Backend hardening
- routers/auth.py: rate limiter X-Real-IP now requires AUTH_TRUSTED_PROXIES
  whitelist (CIDR/IP allowlist). Without nginx in front, the previous code
  honored arbitrary X-Real-IP, letting an attacker rotate the header to
  fully bypass the per-IP login lockout.
- routers/auth.py: 36-entry common-password blocklist via Pydantic
  field_validator on RegisterRequest + ChangePasswordRequest. The shared
  _validate_strong_password helper keeps the constraint in one place.
- routers/threads.py: ThreadCreateRequest + ThreadPatchRequest strip
  server-reserved metadata keys (owner_id, user_id) via Pydantic
  field_validator so a forged value can never round-trip back to other
  clients reading the same thread. The actual ownership invariant stays
  on the threads_meta row; this closes the metadata-blob echo gap.
- authz.py + thread_meta/sql.py: require_permission gains a require_existing
  flag plumbed through check_access(require_existing=True). Destructive
  routes (DELETE/PATCH/state-update/runs/feedback) now treat a missing
  thread_meta row as 404 instead of "untracked legacy thread, allow",
  closing the cross-user delete-idempotence gap where any user could
  successfully DELETE another user's deleted thread.
- repositories/sqlite.py + base.py: update_user raises UserNotFoundError
  on a vanished row instead of silently returning the input. Concurrent
  delete during password reset can no longer look like a successful update.
- runtime/user_context.py: resolve_owner_id() coerces User.id (UUID) to
  str at the contextvar boundary so SQLAlchemy String(64) columns can
  bind it. The whole 2.0-rc isolation pipeline was previously broken
  end-to-end (POST /api/threads → 500 "type 'UUID' is not supported").
- persistence/engine.py: SQLAlchemy listener enables PRAGMA journal_mode=WAL,
  synchronous=NORMAL, foreign_keys=ON on every new SQLite connection.
  TC-UPG-06 in the test plan expects WAL; previous code shipped with the
  default 'delete' journal.
- auth_middleware.py: stamp request.state.auth = AuthContext(...) so
  @require_permission's short-circuit fires; previously every isolation
  request did a duplicate JWT decode + users SELECT. Also unifies the
  401 payload through AuthErrorResponse(...).model_dump().
- app.py: _ensure_admin_user restructure removes the noqa F821 scoping
  bug where 'password' was referenced outside the branch that defined it.
  New _announce_credentials helper absorbs the duplicate log block in
  the fresh-admin and reset-admin branches.

* fix(frontend+nginx): rollout CSRF on every state-changing client path

The frontend was 100% broken in gateway-pro mode for any user trying to
open a specific chat thread. Three cumulative bugs each silently
masked the next.

LangGraph SDK CSRF gap (api-client.ts)
- The Client constructor took only apiUrl, no defaultHeaders, no fetch
  interceptor. The SDK's internal fetch never sent X-CSRF-Token, so
  every state-changing /api/langgraph-compat/* call (runs/stream,
  threads/search, threads/{tid}/history, ...) hit CSRFMiddleware and
  got 403 before reaching the auth check. UI symptom: empty thread page
  with no error message; the SPA's hooks swallowed the rejection.
- Fix: pass an onRequest hook that injects X-CSRF-Token from the
  csrf_token cookie per request. Reading the cookie per call (not at
  construction time) handles login / logout / password-change cookie
  rotation transparently. The SDK's prepareFetchOptions calls
  onRequest for both regular requests AND streaming/SSE/reconnect, so
  the same hook covers runs.stream and runs.joinStream.

Raw fetch CSRF gap (7 files)
- Audit: 11 frontend fetch sites, only 2 included CSRF (login/setup +
  account-settings change-password). The other 7 routed through raw
  fetch() with no header — suggestions, memory, agents, mcp, skills,
  uploads, and the local thread cleanup hook all 403'd silently.
- Fix: enhance fetcher.ts:fetchWithAuth to auto-inject X-CSRF-Token on
  POST/PUT/DELETE/PATCH from a single shared readCsrfCookie() helper.
  Convert all 7 raw fetch() callers to fetchWithAuth so the contract
  is centrally enforced. api-client.ts and fetcher.ts share
  readCsrfCookie + STATE_CHANGING_METHODS to avoid drift.

nginx routing + buffering (nginx.local.conf)
- The auth feature shipped without updating the nginx config: per-API
  explicit location blocks but no /api/v1/auth/, /api/feedback, /api/runs.
  The frontend's client-side fetches to /api/v1/auth/login/local 404'd
  from the Next.js side because nginx routed /api/* to the frontend.
- Fix: add catch-all `location /api/` that proxies to the gateway.
  nginx longest-prefix matching keeps the explicit blocks (/api/models,
  /api/threads regex, /api/langgraph/, ...) winning for their paths.
- Fix: disable proxy_buffering + proxy_request_buffering for the
  frontend `location /` block. Without it, nginx tries to spool large
  Next.js chunks into /var/lib/nginx/proxy (root-owned) and fails with
  Permission denied → ERR_INCOMPLETE_CHUNKED_ENCODING → ChunkLoadError.

* test(auth): release-validation test infra and new coverage

Test fixtures and unit tests added during the validation pass.

Router test helpers (NEW: tests/_router_auth_helpers.py)
- make_authed_test_app(): builds a FastAPI test app with a stub
  middleware that stamps request.state.user + request.state.auth and a
  permissive thread_meta_repo mock. TestClient-based router tests
  (test_artifacts_router, test_threads_router) use it instead of bare
  FastAPI() so the new @require_permission(owner_check=True) decorators
  short-circuit cleanly.
- call_unwrapped(): walks the __wrapped__ chain to invoke the underlying
  handler without going through the authz wrappers. Direct-call tests
  (test_uploads_router) use it. Typed with ParamSpec so the wrapped
  signature flows through.

Backend test additions
- test_auth.py: 7 tests for the new _get_client_ip trust model (no
  proxy / trusted proxy / untrusted peer / XFF rejection / invalid
  CIDR / no client). 5 tests for the password blocklist (literal,
  case-insensitive, strong password accepted, change-password binding,
  short-password length-check still fires before blocklist).
  test_update_user_raises_when_row_concurrently_deleted: closes a
  shipped-without-coverage gap on the new UserNotFoundError contract.
- test_thread_meta_repo.py: 4 tests for check_access(require_existing=True)
  — strict missing-row denial, strict owner match, strict owner mismatch,
  strict null-owner still allowed (shared rows survive the tightening).
- test_ensure_admin.py: 3 tests for _migrate_orphaned_threads /
  _iter_store_items pagination, covering the TC-UPG-02 upgrade story
  end-to-end via mock store. Closes the gap where the cursor pagination
  was untested even though the previous PR rewrote it.
- test_threads_router.py: 5 tests for _strip_reserved_metadata
  (owner_id removal, user_id removal, safe-keys passthrough, empty
  input, both-stripped).
- test_auth_type_system.py: replace "password123" fixtures with
  Tr0ub4dor3a / AnotherStr0ngPwd! so the new password blocklist
  doesn't reject the test data.

* docs(auth): refresh TC-DOCKER-05 + document Docker validation gap

- AUTH_TEST_PLAN.md TC-DOCKER-05: the previous expectation
  ("admin password visible in docker logs") was stale after the simplify
  pass that moved credentials to a 0600 file. The grep "Password:" check
  would have silently failed and given a false sense of coverage. New
  expectation matches the actual file-based path: 0600 file in
  DEER_FLOW_HOME, log shows the path (not the secret), reverse-grep
  asserts no leaked password in container logs.
- NEW: docs/AUTH_TEST_DOCKER_GAP.md documents the only un-executed
  block in the test plan (TC-DOCKER-01..06). Reason: sg_dev validation
  host has no Docker daemon installed. The doc maps each Docker case
  to an already-validated bare-metal equivalent (TC-1.1, TC-REENT-01,
  TC-API-02 etc.) so the gap is auditable, and includes pre-flight
  reproduction steps for whoever has Docker available.

---------

Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>

* fix(persistence): stream hang when run_events.backend=db

DbRunEventStore._user_id_from_context() returned user.id without
coercing it to str. User.id is a Pydantic UUID, and aiosqlite cannot
bind a raw UUID object to a VARCHAR column, so the INSERT for the
initial human_message event silently rolled back and raised out of
the worker task. Because that put() sat outside the worker's try
block, the finally-clause that publishes end-of-stream never ran
and the SSE stream hung forever.

jsonl mode was unaffected because json.dumps(default=str) coerces
UUID objects transparently.

Fixes:
- db.py: coerce user.id to str at the context-read boundary (matches
  what resolve_user_id already does for the other repositories)
- worker.py: move RunJournal init + human_message put inside the try
  block so any failure flows through the finally/publish_end path
  instead of hanging the subscriber

Defense-in-depth:
- engine.py: add PRAGMA busy_timeout=5000 so checkpointer and event
  store wait for each other on the shared deerflow.db file instead
  of failing immediately under write-lock contention
- journal.py: skip fire-and-forget _flush_sync when a previous flush
  task is still in flight, to avoid piling up concurrent put_batch
  writes on the same SQLAlchemy engine during streaming; flush() now
  waits for pending tasks before draining the buffer
- database_config.py: doc-only update clarifying WAL + busy_timeout
  keep the unified deerflow.db safe for both workloads

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore(persistence): drop redundant busy_timeout PRAGMA

Python's sqlite3 driver defaults to a 5-second busy timeout via the
``timeout`` kwarg of ``sqlite3.connect``, and aiosqlite + SQLAlchemy's
aiosqlite dialect inherit that default. Setting ``PRAGMA busy_timeout=5000``
explicitly was a no-op — verified by reading back the PRAGMA on a fresh
connection (it already reports 5000ms without our PRAGMA).

Concurrent stress test (50 checkpoint writes + 20 event batches + 50
thread_meta updates on the same deerflow.db) still completes with zero
errors and 200/200 rows after removing the explicit PRAGMA.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(rebase): remove duplicate definitions and update stale module paths

Rebase left duplicate function blocks in worker.py (triple human_message
write causing 3x user messages in /history), deps.py, and prompt.py.
Also update checkpointer imports from the old deerflow.agents.checkpointer
path to deerflow.runtime.checkpointer, and clean up orphaned feedback
props in the frontend message components.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(user-context): add DEFAULT_USER_ID and get_effective_user_id helper

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(paths): add user-aware path methods with optional user_id parameter

Add _validate_user_id(), user_dir(), user_memory_file(), user_agent_memory_file()
and optional keyword-only user_id parameter to all thread-related path methods.
When user_id is provided, paths resolve under users/{user_id}/threads/{thread_id}/;
when omitted, legacy layout is preserved for backward compatibility.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(memory): add user_id to MemoryStorage interface for per-user isolation

Thread user_id through MemoryStorage.load/reload/save abstract methods and
FileMemoryStorage, re-keying the in-memory cache from bare agent_name to a
(user_id, agent_name) tuple to prevent cross-user cache collisions.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(memory): thread user_id through memory updater layer

Add `user_id` keyword-only parameter to all public updater functions
(_save_memory_to_file, get_memory_data, reload_memory_data, import_memory_data,
clear_memory_data, create/delete/update_memory_fact) and regular keyword param
to MemoryUpdater.update_memory + update_memory_from_conversation, propagating
it to every storage load/save/reload call.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(memory): capture user_id at enqueue time for async-safe thread isolation

Add user_id field to ConversationContext and MemoryUpdateQueue.add() so the
user identity is stored explicitly at request time, before threading.Timer
fires on a different thread where ContextVar values do not propagate.
MemoryMiddleware.after_agent() now calls get_effective_user_id() at enqueue
time and passes the value through to updater.update_memory().

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(isolation): wire user_id through all Paths and memory callsites

Pass user_id=get_effective_user_id() at every callsite that invokes
Paths methods or memory functions, enabling per-user filesystem isolation
throughout the harness and app layers.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(migration): add idempotent script for per-user data migration

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: update CLAUDE.md and config docs for per-user isolation

* feat(events): add pagination to list_messages_by_run on all store backends

Replicates the existing before_seq/after_seq/limit cursor-pagination pattern
from list_messages onto list_messages_by_run across the abstract interface,
MemoryRunEventStore, JsonlRunEventStore, and DbRunEventStore.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(api): add GET /api/runs/{run_id}/messages with cursor pagination

New endpoint resolves thread_id from the run record and delegates to
RunEventStore.list_messages_by_run for cursor-based pagination.
Ownership is enforced implicitly via RunStore.get() user filtering.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(api): add GET /api/runs/{run_id}/feedback

Delegates to FeedbackRepository.list_by_run via the existing _resolve_run
helper; includes tests for success, 404, empty list, and 503 (no DB).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(api): retrofit cursor pagination onto GET /threads/{tid}/runs/{rid}/messages

Replace bare list[dict] response with {data: [...], has_more: bool} envelope,
forwarding limit/before_seq/after_seq query params to the event store.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: add run-level API endpoints to CLAUDE.md routers table

* refactor(threads): remove event-store message loader and feedback from state/history endpoints

State and history endpoints now return messages purely from the
checkpointer's channel_values. The _get_event_store_messages helper
(which loaded the full event-store transcript with feedback attached)
is removed along with its tests. Frontend will use the dedicated
GET /api/runs/{run_id}/messages and /feedback endpoints instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: JilongSun <965640067@qq.com>
Co-authored-by: jie <49781832+stan-fu@users.noreply.github.com>
Co-authored-by: cooper <cooperfu@tencent.com>
Co-authored-by: yangzheli <43645580+yangzheli@users.noreply.github.com>
Co-authored-by: greatmengqi <chenmengqi.0376@gmail.com>
Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
2026-04-26 11:13:01 +08:00
foreleven 00a90bbd3d refactor: Remove init_token handling from admin initialization logic and related tests 2026-04-26 11:09:56 +08:00
JeffJiang 44d9953e2e feat: Add metadata and descriptions to various documentation pages in Chinese
- Added titles and descriptions to workspace usage, configuration, customization, design principles, installation, integration guide, lead agent, MCP integration, memory system, middleware, quick start, sandbox, skills, subagents, and tools documentation.
- Removed outdated API/Gateway reference and concepts glossary pages.
- Updated configuration reference to reflect current structure and removed unnecessary sections.
- Introduced new model provider documentation for Ark and updated the index page for model providers.
- Enhanced tutorials with titles and descriptions for better clarity and navigation.
2026-04-26 11:09:55 +08:00
rayhpeng 56d5fa3337 feat(persistence):Unified persistence layer with event store, feedback, and rebase cleanup (#2134)
* feat(persistence): add unified persistence layer with event store, token tracking, and feedback (#1930)

* feat(persistence): add SQLAlchemy 2.0 async ORM scaffold

Introduce a unified database configuration (DatabaseConfig) that
controls both the LangGraph checkpointer and the DeerFlow application
persistence layer from a single `database:` config section.

New modules:
- deerflow.config.database_config — Pydantic config with memory/sqlite/postgres backends
- deerflow.persistence — async engine lifecycle, DeclarativeBase with to_dict mixin, Alembic skeleton
- deerflow.runtime.runs.store — RunStore ABC + MemoryRunStore implementation

Gateway integration initializes/tears down the persistence engine in
the existing langgraph_runtime() context manager. Legacy checkpointer
config is preserved for backward compatibility.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add RunEventStore ABC + MemoryRunEventStore

Phase 2-A prerequisite for event storage: adds the unified run event
stream interface (RunEventStore) with an in-memory implementation,
RunEventsConfig, gateway integration, and comprehensive tests (27 cases).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add ORM models, repositories, DB/JSONL event stores, RunJournal, and API endpoints

Phase 2-B: run persistence + event storage + token tracking.

- ORM models: RunRow (with token fields), ThreadMetaRow, RunEventRow
- RunRepository implements RunStore ABC via SQLAlchemy ORM
- ThreadMetaRepository with owner access control
- DbRunEventStore with trace content truncation and cursor pagination
- JsonlRunEventStore with per-run files and seq recovery from disk
- RunJournal (BaseCallbackHandler) captures LLM/tool/lifecycle events,
  accumulates token usage by caller type, buffers and flushes to store
- RunManager now accepts optional RunStore for persistent backing
- Worker creates RunJournal, writes human_message, injects callbacks
- Gateway deps use factory functions (RunRepository when DB available)
- New endpoints: messages, run messages, run events, token-usage
- ThreadCreateRequest gains assistant_id field
- 92 tests pass (33 new), zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add user feedback + follow-up run association

Phase 2-C: feedback and follow-up tracking.

- FeedbackRow ORM model (rating +1/-1, optional message_id, comment)
- FeedbackRepository with CRUD, list_by_run/thread, aggregate stats
- Feedback API endpoints: create, list, stats, delete
- follow_up_to_run_id in RunCreateRequest (explicit or auto-detected
  from latest successful run on the thread)
- Worker writes follow_up_to_run_id into human_message event metadata
- Gateway deps: feedback_repo factory + getter
- 17 new tests (14 FeedbackRepository + 3 follow-up association)
- 109 total tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test+config: comprehensive Phase 2 test coverage + deprecate checkpointer config

- config.example.yaml: deprecate standalone checkpointer section, activate
  unified database:sqlite as default (drives both checkpointer + app data)
- New: test_thread_meta_repo.py (14 tests) — full ThreadMetaRepository coverage
  including check_access owner logic, list_by_owner pagination
- Extended test_run_repository.py (+4 tests) — completion preserves fields,
  list ordering desc, limit, owner_none returns all
- Extended test_run_journal.py (+8 tests) — on_chain_error, track_tokens=false,
  middleware no ai_message, unknown caller tokens, convenience fields,
  tool_error, non-summarization custom event
- Extended test_run_event_store.py (+7 tests) — DB batch seq continuity,
  make_run_event_store factory (memory/db/jsonl/fallback/unknown)
- Extended test_phase2b_integration.py (+4 tests) — create_or_reject persists,
  follow-up metadata, summarization in history, full DB-backed lifecycle
- Fixed DB integration test to use proper fake objects (not MagicMock)
  for JSON-serializable metadata
- 157 total Phase 2 tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* config: move default sqlite_dir to .deer-flow/data

Keep SQLite databases alongside other DeerFlow-managed data
(threads, memory) under the .deer-flow/ directory instead of a
top-level ./data folder.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): remove UTFJSON, use engine-level json_serializer + datetime.now()

- Replace custom UTFJSON type with standard sqlalchemy.JSON in all ORM
  models. Add json_serializer=json.dumps(ensure_ascii=False) to all
  create_async_engine calls so non-ASCII text (Chinese etc.) is stored
  as-is in both SQLite and Postgres.
- Change ORM datetime defaults from datetime.now(UTC) to datetime.now(),
  remove UTC imports.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): simplify deps.py with getter factory + inline repos

- Replace 6 identical getter functions with _require() factory.
- Inline 3 _make_*_repo() factories into langgraph_runtime(), call
  get_session_factory() once instead of 3 times.
- Add thread_meta upsert in start_run (services.py).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(docker): add UV_EXTRAS build arg for optional dependencies

Support installing optional dependency groups (e.g. postgres) at
Docker build time via UV_EXTRAS build arg:
  UV_EXTRAS=postgres docker compose build

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(journal): fix flush, token tracking, and consolidate tests

RunJournal fixes:
- _flush_sync: retain events in buffer when no event loop instead of
  dropping them; worker's finally block flushes via async flush().
- on_llm_end: add tool_calls filter and caller=="lead_agent" guard for
  ai_message events; mark message IDs for dedup with record_llm_usage.
- worker.py: persist completion data (tokens, message count) to RunStore
  in finally block.

Model factory:
- Auto-inject stream_usage=True for BaseChatOpenAI subclasses with
  custom api_base, so usage_metadata is populated in streaming responses.

Test consolidation:
- Delete test_phase2b_integration.py (redundant with existing tests).
- Move DB-backed lifecycle test into test_run_journal.py.
- Add tests for stream_usage injection in test_model_factory.py.
- Clean up executor/task_tool dead journal references.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): widen content type to str|dict in all store backends

Allow event content to be a dict (for structured OpenAI-format messages)
in addition to plain strings. Dict values are JSON-serialized for the DB
backend and deserialized on read; memory and JSONL backends handle dicts
natively. Trace truncation now serializes dicts to JSON before measuring.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(events): use metadata flag instead of heuristic for dict content detection

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(converters): add LangChain-to-OpenAI message format converters

Pure functions langchain_to_openai_message, langchain_to_openai_completion,
langchain_messages_to_openai, and _infer_finish_reason for converting
LangChain BaseMessage objects to OpenAI Chat Completions format, used by
RunJournal for event storage. 15 unit tests added.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(converters): handle empty list content as null, clean up test

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): human_message content uses OpenAI user message format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): ai_message uses OpenAI format, add ai_tool_call message event

- ai_message content now uses {"role": "assistant", "content": "..."} format
- New ai_tool_call message event emitted when lead_agent LLM responds with tool_calls
- ai_tool_call uses langchain_to_openai_message converter for consistent format
- Both events include finish_reason in metadata ("stop" or "tool_calls")

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add tool_result message event with OpenAI tool message format

Cache tool_call_id from on_tool_start keyed by run_id as fallback for on_tool_end,
then emit a tool_result message event (role=tool, tool_call_id, content) after each
successful tool completion.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): summary content uses OpenAI system message format

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): replace llm_start/llm_end with llm_request/llm_response in OpenAI format

Add on_chat_model_start to capture structured prompt messages as llm_request events.
Replace llm_end trace events with llm_response using OpenAI Chat Completions format.
Track llm_call_index to pair request/response events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add record_middleware method for middleware trace events

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test(events): add full run sequence integration test for OpenAI content format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): align message events with checkpoint format and add middleware tag injection

- Message events (ai_message, ai_tool_call, tool_result, human_message) now use
  BaseMessage.model_dump() format, matching LangGraph checkpoint values.messages
- on_tool_end extracts tool_call_id/name/status from ToolMessage objects
- on_tool_error now emits tool_result message events with error status
- record_middleware uses middleware:{tag} event_type and middleware category
- Summarization custom events use middleware:summarize category
- TitleMiddleware injects middleware:title tag via get_config() inheritance
- SummarizationMiddleware model bound with middleware:summarize tag
- Worker writes human_message using HumanMessage.model_dump()

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): switch search endpoint to threads_meta table and sync title

- POST /api/threads/search now queries threads_meta table directly,
  removing the two-phase Store + Checkpointer scan approach
- Add ThreadMetaRepository.search() with metadata/status filters
- Add ThreadMetaRepository.update_display_name() for title sync
- Worker syncs checkpoint title to threads_meta.display_name on run completion
- Map display_name to values.title in search response for API compatibility

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): history endpoint reads messages from event store

- POST /api/threads/{thread_id}/history now combines two data sources:
  checkpointer for checkpoint_id, metadata, title, thread_data;
  event store for messages (complete history, not truncated by summarization)
- Strip internal LangGraph metadata keys from response
- Remove full channel_values serialization in favor of selective fields

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: remove duplicate optional-dependencies header in pyproject.toml

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(middleware): pass tagged config to TitleMiddleware ainvoke call

Without the config, the middleware:title tag was not injected,
causing the LLM response to be recorded as a lead_agent ai_message
in run_events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: resolve merge conflict in .env.example

Keep both DATABASE_URL (from persistence-scaffold) and WECOM
credentials (from main) after the merge.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address review feedback on PR #1851

- Fix naive datetime.now() → datetime.now(UTC) in all ORM models
- Fix seq race condition in DbRunEventStore.put() with FOR UPDATE
  and UNIQUE(thread_id, seq) constraint
- Encapsulate _store access in RunManager.update_run_completion()
- Deduplicate _store.put() logic in RunManager via _persist_to_store()
- Add update_run_completion to RunStore ABC + MemoryRunStore
- Wire follow_up_to_run_id through the full create path
- Add error recovery to RunJournal._flush_sync() lost-event scenario
- Add migration note for search_threads breaking change
- Fix test_checkpointer_none_fix mock to set database=None

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore: update uv.lock

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address 22 review comments from CodeQL, Copilot, and Code Quality

Bug fixes:
- Sanitize log params to prevent log injection (CodeQL)
- Reset threads_meta.status to idle/error when run completes
- Attach messages only to latest checkpoint in /history response
- Write threads_meta on POST /threads so new threads appear in search

Lint fixes:
- Remove unused imports (journal.py, migrations/env.py, test_converters.py)
- Convert lambda to named function (engine.py, Ruff E731)
- Remove unused logger definitions in repos (Ruff F841)
- Add logging to JSONL decode errors and empty except blocks
- Separate assert side-effects in tests (CodeQL)
- Remove unused local variables in tests (Ruff F841)
- Fix max_trace_content truncation to use byte length, not char length

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style: apply ruff format to persistence and runtime files

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Potential fix for pull request finding 'Statement has no effect'

Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>

* refactor(runtime): introduce RunContext to reduce run_agent parameter bloat

Extract checkpointer, store, event_store, run_events_config, thread_meta_repo,
and follow_up_to_run_id into a frozen RunContext dataclass. Add get_run_context()
in deps.py to build the base context from app.state singletons. start_run() uses
dataclasses.replace() to enrich per-run fields before passing ctx to run_agent.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): move sanitize_log_param to app/gateway/utils.py

Extract the log-injection sanitizer from routers/threads.py into a shared
utils module and rename to sanitize_log_param (public API). Eliminates the
reverse service → router import in services.py.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* perf: use SQL aggregation for feedback stats and thread token usage

Replace Python-side counting in FeedbackRepository.aggregate_by_run with
a single SELECT COUNT/SUM query. Add RunStore.aggregate_tokens_by_thread
abstract method with SQL GROUP BY implementation in RunRepository and
Python fallback in MemoryRunStore. Simplify the thread_token_usage
endpoint to delegate to the new method, eliminating the limit=10000
truncation risk.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: annotate DbRunEventStore.put() as low-frequency path

Add docstring clarifying that put() opens a per-call transaction with
FOR UPDATE and should only be used for infrequent writes (currently
just the initial human_message event). High-throughput callers should
use put_batch() instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(threads): fall back to Store search when ThreadMetaRepository is unavailable

When database.backend=memory (default) or no SQL session factory is
configured, search_threads now queries the LangGraph Store instead of
returning 503. Returns empty list if neither Store nor repo is available.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): introduce ThreadMetaStore ABC for backend-agnostic thread metadata

Add ThreadMetaStore abstract base class with create/get/search/update/delete
interface. ThreadMetaRepository (SQL) now inherits from it. New
MemoryThreadMetaStore wraps LangGraph BaseStore for memory-mode deployments.

deps.py now always provides a non-None thread_meta_repo, eliminating all
`if thread_meta_repo is not None` guards in services.py, worker.py, and
routers/threads.py. search_threads no longer needs a Store fallback branch.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(history): read messages from checkpointer instead of RunEventStore

The /history endpoint now reads messages directly from the
checkpointer's channel_values (the authoritative source) instead of
querying RunEventStore.list_messages(). The RunEventStore API is
preserved for other consumers.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address new Copilot review comments

- feedback.py: validate thread_id/run_id before deleting feedback
- jsonl.py: add path traversal protection with ID validation
- run_repo.py: parse `before` to datetime for PostgreSQL compat
- thread_meta_repo.py: fix pagination when metadata filter is active
- database_config.py: use resolve_path for sqlite_dir consistency

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Implement skill self-evolution and skill_manage flow (#1874)

* chore: ignore .worktrees directory

* Add skill_manage self-evolution flow

* Fix CI regressions for skill_manage

* Address PR review feedback for skill evolution

* fix(skill-evolution): preserve history on delete

* fix(skill-evolution): tighten scanner fallbacks

* docs: add skill_manage e2e evidence screenshot

* fix(skill-manage): avoid blocking fs ops in session runtime

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

* fix(config): resolve sqlite_dir relative to CWD, not Paths.base_dir

resolve_path() resolves relative to Paths.base_dir (.deer-flow),
which double-nested the path to .deer-flow/.deer-flow/data/app.db.
Use Path.resolve() (CWD-relative) instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Feature/feishu receive file (#1608)

* feat(feishu): add channel file materialization hook for inbound messages

- Introduce Channel.receive_file(msg, thread_id) as a base method for file materialization; default is no-op.
- Implement FeishuChannel.receive_file to download files/images from Feishu messages, save to sandbox, and inject virtual paths into msg.text.
- Update ChannelManager to call receive_file for any channel if msg.files is present, enabling downstream model access to user-uploaded files.
- No impact on Slack/Telegram or other channels (they inherit the default no-op).

* style(backend): format code with ruff for lint compliance

- Auto-formatted packages/harness/deerflow/agents/factory.py and tests/test_create_deerflow_agent.py using `ruff format`
- Ensured both files conform to project linting standards
- Fixes CI lint check failures caused by code style issues

* fix(feishu): handle file write operation asynchronously to prevent blocking

* fix(feishu): rename GetMessageResourceRequest to _GetMessageResourceRequest and remove redundant code

* test(feishu): add tests for receive_file method and placeholder replacement

* fix(manager): remove unnecessary type casting for channel retrieval

* fix(feishu): update logging messages to reflect resource handling instead of image

* fix(feishu): sanitize filename by replacing invalid characters in file uploads

* fix(feishu): improve filename sanitization and reorder image key handling in message processing

* fix(feishu): add thread lock to prevent filename conflicts during file downloads

* fix(test): correct bad merge in test_feishu_parser.py

* chore: run ruff and apply formatting cleanup
fix(feishu): preserve rich-text attachment order and improve fallback filename handling

* fix(docker): restore gateway env vars and fix langgraph empty arg issue (#1915)

Two production docker-compose.yaml bugs prevent `make up` from working:

1. Gateway missing DEER_FLOW_CONFIG_PATH and DEER_FLOW_EXTENSIONS_CONFIG_PATH
   environment overrides. Added in fb2d99f (#1836) but accidentally reverted
   by ca2fb95 (#1847). Without them, gateway reads host paths from .env via
   env_file, causing FileNotFoundError inside the container.

2. Langgraph command fails when LANGGRAPH_ALLOW_BLOCKING is unset (default).
   Empty $${allow_blocking} inserts a bare space between flags, causing
   ' --no-reload' to be parsed as unexpected extra argument. Fix by building
   args string first and conditionally appending --allow-blocking.

Co-authored-by: cooper <cooperfu@tencent.com>

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities (#1904)

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities

Fix `<button>` inside `<a>` invalid HTML in artifact components and add
missing `noopener,noreferrer` to `window.open` calls to prevent reverse
tabnabbing.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(frontend): address Copilot review on tabnabbing and double-tab-open

Remove redundant parent onClick on web_fetch ChainOfThoughtStep to
prevent opening two tabs on link click, and explicitly null out
window.opener after window.open() for defensive tabnabbing hardening.

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* refactor(persistence): organize entities into per-entity directories

Restructure the persistence layer from horizontal "models/ + repositories/"
split into vertical entity-aligned directories. Each entity (thread_meta,
run, feedback) now owns its ORM model, abstract interface (where applicable),
and concrete implementations under a single directory with an aggregating
__init__.py for one-line imports.

Layout:
  persistence/thread_meta/{base,model,sql,memory}.py
  persistence/run/{model,sql}.py
  persistence/feedback/{model,sql}.py

models/__init__.py is kept as a facade so Alembic autogenerate continues to
discover all ORM tables via Base.metadata. RunEventRow remains under
models/run_event.py because its storage implementation lives in
runtime/events/store/db.py and has no matching repository directory.

The repositories/ directory is removed entirely. All call sites in
gateway/deps.py and tests are updated to import from the new entity
packages, e.g.:

    from deerflow.persistence.thread_meta import ThreadMetaRepository
    from deerflow.persistence.run import RunRepository
    from deerflow.persistence.feedback import FeedbackRepository

Full test suite passes (1690 passed, 14 skipped).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(gateway): sync thread rename and delete through ThreadMetaStore

The POST /threads/{id}/state endpoint previously synced title changes
only to the LangGraph Store via _store_upsert. In sqlite mode the search
endpoint reads from the ThreadMetaRepository SQL table, so renames never
appeared in /threads/search until the next agent run completed (worker.py
syncs title from checkpoint to thread_meta in its finally block).

Likewise the DELETE /threads/{id} endpoint cleaned up the filesystem,
Store, and checkpointer but left the threads_meta row orphaned in sqlite,
so deleted threads kept appearing in /threads/search.

Fix both endpoints by routing through the ThreadMetaStore abstraction
which already has the correct sqlite/memory implementations wired up by
deps.py. The rename path now calls update_display_name() and the delete
path calls delete() — both work uniformly across backends.

Verified end-to-end with curl in gateway mode against sqlite backend.
Existing test suite (1690 passed) and focused router/repo tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): route all thread metadata access through ThreadMetaStore

Following the rename/delete bug fix in PR1, migrate the remaining direct
LangGraph Store reads/writes in the threads router and services to the
ThreadMetaStore abstraction so that the sqlite and memory backends behave
identically and the legacy dual-write paths can be removed.

Migrated endpoints (threads.py):
- create_thread: idempotency check + write now use thread_meta_repo.get/create
  instead of dual-writing the LangGraph Store and the SQL row.
- get_thread: reads from thread_meta_repo.get; the checkpoint-only fallback
  for legacy threads is preserved.
- patch_thread: replaced _store_get/_store_put with thread_meta_repo.update_metadata.
- delete_thread_data: dropped the legacy store.adelete; thread_meta_repo.delete
  already covers it.

Removed dead code (services.py):
- _upsert_thread_in_store — redundant with the immediately following
  thread_meta_repo.create() call.
- _sync_thread_title_after_run — worker.py's finally block already syncs
  the title via thread_meta_repo.update_display_name() after each run.

Removed dead code (threads.py):
- _store_get / _store_put / _store_upsert helpers (no remaining callers).
- THREADS_NS constant.
- get_store import (router no longer touches the LangGraph Store directly).

New abstract method:
- ThreadMetaStore.update_metadata(thread_id, metadata) merges metadata into
  the thread's metadata field. Implemented in both ThreadMetaRepository (SQL,
  read-modify-write inside one session) and MemoryThreadMetaStore. Three new
  unit tests cover merge / empty / nonexistent behaviour.

Net change: -134 lines. Full test suite: 1693 passed, 14 skipped.
Verified end-to-end with curl in gateway mode against sqlite backend
(create / patch / get / rename / search / delete).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: JilongSun <965640067@qq.com>
Co-authored-by: jie <49781832+stan-fu@users.noreply.github.com>
Co-authored-by: cooper <cooperfu@tencent.com>
Co-authored-by: yangzheli <43645580+yangzheli@users.noreply.github.com>

* feat(auth): release-validation pass for 2.0-rc — 12 blockers + simplify follow-ups (#2008)

* feat(auth): introduce backend auth module

Port RFC-001 authentication core from PR #1728:
- JWT token handling (create_access_token, decode_token, TokenPayload)
- Password hashing (bcrypt) with verify_password
- SQLite UserRepository with base interface
- Provider Factory pattern (LocalAuthProvider)
- CLI reset_admin tool
- Auth-specific errors (AuthErrorCode, TokenError, AuthErrorResponse)

Deps:
- bcrypt>=4.0.0
- pyjwt>=2.9.0
- email-validator>=2.0.0
- backend/uv.toml pins public PyPI index

Tests: 12 pure unit tests (test_auth_config.py, test_auth_errors.py).

Scope note: authz.py, test_auth.py, and test_auth_type_system.py are
deferred to commit 2 because they depend on middleware and deps wiring
that is not yet in place. Commit 1 stays "pure new files only" as the
spec mandates.

* feat(auth): wire auth end-to-end (middleware + frontend replacement)

Backend:
- Port auth_middleware, csrf_middleware, langgraph_auth, routers/auth
- Port authz decorator (owner_filter_key defaults to 'owner_id')
- Merge app.py: register AuthMiddleware + CSRFMiddleware + CORS, add
  _ensure_admin_user lifespan hook, _migrate_orphaned_threads helper,
  register auth router
- Merge deps.py: add get_local_provider, get_current_user_from_request,
  get_optional_user_from_request; keep get_current_user as thin str|None
  adapter for feedback router
- langgraph.json: add auth path pointing to langgraph_auth.py:auth
- Rename metadata['user_id'] -> metadata['owner_id'] in langgraph_auth
  (both metadata write and LangGraph filter dict) + test fixtures

Frontend:
- Delete better-auth library and api catch-all route
- Remove better-auth npm dependency and env vars (BETTER_AUTH_SECRET,
  BETTER_AUTH_GITHUB_*) from env.js
- Port frontend/src/core/auth/* (AuthProvider, gateway-config,
  proxy-policy, server-side getServerSideUser, types)
- Port frontend/src/core/api/fetcher.ts
- Port (auth)/layout, (auth)/login, (auth)/setup pages
- Rewrite workspace/layout.tsx as server component that calls
  getServerSideUser and wraps in AuthProvider
- Port workspace/workspace-content.tsx for the client-side sidebar logic

Tests:
- Port 5 auth test files (test_auth, test_auth_middleware,
  test_auth_type_system, test_ensure_admin, test_langgraph_auth)
- 176 auth tests PASS

After this commit: login/logout/registration flow works, but persistence
layer does not yet filter by owner_id. Commit 4 closes that gap.

* feat(auth): account settings page + i18n

- Port account-settings-page.tsx (change password, change email, logout)
- Wire into settings-dialog.tsx as new "account" section with UserIcon,
  rendered first in the section list
- Add i18n keys:
  - en-US/zh-CN: settings.sections.account ("Account" / "账号")
  - en-US/zh-CN: button.logout ("Log out" / "退出登录")
  - types.ts: matching type declarations

* feat(auth): enforce owner_id across 2.0-rc persistence layer

Add request-scoped contextvar-based owner filtering to threads_meta,
runs, run_events, and feedback repositories. Router code is unchanged
— isolation is enforced at the storage layer so that any caller that
forgets to pass owner_id still gets filtered results, and new routes
cannot accidentally leak data.

Core infrastructure
-------------------
- deerflow/runtime/user_context.py (new):
  - ContextVar[CurrentUser | None] with default None
  - runtime_checkable CurrentUser Protocol (structural subtype with .id)
  - set/reset/get/require helpers
  - AUTO sentinel + resolve_owner_id(value, method_name) for sentinel
    three-state resolution: AUTO reads contextvar, explicit str
    overrides, explicit None bypasses the filter (for migration/CLI)

Repository changes
------------------
- ThreadMetaRepository: create/get/search/update_*/delete gain
  owner_id=AUTO kwarg; read paths filter by owner, writes stamp it,
  mutations check ownership before applying
- RunRepository: put/get/list_by_thread/delete gain owner_id=AUTO kwarg
- FeedbackRepository: create/get/list_by_run/list_by_thread/delete
  gain owner_id=AUTO kwarg
- DbRunEventStore: list_messages/list_events/list_messages_by_run/
  count_messages/delete_by_thread/delete_by_run gain owner_id=AUTO
  kwarg. Write paths (put/put_batch) read contextvar softly: when a
  request-scoped user is available, owner_id is stamped; background
  worker writes without a user context pass None which is valid
  (orphan row to be bound by migration)

Schema
------
- persistence/models/run_event.py: RunEventRow.owner_id = Mapped[
  str | None] = mapped_column(String(64), nullable=True, index=True)
- No alembic migration needed: 2.0 ships fresh, Base.metadata.create_all
  picks up the new column automatically

Middleware
----------
- auth_middleware.py: after cookie check, call get_optional_user_from_
  request to load the real User, stamp it into request.state.user AND
  the contextvar via set_current_user, reset in a try/finally. Public
  paths and unauthenticated requests continue without contextvar, and
  @require_auth handles the strict 401 path

Test infrastructure
-------------------
- tests/conftest.py: @pytest.fixture(autouse=True) _auto_user_context
  sets a default SimpleNamespace(id="test-user-autouse") on every test
  unless marked @pytest.mark.no_auto_user. Keeps existing 20+
  persistence tests passing without modification
- pyproject.toml [tool.pytest.ini_options]: register no_auto_user
  marker so pytest does not emit warnings for opt-out tests
- tests/test_user_context.py: 6 tests covering three-state semantics,
  Protocol duck typing, and require/optional APIs
- tests/test_thread_meta_repo.py: one test updated to pass owner_id=
  None explicitly where it was previously relying on the old default

Test results
------------
- test_user_context.py: 6 passed
- test_auth*.py + test_langgraph_auth.py + test_ensure_admin.py: 127
- test_run_event_store / test_run_repository / test_thread_meta_repo
  / test_feedback: 92 passed
- Full backend suite: 1905 passed, 2 failed (both @requires_llm flaky
  integration tests unrelated to auth), 1 skipped

* feat(auth): extend orphan migration to 2.0-rc persistence tables

_ensure_admin_user now runs a three-step pipeline on every boot:

  Step 1 (fatal):     admin user exists / is created / password is reset
  Step 2 (non-fatal): LangGraph store orphan threads → admin
  Step 3 (non-fatal): SQL persistence tables → admin
    - threads_meta
    - runs
    - run_events
    - feedback

Each step is idempotent. The fatal/non-fatal split mirrors PR #1728's
original philosophy: admin creation failure blocks startup (the system
is unusable without an admin), whereas migration failures log a warning
and let the service proceed (a partial migration is recoverable; a
missing admin is not).

Key helpers
-----------
- _iter_store_items(store, namespace, *, page_size=500):
  async generator that cursor-paginates across LangGraph store pages.
  Fixes PR #1728's hardcoded limit=1000 bug that would silently lose
  orphans beyond the first page.

- _migrate_orphaned_threads(store, admin_user_id):
  Rewritten to use _iter_store_items. Returns the migrated count so the
  caller can log it; raises only on unhandled exceptions.

- _migrate_orphan_sql_tables(admin_user_id):
  Imports the 4 ORM models lazily, grabs the shared session factory,
  runs one UPDATE per table in a single transaction, commits once.
  No-op when no persistence backend is configured (in-memory dev).

Tests: test_ensure_admin.py (8 passed)

* test(auth): port AUTH test plan docs + lint/format pass

- Port backend/docs/AUTH_TEST_PLAN.md and AUTH_UPGRADE.md from PR #1728
- Rename metadata.user_id → metadata.owner_id in AUTH_TEST_PLAN.md
  (4 occurrences from the original PR doc)
- ruff auto-fix UP037 in sentinel type annotations: drop quotes around
  "str | None | _AutoSentinel" now that from __future__ import
  annotations makes them implicit string forms
- ruff format: 2 files (app/gateway/app.py, runtime/user_context.py)

Note on test coverage additions:
- conftest.py autouse fixture was already added in commit 4 (had to
  be co-located with the repository changes to keep pre-existing
  persistence tests passing)
- cross-user isolation E2E tests (test_owner_isolation.py) deferred
  — enforcement is already proven by the 98-test repository suite
  via the autouse fixture + explicit _AUTO sentinel exercises
- New test cases (TC-API-17..20, TC-ATK-13, TC-MIG-01..07) listed
  in AUTH_TEST_PLAN.md are deferred to a follow-up PR — they are
  manual-QA test cases rather than pytest code, and the spec-level
  coverage is already met by test_user_context.py + the 98-test
  repository suite.

Final test results:
- Auth suite (test_auth*, test_langgraph_auth, test_ensure_admin,
  test_user_context): 186 passed
- Persistence suite (test_run_event_store, test_run_repository,
  test_thread_meta_repo, test_feedback): 98 passed
- Lint: ruff check + ruff format both clean

* test(auth): add cross-user isolation test suite

10 tests exercising the storage-layer owner filter by manually
switching the user_context contextvar between two users. Verifies
the safety invariant:

  After a repository write with owner_id=A, a subsequent read with
  owner_id=B must not return the row, and vice versa.

Covers all 4 tables that own user-scoped data:

TC-API-17  threads_meta  — read, search, update, delete cross-user
TC-API-18  runs          — get, list_by_thread, delete cross-user
TC-API-19  run_events    — list_messages, list_events, count_messages,
                           delete_by_thread (CRITICAL: raw conversation
                           content leak vector)
TC-API-20  feedback      — get, list_by_run, delete cross-user

Plus two meta-tests verifying the sentinel pattern itself:
- AUTO + unset contextvar raises RuntimeError
- explicit owner_id=None bypasses the filter (migration escape hatch)

Architecture note
-----------------
These tests bypass the HTTP layer by design. The full chain
(cookie → middleware → contextvar → repository) is covered piecewise:

- test_auth_middleware.py: middleware sets contextvar from cookies
- test_owner_isolation.py: repositories enforce isolation when
  contextvar is set to different users

Together they prove the end-to-end safety property without the
ceremony of spinning up a full TestClient + in-memory DB for every
router endpoint.

Tests pass: 231 (full auth + persistence + isolation suite)
Lint: clean

* refactor(auth): migrate user repository to SQLAlchemy ORM

Move the users table into the shared persistence engine so auth
matches the pattern of threads_meta, runs, run_events, and feedback —
one engine, one session factory, one schema init codepath.

New files
---------
- persistence/user/__init__.py, persistence/user/model.py: UserRow
  ORM class with partial unique index on (oauth_provider, oauth_id)
- Registered in persistence/models/__init__.py so
  Base.metadata.create_all() picks it up

Modified
--------
- auth/repositories/sqlite.py: rewritten as async SQLAlchemy,
  identical constructor pattern to the other four repositories
  (def __init__(self, session_factory) + self._sf = session_factory)
- auth/config.py: drop users_db_path field — storage is configured
  through config.database like every other table
- deps.py/get_local_provider: construct SQLiteUserRepository with
  the shared session factory, fail fast if engine is not initialised
- tests/test_auth.py: rewrite test_sqlite_round_trip_new_fields to
  use the shared engine (init_engine + close_engine in a tempdir)
- tests/test_auth_type_system.py: add per-test autouse fixture that
  spins up a scratch engine and resets deps._cached_* singletons

* refactor(auth): remove SQL orphan migration (unused in supported scenarios)

The _migrate_orphan_sql_tables helper existed to bind NULL owner_id
rows in threads_meta, runs, run_events, and feedback to the admin on
first boot. But in every supported upgrade path, it's a no-op:

  1. Fresh install: create_all builds fresh tables, no legacy rows
  2. No-auth → with-auth (no existing persistence DB): persistence
     tables are created fresh by create_all, no legacy rows
  3. No-auth → with-auth (has existing persistence DB from #1930):
     NOT a supported upgrade path — "有 DB 到有 DB" schema evolution
     is out of scope; users wipe DB or run manual ALTER

So the SQL orphan migration never has anything to do in the
supported matrix. Delete the function, simplify _ensure_admin_user
from a 3-step pipeline to a 2-step one (admin creation + LangGraph
store orphan migration only).

LangGraph store orphan migration stays: it serves the real
"no-auth → with-auth" upgrade path where a user's existing LangGraph
thread metadata has no owner_id field and needs to be stamped with
the newly-created admin's id.

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): write initial admin password to 0600 file instead of logs

CodeQL py/clear-text-logging-sensitive-data flagged 3 call sites that
logged the auto-generated admin password to stdout via logger.info().
Production log aggregators (ELK/Splunk/etc) would have captured those
cleartext secrets. Replace with a shared helper that writes to
.deer-flow/admin_initial_credentials.txt with mode 0600, and log only
the path.

New file
--------
- app/gateway/auth/credential_file.py: write_initial_credentials()
  helper. Takes email, password, and a "initial"/"reset" label.
  Creates .deer-flow/ if missing, writes a header comment plus the
  email+password, chmods 0o600, returns the absolute Path.

Modified
--------
- app/gateway/app.py: both _ensure_admin_user paths (fresh creation
  + needs_setup password reset) now write to file and log the path
- app/gateway/auth/reset_admin.py: rewritten to use the shared ORM
  repo (SQLiteUserRepository with session_factory) and the
  credential_file helper. The previous implementation was broken
  after the earlier ORM refactor — it still imported _get_users_conn
  and constructed SQLiteUserRepository() without a session factory.

No tests changed — the three password-log sites are all exercised
via existing test_ensure_admin.py which checks that startup
succeeds, not that a specific string appears in logs.

CodeQL alerts 272, 283, 284: all resolved.

* security(auth): strict JWT validation in middleware (fix junk cookie bypass)

AUTH_TEST_PLAN test 7.5.8 expects junk cookies to be rejected with
401. The previous middleware behaviour was "presence-only": check
that some access_token cookie exists, then pass through. In
combination with my Task-12 decision to skip @require_auth
decorators on routes, this created a gap where a request with any
cookie-shaped string (e.g. access_token=not-a-jwt) would bypass
authentication on routes that do not touch the repository
(/api/models, /api/mcp/config, /api/memory, /api/skills, …).

Fix: middleware now calls get_current_user_from_request() strictly
and catches the resulting HTTPException to render a 401 with the
proper fine-grained error code (token_invalid, token_expired,
user_not_found, …). On success it stamps request.state.user and
the contextvar so repository-layer owner filters work downstream.

The 4 old "_with_cookie_passes" tests in test_auth_middleware.py
were written for the presence-only behaviour; they asserted that
a junk cookie would make the handler return 200. They are renamed
to "_with_junk_cookie_rejected" and their assertions flipped to
401. The negative path (no cookie → 401 not_authenticated)
is unchanged.

Verified:
  no cookie       → 401 not_authenticated
  junk cookie     → 401 token_invalid     (the fixed bug)
  expired cookie  → 401 token_expired

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): wire @require_permission(owner_check=True) on isolation routes

Apply the require_permission decorator to all 28 routes that take a
{thread_id} path parameter. Combined with the strict middleware
(previous commit), this gives the double-layer protection that
AUTH_TEST_PLAN test 7.5.9 documents:

  Layer 1 (AuthMiddleware): cookie + JWT validation, rejects junk
                            cookies and stamps request.state.user
  Layer 2 (@require_permission with owner_check=True): per-resource
                            ownership verification via
                            ThreadMetaStore.check_access — returns
                            404 if a different user owns the thread

The decorator's owner_check branch is rewritten to use the SQL
thread_meta_repo (the 2.0-rc persistence layer) instead of the
LangGraph store path that PR #1728 used (_store_get / get_store
in routers/threads.py). The inject_record convenience is dropped
— no caller in 2.0 needs the LangGraph blob, and the SQL repo has
a different shape.

Routes decorated (28 total):
- threads.py: delete, patch, get, get-state, post-state, post-history
- thread_runs.py: post-runs, post-runs-stream, post-runs-wait,
  list_runs, get_run, cancel_run, join_run, stream_existing_run,
  list_thread_messages, list_run_messages, list_run_events,
  thread_token_usage
- feedback.py: create, list, stats, delete
- uploads.py: upload (added Request param), list, delete
- artifacts.py: get_artifact
- suggestions.py: generate (renamed body parameter to avoid
  conflict with FastAPI Request)

Test fixes:
- test_suggestions_router.py: bypass the decorator via __wrapped__
  (the unit tests cover parsing logic, not auth — no point spinning
  up a thread_meta_repo just to test JSON unwrapping)
- test_auth_middleware.py 4 fake-cookie tests: already updated in
  the previous commit (745bf432)

Tests: 293 passed (auth + persistence + isolation + suggestions)
Lint: clean

* security(auth): defense-in-depth fixes from release validation pass

Eight findings caught while running the AUTH_TEST_PLAN end-to-end against
the deployed sg_dev stack. Each is a pre-condition for shipping
release/2.0-rc that the previous PRs missed.

Backend hardening
- routers/auth.py: rate limiter X-Real-IP now requires AUTH_TRUSTED_PROXIES
  whitelist (CIDR/IP allowlist). Without nginx in front, the previous code
  honored arbitrary X-Real-IP, letting an attacker rotate the header to
  fully bypass the per-IP login lockout.
- routers/auth.py: 36-entry common-password blocklist via Pydantic
  field_validator on RegisterRequest + ChangePasswordRequest. The shared
  _validate_strong_password helper keeps the constraint in one place.
- routers/threads.py: ThreadCreateRequest + ThreadPatchRequest strip
  server-reserved metadata keys (owner_id, user_id) via Pydantic
  field_validator so a forged value can never round-trip back to other
  clients reading the same thread. The actual ownership invariant stays
  on the threads_meta row; this closes the metadata-blob echo gap.
- authz.py + thread_meta/sql.py: require_permission gains a require_existing
  flag plumbed through check_access(require_existing=True). Destructive
  routes (DELETE/PATCH/state-update/runs/feedback) now treat a missing
  thread_meta row as 404 instead of "untracked legacy thread, allow",
  closing the cross-user delete-idempotence gap where any user could
  successfully DELETE another user's deleted thread.
- repositories/sqlite.py + base.py: update_user raises UserNotFoundError
  on a vanished row instead of silently returning the input. Concurrent
  delete during password reset can no longer look like a successful update.
- runtime/user_context.py: resolve_owner_id() coerces User.id (UUID) to
  str at the contextvar boundary so SQLAlchemy String(64) columns can
  bind it. The whole 2.0-rc isolation pipeline was previously broken
  end-to-end (POST /api/threads → 500 "type 'UUID' is not supported").
- persistence/engine.py: SQLAlchemy listener enables PRAGMA journal_mode=WAL,
  synchronous=NORMAL, foreign_keys=ON on every new SQLite connection.
  TC-UPG-06 in the test plan expects WAL; previous code shipped with the
  default 'delete' journal.
- auth_middleware.py: stamp request.state.auth = AuthContext(...) so
  @require_permission's short-circuit fires; previously every isolation
  request did a duplicate JWT decode + users SELECT. Also unifies the
  401 payload through AuthErrorResponse(...).model_dump().
- app.py: _ensure_admin_user restructure removes the noqa F821 scoping
  bug where 'password' was referenced outside the branch that defined it.
  New _announce_credentials helper absorbs the duplicate log block in
  the fresh-admin and reset-admin branches.

* fix(frontend+nginx): rollout CSRF on every state-changing client path

The frontend was 100% broken in gateway-pro mode for any user trying to
open a specific chat thread. Three cumulative bugs each silently
masked the next.

LangGraph SDK CSRF gap (api-client.ts)
- The Client constructor took only apiUrl, no defaultHeaders, no fetch
  interceptor. The SDK's internal fetch never sent X-CSRF-Token, so
  every state-changing /api/langgraph-compat/* call (runs/stream,
  threads/search, threads/{tid}/history, ...) hit CSRFMiddleware and
  got 403 before reaching the auth check. UI symptom: empty thread page
  with no error message; the SPA's hooks swallowed the rejection.
- Fix: pass an onRequest hook that injects X-CSRF-Token from the
  csrf_token cookie per request. Reading the cookie per call (not at
  construction time) handles login / logout / password-change cookie
  rotation transparently. The SDK's prepareFetchOptions calls
  onRequest for both regular requests AND streaming/SSE/reconnect, so
  the same hook covers runs.stream and runs.joinStream.

Raw fetch CSRF gap (7 files)
- Audit: 11 frontend fetch sites, only 2 included CSRF (login/setup +
  account-settings change-password). The other 7 routed through raw
  fetch() with no header — suggestions, memory, agents, mcp, skills,
  uploads, and the local thread cleanup hook all 403'd silently.
- Fix: enhance fetcher.ts:fetchWithAuth to auto-inject X-CSRF-Token on
  POST/PUT/DELETE/PATCH from a single shared readCsrfCookie() helper.
  Convert all 7 raw fetch() callers to fetchWithAuth so the contract
  is centrally enforced. api-client.ts and fetcher.ts share
  readCsrfCookie + STATE_CHANGING_METHODS to avoid drift.

nginx routing + buffering (nginx.local.conf)
- The auth feature shipped without updating the nginx config: per-API
  explicit location blocks but no /api/v1/auth/, /api/feedback, /api/runs.
  The frontend's client-side fetches to /api/v1/auth/login/local 404'd
  from the Next.js side because nginx routed /api/* to the frontend.
- Fix: add catch-all `location /api/` that proxies to the gateway.
  nginx longest-prefix matching keeps the explicit blocks (/api/models,
  /api/threads regex, /api/langgraph/, ...) winning for their paths.
- Fix: disable proxy_buffering + proxy_request_buffering for the
  frontend `location /` block. Without it, nginx tries to spool large
  Next.js chunks into /var/lib/nginx/proxy (root-owned) and fails with
  Permission denied → ERR_INCOMPLETE_CHUNKED_ENCODING → ChunkLoadError.

* test(auth): release-validation test infra and new coverage

Test fixtures and unit tests added during the validation pass.

Router test helpers (NEW: tests/_router_auth_helpers.py)
- make_authed_test_app(): builds a FastAPI test app with a stub
  middleware that stamps request.state.user + request.state.auth and a
  permissive thread_meta_repo mock. TestClient-based router tests
  (test_artifacts_router, test_threads_router) use it instead of bare
  FastAPI() so the new @require_permission(owner_check=True) decorators
  short-circuit cleanly.
- call_unwrapped(): walks the __wrapped__ chain to invoke the underlying
  handler without going through the authz wrappers. Direct-call tests
  (test_uploads_router) use it. Typed with ParamSpec so the wrapped
  signature flows through.

Backend test additions
- test_auth.py: 7 tests for the new _get_client_ip trust model (no
  proxy / trusted proxy / untrusted peer / XFF rejection / invalid
  CIDR / no client). 5 tests for the password blocklist (literal,
  case-insensitive, strong password accepted, change-password binding,
  short-password length-check still fires before blocklist).
  test_update_user_raises_when_row_concurrently_deleted: closes a
  shipped-without-coverage gap on the new UserNotFoundError contract.
- test_thread_meta_repo.py: 4 tests for check_access(require_existing=True)
  — strict missing-row denial, strict owner match, strict owner mismatch,
  strict null-owner still allowed (shared rows survive the tightening).
- test_ensure_admin.py: 3 tests for _migrate_orphaned_threads /
  _iter_store_items pagination, covering the TC-UPG-02 upgrade story
  end-to-end via mock store. Closes the gap where the cursor pagination
  was untested even though the previous PR rewrote it.
- test_threads_router.py: 5 tests for _strip_reserved_metadata
  (owner_id removal, user_id removal, safe-keys passthrough, empty
  input, both-stripped).
- test_auth_type_system.py: replace "password123" fixtures with
  Tr0ub4dor3a / AnotherStr0ngPwd! so the new password blocklist
  doesn't reject the test data.

* docs(auth): refresh TC-DOCKER-05 + document Docker validation gap

- AUTH_TEST_PLAN.md TC-DOCKER-05: the previous expectation
  ("admin password visible in docker logs") was stale after the simplify
  pass that moved credentials to a 0600 file. The grep "Password:" check
  would have silently failed and given a false sense of coverage. New
  expectation matches the actual file-based path: 0600 file in
  DEER_FLOW_HOME, log shows the path (not the secret), reverse-grep
  asserts no leaked password in container logs.
- NEW: docs/AUTH_TEST_DOCKER_GAP.md documents the only un-executed
  block in the test plan (TC-DOCKER-01..06). Reason: sg_dev validation
  host has no Docker daemon installed. The doc maps each Docker case
  to an already-validated bare-metal equivalent (TC-1.1, TC-REENT-01,
  TC-API-02 etc.) so the gap is auditable, and includes pre-flight
  reproduction steps for whoever has Docker available.

---------

Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>

* refactor(persistence): unify SQLite to single deerflow.db and move checkpointer to runtime

Merge checkpoints.db and app.db into a single deerflow.db file (WAL mode
handles concurrent access safely). Move checkpointer module from
agents/checkpointer to runtime/checkpointer to better reflect its role
as a runtime infrastructure concern.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): rename owner_id to user_id and thread_meta_repo to thread_store

Rename owner_id to user_id across all persistence models, repositories,
stores, routers, and tests for clearer semantics. Rename thread_meta_repo
to thread_store for consistency with run_store/run_event_store naming.
Add ThreadMetaStore return type annotation to get_thread_store().

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): unify ThreadMetaStore interface with user isolation and factory

Add user_id parameter to all ThreadMetaStore abstract methods. Implement
owner isolation in MemoryThreadMetaStore with _get_owned_record helper.
Add check_access to base class and memory implementation. Add
make_thread_store factory to simplify deps.py initialization. Add
memory-backend isolation tests.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(feedback): add UNIQUE(thread_id, run_id, user_id) constraint

Add UNIQUE constraint to FeedbackRow to enforce one feedback per user per run,
enabling upsert behavior in Task 2. Update tests to use distinct user_ids for
multiple feedback records per run, and pass user_id=None to list_by_run for
admin-style queries that bypass user isolation.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(feedback): add upsert() method with UNIQUE enforcement

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): add delete_by_run() and list_by_thread_grouped()

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): add PUT upsert and DELETE-by-run endpoints

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): enrich messages endpoint with per-run feedback data

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): add frontend feedback API client

Adds upsertFeedback and deleteFeedback API functions backed by
fetchWithAuth, targeting the /api/threads/{id}/runs/{id}/feedback
endpoint.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(feedback): wire feedback data into message rendering for history echo

Adds useThreadFeedback hook that fetches run-level feedback from the
messages API and builds a runId->FeedbackData map. MessageList now calls
this hook and passes feedback and runId to each MessageListItem so
previously-submitted thumbs are pre-filled when revisiting a thread.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(feedback): correct run_id mapping for feedback echo

The feedbackMap was keyed by run_id but looked up by LangGraph message ID.
Fixed by tracking AI message ordinal index to correlate event store
run_ids with LangGraph SDK messages.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(feedback): use real threadId and refresh after stream

- Pass threadId prop to MessageListItem instead of reading "new" from URL params
- Invalidate thread-feedback query on stream finish so buttons appear immediately
- Show feedback buttons always visible, copy button on hover only

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style(feedback): group copy and feedback buttons together on the left

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style(feedback): always show toolbar buttons without hover

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): stream hang when run_events.backend=db

DbRunEventStore._user_id_from_context() returned user.id without
coercing it to str. User.id is a Pydantic UUID, and aiosqlite cannot
bind a raw UUID object to a VARCHAR column, so the INSERT for the
initial human_message event silently rolled back and raised out of
the worker task. Because that put() sat outside the worker's try
block, the finally-clause that publishes end-of-stream never ran
and the SSE stream hung forever.

jsonl mode was unaffected because json.dumps(default=str) coerces
UUID objects transparently.

Fixes:
- db.py: coerce user.id to str at the context-read boundary (matches
  what resolve_user_id already does for the other repositories)
- worker.py: move RunJournal init + human_message put inside the try
  block so any failure flows through the finally/publish_end path
  instead of hanging the subscriber

Defense-in-depth:
- engine.py: add PRAGMA busy_timeout=5000 so checkpointer and event
  store wait for each other on the shared deerflow.db file instead
  of failing immediately under write-lock contention
- journal.py: skip fire-and-forget _flush_sync when a previous flush
  task is still in flight, to avoid piling up concurrent put_batch
  writes on the same SQLAlchemy engine during streaming; flush() now
  waits for pending tasks before draining the buffer
- database_config.py: doc-only update clarifying WAL + busy_timeout
  keep the unified deerflow.db safe for both workloads

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore(persistence): drop redundant busy_timeout PRAGMA

Python's sqlite3 driver defaults to a 5-second busy timeout via the
``timeout`` kwarg of ``sqlite3.connect``, and aiosqlite + SQLAlchemy's
aiosqlite dialect inherit that default. Setting ``PRAGMA busy_timeout=5000``
explicitly was a no-op — verified by reading back the PRAGMA on a fresh
connection (it already reports 5000ms without our PRAGMA).

Concurrent stress test (50 checkpoint writes + 20 event batches + 50
thread_meta updates on the same deerflow.db) still completes with zero
errors and 200/200 rows after removing the explicit PRAGMA.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(journal): unwrap Command tool results in on_tool_end

Tools that update graph state (e.g. ``present_files``) return
``Command(update={'messages': [ToolMessage(...)], 'artifacts': [...]})``.
LangGraph later unwraps the inner ``ToolMessage`` into checkpoint state,
but ``RunJournal.on_tool_end`` was receiving the ``Command`` object
directly via the LangChain callback chain and storing
``str(Command(update={...}))`` as the tool_result content.

This produced a visible divergence between the event-store and the
checkpoint for any thread that used a Command-returning tool, blocking
the event-store-backed history fix in the follow-up commit. Concrete
example from thread ``6d30913e-dcd4-41c8-8941-f66c716cf359`` (seq=48):
checkpoint had ``'Successfully presented files'`` while event_store
stored the full Command repr.

The fix detects ``Command`` in ``on_tool_end``, extracts the first
``ToolMessage`` from ``update['messages']``, and lets the existing
ToolMessage branch handle the ``model_dump()`` path. Legacy rows still
containing the Command repr are separately cleaned up by the history
helper in the follow-up commit.

Tests:
- ``test_tool_end_unwraps_command_with_inner_tool_message`` — unit test
  of the unwrap branch with a constructed Command
- ``test_tool_invoke_end_to_end_unwraps_command`` — end-to-end via
  ``CallbackManager`` + ``tool.invoke`` to exercise the real LangChain
  dispatch path that production uses, matching the repro shape from
  ``present_files``
- Counter-proof: temporarily reverted the patch, both tests failed with
  the exact ``Command(update={...})`` repr that was stored in the
  production SQLite row at seq=48, confirming LangChain does pass the
  ``Command`` through callbacks (the unwrap is load-bearing)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(threads): load history messages from event store, immune to summarize

``get_thread_history`` and ``get_thread_state`` in Gateway mode read
messages from ``checkpoint.channel_values["messages"]``. After
SummarizationMiddleware runs mid-run, that list is rewritten in-place:
pre-summarize messages are dropped and a synthetic summary-as-human
message takes position 0. The frontend then renders a chat history that
starts with ``"Here is a summary of the conversation to date:..."``
instead of the user's original query, and all earlier turns are gone.

The event store (``RunEventStore``) is append-only and never rewritten,
so it retains the full transcript. This commit adds a helper
``_get_event_store_messages`` that loads the event store's message
stream and overrides ``values["messages"]`` in both endpoints; the
checkpoint fallback kicks in only when the event store is unavailable.

Behavior contract of the helper:

- **Full pagination.** ``list_messages`` returns the newest ``limit``
  records when no cursor is given, so a fixed limit silently drops
  older messages on long threads. The helper sizes the read from
  ``count_messages()`` and pages forward with ``after_seq`` cursors.
- **Copy-on-read.** Each content dict is copied before ``id`` is
  patched so the live store object (``MemoryRunEventStore`` returns
  references) is never mutated.
- **Stable ids.** Messages with ``id=None`` (human + tool_result,
  which don't receive an id until checkpoint persistence) get a
  deterministic ``uuid5(NAMESPACE_URL, f"{thread_id}:{seq}")`` so
  React keys stay stable across requests. AI messages keep their
  LLM-assigned ``lc_run--*`` ids.
- **Legacy ``Command`` repr sanitization.** Rows captured before the
  ``journal.py`` ``on_tool_end`` fix (previous commit) stored
  ``str(Command(update={'messages': [ToolMessage(content='X', ...)]}))``
  as the tool_result content. ``_sanitize_legacy_command_repr``
  regex-extracts the inner text so old threads render cleanly.
- **Inline feedback.** When loading the stream, the helper also pulls
  ``feedback_repo.list_by_thread_grouped`` and attaches ``run_id`` to
  every message plus ``feedback`` to the final ``ai_message`` of each
  run. This removes the frontend's need to fetch a second endpoint
  and positional-index-map its way back to the right run. When the
  feedback subsystem is unavailable, the ``feedback`` field is left
  absent entirely so the frontend hides the button rather than
  rendering it over a broken write path.
- **User context.** ``DbRunEventStore`` is user-scoped by default via
  ``resolve_user_id(AUTO)``. The helper relies on the ``@require_permission``
  decorator having populated the user contextvar on both callers; the
  docstring documents this dependency explicitly so nobody wires it
  into a CLI or migration script without passing ``user_id=None``.

Real data verification against thread
``6d30913e-dcd4-41c8-8941-f66c716cf359``: checkpoint showed 12 messages
(summarize-corrupted), event store had 16. The original human message
``"最新伊美局势"`` was preserved as seq=1 in the event store and
correctly restored to position 0 in the helper output. Helper output
for AI messages was byte-identical to checkpoint for every overlapping
message; only tool_result ids differed (patched to uuid5) and the
legacy Command repr at seq=48 was sanitized.

Tests:
- ``test_thread_state_event_store.py`` — 18 tests covering
  ``_sanitize_legacy_command_repr`` (passthrough, single/double-quote
  extraction, unparseable fallback), helper happy path (all message
  types, stable uuid5, store non-mutation), multi-page pagination,
  summarize regression (recovers pre-summarize messages), feedback
  attachment (per-run, multi-run threads, repo failure graceful),
  and dependency failure fallback to ``None``.

Docs:
- ``docs/superpowers/plans/2026-04-10-event-store-history.md`` — the
  implementation plan this commit realizes, with Task 1 revised after
  the evaluation findings (pagination, copy-on-read, Command wrap
  already landed in journal.py, frontend feedback pagination in the
  follow-up commit, Standard-mode follow-up noted).
- ``docs/superpowers/specs/2026-04-11-runjournal-history-evaluation.md``
  — the Claude + second-opinion evaluation document that drove the
  plan revisions (pagination bug, dict-mutation bug, feedback hidden
  bug, Command bug).
- ``docs/superpowers/specs/2026-04-11-summarize-marker-design.md`` —
  design for a follow-up PR that visually marks summarize events in
  history, based on a verified ``adispatch_custom_event`` experiment
  (``trace=False`` middleware nodes can still forward the Pregel task
  config via explicit signature injection).

Scope: Gateway mode only (``make dev-pro``). Standard mode
(``make dev``) hits LangGraph Server directly and bypasses these
endpoints; the summarize symptom is still present there and is
tracked as a separate follow-up in the plan.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(feedback): inline feedback on history and drop positional mapping

The old ``useThreadFeedback`` hook loaded ``GET /api/threads/{id}/messages?limit=200``
and built two parallel lookup tables: ``runIdByAiIndex`` (an ordinal array of
run_ids for every ``ai_message``-typed event) and ``feedbackByRunId``. The render
loop in ``message-list.tsx`` walked the AI messages in order, incrementing
``aiMessageIndex`` on each non-human message, and used that ordinal to look up
the run_id and feedback.

This shape had three latent bugs we could observe on real threads:

1. **Fetch was capped at 200 messages.** Long or tool-heavy threads silently
   dropped earlier entries from the map, so feedback buttons could be missing
   on messages they should own.
2. **Ordinal mismatch.** The render loop counted every non-human message
   (including each intermediate ``ai_tool_call``), but ``runIdByAiIndex`` only
   pushed entries for ``event_type == "ai_message"``. A run with 3 tool_calls
   + 1 final AI message would push 1 entry while the render consumed 4
   positions, so buttons mapped to the wrong positions across multi-run
   threads.
3. **Two parallel data paths.** The ``/history`` render path and the
   ``/messages`` feedback-lookup path could drift in-between an
   ``invalidateQueries`` call and the next refetch, producing transient
   mismaps.

The previous commit moved the authoritative message source for history to
the event store and added ``run_id`` + ``feedback`` inline on each message
dict returned by ``_get_event_store_messages``. This commit aligns the
frontend with that contract:

- **Delete** ``useThreadFeedback``, ``ThreadFeedbackData``,
  ``runIdByAiIndex``, ``feedbackByRunId``, and ``fetchAllThreadMessages``.
- **Introduce** ``useThreadMessageEnrichment`` that fetches
  ``POST /history?limit=1`` once, indexes the returned messages by
  ``message.id`` into a ``Map<id, {run_id, feedback?}>``, and invalidates
  on stream completion (``onFinish`` in ``useThreadStream``). Keying by
  ``message.id`` is stable across runs, tool_call chains, and summarize.
- **Simplify** ``message-list.tsx`` to drop the ``aiMessageIndex``
  counter and read ``enrichment?.get(msg.id)`` at each render step.
- **Rewire** ``message-list-item.tsx`` so the feedback button renders
  when ``feedback !== undefined`` rather than when the message happens
  to be non-human. ``feedback`` is ``undefined`` for non-eligible
  messages (humans, non-final AI, tools), ``null`` for the final
  ai_message of an unrated run, and a ``FeedbackData`` object once
  rated — cleanly distinguishing "not eligible" from "eligible but
  unrated".

``/api/threads/{id}/messages`` is kept as a debug/export surface; no
frontend code calls it anymore but the backend router is untouched.

Validation:
- ``pnpm check`` clean (0 errors, 1 pre-existing unrelated warning)
- Live test on thread ``3d5dea4a`` after gateway restart confirmed the
  original user query is restored to position 0 and the feedback
  button behaves correctly on the final AI message.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(rebase): remove duplicate definitions and update stale module paths

Rebase left duplicate function blocks in worker.py (triple human_message
write causing 3x user messages in /history), deps.py, and prompt.py.
Also update checkpointer imports from the old deerflow.agents.checkpointer
path to deerflow.runtime.checkpointer, and clean up orphaned feedback
props in the frontend message components.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(rebase): restore FeedbackButtons component and enrichment lost during rebase

The FeedbackButtons component (defined inline in message-list-item.tsx)
was introduced in commit 95df8d13 but lost during rebase. The previous
rebase cleanup commit incorrectly removed the feedback/runId props and
enrichment hook as "orphaned code" instead of restoring the missing
component. This commit restores:

- FeedbackButtons component with thumbs up/down toggle and optimistic state
- FeedbackData/upsertFeedback/deleteFeedback imports
- feedback and runId props on MessageListItem
- useThreadMessageEnrichment hook and entry lookup in message-list.tsx

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: JilongSun <965640067@qq.com>
Co-authored-by: jie <49781832+stan-fu@users.noreply.github.com>
Co-authored-by: cooper <cooperfu@tencent.com>
Co-authored-by: yangzheli <43645580+yangzheli@users.noreply.github.com>
Co-authored-by: greatmengqi <chenmengqi.0376@gmail.com>
Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
2026-04-26 11:09:55 +08:00
copilot-swe-agent[bot] 88f822a8b3 docs: fill all TBD documentation pages and add new harness module pages
Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/ff389ed8-31c9-430c-85ff-cc1b52b8239c

Co-authored-by: foreleven <4785594+foreleven@users.noreply.github.com>
2026-04-26 11:08:49 +08:00
copilot-swe-agent[bot] 814a488bcb docs: complete all English and Chinese documentation pages
Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/a5f192e7-8034-4e46-af22-60b90ee27d40

Co-authored-by: foreleven <4785594+foreleven@users.noreply.github.com>
2026-04-26 11:08:49 +08:00
copilot-swe-agent[bot] 716cae20c6 docs: fix review feedback - source-map paths, memory API routes, supports_thinking, checkpointer callout
Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/fb75dc8c-18a4-4a23-9229-25b3c5e545cf

Co-authored-by: foreleven <4785594+foreleven@users.noreply.github.com>
2026-04-26 11:08:49 +08:00
jiangfeng.11 7ff9077074 feat(dependencies): add langchain-ollama and ollama packages with optional dependencies 2026-04-26 11:08:49 +08:00
Copilot 848ace98cb feat: replace auto-admin creation with secure interactive first-boot setup (#2063)
* feat(persistence): add unified persistence layer with event store, token tracking, and feedback (#1930)

* feat(persistence): add SQLAlchemy 2.0 async ORM scaffold

Introduce a unified database configuration (DatabaseConfig) that
controls both the LangGraph checkpointer and the DeerFlow application
persistence layer from a single `database:` config section.

New modules:
- deerflow.config.database_config — Pydantic config with memory/sqlite/postgres backends
- deerflow.persistence — async engine lifecycle, DeclarativeBase with to_dict mixin, Alembic skeleton
- deerflow.runtime.runs.store — RunStore ABC + MemoryRunStore implementation

Gateway integration initializes/tears down the persistence engine in
the existing langgraph_runtime() context manager. Legacy checkpointer
config is preserved for backward compatibility.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add RunEventStore ABC + MemoryRunEventStore

Phase 2-A prerequisite for event storage: adds the unified run event
stream interface (RunEventStore) with an in-memory implementation,
RunEventsConfig, gateway integration, and comprehensive tests (27 cases).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add ORM models, repositories, DB/JSONL event stores, RunJournal, and API endpoints

Phase 2-B: run persistence + event storage + token tracking.

- ORM models: RunRow (with token fields), ThreadMetaRow, RunEventRow
- RunRepository implements RunStore ABC via SQLAlchemy ORM
- ThreadMetaRepository with owner access control
- DbRunEventStore with trace content truncation and cursor pagination
- JsonlRunEventStore with per-run files and seq recovery from disk
- RunJournal (BaseCallbackHandler) captures LLM/tool/lifecycle events,
  accumulates token usage by caller type, buffers and flushes to store
- RunManager now accepts optional RunStore for persistent backing
- Worker creates RunJournal, writes human_message, injects callbacks
- Gateway deps use factory functions (RunRepository when DB available)
- New endpoints: messages, run messages, run events, token-usage
- ThreadCreateRequest gains assistant_id field
- 92 tests pass (33 new), zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add user feedback + follow-up run association

Phase 2-C: feedback and follow-up tracking.

- FeedbackRow ORM model (rating +1/-1, optional message_id, comment)
- FeedbackRepository with CRUD, list_by_run/thread, aggregate stats
- Feedback API endpoints: create, list, stats, delete
- follow_up_to_run_id in RunCreateRequest (explicit or auto-detected
  from latest successful run on the thread)
- Worker writes follow_up_to_run_id into human_message event metadata
- Gateway deps: feedback_repo factory + getter
- 17 new tests (14 FeedbackRepository + 3 follow-up association)
- 109 total tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test+config: comprehensive Phase 2 test coverage + deprecate checkpointer config

- config.example.yaml: deprecate standalone checkpointer section, activate
  unified database:sqlite as default (drives both checkpointer + app data)
- New: test_thread_meta_repo.py (14 tests) — full ThreadMetaRepository coverage
  including check_access owner logic, list_by_owner pagination
- Extended test_run_repository.py (+4 tests) — completion preserves fields,
  list ordering desc, limit, owner_none returns all
- Extended test_run_journal.py (+8 tests) — on_chain_error, track_tokens=false,
  middleware no ai_message, unknown caller tokens, convenience fields,
  tool_error, non-summarization custom event
- Extended test_run_event_store.py (+7 tests) — DB batch seq continuity,
  make_run_event_store factory (memory/db/jsonl/fallback/unknown)
- Extended test_phase2b_integration.py (+4 tests) — create_or_reject persists,
  follow-up metadata, summarization in history, full DB-backed lifecycle
- Fixed DB integration test to use proper fake objects (not MagicMock)
  for JSON-serializable metadata
- 157 total Phase 2 tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* config: move default sqlite_dir to .deer-flow/data

Keep SQLite databases alongside other DeerFlow-managed data
(threads, memory) under the .deer-flow/ directory instead of a
top-level ./data folder.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): remove UTFJSON, use engine-level json_serializer + datetime.now()

- Replace custom UTFJSON type with standard sqlalchemy.JSON in all ORM
  models. Add json_serializer=json.dumps(ensure_ascii=False) to all
  create_async_engine calls so non-ASCII text (Chinese etc.) is stored
  as-is in both SQLite and Postgres.
- Change ORM datetime defaults from datetime.now(UTC) to datetime.now(),
  remove UTC imports.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): simplify deps.py with getter factory + inline repos

- Replace 6 identical getter functions with _require() factory.
- Inline 3 _make_*_repo() factories into langgraph_runtime(), call
  get_session_factory() once instead of 3 times.
- Add thread_meta upsert in start_run (services.py).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(docker): add UV_EXTRAS build arg for optional dependencies

Support installing optional dependency groups (e.g. postgres) at
Docker build time via UV_EXTRAS build arg:
  UV_EXTRAS=postgres docker compose build

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(journal): fix flush, token tracking, and consolidate tests

RunJournal fixes:
- _flush_sync: retain events in buffer when no event loop instead of
  dropping them; worker's finally block flushes via async flush().
- on_llm_end: add tool_calls filter and caller=="lead_agent" guard for
  ai_message events; mark message IDs for dedup with record_llm_usage.
- worker.py: persist completion data (tokens, message count) to RunStore
  in finally block.

Model factory:
- Auto-inject stream_usage=True for BaseChatOpenAI subclasses with
  custom api_base, so usage_metadata is populated in streaming responses.

Test consolidation:
- Delete test_phase2b_integration.py (redundant with existing tests).
- Move DB-backed lifecycle test into test_run_journal.py.
- Add tests for stream_usage injection in test_model_factory.py.
- Clean up executor/task_tool dead journal references.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): widen content type to str|dict in all store backends

Allow event content to be a dict (for structured OpenAI-format messages)
in addition to plain strings. Dict values are JSON-serialized for the DB
backend and deserialized on read; memory and JSONL backends handle dicts
natively. Trace truncation now serializes dicts to JSON before measuring.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(events): use metadata flag instead of heuristic for dict content detection

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(converters): add LangChain-to-OpenAI message format converters

Pure functions langchain_to_openai_message, langchain_to_openai_completion,
langchain_messages_to_openai, and _infer_finish_reason for converting
LangChain BaseMessage objects to OpenAI Chat Completions format, used by
RunJournal for event storage. 15 unit tests added.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(converters): handle empty list content as null, clean up test

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): human_message content uses OpenAI user message format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): ai_message uses OpenAI format, add ai_tool_call message event

- ai_message content now uses {"role": "assistant", "content": "..."} format
- New ai_tool_call message event emitted when lead_agent LLM responds with tool_calls
- ai_tool_call uses langchain_to_openai_message converter for consistent format
- Both events include finish_reason in metadata ("stop" or "tool_calls")

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add tool_result message event with OpenAI tool message format

Cache tool_call_id from on_tool_start keyed by run_id as fallback for on_tool_end,
then emit a tool_result message event (role=tool, tool_call_id, content) after each
successful tool completion.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): summary content uses OpenAI system message format

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): replace llm_start/llm_end with llm_request/llm_response in OpenAI format

Add on_chat_model_start to capture structured prompt messages as llm_request events.
Replace llm_end trace events with llm_response using OpenAI Chat Completions format.
Track llm_call_index to pair request/response events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add record_middleware method for middleware trace events

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test(events): add full run sequence integration test for OpenAI content format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): align message events with checkpoint format and add middleware tag injection

- Message events (ai_message, ai_tool_call, tool_result, human_message) now use
  BaseMessage.model_dump() format, matching LangGraph checkpoint values.messages
- on_tool_end extracts tool_call_id/name/status from ToolMessage objects
- on_tool_error now emits tool_result message events with error status
- record_middleware uses middleware:{tag} event_type and middleware category
- Summarization custom events use middleware:summarize category
- TitleMiddleware injects middleware:title tag via get_config() inheritance
- SummarizationMiddleware model bound with middleware:summarize tag
- Worker writes human_message using HumanMessage.model_dump()

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): switch search endpoint to threads_meta table and sync title

- POST /api/threads/search now queries threads_meta table directly,
  removing the two-phase Store + Checkpointer scan approach
- Add ThreadMetaRepository.search() with metadata/status filters
- Add ThreadMetaRepository.update_display_name() for title sync
- Worker syncs checkpoint title to threads_meta.display_name on run completion
- Map display_name to values.title in search response for API compatibility

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): history endpoint reads messages from event store

- POST /api/threads/{thread_id}/history now combines two data sources:
  checkpointer for checkpoint_id, metadata, title, thread_data;
  event store for messages (complete history, not truncated by summarization)
- Strip internal LangGraph metadata keys from response
- Remove full channel_values serialization in favor of selective fields

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: remove duplicate optional-dependencies header in pyproject.toml

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(middleware): pass tagged config to TitleMiddleware ainvoke call

Without the config, the middleware:title tag was not injected,
causing the LLM response to be recorded as a lead_agent ai_message
in run_events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: resolve merge conflict in .env.example

Keep both DATABASE_URL (from persistence-scaffold) and WECOM
credentials (from main) after the merge.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address review feedback on PR #1851

- Fix naive datetime.now() → datetime.now(UTC) in all ORM models
- Fix seq race condition in DbRunEventStore.put() with FOR UPDATE
  and UNIQUE(thread_id, seq) constraint
- Encapsulate _store access in RunManager.update_run_completion()
- Deduplicate _store.put() logic in RunManager via _persist_to_store()
- Add update_run_completion to RunStore ABC + MemoryRunStore
- Wire follow_up_to_run_id through the full create path
- Add error recovery to RunJournal._flush_sync() lost-event scenario
- Add migration note for search_threads breaking change
- Fix test_checkpointer_none_fix mock to set database=None

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore: update uv.lock

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address 22 review comments from CodeQL, Copilot, and Code Quality

Bug fixes:
- Sanitize log params to prevent log injection (CodeQL)
- Reset threads_meta.status to idle/error when run completes
- Attach messages only to latest checkpoint in /history response
- Write threads_meta on POST /threads so new threads appear in search

Lint fixes:
- Remove unused imports (journal.py, migrations/env.py, test_converters.py)
- Convert lambda to named function (engine.py, Ruff E731)
- Remove unused logger definitions in repos (Ruff F841)
- Add logging to JSONL decode errors and empty except blocks
- Separate assert side-effects in tests (CodeQL)
- Remove unused local variables in tests (Ruff F841)
- Fix max_trace_content truncation to use byte length, not char length

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style: apply ruff format to persistence and runtime files

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Potential fix for pull request finding 'Statement has no effect'

Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>

* refactor(runtime): introduce RunContext to reduce run_agent parameter bloat

Extract checkpointer, store, event_store, run_events_config, thread_meta_repo,
and follow_up_to_run_id into a frozen RunContext dataclass. Add get_run_context()
in deps.py to build the base context from app.state singletons. start_run() uses
dataclasses.replace() to enrich per-run fields before passing ctx to run_agent.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): move sanitize_log_param to app/gateway/utils.py

Extract the log-injection sanitizer from routers/threads.py into a shared
utils module and rename to sanitize_log_param (public API). Eliminates the
reverse service → router import in services.py.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* perf: use SQL aggregation for feedback stats and thread token usage

Replace Python-side counting in FeedbackRepository.aggregate_by_run with
a single SELECT COUNT/SUM query. Add RunStore.aggregate_tokens_by_thread
abstract method with SQL GROUP BY implementation in RunRepository and
Python fallback in MemoryRunStore. Simplify the thread_token_usage
endpoint to delegate to the new method, eliminating the limit=10000
truncation risk.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: annotate DbRunEventStore.put() as low-frequency path

Add docstring clarifying that put() opens a per-call transaction with
FOR UPDATE and should only be used for infrequent writes (currently
just the initial human_message event). High-throughput callers should
use put_batch() instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(threads): fall back to Store search when ThreadMetaRepository is unavailable

When database.backend=memory (default) or no SQL session factory is
configured, search_threads now queries the LangGraph Store instead of
returning 503. Returns empty list if neither Store nor repo is available.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): introduce ThreadMetaStore ABC for backend-agnostic thread metadata

Add ThreadMetaStore abstract base class with create/get/search/update/delete
interface. ThreadMetaRepository (SQL) now inherits from it. New
MemoryThreadMetaStore wraps LangGraph BaseStore for memory-mode deployments.

deps.py now always provides a non-None thread_meta_repo, eliminating all
`if thread_meta_repo is not None` guards in services.py, worker.py, and
routers/threads.py. search_threads no longer needs a Store fallback branch.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(history): read messages from checkpointer instead of RunEventStore

The /history endpoint now reads messages directly from the
checkpointer's channel_values (the authoritative source) instead of
querying RunEventStore.list_messages(). The RunEventStore API is
preserved for other consumers.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address new Copilot review comments

- feedback.py: validate thread_id/run_id before deleting feedback
- jsonl.py: add path traversal protection with ID validation
- run_repo.py: parse `before` to datetime for PostgreSQL compat
- thread_meta_repo.py: fix pagination when metadata filter is active
- database_config.py: use resolve_path for sqlite_dir consistency

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Implement skill self-evolution and skill_manage flow (#1874)

* chore: ignore .worktrees directory

* Add skill_manage self-evolution flow

* Fix CI regressions for skill_manage

* Address PR review feedback for skill evolution

* fix(skill-evolution): preserve history on delete

* fix(skill-evolution): tighten scanner fallbacks

* docs: add skill_manage e2e evidence screenshot

* fix(skill-manage): avoid blocking fs ops in session runtime

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

* fix(config): resolve sqlite_dir relative to CWD, not Paths.base_dir

resolve_path() resolves relative to Paths.base_dir (.deer-flow),
which double-nested the path to .deer-flow/.deer-flow/data/app.db.
Use Path.resolve() (CWD-relative) instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Feature/feishu receive file (#1608)

* feat(feishu): add channel file materialization hook for inbound messages

- Introduce Channel.receive_file(msg, thread_id) as a base method for file materialization; default is no-op.
- Implement FeishuChannel.receive_file to download files/images from Feishu messages, save to sandbox, and inject virtual paths into msg.text.
- Update ChannelManager to call receive_file for any channel if msg.files is present, enabling downstream model access to user-uploaded files.
- No impact on Slack/Telegram or other channels (they inherit the default no-op).

* style(backend): format code with ruff for lint compliance

- Auto-formatted packages/harness/deerflow/agents/factory.py and tests/test_create_deerflow_agent.py using `ruff format`
- Ensured both files conform to project linting standards
- Fixes CI lint check failures caused by code style issues

* fix(feishu): handle file write operation asynchronously to prevent blocking

* fix(feishu): rename GetMessageResourceRequest to _GetMessageResourceRequest and remove redundant code

* test(feishu): add tests for receive_file method and placeholder replacement

* fix(manager): remove unnecessary type casting for channel retrieval

* fix(feishu): update logging messages to reflect resource handling instead of image

* fix(feishu): sanitize filename by replacing invalid characters in file uploads

* fix(feishu): improve filename sanitization and reorder image key handling in message processing

* fix(feishu): add thread lock to prevent filename conflicts during file downloads

* fix(test): correct bad merge in test_feishu_parser.py

* chore: run ruff and apply formatting cleanup
fix(feishu): preserve rich-text attachment order and improve fallback filename handling

* fix(docker): restore gateway env vars and fix langgraph empty arg issue (#1915)

Two production docker-compose.yaml bugs prevent `make up` from working:

1. Gateway missing DEER_FLOW_CONFIG_PATH and DEER_FLOW_EXTENSIONS_CONFIG_PATH
   environment overrides. Added in fb2d99f (#1836) but accidentally reverted
   by ca2fb95 (#1847). Without them, gateway reads host paths from .env via
   env_file, causing FileNotFoundError inside the container.

2. Langgraph command fails when LANGGRAPH_ALLOW_BLOCKING is unset (default).
   Empty $${allow_blocking} inserts a bare space between flags, causing
   ' --no-reload' to be parsed as unexpected extra argument. Fix by building
   args string first and conditionally appending --allow-blocking.

Co-authored-by: cooper <cooperfu@tencent.com>

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities (#1904)

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities

Fix `<button>` inside `<a>` invalid HTML in artifact components and add
missing `noopener,noreferrer` to `window.open` calls to prevent reverse
tabnabbing.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(frontend): address Copilot review on tabnabbing and double-tab-open

Remove redundant parent onClick on web_fetch ChainOfThoughtStep to
prevent opening two tabs on link click, and explicitly null out
window.opener after window.open() for defensive tabnabbing hardening.

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* refactor(persistence): organize entities into per-entity directories

Restructure the persistence layer from horizontal "models/ + repositories/"
split into vertical entity-aligned directories. Each entity (thread_meta,
run, feedback) now owns its ORM model, abstract interface (where applicable),
and concrete implementations under a single directory with an aggregating
__init__.py for one-line imports.

Layout:
  persistence/thread_meta/{base,model,sql,memory}.py
  persistence/run/{model,sql}.py
  persistence/feedback/{model,sql}.py

models/__init__.py is kept as a facade so Alembic autogenerate continues to
discover all ORM tables via Base.metadata. RunEventRow remains under
models/run_event.py because its storage implementation lives in
runtime/events/store/db.py and has no matching repository directory.

The repositories/ directory is removed entirely. All call sites in
gateway/deps.py and tests are updated to import from the new entity
packages, e.g.:

    from deerflow.persistence.thread_meta import ThreadMetaRepository
    from deerflow.persistence.run import RunRepository
    from deerflow.persistence.feedback import FeedbackRepository

Full test suite passes (1690 passed, 14 skipped).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(gateway): sync thread rename and delete through ThreadMetaStore

The POST /threads/{id}/state endpoint previously synced title changes
only to the LangGraph Store via _store_upsert. In sqlite mode the search
endpoint reads from the ThreadMetaRepository SQL table, so renames never
appeared in /threads/search until the next agent run completed (worker.py
syncs title from checkpoint to thread_meta in its finally block).

Likewise the DELETE /threads/{id} endpoint cleaned up the filesystem,
Store, and checkpointer but left the threads_meta row orphaned in sqlite,
so deleted threads kept appearing in /threads/search.

Fix both endpoints by routing through the ThreadMetaStore abstraction
which already has the correct sqlite/memory implementations wired up by
deps.py. The rename path now calls update_display_name() and the delete
path calls delete() — both work uniformly across backends.

Verified end-to-end with curl in gateway mode against sqlite backend.
Existing test suite (1690 passed) and focused router/repo tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): route all thread metadata access through ThreadMetaStore

Following the rename/delete bug fix in PR1, migrate the remaining direct
LangGraph Store reads/writes in the threads router and services to the
ThreadMetaStore abstraction so that the sqlite and memory backends behave
identically and the legacy dual-write paths can be removed.

Migrated endpoints (threads.py):
- create_thread: idempotency check + write now use thread_meta_repo.get/create
  instead of dual-writing the LangGraph Store and the SQL row.
- get_thread: reads from thread_meta_repo.get; the checkpoint-only fallback
  for legacy threads is preserved.
- patch_thread: replaced _store_get/_store_put with thread_meta_repo.update_metadata.
- delete_thread_data: dropped the legacy store.adelete; thread_meta_repo.delete
  already covers it.

Removed dead code (services.py):
- _upsert_thread_in_store — redundant with the immediately following
  thread_meta_repo.create() call.
- _sync_thread_title_after_run — worker.py's finally block already syncs
  the title via thread_meta_repo.update_display_name() after each run.

Removed dead code (threads.py):
- _store_get / _store_put / _store_upsert helpers (no remaining callers).
- THREADS_NS constant.
- get_store import (router no longer touches the LangGraph Store directly).

New abstract method:
- ThreadMetaStore.update_metadata(thread_id, metadata) merges metadata into
  the thread's metadata field. Implemented in both ThreadMetaRepository (SQL,
  read-modify-write inside one session) and MemoryThreadMetaStore. Three new
  unit tests cover merge / empty / nonexistent behaviour.

Net change: -134 lines. Full test suite: 1693 passed, 14 skipped.
Verified end-to-end with curl in gateway mode against sqlite backend
(create / patch / get / rename / search / delete).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: JilongSun <965640067@qq.com>
Co-authored-by: jie <49781832+stan-fu@users.noreply.github.com>
Co-authored-by: cooper <cooperfu@tencent.com>
Co-authored-by: yangzheli <43645580+yangzheli@users.noreply.github.com>

* feat(auth): release-validation pass for 2.0-rc — 12 blockers + simplify follow-ups (#2008)

* feat(auth): introduce backend auth module

Port RFC-001 authentication core from PR #1728:
- JWT token handling (create_access_token, decode_token, TokenPayload)
- Password hashing (bcrypt) with verify_password
- SQLite UserRepository with base interface
- Provider Factory pattern (LocalAuthProvider)
- CLI reset_admin tool
- Auth-specific errors (AuthErrorCode, TokenError, AuthErrorResponse)

Deps:
- bcrypt>=4.0.0
- pyjwt>=2.9.0
- email-validator>=2.0.0
- backend/uv.toml pins public PyPI index

Tests: 12 pure unit tests (test_auth_config.py, test_auth_errors.py).

Scope note: authz.py, test_auth.py, and test_auth_type_system.py are
deferred to commit 2 because they depend on middleware and deps wiring
that is not yet in place. Commit 1 stays "pure new files only" as the
spec mandates.

* feat(auth): wire auth end-to-end (middleware + frontend replacement)

Backend:
- Port auth_middleware, csrf_middleware, langgraph_auth, routers/auth
- Port authz decorator (owner_filter_key defaults to 'owner_id')
- Merge app.py: register AuthMiddleware + CSRFMiddleware + CORS, add
  _ensure_admin_user lifespan hook, _migrate_orphaned_threads helper,
  register auth router
- Merge deps.py: add get_local_provider, get_current_user_from_request,
  get_optional_user_from_request; keep get_current_user as thin str|None
  adapter for feedback router
- langgraph.json: add auth path pointing to langgraph_auth.py:auth
- Rename metadata['user_id'] -> metadata['owner_id'] in langgraph_auth
  (both metadata write and LangGraph filter dict) + test fixtures

Frontend:
- Delete better-auth library and api catch-all route
- Remove better-auth npm dependency and env vars (BETTER_AUTH_SECRET,
  BETTER_AUTH_GITHUB_*) from env.js
- Port frontend/src/core/auth/* (AuthProvider, gateway-config,
  proxy-policy, server-side getServerSideUser, types)
- Port frontend/src/core/api/fetcher.ts
- Port (auth)/layout, (auth)/login, (auth)/setup pages
- Rewrite workspace/layout.tsx as server component that calls
  getServerSideUser and wraps in AuthProvider
- Port workspace/workspace-content.tsx for the client-side sidebar logic

Tests:
- Port 5 auth test files (test_auth, test_auth_middleware,
  test_auth_type_system, test_ensure_admin, test_langgraph_auth)
- 176 auth tests PASS

After this commit: login/logout/registration flow works, but persistence
layer does not yet filter by owner_id. Commit 4 closes that gap.

* feat(auth): account settings page + i18n

- Port account-settings-page.tsx (change password, change email, logout)
- Wire into settings-dialog.tsx as new "account" section with UserIcon,
  rendered first in the section list
- Add i18n keys:
  - en-US/zh-CN: settings.sections.account ("Account" / "账号")
  - en-US/zh-CN: button.logout ("Log out" / "退出登录")
  - types.ts: matching type declarations

* feat(auth): enforce owner_id across 2.0-rc persistence layer

Add request-scoped contextvar-based owner filtering to threads_meta,
runs, run_events, and feedback repositories. Router code is unchanged
— isolation is enforced at the storage layer so that any caller that
forgets to pass owner_id still gets filtered results, and new routes
cannot accidentally leak data.

Core infrastructure
-------------------
- deerflow/runtime/user_context.py (new):
  - ContextVar[CurrentUser | None] with default None
  - runtime_checkable CurrentUser Protocol (structural subtype with .id)
  - set/reset/get/require helpers
  - AUTO sentinel + resolve_owner_id(value, method_name) for sentinel
    three-state resolution: AUTO reads contextvar, explicit str
    overrides, explicit None bypasses the filter (for migration/CLI)

Repository changes
------------------
- ThreadMetaRepository: create/get/search/update_*/delete gain
  owner_id=AUTO kwarg; read paths filter by owner, writes stamp it,
  mutations check ownership before applying
- RunRepository: put/get/list_by_thread/delete gain owner_id=AUTO kwarg
- FeedbackRepository: create/get/list_by_run/list_by_thread/delete
  gain owner_id=AUTO kwarg
- DbRunEventStore: list_messages/list_events/list_messages_by_run/
  count_messages/delete_by_thread/delete_by_run gain owner_id=AUTO
  kwarg. Write paths (put/put_batch) read contextvar softly: when a
  request-scoped user is available, owner_id is stamped; background
  worker writes without a user context pass None which is valid
  (orphan row to be bound by migration)

Schema
------
- persistence/models/run_event.py: RunEventRow.owner_id = Mapped[
  str | None] = mapped_column(String(64), nullable=True, index=True)
- No alembic migration needed: 2.0 ships fresh, Base.metadata.create_all
  picks up the new column automatically

Middleware
----------
- auth_middleware.py: after cookie check, call get_optional_user_from_
  request to load the real User, stamp it into request.state.user AND
  the contextvar via set_current_user, reset in a try/finally. Public
  paths and unauthenticated requests continue without contextvar, and
  @require_auth handles the strict 401 path

Test infrastructure
-------------------
- tests/conftest.py: @pytest.fixture(autouse=True) _auto_user_context
  sets a default SimpleNamespace(id="test-user-autouse") on every test
  unless marked @pytest.mark.no_auto_user. Keeps existing 20+
  persistence tests passing without modification
- pyproject.toml [tool.pytest.ini_options]: register no_auto_user
  marker so pytest does not emit warnings for opt-out tests
- tests/test_user_context.py: 6 tests covering three-state semantics,
  Protocol duck typing, and require/optional APIs
- tests/test_thread_meta_repo.py: one test updated to pass owner_id=
  None explicitly where it was previously relying on the old default

Test results
------------
- test_user_context.py: 6 passed
- test_auth*.py + test_langgraph_auth.py + test_ensure_admin.py: 127
- test_run_event_store / test_run_repository / test_thread_meta_repo
  / test_feedback: 92 passed
- Full backend suite: 1905 passed, 2 failed (both @requires_llm flaky
  integration tests unrelated to auth), 1 skipped

* feat(auth): extend orphan migration to 2.0-rc persistence tables

_ensure_admin_user now runs a three-step pipeline on every boot:

  Step 1 (fatal):     admin user exists / is created / password is reset
  Step 2 (non-fatal): LangGraph store orphan threads → admin
  Step 3 (non-fatal): SQL persistence tables → admin
    - threads_meta
    - runs
    - run_events
    - feedback

Each step is idempotent. The fatal/non-fatal split mirrors PR #1728's
original philosophy: admin creation failure blocks startup (the system
is unusable without an admin), whereas migration failures log a warning
and let the service proceed (a partial migration is recoverable; a
missing admin is not).

Key helpers
-----------
- _iter_store_items(store, namespace, *, page_size=500):
  async generator that cursor-paginates across LangGraph store pages.
  Fixes PR #1728's hardcoded limit=1000 bug that would silently lose
  orphans beyond the first page.

- _migrate_orphaned_threads(store, admin_user_id):
  Rewritten to use _iter_store_items. Returns the migrated count so the
  caller can log it; raises only on unhandled exceptions.

- _migrate_orphan_sql_tables(admin_user_id):
  Imports the 4 ORM models lazily, grabs the shared session factory,
  runs one UPDATE per table in a single transaction, commits once.
  No-op when no persistence backend is configured (in-memory dev).

Tests: test_ensure_admin.py (8 passed)

* test(auth): port AUTH test plan docs + lint/format pass

- Port backend/docs/AUTH_TEST_PLAN.md and AUTH_UPGRADE.md from PR #1728
- Rename metadata.user_id → metadata.owner_id in AUTH_TEST_PLAN.md
  (4 occurrences from the original PR doc)
- ruff auto-fix UP037 in sentinel type annotations: drop quotes around
  "str | None | _AutoSentinel" now that from __future__ import
  annotations makes them implicit string forms
- ruff format: 2 files (app/gateway/app.py, runtime/user_context.py)

Note on test coverage additions:
- conftest.py autouse fixture was already added in commit 4 (had to
  be co-located with the repository changes to keep pre-existing
  persistence tests passing)
- cross-user isolation E2E tests (test_owner_isolation.py) deferred
  — enforcement is already proven by the 98-test repository suite
  via the autouse fixture + explicit _AUTO sentinel exercises
- New test cases (TC-API-17..20, TC-ATK-13, TC-MIG-01..07) listed
  in AUTH_TEST_PLAN.md are deferred to a follow-up PR — they are
  manual-QA test cases rather than pytest code, and the spec-level
  coverage is already met by test_user_context.py + the 98-test
  repository suite.

Final test results:
- Auth suite (test_auth*, test_langgraph_auth, test_ensure_admin,
  test_user_context): 186 passed
- Persistence suite (test_run_event_store, test_run_repository,
  test_thread_meta_repo, test_feedback): 98 passed
- Lint: ruff check + ruff format both clean

* test(auth): add cross-user isolation test suite

10 tests exercising the storage-layer owner filter by manually
switching the user_context contextvar between two users. Verifies
the safety invariant:

  After a repository write with owner_id=A, a subsequent read with
  owner_id=B must not return the row, and vice versa.

Covers all 4 tables that own user-scoped data:

TC-API-17  threads_meta  — read, search, update, delete cross-user
TC-API-18  runs          — get, list_by_thread, delete cross-user
TC-API-19  run_events    — list_messages, list_events, count_messages,
                           delete_by_thread (CRITICAL: raw conversation
                           content leak vector)
TC-API-20  feedback      — get, list_by_run, delete cross-user

Plus two meta-tests verifying the sentinel pattern itself:
- AUTO + unset contextvar raises RuntimeError
- explicit owner_id=None bypasses the filter (migration escape hatch)

Architecture note
-----------------
These tests bypass the HTTP layer by design. The full chain
(cookie → middleware → contextvar → repository) is covered piecewise:

- test_auth_middleware.py: middleware sets contextvar from cookies
- test_owner_isolation.py: repositories enforce isolation when
  contextvar is set to different users

Together they prove the end-to-end safety property without the
ceremony of spinning up a full TestClient + in-memory DB for every
router endpoint.

Tests pass: 231 (full auth + persistence + isolation suite)
Lint: clean

* refactor(auth): migrate user repository to SQLAlchemy ORM

Move the users table into the shared persistence engine so auth
matches the pattern of threads_meta, runs, run_events, and feedback —
one engine, one session factory, one schema init codepath.

New files
---------
- persistence/user/__init__.py, persistence/user/model.py: UserRow
  ORM class with partial unique index on (oauth_provider, oauth_id)
- Registered in persistence/models/__init__.py so
  Base.metadata.create_all() picks it up

Modified
--------
- auth/repositories/sqlite.py: rewritten as async SQLAlchemy,
  identical constructor pattern to the other four repositories
  (def __init__(self, session_factory) + self._sf = session_factory)
- auth/config.py: drop users_db_path field — storage is configured
  through config.database like every other table
- deps.py/get_local_provider: construct SQLiteUserRepository with
  the shared session factory, fail fast if engine is not initialised
- tests/test_auth.py: rewrite test_sqlite_round_trip_new_fields to
  use the shared engine (init_engine + close_engine in a tempdir)
- tests/test_auth_type_system.py: add per-test autouse fixture that
  spins up a scratch engine and resets deps._cached_* singletons

* refactor(auth): remove SQL orphan migration (unused in supported scenarios)

The _migrate_orphan_sql_tables helper existed to bind NULL owner_id
rows in threads_meta, runs, run_events, and feedback to the admin on
first boot. But in every supported upgrade path, it's a no-op:

  1. Fresh install: create_all builds fresh tables, no legacy rows
  2. No-auth → with-auth (no existing persistence DB): persistence
     tables are created fresh by create_all, no legacy rows
  3. No-auth → with-auth (has existing persistence DB from #1930):
     NOT a supported upgrade path — "有 DB 到有 DB" schema evolution
     is out of scope; users wipe DB or run manual ALTER

So the SQL orphan migration never has anything to do in the
supported matrix. Delete the function, simplify _ensure_admin_user
from a 3-step pipeline to a 2-step one (admin creation + LangGraph
store orphan migration only).

LangGraph store orphan migration stays: it serves the real
"no-auth → with-auth" upgrade path where a user's existing LangGraph
thread metadata has no owner_id field and needs to be stamped with
the newly-created admin's id.

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): write initial admin password to 0600 file instead of logs

CodeQL py/clear-text-logging-sensitive-data flagged 3 call sites that
logged the auto-generated admin password to stdout via logger.info().
Production log aggregators (ELK/Splunk/etc) would have captured those
cleartext secrets. Replace with a shared helper that writes to
.deer-flow/admin_initial_credentials.txt with mode 0600, and log only
the path.

New file
--------
- app/gateway/auth/credential_file.py: write_initial_credentials()
  helper. Takes email, password, and a "initial"/"reset" label.
  Creates .deer-flow/ if missing, writes a header comment plus the
  email+password, chmods 0o600, returns the absolute Path.

Modified
--------
- app/gateway/app.py: both _ensure_admin_user paths (fresh creation
  + needs_setup password reset) now write to file and log the path
- app/gateway/auth/reset_admin.py: rewritten to use the shared ORM
  repo (SQLiteUserRepository with session_factory) and the
  credential_file helper. The previous implementation was broken
  after the earlier ORM refactor — it still imported _get_users_conn
  and constructed SQLiteUserRepository() without a session factory.

No tests changed — the three password-log sites are all exercised
via existing test_ensure_admin.py which checks that startup
succeeds, not that a specific string appears in logs.

CodeQL alerts 272, 283, 284: all resolved.

* security(auth): strict JWT validation in middleware (fix junk cookie bypass)

AUTH_TEST_PLAN test 7.5.8 expects junk cookies to be rejected with
401. The previous middleware behaviour was "presence-only": check
that some access_token cookie exists, then pass through. In
combination with my Task-12 decision to skip @require_auth
decorators on routes, this created a gap where a request with any
cookie-shaped string (e.g. access_token=not-a-jwt) would bypass
authentication on routes that do not touch the repository
(/api/models, /api/mcp/config, /api/memory, /api/skills, …).

Fix: middleware now calls get_current_user_from_request() strictly
and catches the resulting HTTPException to render a 401 with the
proper fine-grained error code (token_invalid, token_expired,
user_not_found, …). On success it stamps request.state.user and
the contextvar so repository-layer owner filters work downstream.

The 4 old "_with_cookie_passes" tests in test_auth_middleware.py
were written for the presence-only behaviour; they asserted that
a junk cookie would make the handler return 200. They are renamed
to "_with_junk_cookie_rejected" and their assertions flipped to
401. The negative path (no cookie → 401 not_authenticated)
is unchanged.

Verified:
  no cookie       → 401 not_authenticated
  junk cookie     → 401 token_invalid     (the fixed bug)
  expired cookie  → 401 token_expired

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): wire @require_permission(owner_check=True) on isolation routes

Apply the require_permission decorator to all 28 routes that take a
{thread_id} path parameter. Combined with the strict middleware
(previous commit), this gives the double-layer protection that
AUTH_TEST_PLAN test 7.5.9 documents:

  Layer 1 (AuthMiddleware): cookie + JWT validation, rejects junk
                            cookies and stamps request.state.user
  Layer 2 (@require_permission with owner_check=True): per-resource
                            ownership verification via
                            ThreadMetaStore.check_access — returns
                            404 if a different user owns the thread

The decorator's owner_check branch is rewritten to use the SQL
thread_meta_repo (the 2.0-rc persistence layer) instead of the
LangGraph store path that PR #1728 used (_store_get / get_store
in routers/threads.py). The inject_record convenience is dropped
— no caller in 2.0 needs the LangGraph blob, and the SQL repo has
a different shape.

Routes decorated (28 total):
- threads.py: delete, patch, get, get-state, post-state, post-history
- thread_runs.py: post-runs, post-runs-stream, post-runs-wait,
  list_runs, get_run, cancel_run, join_run, stream_existing_run,
  list_thread_messages, list_run_messages, list_run_events,
  thread_token_usage
- feedback.py: create, list, stats, delete
- uploads.py: upload (added Request param), list, delete
- artifacts.py: get_artifact
- suggestions.py: generate (renamed body parameter to avoid
  conflict with FastAPI Request)

Test fixes:
- test_suggestions_router.py: bypass the decorator via __wrapped__
  (the unit tests cover parsing logic, not auth — no point spinning
  up a thread_meta_repo just to test JSON unwrapping)
- test_auth_middleware.py 4 fake-cookie tests: already updated in
  the previous commit (745bf432)

Tests: 293 passed (auth + persistence + isolation + suggestions)
Lint: clean

* security(auth): defense-in-depth fixes from release validation pass

Eight findings caught while running the AUTH_TEST_PLAN end-to-end against
the deployed sg_dev stack. Each is a pre-condition for shipping
release/2.0-rc that the previous PRs missed.

Backend hardening
- routers/auth.py: rate limiter X-Real-IP now requires AUTH_TRUSTED_PROXIES
  whitelist (CIDR/IP allowlist). Without nginx in front, the previous code
  honored arbitrary X-Real-IP, letting an attacker rotate the header to
  fully bypass the per-IP login lockout.
- routers/auth.py: 36-entry common-password blocklist via Pydantic
  field_validator on RegisterRequest + ChangePasswordRequest. The shared
  _validate_strong_password helper keeps the constraint in one place.
- routers/threads.py: ThreadCreateRequest + ThreadPatchRequest strip
  server-reserved metadata keys (owner_id, user_id) via Pydantic
  field_validator so a forged value can never round-trip back to other
  clients reading the same thread. The actual ownership invariant stays
  on the threads_meta row; this closes the metadata-blob echo gap.
- authz.py + thread_meta/sql.py: require_permission gains a require_existing
  flag plumbed through check_access(require_existing=True). Destructive
  routes (DELETE/PATCH/state-update/runs/feedback) now treat a missing
  thread_meta row as 404 instead of "untracked legacy thread, allow",
  closing the cross-user delete-idempotence gap where any user could
  successfully DELETE another user's deleted thread.
- repositories/sqlite.py + base.py: update_user raises UserNotFoundError
  on a vanished row instead of silently returning the input. Concurrent
  delete during password reset can no longer look like a successful update.
- runtime/user_context.py: resolve_owner_id() coerces User.id (UUID) to
  str at the contextvar boundary so SQLAlchemy String(64) columns can
  bind it. The whole 2.0-rc isolation pipeline was previously broken
  end-to-end (POST /api/threads → 500 "type 'UUID' is not supported").
- persistence/engine.py: SQLAlchemy listener enables PRAGMA journal_mode=WAL,
  synchronous=NORMAL, foreign_keys=ON on every new SQLite connection.
  TC-UPG-06 in the test plan expects WAL; previous code shipped with the
  default 'delete' journal.
- auth_middleware.py: stamp request.state.auth = AuthContext(...) so
  @require_permission's short-circuit fires; previously every isolation
  request did a duplicate JWT decode + users SELECT. Also unifies the
  401 payload through AuthErrorResponse(...).model_dump().
- app.py: _ensure_admin_user restructure removes the noqa F821 scoping
  bug where 'password' was referenced outside the branch that defined it.
  New _announce_credentials helper absorbs the duplicate log block in
  the fresh-admin and reset-admin branches.

* fix(frontend+nginx): rollout CSRF on every state-changing client path

The frontend was 100% broken in gateway-pro mode for any user trying to
open a specific chat thread. Three cumulative bugs each silently
masked the next.

LangGraph SDK CSRF gap (api-client.ts)
- The Client constructor took only apiUrl, no defaultHeaders, no fetch
  interceptor. The SDK's internal fetch never sent X-CSRF-Token, so
  every state-changing /api/langgraph-compat/* call (runs/stream,
  threads/search, threads/{tid}/history, ...) hit CSRFMiddleware and
  got 403 before reaching the auth check. UI symptom: empty thread page
  with no error message; the SPA's hooks swallowed the rejection.
- Fix: pass an onRequest hook that injects X-CSRF-Token from the
  csrf_token cookie per request. Reading the cookie per call (not at
  construction time) handles login / logout / password-change cookie
  rotation transparently. The SDK's prepareFetchOptions calls
  onRequest for both regular requests AND streaming/SSE/reconnect, so
  the same hook covers runs.stream and runs.joinStream.

Raw fetch CSRF gap (7 files)
- Audit: 11 frontend fetch sites, only 2 included CSRF (login/setup +
  account-settings change-password). The other 7 routed through raw
  fetch() with no header — suggestions, memory, agents, mcp, skills,
  uploads, and the local thread cleanup hook all 403'd silently.
- Fix: enhance fetcher.ts:fetchWithAuth to auto-inject X-CSRF-Token on
  POST/PUT/DELETE/PATCH from a single shared readCsrfCookie() helper.
  Convert all 7 raw fetch() callers to fetchWithAuth so the contract
  is centrally enforced. api-client.ts and fetcher.ts share
  readCsrfCookie + STATE_CHANGING_METHODS to avoid drift.

nginx routing + buffering (nginx.local.conf)
- The auth feature shipped without updating the nginx config: per-API
  explicit location blocks but no /api/v1/auth/, /api/feedback, /api/runs.
  The frontend's client-side fetches to /api/v1/auth/login/local 404'd
  from the Next.js side because nginx routed /api/* to the frontend.
- Fix: add catch-all `location /api/` that proxies to the gateway.
  nginx longest-prefix matching keeps the explicit blocks (/api/models,
  /api/threads regex, /api/langgraph/, ...) winning for their paths.
- Fix: disable proxy_buffering + proxy_request_buffering for the
  frontend `location /` block. Without it, nginx tries to spool large
  Next.js chunks into /var/lib/nginx/proxy (root-owned) and fails with
  Permission denied → ERR_INCOMPLETE_CHUNKED_ENCODING → ChunkLoadError.

* test(auth): release-validation test infra and new coverage

Test fixtures and unit tests added during the validation pass.

Router test helpers (NEW: tests/_router_auth_helpers.py)
- make_authed_test_app(): builds a FastAPI test app with a stub
  middleware that stamps request.state.user + request.state.auth and a
  permissive thread_meta_repo mock. TestClient-based router tests
  (test_artifacts_router, test_threads_router) use it instead of bare
  FastAPI() so the new @require_permission(owner_check=True) decorators
  short-circuit cleanly.
- call_unwrapped(): walks the __wrapped__ chain to invoke the underlying
  handler without going through the authz wrappers. Direct-call tests
  (test_uploads_router) use it. Typed with ParamSpec so the wrapped
  signature flows through.

Backend test additions
- test_auth.py: 7 tests for the new _get_client_ip trust model (no
  proxy / trusted proxy / untrusted peer / XFF rejection / invalid
  CIDR / no client). 5 tests for the password blocklist (literal,
  case-insensitive, strong password accepted, change-password binding,
  short-password length-check still fires before blocklist).
  test_update_user_raises_when_row_concurrently_deleted: closes a
  shipped-without-coverage gap on the new UserNotFoundError contract.
- test_thread_meta_repo.py: 4 tests for check_access(require_existing=True)
  — strict missing-row denial, strict owner match, strict owner mismatch,
  strict null-owner still allowed (shared rows survive the tightening).
- test_ensure_admin.py: 3 tests for _migrate_orphaned_threads /
  _iter_store_items pagination, covering the TC-UPG-02 upgrade story
  end-to-end via mock store. Closes the gap where the cursor pagination
  was untested even though the previous PR rewrote it.
- test_threads_router.py: 5 tests for _strip_reserved_metadata
  (owner_id removal, user_id removal, safe-keys passthrough, empty
  input, both-stripped).
- test_auth_type_system.py: replace "password123" fixtures with
  Tr0ub4dor3a / AnotherStr0ngPwd! so the new password blocklist
  doesn't reject the test data.

* docs(auth): refresh TC-DOCKER-05 + document Docker validation gap

- AUTH_TEST_PLAN.md TC-DOCKER-05: the previous expectation
  ("admin password visible in docker logs") was stale after the simplify
  pass that moved credentials to a 0600 file. The grep "Password:" check
  would have silently failed and given a false sense of coverage. New
  expectation matches the actual file-based path: 0600 file in
  DEER_FLOW_HOME, log shows the path (not the secret), reverse-grep
  asserts no leaked password in container logs.
- NEW: docs/AUTH_TEST_DOCKER_GAP.md documents the only un-executed
  block in the test plan (TC-DOCKER-01..06). Reason: sg_dev validation
  host has no Docker daemon installed. The doc maps each Docker case
  to an already-validated bare-metal equivalent (TC-1.1, TC-REENT-01,
  TC-API-02 etc.) so the gap is auditable, and includes pre-flight
  reproduction steps for whoever has Docker available.

---------

Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>

* feat: replace auto-admin creation with interactive setup flow

On first boot, instead of auto-creating admin@deerflow.dev with a
random password written to a credential file, DeerFlow now redirects
to /setup where the user creates the admin account interactively.

Backend:
- Remove auto admin creation from _ensure_admin_user (now only runs
  orphan thread migration when an admin already exists)
- Add POST /api/v1/auth/initialize endpoint (public, only callable
  when 0 users exist; auto-logs in after creation)
- Add /api/v1/auth/initialize to public paths in auth_middleware.py
  and CSRF exempt paths in csrf_middleware.py
- Update test_ensure_admin.py to match new behavior
- Add test_initialize_admin.py with 8 tests for the new endpoint

Frontend:
- Add system_setup_required to AuthResult type
- getServerSideUser() checks setup-status when unauthenticated
- Auth layout allows system_setup_required (renders children)
- Workspace layout redirects system_setup_required → /setup
- Login page redirects to /setup when system not initialized
- Setup page detects mode via isAuthenticated: unauth = create-admin
  form (calls /initialize), auth = change-password form (existing)

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/9c2471c5-d6e9-4ada-9192-61b56007b8d7

Co-authored-by: foreleven <4785594+foreleven@users.noreply.github.com>

* fix: add cleanup flags to useEffect async fetches in setup/login pages

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/9c2471c5-d6e9-4ada-9192-61b56007b8d7

Co-authored-by: foreleven <4785594+foreleven@users.noreply.github.com>

* fix: address reviewer feedback on /initialize endpoint security and robustness

1. Concurrency/register-blocking: switch setup-status and /initialize to
   check admin_count (via new count_admin_users()) instead of total
   user_count — /register can no longer block admin initialization

2. Dedicated error code: add SYSTEM_ALREADY_INITIALIZED to AuthErrorCode
   and use it in /initialize 409 responses; add to frontend types

3. Init token security: generate a one-time token at startup (logged to
   stdout) and require it in the /initialize request body — prevents
   an attacker from claiming admin on an exposed first-boot instance

4. Setup-status fetch timeout: apply SSR_AUTH_TIMEOUT_MS abort-controller
   pattern to the setup-status fetch in server.ts (same as /auth/me)

Backend repo/provider: add count_admin_users() to base, SQLite, and
LocalAuthProvider. Tests updated + new token-validation/register-blocking
test cases added.

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/b9f531fc-8ed3-41db-b416-237f243b45fd

Co-authored-by: foreleven <4785594+foreleven@users.noreply.github.com>

* fix: address code review nits — move secrets import, add INVALID_INIT_TOKEN error code, fix test assertions

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/b9f531fc-8ed3-41db-b416-237f243b45fd

Co-authored-by: foreleven <4785594+foreleven@users.noreply.github.com>

* refactor: remove init_token generation and validation from admin setup flow

* fix: re-apply init_token security for /initialize endpoint

Re-adds the one-time init_token requirement to the /initialize endpoint,
building on the human's UI improvements in 5eeeb09. This addresses the
two remaining unresolved review threads:

1. Dedicated error code (SYSTEM_ALREADY_INITIALIZED + INVALID_INIT_TOKEN)
2. Init token security gate — requires the token logged at startup

Changes:
- errors.py: re-add INVALID_INIT_TOKEN error code
- routers/auth.py: re-add `import secrets`, `init_token` field,
  token validation with secrets.compare_digest, and token consumption
- app.py: re-add token generation/logging and app.state.init_token = None
- setup/page.tsx: re-add initToken state + input field (human's UI kept)
- types.ts: re-add invalid_init_token error code
- test_initialize_admin.py: restore full token test coverage
- test_ensure_admin.py: restore init_token assertions

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/646fb5c0-ec09-41aa-9fe9-e6f7c32364e8

Co-authored-by: foreleven <4785594+foreleven@users.noreply.github.com>

* fix: make init_token optional (403 not 422 on missing), don't consume token on error paths

Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/646fb5c0-ec09-41aa-9fe9-e6f7c32364e8

Co-authored-by: foreleven <4785594+foreleven@users.noreply.github.com>

* refactor: remove redundant skill-related functions and documentation

---------

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2026-04-26 11:08:41 +08:00
greatmengqi 94eee95fe0 feat(auth): release-validation pass for 2.0-rc — 12 blockers + simplify follow-ups (#2008)
* feat(auth): introduce backend auth module

Port RFC-001 authentication core from PR #1728:
- JWT token handling (create_access_token, decode_token, TokenPayload)
- Password hashing (bcrypt) with verify_password
- SQLite UserRepository with base interface
- Provider Factory pattern (LocalAuthProvider)
- CLI reset_admin tool
- Auth-specific errors (AuthErrorCode, TokenError, AuthErrorResponse)

Deps:
- bcrypt>=4.0.0
- pyjwt>=2.9.0
- email-validator>=2.0.0
- backend/uv.toml pins public PyPI index

Tests: 12 pure unit tests (test_auth_config.py, test_auth_errors.py).

Scope note: authz.py, test_auth.py, and test_auth_type_system.py are
deferred to commit 2 because they depend on middleware and deps wiring
that is not yet in place. Commit 1 stays "pure new files only" as the
spec mandates.

* feat(auth): wire auth end-to-end (middleware + frontend replacement)

Backend:
- Port auth_middleware, csrf_middleware, langgraph_auth, routers/auth
- Port authz decorator (owner_filter_key defaults to 'owner_id')
- Merge app.py: register AuthMiddleware + CSRFMiddleware + CORS, add
  _ensure_admin_user lifespan hook, _migrate_orphaned_threads helper,
  register auth router
- Merge deps.py: add get_local_provider, get_current_user_from_request,
  get_optional_user_from_request; keep get_current_user as thin str|None
  adapter for feedback router
- langgraph.json: add auth path pointing to langgraph_auth.py:auth
- Rename metadata['user_id'] -> metadata['owner_id'] in langgraph_auth
  (both metadata write and LangGraph filter dict) + test fixtures

Frontend:
- Delete better-auth library and api catch-all route
- Remove better-auth npm dependency and env vars (BETTER_AUTH_SECRET,
  BETTER_AUTH_GITHUB_*) from env.js
- Port frontend/src/core/auth/* (AuthProvider, gateway-config,
  proxy-policy, server-side getServerSideUser, types)
- Port frontend/src/core/api/fetcher.ts
- Port (auth)/layout, (auth)/login, (auth)/setup pages
- Rewrite workspace/layout.tsx as server component that calls
  getServerSideUser and wraps in AuthProvider
- Port workspace/workspace-content.tsx for the client-side sidebar logic

Tests:
- Port 5 auth test files (test_auth, test_auth_middleware,
  test_auth_type_system, test_ensure_admin, test_langgraph_auth)
- 176 auth tests PASS

After this commit: login/logout/registration flow works, but persistence
layer does not yet filter by owner_id. Commit 4 closes that gap.

* feat(auth): account settings page + i18n

- Port account-settings-page.tsx (change password, change email, logout)
- Wire into settings-dialog.tsx as new "account" section with UserIcon,
  rendered first in the section list
- Add i18n keys:
  - en-US/zh-CN: settings.sections.account ("Account" / "账号")
  - en-US/zh-CN: button.logout ("Log out" / "退出登录")
  - types.ts: matching type declarations

* feat(auth): enforce owner_id across 2.0-rc persistence layer

Add request-scoped contextvar-based owner filtering to threads_meta,
runs, run_events, and feedback repositories. Router code is unchanged
— isolation is enforced at the storage layer so that any caller that
forgets to pass owner_id still gets filtered results, and new routes
cannot accidentally leak data.

Core infrastructure
-------------------
- deerflow/runtime/user_context.py (new):
  - ContextVar[CurrentUser | None] with default None
  - runtime_checkable CurrentUser Protocol (structural subtype with .id)
  - set/reset/get/require helpers
  - AUTO sentinel + resolve_owner_id(value, method_name) for sentinel
    three-state resolution: AUTO reads contextvar, explicit str
    overrides, explicit None bypasses the filter (for migration/CLI)

Repository changes
------------------
- ThreadMetaRepository: create/get/search/update_*/delete gain
  owner_id=AUTO kwarg; read paths filter by owner, writes stamp it,
  mutations check ownership before applying
- RunRepository: put/get/list_by_thread/delete gain owner_id=AUTO kwarg
- FeedbackRepository: create/get/list_by_run/list_by_thread/delete
  gain owner_id=AUTO kwarg
- DbRunEventStore: list_messages/list_events/list_messages_by_run/
  count_messages/delete_by_thread/delete_by_run gain owner_id=AUTO
  kwarg. Write paths (put/put_batch) read contextvar softly: when a
  request-scoped user is available, owner_id is stamped; background
  worker writes without a user context pass None which is valid
  (orphan row to be bound by migration)

Schema
------
- persistence/models/run_event.py: RunEventRow.owner_id = Mapped[
  str | None] = mapped_column(String(64), nullable=True, index=True)
- No alembic migration needed: 2.0 ships fresh, Base.metadata.create_all
  picks up the new column automatically

Middleware
----------
- auth_middleware.py: after cookie check, call get_optional_user_from_
  request to load the real User, stamp it into request.state.user AND
  the contextvar via set_current_user, reset in a try/finally. Public
  paths and unauthenticated requests continue without contextvar, and
  @require_auth handles the strict 401 path

Test infrastructure
-------------------
- tests/conftest.py: @pytest.fixture(autouse=True) _auto_user_context
  sets a default SimpleNamespace(id="test-user-autouse") on every test
  unless marked @pytest.mark.no_auto_user. Keeps existing 20+
  persistence tests passing without modification
- pyproject.toml [tool.pytest.ini_options]: register no_auto_user
  marker so pytest does not emit warnings for opt-out tests
- tests/test_user_context.py: 6 tests covering three-state semantics,
  Protocol duck typing, and require/optional APIs
- tests/test_thread_meta_repo.py: one test updated to pass owner_id=
  None explicitly where it was previously relying on the old default

Test results
------------
- test_user_context.py: 6 passed
- test_auth*.py + test_langgraph_auth.py + test_ensure_admin.py: 127
- test_run_event_store / test_run_repository / test_thread_meta_repo
  / test_feedback: 92 passed
- Full backend suite: 1905 passed, 2 failed (both @requires_llm flaky
  integration tests unrelated to auth), 1 skipped

* feat(auth): extend orphan migration to 2.0-rc persistence tables

_ensure_admin_user now runs a three-step pipeline on every boot:

  Step 1 (fatal):     admin user exists / is created / password is reset
  Step 2 (non-fatal): LangGraph store orphan threads → admin
  Step 3 (non-fatal): SQL persistence tables → admin
    - threads_meta
    - runs
    - run_events
    - feedback

Each step is idempotent. The fatal/non-fatal split mirrors PR #1728's
original philosophy: admin creation failure blocks startup (the system
is unusable without an admin), whereas migration failures log a warning
and let the service proceed (a partial migration is recoverable; a
missing admin is not).

Key helpers
-----------
- _iter_store_items(store, namespace, *, page_size=500):
  async generator that cursor-paginates across LangGraph store pages.
  Fixes PR #1728's hardcoded limit=1000 bug that would silently lose
  orphans beyond the first page.

- _migrate_orphaned_threads(store, admin_user_id):
  Rewritten to use _iter_store_items. Returns the migrated count so the
  caller can log it; raises only on unhandled exceptions.

- _migrate_orphan_sql_tables(admin_user_id):
  Imports the 4 ORM models lazily, grabs the shared session factory,
  runs one UPDATE per table in a single transaction, commits once.
  No-op when no persistence backend is configured (in-memory dev).

Tests: test_ensure_admin.py (8 passed)

* test(auth): port AUTH test plan docs + lint/format pass

- Port backend/docs/AUTH_TEST_PLAN.md and AUTH_UPGRADE.md from PR #1728
- Rename metadata.user_id → metadata.owner_id in AUTH_TEST_PLAN.md
  (4 occurrences from the original PR doc)
- ruff auto-fix UP037 in sentinel type annotations: drop quotes around
  "str | None | _AutoSentinel" now that from __future__ import
  annotations makes them implicit string forms
- ruff format: 2 files (app/gateway/app.py, runtime/user_context.py)

Note on test coverage additions:
- conftest.py autouse fixture was already added in commit 4 (had to
  be co-located with the repository changes to keep pre-existing
  persistence tests passing)
- cross-user isolation E2E tests (test_owner_isolation.py) deferred
  — enforcement is already proven by the 98-test repository suite
  via the autouse fixture + explicit _AUTO sentinel exercises
- New test cases (TC-API-17..20, TC-ATK-13, TC-MIG-01..07) listed
  in AUTH_TEST_PLAN.md are deferred to a follow-up PR — they are
  manual-QA test cases rather than pytest code, and the spec-level
  coverage is already met by test_user_context.py + the 98-test
  repository suite.

Final test results:
- Auth suite (test_auth*, test_langgraph_auth, test_ensure_admin,
  test_user_context): 186 passed
- Persistence suite (test_run_event_store, test_run_repository,
  test_thread_meta_repo, test_feedback): 98 passed
- Lint: ruff check + ruff format both clean

* test(auth): add cross-user isolation test suite

10 tests exercising the storage-layer owner filter by manually
switching the user_context contextvar between two users. Verifies
the safety invariant:

  After a repository write with owner_id=A, a subsequent read with
  owner_id=B must not return the row, and vice versa.

Covers all 4 tables that own user-scoped data:

TC-API-17  threads_meta  — read, search, update, delete cross-user
TC-API-18  runs          — get, list_by_thread, delete cross-user
TC-API-19  run_events    — list_messages, list_events, count_messages,
                           delete_by_thread (CRITICAL: raw conversation
                           content leak vector)
TC-API-20  feedback      — get, list_by_run, delete cross-user

Plus two meta-tests verifying the sentinel pattern itself:
- AUTO + unset contextvar raises RuntimeError
- explicit owner_id=None bypasses the filter (migration escape hatch)

Architecture note
-----------------
These tests bypass the HTTP layer by design. The full chain
(cookie → middleware → contextvar → repository) is covered piecewise:

- test_auth_middleware.py: middleware sets contextvar from cookies
- test_owner_isolation.py: repositories enforce isolation when
  contextvar is set to different users

Together they prove the end-to-end safety property without the
ceremony of spinning up a full TestClient + in-memory DB for every
router endpoint.

Tests pass: 231 (full auth + persistence + isolation suite)
Lint: clean

* refactor(auth): migrate user repository to SQLAlchemy ORM

Move the users table into the shared persistence engine so auth
matches the pattern of threads_meta, runs, run_events, and feedback —
one engine, one session factory, one schema init codepath.

New files
---------
- persistence/user/__init__.py, persistence/user/model.py: UserRow
  ORM class with partial unique index on (oauth_provider, oauth_id)
- Registered in persistence/models/__init__.py so
  Base.metadata.create_all() picks it up

Modified
--------
- auth/repositories/sqlite.py: rewritten as async SQLAlchemy,
  identical constructor pattern to the other four repositories
  (def __init__(self, session_factory) + self._sf = session_factory)
- auth/config.py: drop users_db_path field — storage is configured
  through config.database like every other table
- deps.py/get_local_provider: construct SQLiteUserRepository with
  the shared session factory, fail fast if engine is not initialised
- tests/test_auth.py: rewrite test_sqlite_round_trip_new_fields to
  use the shared engine (init_engine + close_engine in a tempdir)
- tests/test_auth_type_system.py: add per-test autouse fixture that
  spins up a scratch engine and resets deps._cached_* singletons

* refactor(auth): remove SQL orphan migration (unused in supported scenarios)

The _migrate_orphan_sql_tables helper existed to bind NULL owner_id
rows in threads_meta, runs, run_events, and feedback to the admin on
first boot. But in every supported upgrade path, it's a no-op:

  1. Fresh install: create_all builds fresh tables, no legacy rows
  2. No-auth → with-auth (no existing persistence DB): persistence
     tables are created fresh by create_all, no legacy rows
  3. No-auth → with-auth (has existing persistence DB from #1930):
     NOT a supported upgrade path — "有 DB 到有 DB" schema evolution
     is out of scope; users wipe DB or run manual ALTER

So the SQL orphan migration never has anything to do in the
supported matrix. Delete the function, simplify _ensure_admin_user
from a 3-step pipeline to a 2-step one (admin creation + LangGraph
store orphan migration only).

LangGraph store orphan migration stays: it serves the real
"no-auth → with-auth" upgrade path where a user's existing LangGraph
thread metadata has no owner_id field and needs to be stamped with
the newly-created admin's id.

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): write initial admin password to 0600 file instead of logs

CodeQL py/clear-text-logging-sensitive-data flagged 3 call sites that
logged the auto-generated admin password to stdout via logger.info().
Production log aggregators (ELK/Splunk/etc) would have captured those
cleartext secrets. Replace with a shared helper that writes to
.deer-flow/admin_initial_credentials.txt with mode 0600, and log only
the path.

New file
--------
- app/gateway/auth/credential_file.py: write_initial_credentials()
  helper. Takes email, password, and a "initial"/"reset" label.
  Creates .deer-flow/ if missing, writes a header comment plus the
  email+password, chmods 0o600, returns the absolute Path.

Modified
--------
- app/gateway/app.py: both _ensure_admin_user paths (fresh creation
  + needs_setup password reset) now write to file and log the path
- app/gateway/auth/reset_admin.py: rewritten to use the shared ORM
  repo (SQLiteUserRepository with session_factory) and the
  credential_file helper. The previous implementation was broken
  after the earlier ORM refactor — it still imported _get_users_conn
  and constructed SQLiteUserRepository() without a session factory.

No tests changed — the three password-log sites are all exercised
via existing test_ensure_admin.py which checks that startup
succeeds, not that a specific string appears in logs.

CodeQL alerts 272, 283, 284: all resolved.

* security(auth): strict JWT validation in middleware (fix junk cookie bypass)

AUTH_TEST_PLAN test 7.5.8 expects junk cookies to be rejected with
401. The previous middleware behaviour was "presence-only": check
that some access_token cookie exists, then pass through. In
combination with my Task-12 decision to skip @require_auth
decorators on routes, this created a gap where a request with any
cookie-shaped string (e.g. access_token=not-a-jwt) would bypass
authentication on routes that do not touch the repository
(/api/models, /api/mcp/config, /api/memory, /api/skills, …).

Fix: middleware now calls get_current_user_from_request() strictly
and catches the resulting HTTPException to render a 401 with the
proper fine-grained error code (token_invalid, token_expired,
user_not_found, …). On success it stamps request.state.user and
the contextvar so repository-layer owner filters work downstream.

The 4 old "_with_cookie_passes" tests in test_auth_middleware.py
were written for the presence-only behaviour; they asserted that
a junk cookie would make the handler return 200. They are renamed
to "_with_junk_cookie_rejected" and their assertions flipped to
401. The negative path (no cookie → 401 not_authenticated)
is unchanged.

Verified:
  no cookie       → 401 not_authenticated
  junk cookie     → 401 token_invalid     (the fixed bug)
  expired cookie  → 401 token_expired

Tests: 284 passed (auth + persistence + isolation)
Lint: clean

* security(auth): wire @require_permission(owner_check=True) on isolation routes

Apply the require_permission decorator to all 28 routes that take a
{thread_id} path parameter. Combined with the strict middleware
(previous commit), this gives the double-layer protection that
AUTH_TEST_PLAN test 7.5.9 documents:

  Layer 1 (AuthMiddleware): cookie + JWT validation, rejects junk
                            cookies and stamps request.state.user
  Layer 2 (@require_permission with owner_check=True): per-resource
                            ownership verification via
                            ThreadMetaStore.check_access — returns
                            404 if a different user owns the thread

The decorator's owner_check branch is rewritten to use the SQL
thread_meta_repo (the 2.0-rc persistence layer) instead of the
LangGraph store path that PR #1728 used (_store_get / get_store
in routers/threads.py). The inject_record convenience is dropped
— no caller in 2.0 needs the LangGraph blob, and the SQL repo has
a different shape.

Routes decorated (28 total):
- threads.py: delete, patch, get, get-state, post-state, post-history
- thread_runs.py: post-runs, post-runs-stream, post-runs-wait,
  list_runs, get_run, cancel_run, join_run, stream_existing_run,
  list_thread_messages, list_run_messages, list_run_events,
  thread_token_usage
- feedback.py: create, list, stats, delete
- uploads.py: upload (added Request param), list, delete
- artifacts.py: get_artifact
- suggestions.py: generate (renamed body parameter to avoid
  conflict with FastAPI Request)

Test fixes:
- test_suggestions_router.py: bypass the decorator via __wrapped__
  (the unit tests cover parsing logic, not auth — no point spinning
  up a thread_meta_repo just to test JSON unwrapping)
- test_auth_middleware.py 4 fake-cookie tests: already updated in
  the previous commit (745bf432)

Tests: 293 passed (auth + persistence + isolation + suggestions)
Lint: clean

* security(auth): defense-in-depth fixes from release validation pass

Eight findings caught while running the AUTH_TEST_PLAN end-to-end against
the deployed sg_dev stack. Each is a pre-condition for shipping
release/2.0-rc that the previous PRs missed.

Backend hardening
- routers/auth.py: rate limiter X-Real-IP now requires AUTH_TRUSTED_PROXIES
  whitelist (CIDR/IP allowlist). Without nginx in front, the previous code
  honored arbitrary X-Real-IP, letting an attacker rotate the header to
  fully bypass the per-IP login lockout.
- routers/auth.py: 36-entry common-password blocklist via Pydantic
  field_validator on RegisterRequest + ChangePasswordRequest. The shared
  _validate_strong_password helper keeps the constraint in one place.
- routers/threads.py: ThreadCreateRequest + ThreadPatchRequest strip
  server-reserved metadata keys (owner_id, user_id) via Pydantic
  field_validator so a forged value can never round-trip back to other
  clients reading the same thread. The actual ownership invariant stays
  on the threads_meta row; this closes the metadata-blob echo gap.
- authz.py + thread_meta/sql.py: require_permission gains a require_existing
  flag plumbed through check_access(require_existing=True). Destructive
  routes (DELETE/PATCH/state-update/runs/feedback) now treat a missing
  thread_meta row as 404 instead of "untracked legacy thread, allow",
  closing the cross-user delete-idempotence gap where any user could
  successfully DELETE another user's deleted thread.
- repositories/sqlite.py + base.py: update_user raises UserNotFoundError
  on a vanished row instead of silently returning the input. Concurrent
  delete during password reset can no longer look like a successful update.
- runtime/user_context.py: resolve_owner_id() coerces User.id (UUID) to
  str at the contextvar boundary so SQLAlchemy String(64) columns can
  bind it. The whole 2.0-rc isolation pipeline was previously broken
  end-to-end (POST /api/threads → 500 "type 'UUID' is not supported").
- persistence/engine.py: SQLAlchemy listener enables PRAGMA journal_mode=WAL,
  synchronous=NORMAL, foreign_keys=ON on every new SQLite connection.
  TC-UPG-06 in the test plan expects WAL; previous code shipped with the
  default 'delete' journal.
- auth_middleware.py: stamp request.state.auth = AuthContext(...) so
  @require_permission's short-circuit fires; previously every isolation
  request did a duplicate JWT decode + users SELECT. Also unifies the
  401 payload through AuthErrorResponse(...).model_dump().
- app.py: _ensure_admin_user restructure removes the noqa F821 scoping
  bug where 'password' was referenced outside the branch that defined it.
  New _announce_credentials helper absorbs the duplicate log block in
  the fresh-admin and reset-admin branches.

* fix(frontend+nginx): rollout CSRF on every state-changing client path

The frontend was 100% broken in gateway-pro mode for any user trying to
open a specific chat thread. Three cumulative bugs each silently
masked the next.

LangGraph SDK CSRF gap (api-client.ts)
- The Client constructor took only apiUrl, no defaultHeaders, no fetch
  interceptor. The SDK's internal fetch never sent X-CSRF-Token, so
  every state-changing /api/langgraph-compat/* call (runs/stream,
  threads/search, threads/{tid}/history, ...) hit CSRFMiddleware and
  got 403 before reaching the auth check. UI symptom: empty thread page
  with no error message; the SPA's hooks swallowed the rejection.
- Fix: pass an onRequest hook that injects X-CSRF-Token from the
  csrf_token cookie per request. Reading the cookie per call (not at
  construction time) handles login / logout / password-change cookie
  rotation transparently. The SDK's prepareFetchOptions calls
  onRequest for both regular requests AND streaming/SSE/reconnect, so
  the same hook covers runs.stream and runs.joinStream.

Raw fetch CSRF gap (7 files)
- Audit: 11 frontend fetch sites, only 2 included CSRF (login/setup +
  account-settings change-password). The other 7 routed through raw
  fetch() with no header — suggestions, memory, agents, mcp, skills,
  uploads, and the local thread cleanup hook all 403'd silently.
- Fix: enhance fetcher.ts:fetchWithAuth to auto-inject X-CSRF-Token on
  POST/PUT/DELETE/PATCH from a single shared readCsrfCookie() helper.
  Convert all 7 raw fetch() callers to fetchWithAuth so the contract
  is centrally enforced. api-client.ts and fetcher.ts share
  readCsrfCookie + STATE_CHANGING_METHODS to avoid drift.

nginx routing + buffering (nginx.local.conf)
- The auth feature shipped without updating the nginx config: per-API
  explicit location blocks but no /api/v1/auth/, /api/feedback, /api/runs.
  The frontend's client-side fetches to /api/v1/auth/login/local 404'd
  from the Next.js side because nginx routed /api/* to the frontend.
- Fix: add catch-all `location /api/` that proxies to the gateway.
  nginx longest-prefix matching keeps the explicit blocks (/api/models,
  /api/threads regex, /api/langgraph/, ...) winning for their paths.
- Fix: disable proxy_buffering + proxy_request_buffering for the
  frontend `location /` block. Without it, nginx tries to spool large
  Next.js chunks into /var/lib/nginx/proxy (root-owned) and fails with
  Permission denied → ERR_INCOMPLETE_CHUNKED_ENCODING → ChunkLoadError.

* test(auth): release-validation test infra and new coverage

Test fixtures and unit tests added during the validation pass.

Router test helpers (NEW: tests/_router_auth_helpers.py)
- make_authed_test_app(): builds a FastAPI test app with a stub
  middleware that stamps request.state.user + request.state.auth and a
  permissive thread_meta_repo mock. TestClient-based router tests
  (test_artifacts_router, test_threads_router) use it instead of bare
  FastAPI() so the new @require_permission(owner_check=True) decorators
  short-circuit cleanly.
- call_unwrapped(): walks the __wrapped__ chain to invoke the underlying
  handler without going through the authz wrappers. Direct-call tests
  (test_uploads_router) use it. Typed with ParamSpec so the wrapped
  signature flows through.

Backend test additions
- test_auth.py: 7 tests for the new _get_client_ip trust model (no
  proxy / trusted proxy / untrusted peer / XFF rejection / invalid
  CIDR / no client). 5 tests for the password blocklist (literal,
  case-insensitive, strong password accepted, change-password binding,
  short-password length-check still fires before blocklist).
  test_update_user_raises_when_row_concurrently_deleted: closes a
  shipped-without-coverage gap on the new UserNotFoundError contract.
- test_thread_meta_repo.py: 4 tests for check_access(require_existing=True)
  — strict missing-row denial, strict owner match, strict owner mismatch,
  strict null-owner still allowed (shared rows survive the tightening).
- test_ensure_admin.py: 3 tests for _migrate_orphaned_threads /
  _iter_store_items pagination, covering the TC-UPG-02 upgrade story
  end-to-end via mock store. Closes the gap where the cursor pagination
  was untested even though the previous PR rewrote it.
- test_threads_router.py: 5 tests for _strip_reserved_metadata
  (owner_id removal, user_id removal, safe-keys passthrough, empty
  input, both-stripped).
- test_auth_type_system.py: replace "password123" fixtures with
  Tr0ub4dor3a / AnotherStr0ngPwd! so the new password blocklist
  doesn't reject the test data.

* docs(auth): refresh TC-DOCKER-05 + document Docker validation gap

- AUTH_TEST_PLAN.md TC-DOCKER-05: the previous expectation
  ("admin password visible in docker logs") was stale after the simplify
  pass that moved credentials to a 0600 file. The grep "Password:" check
  would have silently failed and given a false sense of coverage. New
  expectation matches the actual file-based path: 0600 file in
  DEER_FLOW_HOME, log shows the path (not the secret), reverse-grep
  asserts no leaked password in container logs.
- NEW: docs/AUTH_TEST_DOCKER_GAP.md documents the only un-executed
  block in the test plan (TC-DOCKER-01..06). Reason: sg_dev validation
  host has no Docker daemon installed. The doc maps each Docker case
  to an already-validated bare-metal equivalent (TC-1.1, TC-REENT-01,
  TC-API-02 etc.) so the gap is auditable, and includes pre-flight
  reproduction steps for whoever has Docker available.

---------

Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
2026-04-26 11:08:11 +08:00
rayhpeng d8ecaf46c9 feat(persistence): add unified persistence layer with event store, token tracking, and feedback (#1930)
* feat(persistence): add SQLAlchemy 2.0 async ORM scaffold

Introduce a unified database configuration (DatabaseConfig) that
controls both the LangGraph checkpointer and the DeerFlow application
persistence layer from a single `database:` config section.

New modules:
- deerflow.config.database_config — Pydantic config with memory/sqlite/postgres backends
- deerflow.persistence — async engine lifecycle, DeclarativeBase with to_dict mixin, Alembic skeleton
- deerflow.runtime.runs.store — RunStore ABC + MemoryRunStore implementation

Gateway integration initializes/tears down the persistence engine in
the existing langgraph_runtime() context manager. Legacy checkpointer
config is preserved for backward compatibility.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add RunEventStore ABC + MemoryRunEventStore

Phase 2-A prerequisite for event storage: adds the unified run event
stream interface (RunEventStore) with an in-memory implementation,
RunEventsConfig, gateway integration, and comprehensive tests (27 cases).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add ORM models, repositories, DB/JSONL event stores, RunJournal, and API endpoints

Phase 2-B: run persistence + event storage + token tracking.

- ORM models: RunRow (with token fields), ThreadMetaRow, RunEventRow
- RunRepository implements RunStore ABC via SQLAlchemy ORM
- ThreadMetaRepository with owner access control
- DbRunEventStore with trace content truncation and cursor pagination
- JsonlRunEventStore with per-run files and seq recovery from disk
- RunJournal (BaseCallbackHandler) captures LLM/tool/lifecycle events,
  accumulates token usage by caller type, buffers and flushes to store
- RunManager now accepts optional RunStore for persistent backing
- Worker creates RunJournal, writes human_message, injects callbacks
- Gateway deps use factory functions (RunRepository when DB available)
- New endpoints: messages, run messages, run events, token-usage
- ThreadCreateRequest gains assistant_id field
- 92 tests pass (33 new), zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(persistence): add user feedback + follow-up run association

Phase 2-C: feedback and follow-up tracking.

- FeedbackRow ORM model (rating +1/-1, optional message_id, comment)
- FeedbackRepository with CRUD, list_by_run/thread, aggregate stats
- Feedback API endpoints: create, list, stats, delete
- follow_up_to_run_id in RunCreateRequest (explicit or auto-detected
  from latest successful run on the thread)
- Worker writes follow_up_to_run_id into human_message event metadata
- Gateway deps: feedback_repo factory + getter
- 17 new tests (14 FeedbackRepository + 3 follow-up association)
- 109 total tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test+config: comprehensive Phase 2 test coverage + deprecate checkpointer config

- config.example.yaml: deprecate standalone checkpointer section, activate
  unified database:sqlite as default (drives both checkpointer + app data)
- New: test_thread_meta_repo.py (14 tests) — full ThreadMetaRepository coverage
  including check_access owner logic, list_by_owner pagination
- Extended test_run_repository.py (+4 tests) — completion preserves fields,
  list ordering desc, limit, owner_none returns all
- Extended test_run_journal.py (+8 tests) — on_chain_error, track_tokens=false,
  middleware no ai_message, unknown caller tokens, convenience fields,
  tool_error, non-summarization custom event
- Extended test_run_event_store.py (+7 tests) — DB batch seq continuity,
  make_run_event_store factory (memory/db/jsonl/fallback/unknown)
- Extended test_phase2b_integration.py (+4 tests) — create_or_reject persists,
  follow-up metadata, summarization in history, full DB-backed lifecycle
- Fixed DB integration test to use proper fake objects (not MagicMock)
  for JSON-serializable metadata
- 157 total Phase 2 tests pass, zero regressions

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* config: move default sqlite_dir to .deer-flow/data

Keep SQLite databases alongside other DeerFlow-managed data
(threads, memory) under the .deer-flow/ directory instead of a
top-level ./data folder.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): remove UTFJSON, use engine-level json_serializer + datetime.now()

- Replace custom UTFJSON type with standard sqlalchemy.JSON in all ORM
  models. Add json_serializer=json.dumps(ensure_ascii=False) to all
  create_async_engine calls so non-ASCII text (Chinese etc.) is stored
  as-is in both SQLite and Postgres.
- Change ORM datetime defaults from datetime.now(UTC) to datetime.now(),
  remove UTC imports.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): simplify deps.py with getter factory + inline repos

- Replace 6 identical getter functions with _require() factory.
- Inline 3 _make_*_repo() factories into langgraph_runtime(), call
  get_session_factory() once instead of 3 times.
- Add thread_meta upsert in start_run (services.py).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(docker): add UV_EXTRAS build arg for optional dependencies

Support installing optional dependency groups (e.g. postgres) at
Docker build time via UV_EXTRAS build arg:
  UV_EXTRAS=postgres docker compose build

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(journal): fix flush, token tracking, and consolidate tests

RunJournal fixes:
- _flush_sync: retain events in buffer when no event loop instead of
  dropping them; worker's finally block flushes via async flush().
- on_llm_end: add tool_calls filter and caller=="lead_agent" guard for
  ai_message events; mark message IDs for dedup with record_llm_usage.
- worker.py: persist completion data (tokens, message count) to RunStore
  in finally block.

Model factory:
- Auto-inject stream_usage=True for BaseChatOpenAI subclasses with
  custom api_base, so usage_metadata is populated in streaming responses.

Test consolidation:
- Delete test_phase2b_integration.py (redundant with existing tests).
- Move DB-backed lifecycle test into test_run_journal.py.
- Add tests for stream_usage injection in test_model_factory.py.
- Clean up executor/task_tool dead journal references.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): widen content type to str|dict in all store backends

Allow event content to be a dict (for structured OpenAI-format messages)
in addition to plain strings. Dict values are JSON-serialized for the DB
backend and deserialized on read; memory and JSONL backends handle dicts
natively. Trace truncation now serializes dicts to JSON before measuring.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(events): use metadata flag instead of heuristic for dict content detection

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(converters): add LangChain-to-OpenAI message format converters

Pure functions langchain_to_openai_message, langchain_to_openai_completion,
langchain_messages_to_openai, and _infer_finish_reason for converting
LangChain BaseMessage objects to OpenAI Chat Completions format, used by
RunJournal for event storage. 15 unit tests added.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(converters): handle empty list content as null, clean up test

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): human_message content uses OpenAI user message format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): ai_message uses OpenAI format, add ai_tool_call message event

- ai_message content now uses {"role": "assistant", "content": "..."} format
- New ai_tool_call message event emitted when lead_agent LLM responds with tool_calls
- ai_tool_call uses langchain_to_openai_message converter for consistent format
- Both events include finish_reason in metadata ("stop" or "tool_calls")

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add tool_result message event with OpenAI tool message format

Cache tool_call_id from on_tool_start keyed by run_id as fallback for on_tool_end,
then emit a tool_result message event (role=tool, tool_call_id, content) after each
successful tool completion.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): summary content uses OpenAI system message format

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): replace llm_start/llm_end with llm_request/llm_response in OpenAI format

Add on_chat_model_start to capture structured prompt messages as llm_request events.
Replace llm_end trace events with llm_response using OpenAI Chat Completions format.
Track llm_call_index to pair request/response events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(events): add record_middleware method for middleware trace events

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test(events): add full run sequence integration test for OpenAI content format

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(events): align message events with checkpoint format and add middleware tag injection

- Message events (ai_message, ai_tool_call, tool_result, human_message) now use
  BaseMessage.model_dump() format, matching LangGraph checkpoint values.messages
- on_tool_end extracts tool_call_id/name/status from ToolMessage objects
- on_tool_error now emits tool_result message events with error status
- record_middleware uses middleware:{tag} event_type and middleware category
- Summarization custom events use middleware:summarize category
- TitleMiddleware injects middleware:title tag via get_config() inheritance
- SummarizationMiddleware model bound with middleware:summarize tag
- Worker writes human_message using HumanMessage.model_dump()

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): switch search endpoint to threads_meta table and sync title

- POST /api/threads/search now queries threads_meta table directly,
  removing the two-phase Store + Checkpointer scan approach
- Add ThreadMetaRepository.search() with metadata/status filters
- Add ThreadMetaRepository.update_display_name() for title sync
- Worker syncs checkpoint title to threads_meta.display_name on run completion
- Map display_name to values.title in search response for API compatibility

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat(threads): history endpoint reads messages from event store

- POST /api/threads/{thread_id}/history now combines two data sources:
  checkpointer for checkpoint_id, metadata, title, thread_data;
  event store for messages (complete history, not truncated by summarization)
- Strip internal LangGraph metadata keys from response
- Remove full channel_values serialization in favor of selective fields

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: remove duplicate optional-dependencies header in pyproject.toml

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(middleware): pass tagged config to TitleMiddleware ainvoke call

Without the config, the middleware:title tag was not injected,
causing the LLM response to be recorded as a lead_agent ai_message
in run_events.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: resolve merge conflict in .env.example

Keep both DATABASE_URL (from persistence-scaffold) and WECOM
credentials (from main) after the merge.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address review feedback on PR #1851

- Fix naive datetime.now() → datetime.now(UTC) in all ORM models
- Fix seq race condition in DbRunEventStore.put() with FOR UPDATE
  and UNIQUE(thread_id, seq) constraint
- Encapsulate _store access in RunManager.update_run_completion()
- Deduplicate _store.put() logic in RunManager via _persist_to_store()
- Add update_run_completion to RunStore ABC + MemoryRunStore
- Wire follow_up_to_run_id through the full create path
- Add error recovery to RunJournal._flush_sync() lost-event scenario
- Add migration note for search_threads breaking change
- Fix test_checkpointer_none_fix mock to set database=None

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* chore: update uv.lock

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address 22 review comments from CodeQL, Copilot, and Code Quality

Bug fixes:
- Sanitize log params to prevent log injection (CodeQL)
- Reset threads_meta.status to idle/error when run completes
- Attach messages only to latest checkpoint in /history response
- Write threads_meta on POST /threads so new threads appear in search

Lint fixes:
- Remove unused imports (journal.py, migrations/env.py, test_converters.py)
- Convert lambda to named function (engine.py, Ruff E731)
- Remove unused logger definitions in repos (Ruff F841)
- Add logging to JSONL decode errors and empty except blocks
- Separate assert side-effects in tests (CodeQL)
- Remove unused local variables in tests (Ruff F841)
- Fix max_trace_content truncation to use byte length, not char length

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* style: apply ruff format to persistence and runtime files

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Potential fix for pull request finding 'Statement has no effect'

Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>

* refactor(runtime): introduce RunContext to reduce run_agent parameter bloat

Extract checkpointer, store, event_store, run_events_config, thread_meta_repo,
and follow_up_to_run_id into a frozen RunContext dataclass. Add get_run_context()
in deps.py to build the base context from app.state singletons. start_run() uses
dataclasses.replace() to enrich per-run fields before passing ctx to run_agent.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): move sanitize_log_param to app/gateway/utils.py

Extract the log-injection sanitizer from routers/threads.py into a shared
utils module and rename to sanitize_log_param (public API). Eliminates the
reverse service → router import in services.py.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* perf: use SQL aggregation for feedback stats and thread token usage

Replace Python-side counting in FeedbackRepository.aggregate_by_run with
a single SELECT COUNT/SUM query. Add RunStore.aggregate_tokens_by_thread
abstract method with SQL GROUP BY implementation in RunRepository and
Python fallback in MemoryRunStore. Simplify the thread_token_usage
endpoint to delegate to the new method, eliminating the limit=10000
truncation risk.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: annotate DbRunEventStore.put() as low-frequency path

Add docstring clarifying that put() opens a per-call transaction with
FOR UPDATE and should only be used for infrequent writes (currently
just the initial human_message event). High-throughput callers should
use put_batch() instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(threads): fall back to Store search when ThreadMetaRepository is unavailable

When database.backend=memory (default) or no SQL session factory is
configured, search_threads now queries the LangGraph Store instead of
returning 503. Returns empty list if neither Store nor repo is available.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(persistence): introduce ThreadMetaStore ABC for backend-agnostic thread metadata

Add ThreadMetaStore abstract base class with create/get/search/update/delete
interface. ThreadMetaRepository (SQL) now inherits from it. New
MemoryThreadMetaStore wraps LangGraph BaseStore for memory-mode deployments.

deps.py now always provides a non-None thread_meta_repo, eliminating all
`if thread_meta_repo is not None` guards in services.py, worker.py, and
routers/threads.py. search_threads no longer needs a Store fallback branch.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(history): read messages from checkpointer instead of RunEventStore

The /history endpoint now reads messages directly from the
checkpointer's channel_values (the authoritative source) instead of
querying RunEventStore.list_messages(). The RunEventStore API is
preserved for other consumers.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(persistence): address new Copilot review comments

- feedback.py: validate thread_id/run_id before deleting feedback
- jsonl.py: add path traversal protection with ID validation
- run_repo.py: parse `before` to datetime for PostgreSQL compat
- thread_meta_repo.py: fix pagination when metadata filter is active
- database_config.py: use resolve_path for sqlite_dir consistency

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Implement skill self-evolution and skill_manage flow (#1874)

* chore: ignore .worktrees directory

* Add skill_manage self-evolution flow

* Fix CI regressions for skill_manage

* Address PR review feedback for skill evolution

* fix(skill-evolution): preserve history on delete

* fix(skill-evolution): tighten scanner fallbacks

* docs: add skill_manage e2e evidence screenshot

* fix(skill-manage): avoid blocking fs ops in session runtime

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

* fix(config): resolve sqlite_dir relative to CWD, not Paths.base_dir

resolve_path() resolves relative to Paths.base_dir (.deer-flow),
which double-nested the path to .deer-flow/.deer-flow/data/app.db.
Use Path.resolve() (CWD-relative) instead.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Feature/feishu receive file (#1608)

* feat(feishu): add channel file materialization hook for inbound messages

- Introduce Channel.receive_file(msg, thread_id) as a base method for file materialization; default is no-op.
- Implement FeishuChannel.receive_file to download files/images from Feishu messages, save to sandbox, and inject virtual paths into msg.text.
- Update ChannelManager to call receive_file for any channel if msg.files is present, enabling downstream model access to user-uploaded files.
- No impact on Slack/Telegram or other channels (they inherit the default no-op).

* style(backend): format code with ruff for lint compliance

- Auto-formatted packages/harness/deerflow/agents/factory.py and tests/test_create_deerflow_agent.py using `ruff format`
- Ensured both files conform to project linting standards
- Fixes CI lint check failures caused by code style issues

* fix(feishu): handle file write operation asynchronously to prevent blocking

* fix(feishu): rename GetMessageResourceRequest to _GetMessageResourceRequest and remove redundant code

* test(feishu): add tests for receive_file method and placeholder replacement

* fix(manager): remove unnecessary type casting for channel retrieval

* fix(feishu): update logging messages to reflect resource handling instead of image

* fix(feishu): sanitize filename by replacing invalid characters in file uploads

* fix(feishu): improve filename sanitization and reorder image key handling in message processing

* fix(feishu): add thread lock to prevent filename conflicts during file downloads

* fix(test): correct bad merge in test_feishu_parser.py

* chore: run ruff and apply formatting cleanup
fix(feishu): preserve rich-text attachment order and improve fallback filename handling

* fix(docker): restore gateway env vars and fix langgraph empty arg issue (#1915)

Two production docker-compose.yaml bugs prevent `make up` from working:

1. Gateway missing DEER_FLOW_CONFIG_PATH and DEER_FLOW_EXTENSIONS_CONFIG_PATH
   environment overrides. Added in fb2d99f (#1836) but accidentally reverted
   by ca2fb95 (#1847). Without them, gateway reads host paths from .env via
   env_file, causing FileNotFoundError inside the container.

2. Langgraph command fails when LANGGRAPH_ALLOW_BLOCKING is unset (default).
   Empty $${allow_blocking} inserts a bare space between flags, causing
   ' --no-reload' to be parsed as unexpected extra argument. Fix by building
   args string first and conditionally appending --allow-blocking.

Co-authored-by: cooper <cooperfu@tencent.com>

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities (#1904)

* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities

Fix `<button>` inside `<a>` invalid HTML in artifact components and add
missing `noopener,noreferrer` to `window.open` calls to prevent reverse
tabnabbing.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix(frontend): address Copilot review on tabnabbing and double-tab-open

Remove redundant parent onClick on web_fetch ChainOfThoughtStep to
prevent opening two tabs on link click, and explicitly null out
window.opener after window.open() for defensive tabnabbing hardening.

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

* refactor(persistence): organize entities into per-entity directories

Restructure the persistence layer from horizontal "models/ + repositories/"
split into vertical entity-aligned directories. Each entity (thread_meta,
run, feedback) now owns its ORM model, abstract interface (where applicable),
and concrete implementations under a single directory with an aggregating
__init__.py for one-line imports.

Layout:
  persistence/thread_meta/{base,model,sql,memory}.py
  persistence/run/{model,sql}.py
  persistence/feedback/{model,sql}.py

models/__init__.py is kept as a facade so Alembic autogenerate continues to
discover all ORM tables via Base.metadata. RunEventRow remains under
models/run_event.py because its storage implementation lives in
runtime/events/store/db.py and has no matching repository directory.

The repositories/ directory is removed entirely. All call sites in
gateway/deps.py and tests are updated to import from the new entity
packages, e.g.:

    from deerflow.persistence.thread_meta import ThreadMetaRepository
    from deerflow.persistence.run import RunRepository
    from deerflow.persistence.feedback import FeedbackRepository

Full test suite passes (1690 passed, 14 skipped).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(gateway): sync thread rename and delete through ThreadMetaStore

The POST /threads/{id}/state endpoint previously synced title changes
only to the LangGraph Store via _store_upsert. In sqlite mode the search
endpoint reads from the ThreadMetaRepository SQL table, so renames never
appeared in /threads/search until the next agent run completed (worker.py
syncs title from checkpoint to thread_meta in its finally block).

Likewise the DELETE /threads/{id} endpoint cleaned up the filesystem,
Store, and checkpointer but left the threads_meta row orphaned in sqlite,
so deleted threads kept appearing in /threads/search.

Fix both endpoints by routing through the ThreadMetaStore abstraction
which already has the correct sqlite/memory implementations wired up by
deps.py. The rename path now calls update_display_name() and the delete
path calls delete() — both work uniformly across backends.

Verified end-to-end with curl in gateway mode against sqlite backend.
Existing test suite (1690 passed) and focused router/repo tests pass.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(gateway): route all thread metadata access through ThreadMetaStore

Following the rename/delete bug fix in PR1, migrate the remaining direct
LangGraph Store reads/writes in the threads router and services to the
ThreadMetaStore abstraction so that the sqlite and memory backends behave
identically and the legacy dual-write paths can be removed.

Migrated endpoints (threads.py):
- create_thread: idempotency check + write now use thread_meta_repo.get/create
  instead of dual-writing the LangGraph Store and the SQL row.
- get_thread: reads from thread_meta_repo.get; the checkpoint-only fallback
  for legacy threads is preserved.
- patch_thread: replaced _store_get/_store_put with thread_meta_repo.update_metadata.
- delete_thread_data: dropped the legacy store.adelete; thread_meta_repo.delete
  already covers it.

Removed dead code (services.py):
- _upsert_thread_in_store — redundant with the immediately following
  thread_meta_repo.create() call.
- _sync_thread_title_after_run — worker.py's finally block already syncs
  the title via thread_meta_repo.update_display_name() after each run.

Removed dead code (threads.py):
- _store_get / _store_put / _store_upsert helpers (no remaining callers).
- THREADS_NS constant.
- get_store import (router no longer touches the LangGraph Store directly).

New abstract method:
- ThreadMetaStore.update_metadata(thread_id, metadata) merges metadata into
  the thread's metadata field. Implemented in both ThreadMetaRepository (SQL,
  read-modify-write inside one session) and MemoryThreadMetaStore. Three new
  unit tests cover merge / empty / nonexistent behaviour.

Net change: -134 lines. Full test suite: 1693 passed, 14 skipped.
Verified end-to-end with curl in gateway mode against sqlite backend
(create / patch / get / rename / search / delete).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: JilongSun <965640067@qq.com>
Co-authored-by: jie <49781832+stan-fu@users.noreply.github.com>
Co-authored-by: cooper <cooperfu@tencent.com>
Co-authored-by: yangzheli <43645580+yangzheli@users.noreply.github.com>
2026-04-26 11:05:47 +08:00
343 changed files with 20044 additions and 10106 deletions
+8
View File
@@ -1,3 +1,6 @@
# Serper API Key (Google Search) - https://serper.dev
SERPER_API_KEY=your-serper-api-key
# TAVILY API Key
TAVILY_API_KEY=your-tavily-api-key
@@ -40,3 +43,8 @@ INFOQUEST_API_KEY=your-infoquest-api-key
#
# WECOM_BOT_ID=your-wecom-bot-id
# WECOM_BOT_SECRET=your-wecom-bot-secret
# DINGTALK_CLIENT_ID=your-dingtalk-client-id
# DINGTALK_CLIENT_SECRET=your-dingtalk-client-secret
# Set to "false" to disable Swagger UI, ReDoc, and OpenAPI schema in production
# GATEWAY_ENABLE_DOCS=false
+101
View File
@@ -0,0 +1,101 @@
name: Publish Containers
on:
push:
tags:
- "v*"
jobs:
backend-container:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
attestations: write
id-token: write
env:
REGISTRY: ghcr.io
IMAGE_NAME: ${{ github.repository }}-backend
steps:
- name: Checkout repository
uses: actions/checkout@v6
- name: Log in to the Container registry
uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 #v3.4.0
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract metadata (tags, labels) for Docker
id: meta
uses: docker/metadata-action@902fa8ec7d6ecbf8d84d538b9b233a880e428804 #v5.7.0
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=tag
type=ref,event=branch
type=sha
type=raw,value=latest,enable={{is_default_branch}}
- name: Build and push Docker image
id: push
uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 #v6.18.0
with:
context: .
file: backend/Dockerfile
push: true
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
- name: Generate artifact attestation
uses: actions/attest-build-provenance@v2
with:
subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
subject-digest: ${{ steps.push.outputs.digest }}
push-to-registry: true
frontend-container:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
attestations: write
id-token: write
env:
REGISTRY: ghcr.io
IMAGE_NAME: ${{ github.repository }}-frontend
steps:
- name: Checkout repository
uses: actions/checkout@v6
- name: Log in to the Container registry
uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772 #v3.4.0
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract metadata (tags, labels) for Docker
id: meta
uses: docker/metadata-action@902fa8ec7d6ecbf8d84d538b9b233a880e428804 #v5.7.0
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=tag
type=ref,event=branch
type=sha
type=raw,value=latest,enable={{is_default_branch}}
- name: Build and push Docker image
id: push
uses: docker/build-push-action@263435318d21b8e681c14492fe198d362a7d2c83 #v6.18.0
with:
context: .
file: frontend/Dockerfile
push: true
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
- name: Generate artifact attestation
uses: actions/attest-build-provenance@v2
with:
subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
subject-digest: ${{ steps.push.outputs.digest }}
push-to-registry: true
+1 -35
View File
@@ -1,6 +1,6 @@
# DeerFlow - Unified Development Environment
.PHONY: help config config-upgrade check install setup doctor dev dev-pro dev-daemon dev-daemon-pro start start-pro start-daemon start-daemon-pro stop up up-pro down clean docker-init docker-start docker-start-pro docker-stop docker-logs docker-logs-frontend docker-logs-gateway
.PHONY: help config config-upgrade check install setup doctor dev dev-daemon start start-daemon stop up down clean docker-init docker-start docker-stop docker-logs docker-logs-frontend docker-logs-gateway
BASH ?= bash
BACKEND_UV_RUN = cd backend && uv run
@@ -26,25 +26,19 @@ help:
@echo " make install - Install all dependencies (frontend + backend + pre-commit hooks)"
@echo " make setup-sandbox - Pre-pull sandbox container image (recommended)"
@echo " make dev - Start all services in development mode (with hot-reloading)"
@echo " make dev-pro - Start in dev + Gateway mode (experimental, no LangGraph server)"
@echo " make dev-daemon - Start dev services in background (daemon mode)"
@echo " make dev-daemon-pro - Start dev daemon + Gateway mode (experimental)"
@echo " make start - Start all services in production mode (optimized, no hot-reloading)"
@echo " make start-pro - Start in prod + Gateway mode (experimental)"
@echo " make start-daemon - Start prod services in background (daemon mode)"
@echo " make start-daemon-pro - Start prod daemon + Gateway mode (experimental)"
@echo " make stop - Stop all running services"
@echo " make clean - Clean up processes and temporary files"
@echo ""
@echo "Docker Production Commands:"
@echo " make up - Build and start production Docker services (localhost:2026)"
@echo " make up-pro - Build and start production Docker in Gateway mode (experimental)"
@echo " make down - Stop and remove production Docker containers"
@echo ""
@echo "Docker Development Commands:"
@echo " make docker-init - Pull the sandbox image"
@echo " make docker-start - Start Docker services (mode-aware from config.yaml, localhost:2026)"
@echo " make docker-start-pro - Start Docker in Gateway mode (experimental, no LangGraph container)"
@echo " make docker-stop - Stop Docker development services"
@echo " make docker-logs - View Docker development logs"
@echo " make docker-logs-frontend - View Docker frontend logs"
@@ -123,41 +117,21 @@ dev:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev
# Start all services in dev + Gateway mode (experimental: agent runtime embedded in Gateway)
dev-pro:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --gateway
# Start all services in production mode (with optimizations)
start:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod
# Start all services in prod + Gateway mode (experimental)
start-pro:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --gateway
# Start all services in daemon mode (background)
dev-daemon:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --daemon
# Start daemon + Gateway mode (experimental)
dev-daemon-pro:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --gateway --daemon
# Start prod services in daemon mode (background)
start-daemon:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --daemon
# Start prod daemon + Gateway mode (experimental)
start-daemon-pro:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --gateway --daemon
# Stop all services
stop:
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --stop
@@ -182,10 +156,6 @@ docker-init:
docker-start:
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh start
# Start Docker in Gateway mode (experimental)
docker-start-pro:
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh start --gateway
# Stop Docker development environment
docker-stop:
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh stop
@@ -208,10 +178,6 @@ docker-logs-gateway:
up:
@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh
# Build and start production services in Gateway mode
up-pro:
@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh --gateway
# Stop and remove production containers
down:
@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh down
+31 -35
View File
@@ -243,9 +243,6 @@ make up # Build images and start all production services
make down # Stop and remove containers
```
> [!NOTE]
> The LangGraph agent server currently runs via `langgraph dev` (the open-source CLI server).
Access: http://localhost:2026
See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
@@ -254,7 +251,7 @@ See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
If you prefer running services locally:
Prerequisite: complete the "Configuration" steps above first (`make setup`). `make dev` requires a valid `config.yaml` in the project root (can be overridden via `DEER_FLOW_CONFIG_PATH`). Run `make doctor` to verify your setup before starting.
Prerequisite: complete the "Configuration" steps above first (`make setup`). `make dev` requires a valid `config.yaml` in the project root. Set `DEER_FLOW_PROJECT_ROOT` to define that root explicitly, or `DEER_FLOW_CONFIG_PATH` to point at a specific config file. Runtime state defaults to `.deer-flow` under the project root and can be moved with `DEER_FLOW_HOME`; skills default to `skills/` under the project root and can be moved with `DEER_FLOW_SKILLS_PATH`. Run `make doctor` to verify your setup before starting.
On Windows, run the local development flow from Git Bash. Native `cmd.exe` and PowerShell shells are not supported for the bash-based service scripts, and WSL is not guaranteed because some scripts rely on Git for Windows utilities such as `cygpath`.
1. **Check prerequisites**:
@@ -289,53 +286,31 @@ On Windows, run the local development flow from Git Bash. Native `cmd.exe` and P
#### Startup Modes
DeerFlow supports multiple startup modes across two dimensions:
- **Dev / Prod** — dev enables hot-reload; prod uses pre-built frontend
- **Standard / Gateway** — standard uses a separate LangGraph server (4 processes); Gateway mode (experimental) embeds the agent runtime in the Gateway API (3 processes)
DeerFlow runs the agent runtime inside the Gateway API. Development mode enables hot-reload; production mode uses a pre-built frontend.
| | **Local Foreground** | **Local Daemon** | **Docker Dev** | **Docker Prod** |
|---|---|---|---|---|
| **Dev** | `./scripts/serve.sh --dev`<br/>`make dev` | `./scripts/serve.sh --dev --daemon`<br/>`make dev-daemon` | `./scripts/docker.sh start`<br/>`make docker-start` | — |
| **Dev + Gateway** | `./scripts/serve.sh --dev --gateway`<br/>`make dev-pro` | `./scripts/serve.sh --dev --gateway --daemon`<br/>`make dev-daemon-pro` | `./scripts/docker.sh start --gateway`<br/>`make docker-start-pro` | — |
| **Prod** | `./scripts/serve.sh --prod`<br/>`make start` | `./scripts/serve.sh --prod --daemon`<br/>`make start-daemon` | — | `./scripts/deploy.sh`<br/>`make up` |
| **Prod + Gateway** | `./scripts/serve.sh --prod --gateway`<br/>`make start-pro` | `./scripts/serve.sh --prod --gateway --daemon`<br/>`make start-daemon-pro` | — | `./scripts/deploy.sh --gateway`<br/>`make up-pro` |
| Action | Local | Docker Dev | Docker Prod |
|---|---|---|---|
| **Stop** | `./scripts/serve.sh --stop`<br/>`make stop` | `./scripts/docker.sh stop`<br/>`make docker-stop` | `./scripts/deploy.sh down`<br/>`make down` |
| **Restart** | `./scripts/serve.sh --restart [flags]` | `./scripts/docker.sh restart` | — |
> **Gateway mode** eliminates the LangGraph server process — the Gateway API handles agent execution directly via async tasks, managing its own concurrency.
#### Why Gateway Mode?
In standard mode, DeerFlow runs a dedicated [LangGraph Platform](https://langchain-ai.github.io/langgraph/) server alongside the Gateway API. This architecture works well but has trade-offs:
| | Standard Mode | Gateway Mode |
|---|---|---|
| **Architecture** | Gateway (REST API) + LangGraph (agent runtime) | Gateway embeds agent runtime |
| **Concurrency** | `--n-jobs-per-worker` per worker (requires license) | `--workers` × async tasks (no per-worker cap) |
| **Containers / Processes** | 4 (frontend, gateway, langgraph, nginx) | 3 (frontend, gateway, nginx) |
| **Resource usage** | Higher (two Python runtimes) | Lower (single Python runtime) |
| **LangGraph Platform license** | Required for production images | Not required |
| **Cold start** | Slower (two services to initialize) | Faster |
Both modes are functionally equivalent — the same agents, tools, and skills work in either mode.
Gateway owns `/api/langgraph/*` and translates those public LangGraph-compatible paths to its native `/api/*` routers behind nginx.
#### Docker Production Deployment
`deploy.sh` supports building and starting separately. Images are mode-agnostic — runtime mode is selected at start time:
`deploy.sh` supports building and starting separately:
```bash
# One-step (build + start)
deploy.sh # standard mode (default)
deploy.sh --gateway # gateway mode
deploy.sh
# Two-step (build once, start with any mode)
# Two-step (build once, start later)
deploy.sh build # build all images
deploy.sh start # start in standard mode
deploy.sh start --gateway # start in gateway mode
deploy.sh start # start pre-built images
# Stop
deploy.sh down
@@ -370,13 +345,14 @@ DeerFlow supports receiving tasks from messaging apps. Channels auto-start when
| Feishu / Lark | WebSocket | Moderate |
| WeChat | Tencent iLink (long-polling) | Moderate |
| WeCom | WebSocket | Moderate |
| DingTalk | Stream Push (WebSocket) | Moderate |
**Configuration in `config.yaml`:**
```yaml
channels:
# LangGraph Server URL (default: http://localhost:2024)
langgraph_url: http://localhost:2024
# LangGraph-compatible Gateway API base URL (default: http://localhost:8001/api)
langgraph_url: http://localhost:8001/api
# Gateway API URL (default: http://localhost:8001)
gateway_url: http://localhost:8001
@@ -439,11 +415,19 @@ channels:
context:
thinking_enabled: true
subagent_enabled: true
dingtalk:
enabled: true
client_id: $DINGTALK_CLIENT_ID # Client ID of your DingTalk application
client_secret: $DINGTALK_CLIENT_SECRET # Client Secret of your DingTalk application
allowed_users: [] # empty = allow all
card_template_id: "" # Optional: AI Card template ID for streaming typewriter effect
```
Notes:
- `assistant_id: lead_agent` calls the default LangGraph assistant directly.
- If `assistant_id` is set to a custom agent name, DeerFlow still routes through `lead_agent` and injects that value as `agent_name`, so the custom agent's SOUL/config takes effect for IM channels.
- IM channel workers call Gateway's LangGraph-compatible API internally and automatically attach process-local internal auth plus the CSRF cookie/header pair required for thread and run creation.
Set the corresponding API keys in your `.env` file:
@@ -466,6 +450,10 @@ WECHAT_ILINK_BOT_ID=your_ilink_bot_id
# WeCom
WECOM_BOT_ID=your_bot_id
WECOM_BOT_SECRET=your_bot_secret
# DingTalk
DINGTALK_CLIENT_ID=your_client_id
DINGTALK_CLIENT_SECRET=your_client_secret
```
**Telegram Setup**
@@ -504,7 +492,15 @@ WECOM_BOT_SECRET=your_bot_secret
4. Make sure backend dependencies include `wecom-aibot-python-sdk`. The channel uses a WebSocket long connection and does not require a public callback URL.
5. The current integration supports inbound text, image, and file messages. Final images/files generated by the agent are also sent back to the WeCom conversation.
When DeerFlow runs in Docker Compose, IM channels execute inside the `gateway` container. In that case, do not point `channels.langgraph_url` or `channels.gateway_url` at `localhost`; use container service names such as `http://langgraph:2024` and `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` and `DEER_FLOW_CHANNELS_GATEWAY_URL`.
**DingTalk Setup**
1. Create a DingTalk application in the [DingTalk Developer Console](https://open.dingtalk.com/) and enable **Robot** capability.
2. Set the message receiving mode to **Stream Mode** in the robot configuration page.
3. Copy the `Client ID` and `Client Secret`, set `DINGTALK_CLIENT_ID` and `DINGTALK_CLIENT_SECRET` in `.env`, and enable the channel in `config.yaml`.
4. *(Optional)* To enable streaming AI Card replies (typewriter effect), create an **AI Card** template on the [DingTalk Card Platform](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card), then set `card_template_id` in `config.yaml` to the template ID. You also need to apply for the `Card.Streaming.Write` and `Card.Instance.Write` permissions.
When DeerFlow runs in Docker Compose, IM channels execute inside the `gateway` container. In that case, do not point `channels.langgraph_url` or `channels.gateway_url` at `localhost`; use container service names such as `http://gateway:8001/api` and `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` and `DEER_FLOW_CHANNELS_GATEWAY_URL`.
**Commands**
+19
View File
@@ -290,6 +290,7 @@ DeerFlow peut recevoir des tâches depuis des applications de messagerie. Les ca
| Telegram | Bot API (long-polling) | Facile |
| Slack | Socket Mode | Modérée |
| Feishu / Lark | WebSocket | Modérée |
| DingTalk | Stream Push (WebSocket) | Modérée |
**Configuration dans `config.yaml` :**
@@ -341,6 +342,13 @@ channels:
context:
thinking_enabled: true
subagent_enabled: true
dingtalk:
enabled: true
client_id: $DINGTALK_CLIENT_ID # ClientId depuis DingTalk Open Platform
client_secret: $DINGTALK_CLIENT_SECRET # ClientSecret depuis DingTalk Open Platform
allowed_users: [] # vide = tout le monde autorisé
card_template_id: "" # Optionnel : ID de modèle AI Card pour l'effet machine à écrire en streaming
```
Définissez les clés API correspondantes dans votre fichier `.env` :
@@ -356,6 +364,10 @@ SLACK_APP_TOKEN=xapp-...
# Feishu / Lark
FEISHU_APP_ID=cli_xxxx
FEISHU_APP_SECRET=your_app_secret
# DingTalk
DINGTALK_CLIENT_ID=your_client_id
DINGTALK_CLIENT_SECRET=your_client_secret
```
**Configuration Telegram**
@@ -378,6 +390,13 @@ FEISHU_APP_SECRET=your_app_secret
3. Dans **Events**, abonnez-vous à `im.message.receive_v1` et sélectionnez le mode **Long Connection**.
4. Copiez l'App ID et l'App Secret. Définissez `FEISHU_APP_ID` et `FEISHU_APP_SECRET` dans `.env` et activez le canal dans `config.yaml`.
**Configuration DingTalk**
1. Créez une application sur [DingTalk Open Platform](https://open.dingtalk.com/) et activez la capacité **Robot**.
2. Dans la page de configuration du robot, définissez le mode de réception des messages sur **Stream**.
3. Copiez le `Client ID` et le `Client Secret`. Définissez `DINGTALK_CLIENT_ID` et `DINGTALK_CLIENT_SECRET` dans `.env` et activez le canal dans `config.yaml`.
4. *(Optionnel)* Pour activer les réponses en streaming AI Card (effet machine à écrire), créez un modèle **AI Card** sur la [plateforme de cartes DingTalk](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card), puis définissez `card_template_id` dans `config.yaml` avec l'ID du modèle. Vous devez également demander les permissions `Card.Streaming.Write` et `Card.Instance.Write`.
**Commandes**
Une fois un canal connecté, vous pouvez interagir avec DeerFlow directement depuis le chat :
+19
View File
@@ -243,6 +243,7 @@ DeerFlowはメッセージングアプリからのタスク受信をサポート
| Telegram | Bot API(ロングポーリング) | 簡単 |
| Slack | Socket Mode | 中程度 |
| Feishu / Lark | WebSocket | 中程度 |
| DingTalk | Stream PushWebSocket | 中程度 |
**`config.yaml`での設定:**
@@ -294,6 +295,13 @@ channels:
context:
thinking_enabled: true
subagent_enabled: true
dingtalk:
enabled: true
client_id: $DINGTALK_CLIENT_ID # DingTalk Open PlatformのClientId
client_secret: $DINGTALK_CLIENT_SECRET # DingTalk Open PlatformのClientSecret
allowed_users: [] # 空 = 全員許可
card_template_id: "" # オプション:ストリーミングタイプライター効果用のAIカードテンプレートID
```
対応するAPIキーを`.env`ファイルに設定します:
@@ -309,6 +317,10 @@ SLACK_APP_TOKEN=xapp-...
# Feishu / Lark
FEISHU_APP_ID=cli_xxxx
FEISHU_APP_SECRET=your_app_secret
# DingTalk
DINGTALK_CLIENT_ID=your_client_id
DINGTALK_CLIENT_SECRET=your_client_secret
```
**Telegramのセットアップ**
@@ -331,6 +343,13 @@ FEISHU_APP_SECRET=your_app_secret
3. **イベント**で`im.message.receive_v1`を購読し、**ロングコネクション**モードを選択。
4. App IDとApp Secretをコピー。`.env`に`FEISHU_APP_ID`と`FEISHU_APP_SECRET`を設定し、`config.yaml`でチャネルを有効にします。
**DingTalkのセットアップ**
1. [DingTalk Open Platform](https://open.dingtalk.com/)でアプリを作成し、**ロボット**機能を有効化します。
2. ロボット設定ページでメッセージ受信モードを**Streamモード**に設定します。
3. `Client ID`と`Client Secret`をコピー。`.env`に`DINGTALK_CLIENT_ID`と`DINGTALK_CLIENT_SECRET`を設定し、`config.yaml`でチャネルを有効にします。
4. *(オプション)* ストリーミングAIカード返信(タイプライター効果)を有効にするには、[DingTalkカードプラットフォーム](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card)で**AIカード**テンプレートを作成し、`config.yaml`の`card_template_id`にテンプレートIDを設定します。`Card.Streaming.Write` および `Card.Instance.Write` 権限の申請も必要です。
**コマンド**
チャネル接続後、チャットから直接DeerFlowと対話できます:
+15
View File
@@ -256,6 +256,7 @@ DeerFlow принимает задачи прямо из мессенджеро
| Telegram | Bot API (long-polling) | Просто |
| Slack | Socket Mode | Средне |
| Feishu / Lark | WebSocket | Средне |
| DingTalk | Stream Push (WebSocket) | Средне |
**Конфигурация в `config.yaml`:**
@@ -278,6 +279,13 @@ channels:
enabled: true
bot_token: $TELEGRAM_BOT_TOKEN
allowed_users: []
dingtalk:
enabled: true
client_id: $DINGTALK_CLIENT_ID # ClientId с DingTalk Open Platform
client_secret: $DINGTALK_CLIENT_SECRET # ClientSecret с DingTalk Open Platform
allowed_users: [] # пусто = разрешить всем
card_template_id: "" # Опционально: ID шаблона AI Card для потокового эффекта печатной машинки
```
**Настройка Telegram**
@@ -285,6 +293,13 @@ channels:
1. Напишите [@BotFather](https://t.me/BotFather), отправьте `/newbot` и скопируйте HTTP API-токен.
2. Укажите `TELEGRAM_BOT_TOKEN` в `.env` и включите канал в `config.yaml`.
**Настройка DingTalk**
1. Создайте приложение на [DingTalk Open Platform](https://open.dingtalk.com/) и включите возможность **Робот**.
2. На странице настроек робота установите режим приёма сообщений на **Stream**.
3. Скопируйте `Client ID` и `Client Secret`. Укажите `DINGTALK_CLIENT_ID` и `DINGTALK_CLIENT_SECRET` в `.env` и включите канал в `config.yaml`.
4. *(Опционально)* Для включения потоковых ответов AI Card (эффект печатной машинки) создайте шаблон **AI Card** на [платформе карточек DingTalk](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card), затем укажите `card_template_id` в `config.yaml` с ID шаблона. Также необходимо запросить разрешения `Card.Streaming.Write` и `Card.Instance.Write`.
**Доступные команды**
| Команда | Описание |
+20 -1
View File
@@ -194,7 +194,7 @@ make down # 停止并移除容器
如果你更希望直接在本地启动各个服务:
前提:先完成上面的“配置”步骤(`make config` 和模型 API key 配置)。`make dev` 需要有效配置文件,默认读取项目根目录下的 `config.yaml`,也可以通过 `DEER_FLOW_CONFIG_PATH` 覆盖。
前提:先完成上面的“配置”步骤(`make config` 和模型 API key 配置)。`make dev` 需要有效配置文件,默认读取项目根目录下的 `config.yaml`。可以用 `DEER_FLOW_PROJECT_ROOT` 显式指定项目根目录,也可以 `DEER_FLOW_CONFIG_PATH` 指向某个具体配置文件。运行期状态默认写到项目根目录下的 `.deer-flow`,可用 `DEER_FLOW_HOME` 覆盖;skills 默认读取项目根目录下的 `skills/`,可用 `DEER_FLOW_SKILLS_PATH` 覆盖。
在 Windows 上,请使用 Git Bash 运行本地开发流程。基于 bash 的服务脚本不支持直接在原生 `cmd.exe` 或 PowerShell 中执行,且 WSL 也不保证可用,因为部分脚本依赖 Git for Windows 的 `cygpath` 等工具。
1. **检查依赖环境**
@@ -248,6 +248,7 @@ DeerFlow 支持从即时通讯应用接收任务。只要配置完成,对应
| Slack | Socket Mode | 中等 |
| Feishu / Lark | WebSocket | 中等 |
| 企业微信智能机器人 | WebSocket | 中等 |
| 钉钉 | Stream PushWebSocket | 中等 |
**`config.yaml` 中的配置示例:**
@@ -304,6 +305,13 @@ channels:
context:
thinking_enabled: true
subagent_enabled: true
dingtalk:
enabled: true
client_id: $DINGTALK_CLIENT_ID # 钉钉开放平台 ClientId
client_secret: $DINGTALK_CLIENT_SECRET # 钉钉开放平台 ClientSecret
allowed_users: [] # 留空表示允许所有人
card_template_id: "" # 可选:AI 卡片模板 ID,用于流式打字机效果
```
说明:
@@ -327,6 +335,10 @@ FEISHU_APP_SECRET=your_app_secret
# 企业微信智能机器人
WECOM_BOT_ID=your_bot_id
WECOM_BOT_SECRET=your_bot_secret
# 钉钉
DINGTALK_CLIENT_ID=your_client_id
DINGTALK_CLIENT_SECRET=your_client_secret
```
**Telegram 配置**
@@ -357,6 +369,13 @@ WECOM_BOT_SECRET=your_bot_secret
4. 安装后端依赖时确保包含 `wecom-aibot-python-sdk`,渠道会通过 WebSocket 长连接接收消息,无需公网回调地址。
5. 当前支持文本、图片和文件入站消息;agent 生成的最终图片/文件也会回传到企业微信会话中。
**钉钉配置**
1. 在 [钉钉开放平台](https://open.dingtalk.com/) 创建应用,并启用 **机器人** 能力。
2. 在机器人配置页面设置消息接收模式为 **Stream模式**。
3. 复制 `Client ID` 和 `Client Secret`,在 `.env` 中设置 `DINGTALK_CLIENT_ID` 和 `DINGTALK_CLIENT_SECRET`,并在 `config.yaml` 中启用该渠道。
4. *(可选)* 如需开启流式 AI 卡片回复(打字机效果),请在[钉钉卡片平台](https://open.dingtalk.com/document/dingstart/typewriter-effect-streaming-ai-card)创建 **AI 卡片**模板,然后在 `config.yaml` 中将 `card_template_id` 设为该模板 ID。同时需要申请 `Card.Streaming.Write` 和 `Card.Instance.Write` 权限。
**命令**
渠道连接完成后,你可以直接在聊天窗口里和 DeerFlow 交互:
+27 -45
View File
@@ -7,15 +7,13 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
DeerFlow is a LangGraph-based AI super agent system with a full-stack architecture. The backend provides a "super agent" with sandbox execution, persistent memory, subagent delegation, and extensible tool integration - all operating in per-thread isolated environments.
**Architecture**:
- **LangGraph Server** (port 2024): Agent runtime and workflow execution
- **Gateway API** (port 8001): REST API for models, MCP, skills, memory, artifacts, uploads, and local thread cleanup
- **Gateway API** (port 8001): REST API plus embedded LangGraph-compatible agent runtime
- **Frontend** (port 3000): Next.js web interface
- **Nginx** (port 2026): Unified reverse proxy entry point
- **Provisioner** (port 8002, optional in Docker dev): Started only when sandbox is configured for provisioner/Kubernetes mode
**Runtime Modes**:
- **Standard mode** (`make dev`): LangGraph Server handles agent execution as a separate process. 4 processes total.
- **Gateway mode** (`make dev-pro`, experimental): Agent runtime embedded in Gateway via `RunManager` + `run_agent()` + `StreamBridge` (`packages/harness/deerflow/runtime/`). Service manages its own concurrency via async tasks. 3 processes total, no LangGraph Server.
**Runtime**:
- `make dev`, Docker dev, and production all run the agent runtime in Gateway via `RunManager` + `run_agent()` + `StreamBridge` (`packages/harness/deerflow/runtime/`). Nginx exposes that runtime at `/api/langgraph/*` and rewrites it to Gateway's native `/api/*` routers.
**Project Structure**:
```
@@ -25,7 +23,7 @@ deer-flow/
├── extensions_config.json # MCP servers and skills configuration
├── backend/ # Backend application (this directory)
│ ├── Makefile # Backend-only commands (dev, gateway, lint)
│ ├── langgraph.json # LangGraph server configuration
│ ├── langgraph.json # LangGraph Studio graph configuration
│ ├── packages/
│ │ └── harness/ # deerflow-harness package (import: deerflow.*)
│ │ ├── pyproject.toml
@@ -83,16 +81,15 @@ When making code changes, you MUST update the relevant documentation:
```bash
make check # Check system requirements
make install # Install all dependencies (frontend + backend)
make dev # Start all services (LangGraph + Gateway + Frontend + Nginx), with config.yaml preflight
make dev-pro # Gateway mode (experimental): skip LangGraph, agent runtime embedded in Gateway
make start-pro # Production + Gateway mode (experimental)
make dev # Start all services (Gateway + Frontend + Nginx), with config.yaml preflight
make start # Start production services locally
make stop # Stop all services
```
**Backend directory** (for backend development only):
```bash
make install # Install backend dependencies
make dev # Run LangGraph server only (port 2024)
make dev # Run Gateway API with reload (port 8001)
make gateway # Run Gateway API only (port 8001)
make test # Run all backend tests
make lint # Lint with ruff
@@ -115,7 +112,7 @@ CI runs these regression tests for every pull request via [.github/workflows/bac
The backend is split into two layers with a strict dependency direction:
- **Harness** (`packages/harness/deerflow/`): Publishable agent framework package (`deerflow-harness`). Import prefix: `deerflow.*`. Contains agent orchestration, tools, sandbox, models, MCP, skills, config — everything needed to build and run agents.
- **App** (`app/`): Unpublished application code. Import prefix: `app.*`. Contains the FastAPI Gateway API and IM channel integrations (Feishu, Slack, Telegram).
- **App** (`app/`): Unpublished application code. Import prefix: `app.*`. Contains the FastAPI Gateway API and IM channel integrations (Feishu, Slack, Telegram, DingTalk).
**Dependency rule**: App imports deerflow, but deerflow never imports app. This boundary is enforced by `tests/test_harness_boundary.py` which runs in CI.
@@ -130,7 +127,7 @@ from app.gateway.app import app
from app.channels.service import start_channel_service
# App → Harness (allowed)
from deerflow.config.app_config import AppConfig
from deerflow.config import get_app_config
# Harness → App (FORBIDDEN — enforced by test_harness_boundary.py)
# from app.gateway.routers.uploads import ... # ← will fail CI
@@ -185,16 +182,7 @@ Setup: Copy `config.example.yaml` to `config.yaml` in the **project root** direc
**Config Versioning**: `config.example.yaml` has a `config_version` field. On startup, `AppConfig.from_file()` compares user version vs example version and emits a warning if outdated. Missing `config_version` = version 0. Run `make config-upgrade` to auto-merge missing fields. When changing the config schema, bump `config_version` in `config.example.yaml`.
**Config Lifecycle**: All config models are `frozen=True` (immutable after construction). `AppConfig.from_file()` is a pure function — no side effects, no process-global state. The resolved `AppConfig` is passed as an explicit parameter down every consumer lane:
- **Gateway**: `app.state.config` populated in lifespan; routers receive it via `Depends(get_config)` from `app/gateway/deps.py`.
- **Client**: `DeerFlowClient._app_config` captured in the constructor; every method reads `self._app_config`.
- **Agent run**: wrapped in `DeerFlowContext(app_config=…)` and injected via LangGraph `Runtime[DeerFlowContext].context`. Middleware and tools read `runtime.context.app_config` directly or via `resolve_context(runtime)`.
- **LangGraph Server bootstrap**: `make_lead_agent` (registered in `langgraph.json`) calls `AppConfig.from_file()` itself — the only place in production that loads from disk at agent-build time.
To update config at runtime (Gateway API mutations for MCP/Skills), write the new file and call `AppConfig.from_file()` to build a fresh snapshot, then swap `app.state.config`. No mtime detection, no auto-reload, no ambient ContextVar lookup (`AppConfig.current()` has been removed).
**DeerFlowContext**: Per-invocation typed context for the agent execution path, injected via LangGraph `Runtime[DeerFlowContext]`. Holds `app_config: AppConfig`, `thread_id: str`, `agent_name: str | None`. Gateway runtime and `DeerFlowClient` construct full `DeerFlowContext` at invoke time; the LangGraph Server boundary builds one inside `make_lead_agent`. Middleware and tools access context through `resolve_context(runtime)` which returns the typed `DeerFlowContext` — legacy dict/None shapes are rejected. Mutable runtime state (`sandbox_id`) flows through `ThreadState.sandbox`, not context.
**Config Caching**: `get_app_config()` caches the parsed config, but automatically reloads it when the resolved config path changes or the file's mtime increases. This keeps Gateway and LangGraph reads aligned with `config.yaml` edits without requiring a manual process restart.
Configuration priority:
1. Explicit `config_path` argument
@@ -217,7 +205,7 @@ Configuration priority:
### Gateway API (`app/gateway/`)
FastAPI application on port 8001 with health check at `GET /health`.
FastAPI application on port 8001 with health check at `GET /health`. Set `GATEWAY_ENABLE_DOCS=false` to disable `/docs`, `/redoc`, and `/openapi.json` in production (default: enabled).
**Routers**:
@@ -324,9 +312,10 @@ Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` →
### IM Channels System (`app/channels/`)
Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow agent via the LangGraph Server.
Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the DeerFlow agent via the LangGraph Server.
**Architecture**: Channels communicate with the LangGraph Server through `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side.
**Architecture**: Channels communicate with Gateway through the `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side. The internal SDK client injects process-local internal auth plus a matching CSRF cookie/header pair so Gateway accepts state-changing thread/run requests from channel workers without relying on browser session cookies.
**Components**:
- `message_bus.py` - Async pub/sub hub (`InboundMessage` → queue → dispatcher; `OutboundMessage` → callbacks → channels)
@@ -334,23 +323,25 @@ Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow a
- `manager.py` - Core dispatcher: creates threads via `client.threads.create()`, routes commands, keeps Slack/Telegram on `client.runs.wait()`, and uses `client.runs.stream(["messages-tuple", "values"])` for Feishu incremental outbound updates
- `base.py` - Abstract `Channel` base class (start/stop/send lifecycle)
- `service.py` - Manages lifecycle of all configured channels from `config.yaml`
- `slack.py` / `feishu.py` / `telegram.py` - Platform-specific implementations (`feishu.py` tracks the running card `message_id` in memory and patches the same card in place)
- `slack.py` / `feishu.py` / `telegram.py` / `dingtalk.py` - Platform-specific implementations (`feishu.py` tracks the running card `message_id` in memory and patches the same card in place; `dingtalk.py` optionally uses AI Card streaming for in-place updates when `card_template_id` is configured)
**Message Flow**:
1. External platform -> Channel impl -> `MessageBus.publish_inbound()`
2. `ChannelManager._dispatch_loop()` consumes from queue
3. For chat: look up/create thread on LangGraph Server
3. For chat: look up/create thread through Gateway's LangGraph-compatible API
4. Feishu chat: `runs.stream()` → accumulate AI text → publish multiple outbound updates (`is_final=False`) → publish final outbound (`is_final=True`)
5. Slack/Telegram chat: `runs.wait()` → extract final response → publish outbound
6. Feishu channel sends one running reply card up front, then patches the same card for each outbound update (card JSON sets `config.update_multi=true` for Feishu's patch API requirement)
7. For commands (`/new`, `/status`, `/models`, `/memory`, `/help`): handle locally or query Gateway API
8. Outbound → channel callbacks → platform reply
7. DingTalk AI Card mode (when `card_template_id` configured): `runs.stream()` → create card with initial text → stream updates via `PUT /v1.0/card/streaming` → finalize on `is_final=True`. Falls back to `sampleMarkdown` if card creation or streaming fails
8. For commands (`/new`, `/status`, `/models`, `/memory`, `/help`): handle locally or query Gateway API
9. Outbound → channel callbacks → platform reply
**Configuration** (`config.yaml` -> `channels`):
- `langgraph_url` - LangGraph Server URL (default: `http://localhost:2024`)
- `langgraph_url` - LangGraph-compatible Gateway API base URL (default: `http://localhost:8001/api`)
- `gateway_url` - Gateway API URL for auxiliary commands (default: `http://localhost:8001`)
- In Docker Compose, IM channels run inside the `gateway` container, so `localhost` points back to that container. Use `http://langgraph:2024` / `http://gateway:8001`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` / `DEER_FLOW_CHANNELS_GATEWAY_URL`.
- Per-channel configs: `feishu` (app_id, app_secret), `slack` (bot_token, app_token), `telegram` (bot_token)
- In Docker Compose, IM channels run inside the `gateway` container, so `localhost` points back to that container. Use `http://gateway:8001/api` for `langgraph_url` and `http://gateway:8001` for `gateway_url`, or set `DEER_FLOW_CHANNELS_LANGGRAPH_URL` / `DEER_FLOW_CHANNELS_GATEWAY_URL`.
- Per-channel configs: `feishu` (app_id, app_secret), `slack` (bot_token, app_token), `telegram` (bot_token), `dingtalk` (client_id, client_secret, optional `card_template_id` for AI Card streaming)
### Memory System (`packages/harness/deerflow/agents/memory/`)
@@ -419,9 +410,9 @@ Both can be modified at runtime via Gateway API endpoints or `DeerFlowClient` me
`DeerFlowClient` provides direct in-process access to all DeerFlow capabilities without HTTP services. All return types align with the Gateway API response schemas, so consumer code works identically in HTTP and embedded modes.
**Architecture**: Imports the same `deerflow` modules that LangGraph Server and Gateway API use. Shares the same config files and data directories. No FastAPI dependency.
**Architecture**: Imports the same `deerflow` modules that Gateway API uses. Shares the same config files and data directories. No FastAPI dependency.
**Agent Conversation** (replaces LangGraph Server):
**Agent Conversation**:
- `chat(message, thread_id)` — synchronous, accumulates streaming deltas per message-id and returns the final AI text
- `stream(message, thread_id)` — subscribes to LangGraph `stream_mode=["values", "messages", "custom"]` and yields `StreamEvent`:
- `"values"` — full state snapshot (title, messages, artifacts); AI text already delivered via `messages` mode is **not** re-synthesized here to avoid duplicate deliveries
@@ -484,20 +475,15 @@ This starts all services and makes the application available at `http://localhos
| | **Local Foreground** | **Local Daemon** | **Docker Dev** | **Docker Prod** |
|---|---|---|---|---|
| **Dev** | `./scripts/serve.sh --dev`<br/>`make dev` | `./scripts/serve.sh --dev --daemon`<br/>`make dev-daemon` | `./scripts/docker.sh start`<br/>`make docker-start` | — |
| **Dev + Gateway** | `./scripts/serve.sh --dev --gateway`<br/>`make dev-pro` | `./scripts/serve.sh --dev --gateway --daemon`<br/>`make dev-daemon-pro` | `./scripts/docker.sh start --gateway`<br/>`make docker-start-pro` | — |
| **Prod** | `./scripts/serve.sh --prod`<br/>`make start` | `./scripts/serve.sh --prod --daemon`<br/>`make start-daemon` | — | `./scripts/deploy.sh`<br/>`make up` |
| **Prod + Gateway** | `./scripts/serve.sh --prod --gateway`<br/>`make start-pro` | `./scripts/serve.sh --prod --gateway --daemon`<br/>`make start-daemon-pro` | — | `./scripts/deploy.sh --gateway`<br/>`make up-pro` |
| Action | Local | Docker Dev | Docker Prod |
|---|---|---|---|
| **Stop** | `./scripts/serve.sh --stop`<br/>`make stop` | `./scripts/docker.sh stop`<br/>`make docker-stop` | `./scripts/deploy.sh down`<br/>`make down` |
| **Restart** | `./scripts/serve.sh --restart [flags]` | `./scripts/docker.sh restart` | — |
Gateway mode embeds the agent runtime in Gateway, no LangGraph server.
**Nginx routing**:
- Standard mode: `/api/langgraph/*`LangGraph Server (2024)
- Gateway mode: `/api/langgraph/*` → Gateway embedded runtime (8001) (via envsubst)
- `/api/langgraph/*`Gateway embedded runtime (8001), rewritten to `/api/*`
- `/api/*` (other) → Gateway API (8001)
- `/` (non-API) → Frontend (3000)
@@ -506,15 +492,11 @@ Gateway mode embeds the agent runtime in Gateway, no LangGraph server.
From the **backend** directory:
```bash
# Terminal 1: LangGraph server
make dev
# Terminal 2: Gateway API
# Gateway API
make gateway
```
Direct access (without nginx):
- LangGraph: `http://localhost:2024`
- Gateway: `http://localhost:8001`
### Frontend Configuration
+10
View File
@@ -50,6 +50,12 @@ COPY backend ./backend
RUN --mount=type=cache,target=/root/.cache/uv \
sh -c "cd backend && UV_INDEX_URL=${UV_INDEX_URL:-https://pypi.org/simple} uv sync ${UV_EXTRAS:+--extra $UV_EXTRAS}"
# UTF-8 locale prevents UnicodeEncodeError on Chinese/emoji content in minimal
# containers where locale configuration may be missing and the default encoding is not UTF-8.
ENV LANG=C.UTF-8
ENV LC_ALL=C.UTF-8
ENV PYTHONIOENCODING=utf-8
# ── Stage 2: Dev ──────────────────────────────────────────────────────────────
# Retains compiler toolchain from builder so startup-time `uv sync` can build
# source distributions in development containers.
@@ -66,6 +72,10 @@ CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app
# Clean image without build-essential — reduces size (~200 MB) and attack surface.
FROM python:3.12-slim-bookworm
ENV LANG=C.UTF-8
ENV LC_ALL=C.UTF-8
ENV PYTHONIOENCODING=utf-8
# Copy Node.js runtime from builder (provides npx for MCP servers)
COPY --from=builder /usr/bin/node /usr/bin/node
COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
+1 -1
View File
@@ -2,7 +2,7 @@ install:
uv sync
dev:
uv run langgraph dev --no-browser --no-reload --n-jobs-per-worker 10
PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001 --reload
gateway:
PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
+1 -1
View File
@@ -2,7 +2,7 @@
Provides a pluggable channel system that connects external messaging platforms
(Feishu/Lark, Slack, Telegram) to the DeerFlow agent via the ChannelManager,
which uses ``langgraph-sdk`` to communicate with the underlying LangGraph Server.
which uses ``langgraph-sdk`` to communicate with Gateway's LangGraph-compatible API.
"""
from app.channels.base import Channel
+4
View File
@@ -31,6 +31,10 @@ class Channel(ABC):
def is_running(self) -> bool:
return self._running
@property
def supports_streaming(self) -> bool:
return False
# -- lifecycle ---------------------------------------------------------
@abstractmethod
+740
View File
@@ -0,0 +1,740 @@
"""DingTalk channel implementation."""
from __future__ import annotations
import asyncio
import json
import logging
import re
import threading
import time
from pathlib import Path
from typing import Any
import httpx
from app.channels.base import Channel
from app.channels.commands import KNOWN_CHANNEL_COMMANDS
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
logger = logging.getLogger(__name__)
DINGTALK_API_BASE = "https://api.dingtalk.com"
_TOKEN_REFRESH_MARGIN_SECONDS = 300
_CONVERSATION_TYPE_P2P = "1"
_CONVERSATION_TYPE_GROUP = "2"
_MAX_UPLOAD_SIZE_BYTES = 20 * 1024 * 1024
def _normalize_conversation_type(raw: Any) -> str:
"""Normalize ``conversationType`` to ``"1"`` (P2P) or ``"2"`` (group).
Stream payloads may send int or string values.
"""
if raw is None:
return _CONVERSATION_TYPE_P2P
s = str(raw).strip()
if s == _CONVERSATION_TYPE_GROUP:
return _CONVERSATION_TYPE_GROUP
return _CONVERSATION_TYPE_P2P
def _normalize_allowed_users(allowed_users: Any) -> set[str]:
if allowed_users is None:
return set()
if isinstance(allowed_users, str):
values = [allowed_users]
elif isinstance(allowed_users, (list, tuple, set)):
values = allowed_users
else:
logger.warning(
"DingTalk allowed_users should be a list of user IDs; treating %s as one string value",
type(allowed_users).__name__,
)
values = [allowed_users]
return {str(uid) for uid in values if str(uid)}
def _is_dingtalk_command(text: str) -> bool:
if not text.startswith("/"):
return False
return text.split(maxsplit=1)[0].lower() in KNOWN_CHANNEL_COMMANDS
def _extract_text_from_rich_text(rich_text_list: list) -> str:
parts: list[str] = []
for item in rich_text_list:
if isinstance(item, dict) and "text" in item:
parts.append(item["text"])
return " ".join(parts)
_FENCED_CODE_BLOCK_RE = re.compile(r"```(\w*)\n(.*?)```", re.DOTALL)
_INLINE_CODE_RE = re.compile(r"`([^`\n]+)`")
_HORIZONTAL_RULE_RE = re.compile(r"^-{3,}$", re.MULTILINE)
_TABLE_SEPARATOR_RE = re.compile(r"^\|[-:| ]+\|$", re.MULTILINE)
def _convert_markdown_table(text: str) -> str:
# DingTalk sampleMarkdown does not render pipe-delimited tables.
lines = text.split("\n")
result: list[str] = []
i = 0
while i < len(lines):
line = lines[i]
# Detect table: header row followed by separator row
if i + 1 < len(lines) and line.strip().startswith("|") and _TABLE_SEPARATOR_RE.match(lines[i + 1].strip()):
headers = [h.strip() for h in line.strip().strip("|").split("|")]
i += 2 # skip header + separator
while i < len(lines) and lines[i].strip().startswith("|"):
cells = [c.strip() for c in lines[i].strip().strip("|").split("|")]
for h, c in zip(headers, cells):
result.append(f"> **{h}**: {c}")
result.append("")
i += 1
else:
result.append(line)
i += 1
return "\n".join(result)
def _adapt_markdown_for_dingtalk(text: str) -> str:
"""Adapt markdown for DingTalk's limited sampleMarkdown renderer."""
def _code_block_to_quote(match: re.Match) -> str:
lang = match.group(1)
code = match.group(2).rstrip("\n")
prefix = f"> **{lang}**\n" if lang else ""
quoted_lines = "\n".join(f"> {line}" for line in code.split("\n"))
return f"{prefix}{quoted_lines}\n"
text = _FENCED_CODE_BLOCK_RE.sub(_code_block_to_quote, text)
text = _INLINE_CODE_RE.sub(r"**\1**", text)
text = _convert_markdown_table(text)
text = _HORIZONTAL_RULE_RE.sub("───────────", text)
return text
class DingTalkChannel(Channel):
"""DingTalk IM channel using Stream Push (WebSocket, no public IP needed)."""
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
super().__init__(name="dingtalk", bus=bus, config=config)
self._thread: threading.Thread | None = None
self._main_loop: asyncio.AbstractEventLoop | None = None
self._client_id: str = ""
self._client_secret: str = ""
self._allowed_users: set[str] = _normalize_allowed_users(config.get("allowed_users"))
self._cached_token: str = ""
self._token_expires_at: float = 0.0
self._token_lock = asyncio.Lock()
self._card_template_id: str = config.get("card_template_id", "")
self._card_track_ids: dict[str, str] = {}
self._dingtalk_client: Any = None
self._stream_client: Any = None
self._incoming_messages: dict[str, Any] = {}
self._incoming_messages_lock = threading.Lock()
self._card_repliers: dict[str, Any] = {}
@property
def supports_streaming(self) -> bool:
return bool(self._card_template_id)
async def start(self) -> None:
if self._running:
return
try:
import dingtalk_stream # noqa: F401
except ImportError:
logger.error("dingtalk-stream is not installed. Install it with: uv add dingtalk-stream")
return
client_id = self.config.get("client_id", "")
client_secret = self.config.get("client_secret", "")
if not client_id or not client_secret:
logger.error("DingTalk channel requires client_id and client_secret")
return
self._client_id = client_id
self._client_secret = client_secret
self._main_loop = asyncio.get_running_loop()
if self._card_template_id:
logger.info("[DingTalk] AI Card mode enabled (template=%s)", self._card_template_id)
self._running = True
self.bus.subscribe_outbound(self._on_outbound)
self._thread = threading.Thread(
target=self._run_stream,
args=(client_id, client_secret),
daemon=True,
)
self._thread.start()
logger.info("DingTalk channel started")
async def stop(self) -> None:
self._running = False
self.bus.unsubscribe_outbound(self._on_outbound)
stream_client = self._stream_client
if stream_client is not None:
try:
if hasattr(stream_client, "disconnect"):
stream_client.disconnect()
except Exception:
logger.debug("[DingTalk] error disconnecting stream client", exc_info=True)
self._dingtalk_client = None
self._stream_client = None
with self._incoming_messages_lock:
self._incoming_messages.clear()
self._card_repliers.clear()
self._card_track_ids.clear()
if self._thread:
self._thread.join(timeout=5)
self._thread = None
logger.info("DingTalk channel stopped")
def _resolve_routing(self, msg: OutboundMessage) -> tuple[str, str, str]:
"""Return (conversation_type, sender_staff_id, conversation_id).
Uses msg.chat_id as the primary routing key; metadata as fallback.
"""
conversation_type = _normalize_conversation_type(msg.metadata.get("conversation_type"))
sender_staff_id = msg.metadata.get("sender_staff_id", "")
conversation_id = msg.metadata.get("conversation_id", "")
if conversation_type == _CONVERSATION_TYPE_GROUP:
conversation_id = msg.chat_id or conversation_id
else:
sender_staff_id = msg.chat_id or sender_staff_id
return conversation_type, sender_staff_id, conversation_id
async def send(self, msg: OutboundMessage, *, _max_retries: int = 3) -> None:
conversation_type, sender_staff_id, conversation_id = self._resolve_routing(msg)
robot_code = self._client_id
# Card mode: stream update to existing AI card
source_key = self._make_card_source_key_from_outbound(msg)
out_track_id = self._card_track_ids.get(source_key)
# ``card_template_id`` enables ``runs.stream`` (non-final + final outbounds).
# If card creation failed, skip non-final chunks to avoid duplicate messages.
if self._card_template_id and not out_track_id and not msg.is_final:
return
if out_track_id:
try:
await self._stream_update_card(
out_track_id,
msg.text,
is_finalize=msg.is_final,
)
except Exception:
logger.warning("[DingTalk] card stream failed, falling back to sampleMarkdown")
if msg.is_final:
self._card_track_ids.pop(source_key, None)
self._card_repliers.pop(out_track_id, None)
await self._send_markdown_fallback(robot_code, conversation_type, sender_staff_id, conversation_id, msg.text)
return
if msg.is_final:
self._card_track_ids.pop(source_key, None)
self._card_repliers.pop(out_track_id, None)
return
# Non-card mode: send sampleMarkdown with retry
last_exc: Exception | None = None
for attempt in range(_max_retries):
try:
if conversation_type == _CONVERSATION_TYPE_GROUP:
await self._send_group_message(robot_code, conversation_id, msg.text, at_user_ids=[sender_staff_id] if sender_staff_id else None)
else:
await self._send_p2p_message(robot_code, sender_staff_id, msg.text)
return
except Exception as exc:
last_exc = exc
if attempt < _max_retries - 1:
delay = 2**attempt
logger.warning(
"[DingTalk] send failed (attempt %d/%d), retrying in %ds: %s",
attempt + 1,
_max_retries,
delay,
exc,
)
await asyncio.sleep(delay)
logger.error("[DingTalk] send failed after %d attempts: %s", _max_retries, last_exc)
if last_exc is None:
raise RuntimeError("DingTalk send failed without an exception from any attempt")
raise last_exc
async def _send_markdown_fallback(
self,
robot_code: str,
conversation_type: str,
sender_staff_id: str,
conversation_id: str,
text: str,
) -> None:
try:
if conversation_type == _CONVERSATION_TYPE_GROUP:
await self._send_group_message(robot_code, conversation_id, text)
else:
await self._send_p2p_message(robot_code, sender_staff_id, text)
except Exception:
logger.exception("[DingTalk] markdown fallback also failed")
raise
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
if attachment.size > _MAX_UPLOAD_SIZE_BYTES:
logger.warning("[DingTalk] file too large (%d bytes), skipping: %s", attachment.size, attachment.filename)
return False
conversation_type, sender_staff_id, conversation_id = self._resolve_routing(msg)
robot_code = self._client_id
try:
media_id = await self._upload_media(attachment.actual_path, "image" if attachment.is_image else "file")
if not media_id:
return False
if attachment.is_image:
msg_key = "sampleImageMsg"
msg_param = json.dumps({"photoURL": media_id})
else:
msg_key = "sampleFile"
msg_param = json.dumps(
{
"fileUrl": media_id,
"fileName": attachment.filename,
"fileSize": str(attachment.size),
}
)
token = await self._get_access_token()
async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
if conversation_type == _CONVERSATION_TYPE_GROUP:
response = await client.post(
f"{DINGTALK_API_BASE}/v1.0/robot/groupMessages/send",
headers=self._api_headers(token),
json={
"msgKey": msg_key,
"msgParam": msg_param,
"robotCode": robot_code,
"openConversationId": conversation_id,
},
)
else:
response = await client.post(
f"{DINGTALK_API_BASE}/v1.0/robot/oToMessages/batchSend",
headers=self._api_headers(token),
json={
"msgKey": msg_key,
"msgParam": msg_param,
"robotCode": robot_code,
"userIds": [sender_staff_id],
},
)
response.raise_for_status()
logger.info("[DingTalk] file sent: %s", attachment.filename)
return True
except (httpx.HTTPError, OSError, ValueError, TypeError, AttributeError):
logger.exception("[DingTalk] failed to send file: %s", attachment.filename)
return False
# -- stream client (runs in dedicated thread) --------------------------
def _run_stream(self, client_id: str, client_secret: str) -> None:
try:
import dingtalk_stream
credential = dingtalk_stream.Credential(client_id, client_secret)
client = dingtalk_stream.DingTalkStreamClient(credential)
self._stream_client = client
client.register_callback_handler(
dingtalk_stream.chatbot.ChatbotMessage.TOPIC,
_DingTalkMessageHandler(self),
)
client.start_forever()
except Exception:
if self._running:
logger.exception("DingTalk Stream Push error")
finally:
self._stream_client = None
def _on_chatbot_message(self, message: Any) -> None:
if not self._running:
return
try:
sender_staff_id = message.sender_staff_id or ""
conversation_type = _normalize_conversation_type(message.conversation_type)
conversation_id = message.conversation_id or ""
msg_id = message.message_id or ""
sender_nick = message.sender_nick or ""
if self._allowed_users and sender_staff_id not in self._allowed_users:
logger.debug("[DingTalk] ignoring message from non-allowed user: %s", sender_staff_id)
return
text = self._extract_text(message)
if not text:
logger.info("[DingTalk] empty text, ignoring message")
return
logger.info(
"[DingTalk] parsed message: conv_type=%s, msg_id=%s, sender=%s(%s), text=%r",
conversation_type,
msg_id,
sender_staff_id,
sender_nick,
text[:100],
)
if _is_dingtalk_command(text):
msg_type = InboundMessageType.COMMAND
else:
msg_type = InboundMessageType.CHAT
# P2P: topic_id=None (single thread per user, like Telegram private chat)
# Group: topic_id=msg_id (each new message starts a new topic, like Feishu)
topic_id: str | None = msg_id if conversation_type == _CONVERSATION_TYPE_GROUP else None
# chat_id uses conversation_id for groups, sender_staff_id for P2P
chat_id = conversation_id if conversation_type == _CONVERSATION_TYPE_GROUP else sender_staff_id
inbound = self._make_inbound(
chat_id=chat_id,
user_id=sender_staff_id,
text=text,
msg_type=msg_type,
thread_ts=msg_id,
metadata={
"conversation_type": conversation_type,
"conversation_id": conversation_id,
"sender_staff_id": sender_staff_id,
"sender_nick": sender_nick,
"message_id": msg_id,
},
)
inbound.topic_id = topic_id
if self._card_template_id:
source_key = self._make_card_source_key(inbound)
with self._incoming_messages_lock:
self._incoming_messages[source_key] = message
if self._main_loop and self._main_loop.is_running():
logger.info("[DingTalk] publishing inbound message to bus (type=%s, msg_id=%s)", msg_type.value, msg_id)
fut = asyncio.run_coroutine_threadsafe(
self._prepare_inbound(chat_id, inbound),
self._main_loop,
)
fut.add_done_callback(lambda f, mid=msg_id: self._log_future_error(f, "prepare_inbound", mid))
else:
logger.warning("[DingTalk] main loop not running, cannot publish inbound message")
except Exception:
logger.exception("[DingTalk] error processing chatbot message")
@staticmethod
def _extract_text(message: Any) -> str:
msg_type = message.message_type
if msg_type == "text" and message.text:
return message.text.content.strip()
if msg_type == "richText" and message.rich_text_content:
return _extract_text_from_rich_text(message.rich_text_content.rich_text_list).strip()
return ""
async def _prepare_inbound(self, chat_id: str, inbound: InboundMessage) -> None:
# Running reply must finish before publish_inbound so AI card tracks are
# registered before the manager emits streaming outbounds.
await self._send_running_reply(chat_id, inbound)
await self.bus.publish_inbound(inbound)
async def _send_running_reply(self, chat_id: str, inbound: InboundMessage) -> None:
conversation_type = inbound.metadata.get("conversation_type", _CONVERSATION_TYPE_P2P)
sender_staff_id = inbound.metadata.get("sender_staff_id", "")
conversation_id = inbound.metadata.get("conversation_id", "")
text = "\u23f3 Working on it..."
try:
if self._card_template_id:
source_key = self._make_card_source_key(inbound)
with self._incoming_messages_lock:
chatbot_message = self._incoming_messages.pop(source_key, None)
out_track_id = await self._create_and_deliver_card(
text,
chatbot_message=chatbot_message,
)
if out_track_id:
self._card_track_ids[source_key] = out_track_id
logger.info("[DingTalk] AI card running reply sent for chat=%s", chat_id)
return
robot_code = self._client_id
if conversation_type == _CONVERSATION_TYPE_GROUP:
await self._send_text_message_to_group(robot_code, conversation_id, text)
else:
await self._send_text_message_to_user(robot_code, sender_staff_id, text)
logger.info("[DingTalk] 'Working on it...' reply sent for chat=%s", chat_id)
except Exception:
logger.exception("[DingTalk] failed to send running reply for chat=%s", chat_id)
# -- DingTalk API helpers ----------------------------------------------
async def _get_access_token(self) -> str:
if self._cached_token and time.monotonic() < self._token_expires_at:
return self._cached_token
async with self._token_lock:
if self._cached_token and time.monotonic() < self._token_expires_at:
return self._cached_token
async with httpx.AsyncClient(timeout=httpx.Timeout(10.0)) as client:
response = await client.post(
f"{DINGTALK_API_BASE}/v1.0/oauth2/accessToken",
json={"appKey": self._client_id, "appSecret": self._client_secret}, # DingTalk API field names
)
response.raise_for_status()
data = response.json()
if not isinstance(data, dict):
raise ValueError(f"DingTalk access token response must be a JSON object, got {type(data).__name__}")
access_token = data.get("accessToken")
if not isinstance(access_token, str) or not access_token.strip():
raise ValueError("DingTalk access token response did not contain a usable accessToken")
raw_expires_in = data.get("expireIn", 7200)
try:
expires_in = int(raw_expires_in)
except (TypeError, ValueError):
logger.warning("[DingTalk] invalid expireIn value %r, using default 7200s", raw_expires_in)
expires_in = 7200
self._cached_token = access_token.strip()
self._token_expires_at = time.monotonic() + expires_in - _TOKEN_REFRESH_MARGIN_SECONDS
return self._cached_token
@staticmethod
def _api_headers(token: str) -> dict[str, str]:
return {
"x-acs-dingtalk-access-token": token,
"Content-Type": "application/json",
}
async def _send_text_message_to_user(self, robot_code: str, user_id: str, text: str) -> None:
token = await self._get_access_token()
async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
response = await client.post(
f"{DINGTALK_API_BASE}/v1.0/robot/oToMessages/batchSend",
headers=self._api_headers(token),
json={
"msgKey": "sampleText",
"msgParam": json.dumps({"content": text}),
"robotCode": robot_code,
"userIds": [user_id],
},
)
response.raise_for_status()
async def _send_text_message_to_group(self, robot_code: str, conversation_id: str, text: str) -> None:
token = await self._get_access_token()
async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
response = await client.post(
f"{DINGTALK_API_BASE}/v1.0/robot/groupMessages/send",
headers=self._api_headers(token),
json={
"msgKey": "sampleText",
"msgParam": json.dumps({"content": text}),
"robotCode": robot_code,
"openConversationId": conversation_id,
},
)
response.raise_for_status()
async def _send_p2p_message(self, robot_code: str, user_id: str, text: str) -> None:
text = _adapt_markdown_for_dingtalk(text)
token = await self._get_access_token()
async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
response = await client.post(
f"{DINGTALK_API_BASE}/v1.0/robot/oToMessages/batchSend",
headers=self._api_headers(token),
json={
"msgKey": "sampleMarkdown",
"msgParam": json.dumps({"title": "DeerFlow", "text": text}),
"robotCode": robot_code,
"userIds": [user_id],
},
)
response.raise_for_status()
data = response.json()
if data.get("processQueryKey"):
logger.info("[DingTalk] P2P message sent to user=%s", user_id)
else:
logger.warning("[DingTalk] P2P send response: %s", data)
async def _send_group_message(
self,
robot_code: str,
conversation_id: str,
text: str,
*,
at_user_ids: list[str] | None = None, # noqa: ARG002
) -> None:
# at_user_ids accepted for call-site compatibility but not passed to the API
# (sampleMarkdown does not support @mentions).
text = _adapt_markdown_for_dingtalk(text)
token = await self._get_access_token()
async with httpx.AsyncClient(timeout=httpx.Timeout(30.0)) as client:
response = await client.post(
f"{DINGTALK_API_BASE}/v1.0/robot/groupMessages/send",
headers=self._api_headers(token),
json={
"msgKey": "sampleMarkdown",
"msgParam": json.dumps({"title": "DeerFlow", "text": text}),
"robotCode": robot_code,
"openConversationId": conversation_id,
},
)
response.raise_for_status()
data = response.json()
if data.get("processQueryKey"):
logger.info("[DingTalk] group message sent to conversation=%s", conversation_id)
else:
logger.warning("[DingTalk] group send response: %s", data)
# -- AI Card streaming helpers -------------------------------------------
def _make_card_source_key(self, inbound: InboundMessage) -> str:
m = inbound.metadata
return f"{m.get('conversation_type', '')}:{m.get('sender_staff_id', '')}:{m.get('conversation_id', '')}:{m.get('message_id', '')}"
def _make_card_source_key_from_outbound(self, msg: OutboundMessage) -> str:
m = msg.metadata
correlation_id = m.get("message_id") or msg.thread_ts or ""
return f"{m.get('conversation_type', '')}:{m.get('sender_staff_id', '')}:{m.get('conversation_id', '')}:{correlation_id}"
async def _create_and_deliver_card(
self,
initial_text: str,
*,
chatbot_message: Any = None,
) -> str | None:
if self._dingtalk_client is None or chatbot_message is None:
logger.warning("[DingTalk] SDK client or chatbot_message unavailable, skipping AI card")
return None
try:
from dingtalk_stream.card_replier import AICardReplier
except ImportError:
logger.warning("[DingTalk] dingtalk-stream card_replier not available")
return None
try:
replier = AICardReplier(self._dingtalk_client, chatbot_message)
card_instance_id = await replier.async_create_and_deliver_card(
card_template_id=self._card_template_id,
card_data={"content": initial_text},
)
if not card_instance_id:
return None
self._card_repliers[card_instance_id] = replier
logger.info("[DingTalk] AI card created: outTrackId=%s", card_instance_id)
return card_instance_id
except Exception:
logger.exception("[DingTalk] failed to create AI card")
return None
async def _stream_update_card(
self,
out_track_id: str,
content: str,
*,
is_finalize: bool = False,
is_error: bool = False,
) -> None:
replier = self._card_repliers.get(out_track_id)
if not replier:
raise RuntimeError(f"No AICardReplier found for track ID {out_track_id}")
await replier.async_streaming(
card_instance_id=out_track_id,
content_key="content",
content_value=content,
append=False,
finished=is_finalize,
failed=is_error,
)
# -- media upload --------------------------------------------------------
async def _upload_media(self, file_path: str | Path, media_type: str) -> str | None:
try:
file_bytes = await asyncio.to_thread(Path(file_path).read_bytes)
token = await self._get_access_token()
async with httpx.AsyncClient(timeout=httpx.Timeout(60.0)) as client:
response = await client.post(
f"{DINGTALK_API_BASE}/v1.0/files/upload",
headers={"x-acs-dingtalk-access-token": token},
files={"file": ("upload", file_bytes)},
data={"type": media_type},
)
response.raise_for_status()
try:
payload = response.json()
except json.JSONDecodeError:
logger.exception("[DingTalk] failed to decode upload response JSON: %s", file_path)
return None
if not isinstance(payload, dict):
logger.warning("[DingTalk] unexpected upload response type %s for %s", type(payload).__name__, file_path)
return None
return payload.get("mediaId")
except (httpx.HTTPError, OSError):
logger.exception("[DingTalk] failed to upload media: %s", file_path)
return None
@staticmethod
def _log_future_error(fut: Any, name: str, msg_id: str) -> None:
try:
exc = fut.exception()
if exc:
logger.error("[DingTalk] %s failed for msg_id=%s: %s", name, msg_id, exc)
except (asyncio.CancelledError, asyncio.InvalidStateError):
pass
class _DingTalkMessageHandler:
"""Callback handler registered with dingtalk-stream."""
def __init__(self, channel: DingTalkChannel) -> None:
self._channel = channel
def pre_start(self) -> None:
if hasattr(self, "dingtalk_client") and self.dingtalk_client is not None:
self._channel._dingtalk_client = self.dingtalk_client
async def raw_process(self, callback_message: Any) -> Any:
import dingtalk_stream
from dingtalk_stream.frames import Headers
code, message = await self.process(callback_message)
ack_message = dingtalk_stream.AckMessage()
ack_message.code = code
ack_message.headers.message_id = callback_message.headers.message_id
ack_message.headers.content_type = Headers.CONTENT_TYPE_APPLICATION_JSON
ack_message.data = {"response": message}
return ack_message
async def process(self, callback: Any) -> tuple[int, str]:
import dingtalk_stream
incoming_message = dingtalk_stream.ChatbotMessage.from_dict(callback.data)
self._channel._on_chatbot_message(incoming_message)
return dingtalk_stream.AckMessage.STATUS_OK, "OK"
+5 -3
View File
@@ -63,6 +63,10 @@ class FeishuChannel(Channel):
self._GetMessageResourceRequest = None
self._thread_lock = threading.Lock()
@property
def supports_streaming(self) -> bool:
return True
async def start(self) -> None:
if self._running:
return
@@ -375,9 +379,7 @@ class FeishuChannel(Channel):
virtual_path = f"{VIRTUAL_PATH_PREFIX}/uploads/{resolved_target.name}"
try:
from deerflow.config.app_config import AppConfig
sandbox_provider = get_sandbox_provider(AppConfig.from_file())
sandbox_provider = get_sandbox_provider()
sandbox_id = sandbox_provider.acquire(thread_id)
if sandbox_id != "local":
sandbox = sandbox_provider.get(sandbox_id)
+48 -9
View File
@@ -1,4 +1,4 @@
"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via LangGraph Server."""
"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via Gateway."""
from __future__ import annotations
@@ -17,11 +17,13 @@ from langgraph_sdk.errors import ConflictError
from app.channels.commands import KNOWN_CHANNEL_COMMANDS
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
from app.channels.store import ChannelStore
from app.gateway.csrf_middleware import CSRF_COOKIE_NAME, CSRF_HEADER_NAME, generate_csrf_token
from app.gateway.internal_auth import create_internal_auth_headers
from deerflow.runtime.user_context import get_effective_user_id
logger = logging.getLogger(__name__)
DEFAULT_LANGGRAPH_URL = "http://localhost:2024"
DEFAULT_LANGGRAPH_URL = "http://localhost:8001/api"
DEFAULT_GATEWAY_URL = "http://localhost:8001"
DEFAULT_ASSISTANT_ID = "lead_agent"
CUSTOM_AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
@@ -36,6 +38,7 @@ STREAM_UPDATE_MIN_INTERVAL_SECONDS = 0.35
THREAD_BUSY_MESSAGE = "This conversation is already processing another request. Please wait for it to finish and try again."
CHANNEL_CAPABILITIES = {
"dingtalk": {"supports_streaming": False},
"discord": {"supports_streaming": False},
"feishu": {"supports_streaming": True},
"slack": {"supports_streaming": False},
@@ -46,6 +49,13 @@ CHANNEL_CAPABILITIES = {
InboundFileReader = Callable[[dict[str, Any], httpx.AsyncClient], Awaitable[bytes | None]]
_METADATA_DROP_KEYS = frozenset({"raw_message", "ref_msg"})
def _slim_metadata(meta: dict[str, Any]) -> dict[str, Any]:
"""Return a shallow copy of *meta* with known-large keys removed."""
return {k: v for k, v in meta.items() if k not in _METADATA_DROP_KEYS}
INBOUND_FILE_READERS: dict[str, InboundFileReader] = {}
@@ -410,7 +420,13 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic
if not msg.files:
return []
from deerflow.uploads.manager import claim_unique_filename, ensure_uploads_dir, normalize_filename
from deerflow.uploads.manager import (
UnsafeUploadPathError,
claim_unique_filename,
ensure_uploads_dir,
normalize_filename,
write_upload_file_no_symlink,
)
uploads_dir = ensure_uploads_dir(thread_id)
seen_names = {entry.name for entry in uploads_dir.iterdir() if entry.is_file()}
@@ -461,7 +477,10 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic
dest = uploads_dir / safe_name
try:
dest.write_bytes(data)
dest = write_upload_file_no_symlink(uploads_dir, safe_name, data)
except UnsafeUploadPathError:
logger.warning("[Manager] skipping inbound file with unsafe destination: %s", safe_name)
continue
except Exception:
logger.exception("[Manager] failed to write inbound file: %s", dest)
continue
@@ -509,7 +528,7 @@ class ChannelManager:
"""Core dispatcher that bridges IM channels to the DeerFlow agent.
It reads from the MessageBus inbound queue, creates/reuses threads on
the LangGraph Server, sends messages via ``runs.wait``, and publishes
Gateway's LangGraph-compatible API, sends messages via ``runs.wait``, and publishes
outbound responses back through the bus.
"""
@@ -534,12 +553,20 @@ class ChannelManager:
self._default_session = _as_dict(default_session)
self._channel_sessions = dict(channel_sessions or {})
self._client = None # lazy init — langgraph_sdk async client
self._csrf_token = generate_csrf_token()
self._semaphore: asyncio.Semaphore | None = None
self._running = False
self._task: asyncio.Task | None = None
@staticmethod
def _channel_supports_streaming(channel_name: str) -> bool:
from .service import get_channel_service
service = get_channel_service()
if service:
channel = service.get_channel(channel_name)
if channel is not None:
return channel.supports_streaming
return CHANNEL_CAPABILITIES.get(channel_name, {}).get("supports_streaming", False)
def _resolve_session_layer(self, msg: InboundMessage) -> tuple[dict[str, Any], dict[str, Any]]:
@@ -586,7 +613,14 @@ class ChannelManager:
if self._client is None:
from langgraph_sdk import get_client
self._client = get_client(url=self._langgraph_url)
self._client = get_client(
url=self._langgraph_url,
headers={
**create_internal_auth_headers(),
CSRF_HEADER_NAME: self._csrf_token,
"Cookie": f"{CSRF_COOKIE_NAME}={self._csrf_token}",
},
)
return self._client
# -- lifecycle ---------------------------------------------------------
@@ -669,7 +703,7 @@ class ChannelManager:
# -- chat handling -----------------------------------------------------
async def _create_thread(self, client, msg: InboundMessage) -> str:
"""Create a new thread on the LangGraph Server and store the mapping."""
"""Create a new thread through Gateway and store the mapping."""
thread = await client.threads.create()
thread_id = thread["thread_id"]
self.store.set_thread_id(
@@ -679,7 +713,7 @@ class ChannelManager:
topic_id=msg.topic_id,
user_id=msg.user_id,
)
logger.info("[Manager] new thread created on LangGraph Server: thread_id=%s for chat_id=%s topic_id=%s", thread_id, msg.chat_id, msg.topic_id)
logger.info("[Manager] new thread created through Gateway: thread_id=%s for chat_id=%s topic_id=%s", thread_id, msg.chat_id, msg.topic_id)
return thread_id
async def _handle_chat(self, msg: InboundMessage, extra_context: dict[str, Any] | None = None) -> None:
@@ -762,6 +796,7 @@ class ChannelManager:
artifacts=artifacts,
attachments=attachments,
thread_ts=msg.thread_ts,
metadata=_slim_metadata(msg.metadata),
)
logger.info("[Manager] publishing outbound message to bus: channel=%s, chat_id=%s", msg.channel_name, msg.chat_id)
await self.bus.publish_outbound(outbound)
@@ -823,6 +858,7 @@ class ChannelManager:
text=latest_text,
is_final=False,
thread_ts=msg.thread_ts,
metadata=_slim_metadata(msg.metadata),
)
)
last_published_text = latest_text
@@ -867,6 +903,7 @@ class ChannelManager:
attachments=attachments,
is_final=True,
thread_ts=msg.thread_ts,
metadata=_slim_metadata(msg.metadata),
)
)
@@ -886,7 +923,7 @@ class ChannelManager:
return
if command == "new":
# Create a new thread on the LangGraph Server
# Create a new thread through Gateway
client = self._get_client()
thread = await client.threads.create()
new_thread_id = thread["thread_id"]
@@ -925,6 +962,7 @@ class ChannelManager:
thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
text=reply,
thread_ts=msg.thread_ts,
metadata=_slim_metadata(msg.metadata),
)
await self.bus.publish_outbound(outbound)
@@ -958,5 +996,6 @@ class ChannelManager:
thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id) or "",
text=error_text,
thread_ts=msg.thread_ts,
metadata=_slim_metadata(msg.metadata),
)
await self.bus.publish_outbound(outbound)
+17 -6
View File
@@ -11,13 +11,14 @@ from app.channels.manager import DEFAULT_GATEWAY_URL, DEFAULT_LANGGRAPH_URL, Cha
from app.channels.message_bus import MessageBus
from app.channels.store import ChannelStore
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from deerflow.config.app_config import AppConfig
logger = logging.getLogger(__name__)
# Channel name → import path for lazy loading
_CHANNEL_REGISTRY: dict[str, str] = {
"dingtalk": "app.channels.dingtalk:DingTalkChannel",
"discord": "app.channels.discord:DiscordChannel",
"feishu": "app.channels.feishu:FeishuChannel",
"slack": "app.channels.slack:SlackChannel",
@@ -28,6 +29,7 @@ _CHANNEL_REGISTRY: dict[str, str] = {
# Keys that indicate a user has configured credentials for a channel.
_CHANNEL_CREDENTIAL_KEYS: dict[str, list[str]] = {
"dingtalk": ["client_id", "client_secret"],
"discord": ["bot_token"],
"feishu": ["app_id", "app_secret"],
"slack": ["bot_token", "app_token"],
@@ -78,8 +80,12 @@ class ChannelService:
self._running = False
@classmethod
def from_app_config(cls, app_config: AppConfig) -> ChannelService:
"""Create a ChannelService from an explicit application config."""
def from_app_config(cls, app_config: AppConfig | None = None) -> ChannelService:
"""Create a ChannelService from the application config."""
if app_config is None:
from deerflow.config.app_config import get_app_config
app_config = get_app_config()
channels_config = {}
# extra fields are allowed by AppConfig (extra="allow")
extra = app_config.model_extra or {}
@@ -162,11 +168,16 @@ class ChannelService:
try:
channel = channel_cls(bus=self.bus, config=config)
await channel.start()
self._channels[name] = channel
await channel.start()
if not channel.is_running:
self._channels.pop(name, None)
logger.error("Channel %s did not enter a running state after start()", name)
return False
logger.info("Channel %s started", name)
return True
except Exception:
self._channels.pop(name, None)
logger.exception("Failed to start channel %s", name)
return False
@@ -201,7 +212,7 @@ def get_channel_service() -> ChannelService | None:
return _channel_service
async def start_channel_service(app_config: AppConfig) -> ChannelService:
async def start_channel_service(app_config: AppConfig | None = None) -> ChannelService:
"""Create and start the global ChannelService from app config."""
global _channel_service
if _channel_service is not None:
+4
View File
@@ -29,6 +29,10 @@ class WeComChannel(Channel):
self._ws_stream_ids: dict[str, str] = {}
self._working_message = "Working on it..."
@property
def supports_streaming(self) -> bool:
return True
def _clear_ws_context(self, thread_ts: str | None) -> None:
if not thread_ts:
return
+24 -14
View File
@@ -28,9 +28,13 @@ from app.gateway.routers import (
threads,
uploads,
)
from deerflow.config.app_config import AppConfig
from deerflow.config import app_config as deerflow_app_config
from deerflow.config.app_config import apply_logging_level
# Configure logging
AppConfig = deerflow_app_config.AppConfig
get_app_config = deerflow_app_config.get_app_config
# Default logging; lifespan overrides from config.yaml log_level.
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
@@ -72,7 +76,18 @@ async def _ensure_admin_user(app: FastAPI) -> None:
from deerflow.persistence.engine import get_session_factory
from deerflow.persistence.user.model import UserRow
provider = get_local_provider()
try:
provider = get_local_provider()
except RuntimeError:
# Auth persistence may not be initialized in some test/boot paths.
# Skip admin migration work rather than failing gateway startup.
logger.warning("Auth persistence not ready; skipping admin bootstrap check")
return
sf = get_session_factory()
if sf is None:
return
admin_count = await provider.count_admin_users()
if admin_count == 0:
@@ -84,10 +99,6 @@ async def _ensure_admin_user(app: FastAPI) -> None:
# Admin already exists — run orphan thread migration for any
# LangGraph thread metadata that pre-dates the auth module.
sf = get_session_factory()
if sf is None:
return
async with sf() as session:
stmt = select(UserRow).where(UserRow.system_role == "admin").limit(1)
row = (await session.execute(stmt)).scalar_one_or_none()
@@ -151,11 +162,10 @@ async def _migrate_orphaned_threads(store, admin_user_id: str) -> int:
async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
"""Application lifespan handler."""
# Load config and check necessary environment variables at startup
try:
# ``app.state.config`` is the sole source of truth for
# ``Depends(get_config)``. Consumers that want AppConfig must receive
# it as an explicit parameter; there is no ambient singleton.
app.state.config = AppConfig.from_file()
app.state.config = get_app_config()
apply_logging_level(app.state.config.log_level)
logger.info("Configuration loaded successfully")
except Exception as e:
error_msg = f"Failed to load configuration during gateway startup: {e}"
@@ -208,6 +218,8 @@ def create_app() -> FastAPI:
Returns:
Configured FastAPI application instance.
"""
config = get_gateway_config()
docs_kwargs = {"docs_url": "/docs", "redoc_url": "/redoc", "openapi_url": "/openapi.json"} if config.enable_docs else {"docs_url": None, "redoc_url": None, "openapi_url": None}
app = FastAPI(
title="DeerFlow API Gateway",
@@ -232,9 +244,7 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
""",
version="0.1.0",
lifespan=lifespan,
docs_url="/docs",
redoc_url="/redoc",
openapi_url="/openapi.json",
**docs_kwargs,
openapi_tags=[
{
"name": "models",
+3 -3
View File
@@ -4,11 +4,8 @@ import logging
import os
import secrets
from dotenv import load_dotenv
from pydantic import BaseModel, Field
load_dotenv()
logger = logging.getLogger(__name__)
@@ -37,6 +34,9 @@ def get_auth_config() -> AuthConfig:
"""Get the global AuthConfig instance. Parses from env on first call."""
global _auth_config
if _auth_config is None:
from dotenv import load_dotenv
load_dotenv()
jwt_secret = os.environ.get("AUTH_JWT_SECRET")
if not jwt_secret:
jwt_secret = secrets.token_urlsafe(32)
+14 -1
View File
@@ -1,10 +1,14 @@
"""Local email/password authentication provider."""
import logging
from app.gateway.auth.models import User
from app.gateway.auth.password import hash_password_async, verify_password_async
from app.gateway.auth.password import hash_password_async, needs_rehash, verify_password_async
from app.gateway.auth.providers import AuthProvider
from app.gateway.auth.repositories.base import UserRepository
logger = logging.getLogger(__name__)
class LocalAuthProvider(AuthProvider):
"""Email/password authentication provider using local database."""
@@ -43,6 +47,15 @@ class LocalAuthProvider(AuthProvider):
if not await verify_password_async(password, user.password_hash):
return None
if needs_rehash(user.password_hash):
try:
user.password_hash = await hash_password_async(password)
await self._repo.update_user(user)
except Exception:
# Rehash is an opportunistic upgrade; a transient DB error must not
# prevent an otherwise-valid login from succeeding.
logger.warning("Failed to rehash password for user %s; login will still succeed", user.email, exc_info=True)
return user
async def get_user(self, user_id: str) -> User | None:
+53 -5
View File
@@ -1,18 +1,66 @@
"""Password hashing utilities using bcrypt directly."""
"""Password hashing utilities with versioned hash format.
Hash format: ``$dfv<N>$<bcrypt_hash>`` where ``<N>`` is the version.
- **v1** (legacy): ``bcrypt(password)`` plain bcrypt, susceptible to
72-byte silent truncation.
- **v2** (current): ``bcrypt(b64(sha256(password)))`` SHA-256 pre-hash
avoids the 72-byte truncation limit so the full password contributes
to the hash.
Verification auto-detects the version and falls back to v1 for hashes
without a prefix, so existing deployments upgrade transparently on next
login.
"""
import asyncio
import base64
import hashlib
import bcrypt
_CURRENT_VERSION = 2
_PREFIX_V2 = "$dfv2$"
_PREFIX_V1 = "$dfv1$"
def _pre_hash_v2(password: str) -> bytes:
"""SHA-256 pre-hash to bypass bcrypt's 72-byte limit."""
return base64.b64encode(hashlib.sha256(password.encode("utf-8")).digest())
def hash_password(password: str) -> str:
"""Hash a password using bcrypt."""
return bcrypt.hashpw(password.encode("utf-8"), bcrypt.gensalt()).decode("utf-8")
"""Hash a password (current version: v2 — SHA-256 + bcrypt)."""
raw = bcrypt.hashpw(_pre_hash_v2(password), bcrypt.gensalt()).decode("utf-8")
return f"{_PREFIX_V2}{raw}"
def verify_password(plain_password: str, hashed_password: str) -> bool:
"""Verify a password against its hash."""
return bcrypt.checkpw(plain_password.encode("utf-8"), hashed_password.encode("utf-8"))
"""Verify a password, auto-detecting the hash version.
Accepts v2 (``$dfv2$``), v1 (``$dfv1$``), and bare bcrypt hashes
(treated as v1 for backward compatibility with pre-versioning data).
"""
try:
if hashed_password.startswith(_PREFIX_V2):
bcrypt_hash = hashed_password[len(_PREFIX_V2) :]
return bcrypt.checkpw(_pre_hash_v2(plain_password), bcrypt_hash.encode("utf-8"))
if hashed_password.startswith(_PREFIX_V1):
bcrypt_hash = hashed_password[len(_PREFIX_V1) :]
else:
bcrypt_hash = hashed_password
return bcrypt.checkpw(plain_password.encode("utf-8"), bcrypt_hash.encode("utf-8"))
except ValueError:
# bcrypt raises ValueError for malformed or corrupt hashes (e.g., invalid salt).
# Fail closed rather than crashing the request.
return False
def needs_rehash(hashed_password: str) -> bool:
"""Return True if the hash uses an older version and should be rehashed."""
return not hashed_password.startswith(_PREFIX_V2)
async def hash_password_async(password: str) -> str:
+2 -2
View File
@@ -12,12 +12,12 @@ class AuthProvider(ABC):
Returns User if authentication succeeds, None otherwise.
"""
...
raise NotImplementedError
@abstractmethod
async def get_user(self, user_id: str) -> "User | None":
"""Retrieve user by ID."""
...
raise NotImplementedError
# Import User at runtime to avoid circular imports
@@ -35,7 +35,7 @@ class UserRepository(ABC):
Raises:
ValueError: If email already exists
"""
...
raise NotImplementedError
@abstractmethod
async def get_user_by_id(self, user_id: str) -> User | None:
@@ -47,7 +47,7 @@ class UserRepository(ABC):
Returns:
User if found, None otherwise
"""
...
raise NotImplementedError
@abstractmethod
async def get_user_by_email(self, email: str) -> User | None:
@@ -59,7 +59,7 @@ class UserRepository(ABC):
Returns:
User if found, None otherwise
"""
...
raise NotImplementedError
@abstractmethod
async def update_user(self, user: User) -> User:
@@ -76,17 +76,17 @@ class UserRepository(ABC):
a hard failure (not a no-op) so callers cannot mistake a
concurrent-delete race for a successful update.
"""
...
raise NotImplementedError
@abstractmethod
async def count_users(self) -> int:
"""Return total number of registered users."""
...
raise NotImplementedError
@abstractmethod
async def count_admin_users(self) -> int:
"""Return number of users with system_role == 'admin'."""
...
raise NotImplementedError
@abstractmethod
async def get_user_by_oauth(self, provider: str, oauth_id: str) -> User | None:
@@ -99,4 +99,4 @@ class UserRepository(ABC):
Returns:
User if found, None otherwise
"""
...
raise NotImplementedError
+2 -3
View File
@@ -25,15 +25,14 @@ from deerflow.persistence.user.model import UserRow
async def _run(email: str | None) -> int:
from deerflow.config import AppConfig
from deerflow.config import get_app_config
from deerflow.persistence.engine import (
close_engine,
get_session_factory,
init_engine_from_config,
)
# CLI entry: load config explicitly at the top, pass down through the closure.
config = AppConfig.from_file()
config = get_app_config()
await init_engine_from_config(config.database)
try:
sf = get_session_factory()
+13 -5
View File
@@ -18,6 +18,7 @@ from starlette.types import ASGIApp
from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse
from app.gateway.authz import _ALL_PERMISSIONS, AuthContext
from app.gateway.internal_auth import INTERNAL_AUTH_HEADER_NAME, get_internal_user, is_valid_internal_auth_token
from deerflow.runtime.user_context import reset_current_user, set_current_user
# Paths that never require authentication.
@@ -75,8 +76,12 @@ class AuthMiddleware(BaseHTTPMiddleware):
if _is_public(request.url.path):
return await call_next(request)
internal_user = None
if is_valid_internal_auth_token(request.headers.get(INTERNAL_AUTH_HEADER_NAME)):
internal_user = get_internal_user()
# Non-public path: require session cookie
if not request.cookies.get("access_token"):
if internal_user is None and not request.cookies.get("access_token"):
return JSONResponse(
status_code=401,
content={
@@ -100,10 +105,13 @@ class AuthMiddleware(BaseHTTPMiddleware):
# bubble up, so we catch and render it as JSONResponse here.
from app.gateway.deps import get_current_user_from_request
try:
user = await get_current_user_from_request(request)
except HTTPException as exc:
return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail})
if internal_user is not None:
user = internal_user
else:
try:
user = await get_current_user_from_request(request)
except HTTPException as exc:
return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail})
# Stamp both request.state.user (for the contextvar pattern)
# and request.state.auth (so @require_permission's "auth is
+43 -4
View File
@@ -30,7 +30,9 @@ Inspired by LangGraph Auth system: https://github.com/langchain-ai/langgraph/blo
from __future__ import annotations
import functools
import inspect
from collections.abc import Callable
from types import SimpleNamespace
from typing import TYPE_CHECKING, Any, ParamSpec, TypeVar
from fastapi import HTTPException, Request
@@ -117,6 +119,15 @@ _ALL_PERMISSIONS: list[str] = [
]
def _make_test_request_stub() -> Any:
"""Create a minimal request-like object for direct unit calls.
Used when decorated route handlers are invoked without FastAPI's
request injection. Includes fields accessed by auth helpers.
"""
return SimpleNamespace(state=SimpleNamespace(), cookies={}, _deerflow_test_bypass_auth=True)
async def _authenticate(request: Request) -> AuthContext:
"""Authenticate request and return AuthContext.
@@ -134,7 +145,11 @@ async def _authenticate(request: Request) -> AuthContext:
def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
"""Decorator that authenticates the request and sets AuthContext.
"""Decorator that authenticates the request and enforces authentication.
Independently raises HTTP 401 for unauthenticated requests, regardless of
whether ``AuthMiddleware`` is present in the ASGI stack. Sets the resolved
``AuthContext`` on ``request.state.auth`` for downstream handlers.
Must be placed ABOVE other decorators (executes after them).
@@ -147,19 +162,33 @@ def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
...
Raises:
ValueError: If 'request' parameter is missing
HTTPException: 401 if the request is unauthenticated.
ValueError: If 'request' parameter is missing.
"""
@functools.wraps(func)
async def wrapper(*args: Any, **kwargs: Any) -> Any:
request = kwargs.get("request")
if request is None:
raise ValueError("require_auth decorator requires 'request' parameter")
# Unit tests may call decorated handlers directly without a
# FastAPI Request object. Inject a minimal request stub when
# the wrapped function declares `request`.
if "request" in inspect.signature(func).parameters:
kwargs["request"] = _make_test_request_stub()
else:
raise ValueError("require_auth decorator requires 'request' parameter")
request = kwargs["request"]
if getattr(request, "_deerflow_test_bypass_auth", False):
return await func(*args, **kwargs)
# Authenticate and set context
auth_context = await _authenticate(request)
request.state.auth = auth_context
if not auth_context.is_authenticated:
raise HTTPException(status_code=401, detail="Authentication required")
return await func(*args, **kwargs)
return wrapper
@@ -210,7 +239,17 @@ def require_permission(
async def wrapper(*args: Any, **kwargs: Any) -> Any:
request = kwargs.get("request")
if request is None:
raise ValueError("require_permission decorator requires 'request' parameter")
# Unit tests may call decorated route handlers directly without
# constructing a FastAPI Request object. Inject a minimal stub
# when the wrapped function declares `request`.
if "request" in inspect.signature(func).parameters:
kwargs["request"] = _make_test_request_stub()
else:
return await func(*args, **kwargs)
request = kwargs["request"]
if getattr(request, "_deerflow_test_bypass_auth", False):
return await func(*args, **kwargs)
auth: AuthContext = getattr(request.state, "auth", None)
if auth is None:
+2
View File
@@ -9,6 +9,7 @@ class GatewayConfig(BaseModel):
host: str = Field(default="0.0.0.0", description="Host to bind the gateway server")
port: int = Field(default=8001, description="Port to bind the gateway server")
cors_origins: list[str] = Field(default_factory=lambda: ["http://localhost:3000"], description="Allowed CORS origins")
enable_docs: bool = Field(default=True, description="Enable Swagger/ReDoc/OpenAPI endpoints")
_gateway_config: GatewayConfig | None = None
@@ -23,5 +24,6 @@ def get_gateway_config() -> GatewayConfig:
host=os.getenv("GATEWAY_HOST", "0.0.0.0"),
port=int(os.getenv("GATEWAY_PORT", "8001")),
cors_origins=cors_origins_str.split(","),
enable_docs=os.getenv("GATEWAY_ENABLE_DOCS", "true").lower() == "true",
)
return _gateway_config
+27 -29
View File
@@ -8,14 +8,18 @@ Initialization is handled directly in ``app.py`` via :class:`AsyncExitStack`.
from __future__ import annotations
from collections.abc import AsyncGenerator
from collections.abc import AsyncGenerator, Callable
from contextlib import AsyncExitStack, asynccontextmanager
from typing import TYPE_CHECKING
from typing import TYPE_CHECKING, TypeVar, cast
from fastapi import FastAPI, HTTPException, Request
from langgraph.types import Checkpointer
from deerflow.config.app_config import AppConfig
from deerflow.runtime import RunContext, RunManager
from deerflow.persistence.feedback import FeedbackRepository
from deerflow.runtime import RunContext, RunManager, StreamBridge
from deerflow.runtime.events.store.base import RunEventStore
from deerflow.runtime.runs.store.base import RunStore
if TYPE_CHECKING:
from app.gateway.auth.local_provider import LocalAuthProvider
@@ -23,17 +27,15 @@ if TYPE_CHECKING:
from deerflow.persistence.thread_meta.base import ThreadMetaStore
def get_config(request: Request) -> AppConfig:
"""FastAPI dependency returning the app-scoped ``AppConfig``.
T = TypeVar("T")
Reads from ``request.app.state.config`` which is set at startup
(``app.py`` lifespan) and swapped on config reload (``routers/mcp.py``,
``routers/skills.py``).
"""
cfg = getattr(request.app.state, "config", None)
if cfg is None:
def get_config(request: Request) -> AppConfig:
"""Return the app-scoped ``AppConfig`` stored on ``app.state``."""
config = getattr(request.app.state, "config", None)
if config is None:
raise HTTPException(status_code=503, detail="Configuration not available")
return cfg
return config
@asynccontextmanager
@@ -51,9 +53,9 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
from deerflow.runtime.events.store import make_run_event_store
async with AsyncExitStack() as stack:
# app.state.config is populated earlier in lifespan(); thread it
# explicitly into every provider below.
config = app.state.config
config = getattr(app.state, "config", None)
if config is None:
raise RuntimeError("langgraph_runtime() requires app.state.config to be initialized")
app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge(config))
@@ -100,25 +102,25 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
# ---------------------------------------------------------------------------
def _require(attr: str, label: str):
def _require(attr: str, label: str) -> Callable[[Request], T]:
"""Create a FastAPI dependency that returns ``app.state.<attr>`` or 503."""
def dep(request: Request):
def dep(request: Request) -> T:
val = getattr(request.app.state, attr, None)
if val is None:
raise HTTPException(status_code=503, detail=f"{label} not available")
return val
return cast(T, val)
dep.__name__ = dep.__qualname__ = f"get_{attr}"
return dep
get_stream_bridge = _require("stream_bridge", "Stream bridge")
get_run_manager = _require("run_manager", "Run manager")
get_checkpointer = _require("checkpointer", "Checkpointer")
get_run_event_store = _require("run_event_store", "Run event store")
get_feedback_repo = _require("feedback_repo", "Feedback")
get_run_store = _require("run_store", "Run store")
get_stream_bridge: Callable[[Request], StreamBridge] = _require("stream_bridge", "Stream bridge")
get_run_manager: Callable[[Request], RunManager] = _require("run_manager", "Run manager")
get_checkpointer: Callable[[Request], Checkpointer] = _require("checkpointer", "Checkpointer")
get_run_event_store: Callable[[Request], RunEventStore] = _require("run_event_store", "Run event store")
get_feedback_repo: Callable[[Request], FeedbackRepository] = _require("feedback_repo", "Feedback")
get_run_store: Callable[[Request], RunStore] = _require("run_store", "Run store")
def get_store(request: Request):
@@ -137,10 +139,7 @@ def get_thread_store(request: Request) -> ThreadMetaStore:
def get_run_context(request: Request) -> RunContext:
"""Build a :class:`RunContext` from ``app.state`` singletons.
Returns a *base* context with infrastructure dependencies. Callers that
need per-run fields (e.g. ``follow_up_to_run_id``) should use
``dataclasses.replace(ctx, follow_up_to_run_id=...)`` before passing it
to :func:`run_agent`.
Returns a *base* context with infrastructure dependencies.
"""
config = get_config(request)
return RunContext(
@@ -153,7 +152,6 @@ def get_run_context(request: Request) -> RunContext:
)
# ---------------------------------------------------------------------------
# Auth helpers (used by authz.py and auth middleware)
# ---------------------------------------------------------------------------
+26
View File
@@ -0,0 +1,26 @@
"""Process-local authentication for Gateway internal callers."""
from __future__ import annotations
import secrets
from types import SimpleNamespace
from deerflow.runtime.user_context import DEFAULT_USER_ID
INTERNAL_AUTH_HEADER_NAME = "X-DeerFlow-Internal-Token"
_INTERNAL_AUTH_TOKEN = secrets.token_urlsafe(32)
def create_internal_auth_headers() -> dict[str, str]:
"""Return headers that authenticate same-process Gateway internal calls."""
return {INTERNAL_AUTH_HEADER_NAME: _INTERNAL_AUTH_TOKEN}
def is_valid_internal_auth_token(token: str | None) -> bool:
"""Return True when *token* matches the process-local internal token."""
return bool(token) and secrets.compare_digest(token, _INTERNAL_AUTH_TOKEN)
def get_internal_user():
"""Return the synthetic user used for trusted internal channel calls."""
return SimpleNamespace(id=DEFAULT_USER_ID, system_role="internal")
+1 -1
View File
@@ -73,7 +73,7 @@ async def authenticate(request):
if isinstance(payload, TokenError):
raise Auth.exceptions.HTTPException(
status_code=401,
detail=f"Token error: {payload.value}",
detail="Invalid token",
)
user = await get_local_provider().get_user(payload.sub)
+19 -20
View File
@@ -5,12 +5,11 @@ import re
import shutil
import yaml
from fastapi import APIRouter, Depends, HTTPException
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
from app.gateway.deps import get_config
from deerflow.config.agents_api_config import get_agents_api_config
from deerflow.config.agents_config import AgentConfig, list_custom_agents, load_agent_config, load_agent_soul
from deerflow.config.app_config import AppConfig
from deerflow.config.paths import get_paths
logger = logging.getLogger(__name__)
@@ -78,9 +77,9 @@ def _normalize_agent_name(name: str) -> str:
return name.lower()
def _require_agents_api_enabled(app_config: AppConfig) -> None:
def _require_agents_api_enabled() -> None:
"""Reject access unless the custom-agent management API is explicitly enabled."""
if not app_config.agents_api.enabled:
if not get_agents_api_config().enabled:
raise HTTPException(
status_code=403,
detail=("Custom-agent management API is disabled. Set agents_api.enabled=true to expose agent and user-profile routes over HTTP."),
@@ -109,13 +108,13 @@ def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False
summary="List Custom Agents",
description="List all custom agents available in the agents directory, including their soul content.",
)
async def list_agents(app_config: AppConfig = Depends(get_config)) -> AgentsListResponse:
async def list_agents() -> AgentsListResponse:
"""List all custom agents.
Returns:
List of all custom agents with their metadata and soul content.
"""
_require_agents_api_enabled(app_config)
_require_agents_api_enabled()
try:
agents = list_custom_agents()
@@ -142,7 +141,7 @@ async def check_agent_name(name: str) -> dict:
Raises:
HTTPException: 422 if the name is invalid.
"""
_require_agents_api_enabled(app_config)
_require_agents_api_enabled()
_validate_agent_name(name)
normalized = _normalize_agent_name(name)
available = not get_paths().agent_dir(normalized).exists()
@@ -155,7 +154,7 @@ async def check_agent_name(name: str) -> dict:
summary="Get Custom Agent",
description="Retrieve details and SOUL.md content for a specific custom agent.",
)
async def get_agent(name: str, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
async def get_agent(name: str) -> AgentResponse:
"""Get a specific custom agent by name.
Args:
@@ -167,7 +166,7 @@ async def get_agent(name: str, app_config: AppConfig = Depends(get_config)) -> A
Raises:
HTTPException: 404 if agent not found.
"""
_require_agents_api_enabled(app_config)
_require_agents_api_enabled()
_validate_agent_name(name)
name = _normalize_agent_name(name)
@@ -188,7 +187,7 @@ async def get_agent(name: str, app_config: AppConfig = Depends(get_config)) -> A
summary="Create Custom Agent",
description="Create a new custom agent with its config and SOUL.md.",
)
async def create_agent_endpoint(request: AgentCreateRequest, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
"""Create a new custom agent.
Args:
@@ -200,7 +199,7 @@ async def create_agent_endpoint(request: AgentCreateRequest, app_config: AppConf
Raises:
HTTPException: 409 if agent already exists, 422 if name is invalid.
"""
_require_agents_api_enabled(app_config)
_require_agents_api_enabled()
_validate_agent_name(request.name)
normalized_name = _normalize_agent_name(request.name)
@@ -252,7 +251,7 @@ async def create_agent_endpoint(request: AgentCreateRequest, app_config: AppConf
summary="Update Custom Agent",
description="Update an existing custom agent's config and/or SOUL.md.",
)
async def update_agent(name: str, request: AgentUpdateRequest, app_config: AppConfig = Depends(get_config)) -> AgentResponse:
async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
"""Update an existing custom agent.
Args:
@@ -265,7 +264,7 @@ async def update_agent(name: str, request: AgentUpdateRequest, app_config: AppCo
Raises:
HTTPException: 404 if agent not found.
"""
_require_agents_api_enabled(app_config)
_require_agents_api_enabled()
_validate_agent_name(name)
name = _normalize_agent_name(name)
@@ -343,13 +342,13 @@ class UserProfileUpdateRequest(BaseModel):
summary="Get User Profile",
description="Read the global USER.md file that is injected into all custom agents.",
)
async def get_user_profile(app_config: AppConfig = Depends(get_config)) -> UserProfileResponse:
async def get_user_profile() -> UserProfileResponse:
"""Return the current USER.md content.
Returns:
UserProfileResponse with content=None if USER.md does not exist yet.
"""
_require_agents_api_enabled(app_config)
_require_agents_api_enabled()
try:
user_md_path = get_paths().user_md_file
@@ -368,7 +367,7 @@ async def get_user_profile(app_config: AppConfig = Depends(get_config)) -> UserP
summary="Update User Profile",
description="Write the global USER.md file that is injected into all custom agents.",
)
async def update_user_profile(request: UserProfileUpdateRequest, app_config: AppConfig = Depends(get_config)) -> UserProfileResponse:
async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileResponse:
"""Create or overwrite the global USER.md.
Args:
@@ -377,7 +376,7 @@ async def update_user_profile(request: UserProfileUpdateRequest, app_config: App
Returns:
UserProfileResponse with the saved content.
"""
_require_agents_api_enabled(app_config)
_require_agents_api_enabled()
try:
paths = get_paths()
@@ -396,7 +395,7 @@ async def update_user_profile(request: UserProfileUpdateRequest, app_config: App
summary="Delete Custom Agent",
description="Delete a custom agent and all its files (config, SOUL.md, memory).",
)
async def delete_agent(name: str, app_config: AppConfig = Depends(get_config)) -> None:
async def delete_agent(name: str) -> None:
"""Delete a custom agent.
Args:
@@ -405,7 +404,7 @@ async def delete_agent(name: str, app_config: AppConfig = Depends(get_config)) -
Raises:
HTTPException: 404 if agent not found.
"""
_require_agents_api_enabled(app_config)
_require_agents_api_enabled()
_validate_agent_name(name)
name = _normalize_agent_name(name)
+36 -2
View File
@@ -146,7 +146,13 @@ def _set_session_cookie(response: Response, token: str, request: Request) -> Non
# ── Rate Limiting ────────────────────────────────────────────────────────
# In-process dict — not shared across workers. Sufficient for single-worker deployments.
# In-process dict — not shared across workers.
#
# **Limitation**: with multi-worker deployments (e.g., gunicorn -w N), each
# worker maintains its own lockout table, so an attacker effectively gets
# N × _MAX_LOGIN_ATTEMPTS guesses before being locked out everywhere. For
# production multi-worker setups, replace this with a shared store (Redis,
# database-backed counter) to enforce a true per-IP limit.
_MAX_LOGIN_ATTEMPTS = 5
_LOCKOUT_SECONDS = 300 # 5 minutes
@@ -376,9 +382,37 @@ async def get_me(request: Request):
return UserResponse(id=str(user.id), email=user.email, system_role=user.system_role, needs_setup=user.needs_setup)
_SETUP_STATUS_COOLDOWN: dict[str, float] = {}
_SETUP_STATUS_COOLDOWN_SECONDS = 60
_MAX_TRACKED_SETUP_STATUS_IPS = 10000
@router.get("/setup-status")
async def setup_status():
async def setup_status(request: Request):
"""Check if an admin account exists. Returns needs_setup=True when no admin exists."""
client_ip = _get_client_ip(request)
now = time.time()
last_check = _SETUP_STATUS_COOLDOWN.get(client_ip, 0)
elapsed = now - last_check
if elapsed < _SETUP_STATUS_COOLDOWN_SECONDS:
retry_after = max(1, int(_SETUP_STATUS_COOLDOWN_SECONDS - elapsed))
raise HTTPException(
status_code=status.HTTP_429_TOO_MANY_REQUESTS,
detail="Setup status check is rate limited",
headers={"Retry-After": str(retry_after)},
)
# Evict stale entries when dict grows too large to bound memory usage.
if len(_SETUP_STATUS_COOLDOWN) >= _MAX_TRACKED_SETUP_STATUS_IPS:
cutoff = now - _SETUP_STATUS_COOLDOWN_SECONDS
stale = [k for k, t in _SETUP_STATUS_COOLDOWN.items() if t < cutoff]
for k in stale:
del _SETUP_STATUS_COOLDOWN[k]
# If still too large after evicting expired entries, remove oldest half.
if len(_SETUP_STATUS_COOLDOWN) >= _MAX_TRACKED_SETUP_STATUS_IPS:
by_time = sorted(_SETUP_STATUS_COOLDOWN.items(), key=lambda kv: kv[1])
for k, _ in by_time[: len(by_time) // 2]:
del _SETUP_STATUS_COOLDOWN[k]
_SETUP_STATUS_COOLDOWN[client_ip] = now
admin_count = await get_local_provider().count_admin_users()
return {"needs_setup": admin_count == 0}
+12 -20
View File
@@ -3,12 +3,10 @@ import logging
from pathlib import Path
from typing import Literal
from fastapi import APIRouter, Depends, HTTPException, Request
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
from app.gateway.deps import get_config
from deerflow.config.app_config import AppConfig
from deerflow.config.extensions_config import ExtensionsConfig
from deerflow.config.extensions_config import ExtensionsConfig, get_extensions_config, reload_extensions_config
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api", tags=["mcp"])
@@ -71,7 +69,7 @@ class McpConfigUpdateRequest(BaseModel):
summary="Get MCP Configuration",
description="Retrieve the current Model Context Protocol (MCP) server configurations.",
)
async def get_mcp_configuration(config: AppConfig = Depends(get_config)) -> McpConfigResponse:
async def get_mcp_configuration() -> McpConfigResponse:
"""Get the current MCP configuration.
Returns:
@@ -92,9 +90,9 @@ async def get_mcp_configuration(config: AppConfig = Depends(get_config)) -> McpC
}
```
"""
ext = config.extensions
config = get_extensions_config()
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in ext.mcp_servers.items()})
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in config.mcp_servers.items()})
@router.put(
@@ -103,11 +101,7 @@ async def get_mcp_configuration(config: AppConfig = Depends(get_config)) -> McpC
summary="Update MCP Configuration",
description="Update Model Context Protocol (MCP) server configurations and save to file.",
)
async def update_mcp_configuration(
request: McpConfigUpdateRequest,
http_request: Request,
config: AppConfig = Depends(get_config),
) -> McpConfigResponse:
async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfigResponse:
"""Update the MCP configuration.
This will:
@@ -148,13 +142,13 @@ async def update_mcp_configuration(
config_path = Path.cwd().parent / "extensions_config.json"
logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
# Use injected config to preserve skills configuration
current_ext = config.extensions
# Load current config to preserve skills configuration
current_config = get_extensions_config()
# Convert request to dict format for JSON serialization
config_data = {
"mcpServers": {name: server.model_dump() for name, server in request.mcp_servers.items()},
"skills": {name: {"enabled": skill.enabled} for name, skill in current_ext.skills.items()},
"skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
}
# Write the configuration to file
@@ -166,11 +160,9 @@ async def update_mcp_configuration(
# NOTE: No need to reload/reset cache here - LangGraph Server (separate process)
# will detect config file changes via mtime and reinitialize MCP tools automatically
# Reload the configuration and swap ``app.state.config`` so subsequent
# ``Depends(get_config)`` calls see the refreshed value.
reloaded = AppConfig.from_file()
http_request.app.state.config = reloaded
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in reloaded.extensions.mcp_servers.items()})
# Reload the configuration and update the global cache
reloaded_config = reload_extensions_config()
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in reloaded_config.mcp_servers.items()})
except Exception as e:
logger.error(f"Failed to update MCP configuration: {e}", exc_info=True)
+21 -28
View File
@@ -1,9 +1,8 @@
"""Memory API router for retrieving and managing global memory data."""
from fastapi import APIRouter, Depends, HTTPException
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
from app.gateway.deps import get_config
from deerflow.agents.memory.updater import (
clear_memory_data,
create_memory_fact,
@@ -13,7 +12,7 @@ from deerflow.agents.memory.updater import (
reload_memory_data,
update_memory_fact,
)
from deerflow.config.app_config import AppConfig
from deerflow.config.memory_config import get_memory_config
from deerflow.runtime.user_context import get_effective_user_id
router = APIRouter(prefix="/api", tags=["memory"])
@@ -115,7 +114,7 @@ class MemoryStatusResponse(BaseModel):
summary="Get Memory Data",
description="Retrieve the current global memory data including user context, history, and facts.",
)
async def get_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
async def get_memory() -> MemoryResponse:
"""Get the current global memory data.
Returns:
@@ -149,7 +148,7 @@ async def get_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRespo
}
```
"""
memory_data = get_memory_data(app_config.memory, user_id=get_effective_user_id())
memory_data = get_memory_data(user_id=get_effective_user_id())
return MemoryResponse(**memory_data)
@@ -160,7 +159,7 @@ async def get_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRespo
summary="Reload Memory Data",
description="Reload memory data from the storage file, refreshing the in-memory cache.",
)
async def reload_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
async def reload_memory() -> MemoryResponse:
"""Reload memory data from file.
This forces a reload of the memory data from the storage file,
@@ -169,7 +168,7 @@ async def reload_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRe
Returns:
The reloaded memory data.
"""
memory_data = reload_memory_data(app_config.memory, user_id=get_effective_user_id())
memory_data = reload_memory_data(user_id=get_effective_user_id())
return MemoryResponse(**memory_data)
@@ -180,10 +179,10 @@ async def reload_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRe
summary="Clear All Memory Data",
description="Delete all saved memory data and reset the memory structure to an empty state.",
)
async def clear_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
async def clear_memory() -> MemoryResponse:
"""Clear all persisted memory data."""
try:
memory_data = clear_memory_data(app_config.memory, user_id=get_effective_user_id())
memory_data = clear_memory_data(user_id=get_effective_user_id())
except OSError as exc:
raise HTTPException(status_code=500, detail="Failed to clear memory data.") from exc
@@ -197,11 +196,10 @@ async def clear_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRes
summary="Create Memory Fact",
description="Create a single saved memory fact manually.",
)
async def create_memory_fact_endpoint(request: FactCreateRequest, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryResponse:
"""Create a single fact manually."""
try:
memory_data = create_memory_fact(
app_config.memory,
content=request.content,
category=request.category,
confidence=request.confidence,
@@ -222,10 +220,10 @@ async def create_memory_fact_endpoint(request: FactCreateRequest, app_config: Ap
summary="Delete Memory Fact",
description="Delete a single saved memory fact by its fact id.",
)
async def delete_memory_fact_endpoint(fact_id: str, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
async def delete_memory_fact_endpoint(fact_id: str) -> MemoryResponse:
"""Delete a single fact from memory by fact id."""
try:
memory_data = delete_memory_fact(app_config.memory, fact_id, user_id=get_effective_user_id())
memory_data = delete_memory_fact(fact_id, user_id=get_effective_user_id())
except KeyError as exc:
raise HTTPException(status_code=404, detail=f"Memory fact '{fact_id}' not found.") from exc
except OSError as exc:
@@ -241,11 +239,10 @@ async def delete_memory_fact_endpoint(fact_id: str, app_config: AppConfig = Depe
summary="Patch Memory Fact",
description="Partially update a single saved memory fact by its fact id while preserving omitted fields.",
)
async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -> MemoryResponse:
"""Partially update a single fact manually."""
try:
memory_data = update_memory_fact(
app_config.memory,
fact_id=fact_id,
content=request.content,
category=request.category,
@@ -269,9 +266,9 @@ async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest, a
summary="Export Memory Data",
description="Export the current global memory data as JSON for backup or transfer.",
)
async def export_memory(app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
async def export_memory() -> MemoryResponse:
"""Export the current memory data."""
memory_data = get_memory_data(app_config.memory, user_id=get_effective_user_id())
memory_data = get_memory_data(user_id=get_effective_user_id())
return MemoryResponse(**memory_data)
@@ -282,10 +279,10 @@ async def export_memory(app_config: AppConfig = Depends(get_config)) -> MemoryRe
summary="Import Memory Data",
description="Import and overwrite the current global memory data from a JSON payload.",
)
async def import_memory(request: MemoryResponse, app_config: AppConfig = Depends(get_config)) -> MemoryResponse:
async def import_memory(request: MemoryResponse) -> MemoryResponse:
"""Import and persist memory data."""
try:
memory_data = import_memory_data(app_config.memory, request.model_dump(), user_id=get_effective_user_id())
memory_data = import_memory_data(request.model_dump(), user_id=get_effective_user_id())
except OSError as exc:
raise HTTPException(status_code=500, detail="Failed to import memory data.") from exc
@@ -298,9 +295,7 @@ async def import_memory(request: MemoryResponse, app_config: AppConfig = Depends
summary="Get Memory Configuration",
description="Retrieve the current memory system configuration.",
)
async def get_memory_config_endpoint(
app_config: AppConfig = Depends(get_config),
) -> MemoryConfigResponse:
async def get_memory_config_endpoint() -> MemoryConfigResponse:
"""Get the memory system configuration.
Returns:
@@ -319,7 +314,7 @@ async def get_memory_config_endpoint(
}
```
"""
config = app_config.memory
config = get_memory_config()
return MemoryConfigResponse(
enabled=config.enabled,
storage_path=config.storage_path,
@@ -338,16 +333,14 @@ async def get_memory_config_endpoint(
summary="Get Memory Status",
description="Retrieve both memory configuration and current data in a single request.",
)
async def get_memory_status(
app_config: AppConfig = Depends(get_config),
) -> MemoryStatusResponse:
async def get_memory_status() -> MemoryStatusResponse:
"""Get the memory system status including configuration and data.
Returns:
Combined memory configuration and current data.
"""
config = app_config.memory
memory_data = get_memory_data(config, user_id=get_effective_user_id())
config = get_memory_config()
memory_data = get_memory_data(user_id=get_effective_user_id())
return MemoryStatusResponse(
config=MemoryConfigResponse(
+2 -1
View File
@@ -123,7 +123,8 @@ async def run_messages(
run = await _resolve_run(run_id, request)
event_store = get_run_event_store(request)
rows = await event_store.list_messages_by_run(
run["thread_id"], run_id,
run["thread_id"],
run_id,
limit=limit + 1,
before_seq=before_seq,
after_seq=after_seq,
+78 -112
View File
@@ -1,32 +1,20 @@
import errno
import json
import logging
import shutil
from pathlib import Path
from fastapi import APIRouter, Depends, HTTPException, Request
from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel, Field
from app.gateway.deps import get_config
from app.gateway.path_utils import resolve_thread_virtual_path
from deerflow.agents.lead_agent.prompt import refresh_skills_system_prompt_cache_async
from deerflow.config.app_config import AppConfig
from deerflow.config.extensions_config import ExtensionsConfig
from deerflow.skills import Skill, load_skills
from deerflow.skills.installer import SkillAlreadyExistsError, install_skill_from_archive
from deerflow.skills.manager import (
append_history,
atomic_write,
custom_skill_exists,
ensure_custom_skill_is_editable,
get_custom_skill_dir,
get_custom_skill_file,
get_skill_history_file,
read_custom_skill_content,
read_history,
validate_skill_markdown_content,
)
from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
from deerflow.skills import Skill
from deerflow.skills.installer import SkillAlreadyExistsError
from deerflow.skills.security_scanner import scan_skill_content
from deerflow.skills.storage import get_or_new_skill_storage
from deerflow.skills.types import SKILL_MD_FILE, SkillCategory
logger = logging.getLogger(__name__)
@@ -39,7 +27,7 @@ class SkillResponse(BaseModel):
name: str = Field(..., description="Name of the skill")
description: str = Field(..., description="Description of what the skill does")
license: str | None = Field(None, description="License information")
category: str = Field(..., description="Category of the skill (public or custom)")
category: SkillCategory = Field(..., description="Category of the skill (public or custom)")
enabled: bool = Field(default=True, description="Whether this skill is enabled")
@@ -103,9 +91,9 @@ def _skill_to_response(skill: Skill) -> SkillResponse:
summary="List All Skills",
description="Retrieve a list of all available skills from both public and custom directories.",
)
async def list_skills(app_config: AppConfig = Depends(get_config)) -> SkillsListResponse:
async def list_skills(config: AppConfig = Depends(get_config)) -> SkillsListResponse:
try:
skills = load_skills(app_config, enabled_only=False)
skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
except Exception as e:
logger.error(f"Failed to load skills: {e}", exc_info=True)
@@ -118,11 +106,11 @@ async def list_skills(app_config: AppConfig = Depends(get_config)) -> SkillsList
summary="Install Skill",
description="Install a skill from a .skill file (ZIP archive) located in the thread's user-data directory.",
)
async def install_skill(request: SkillInstallRequest, app_config: AppConfig = Depends(get_config)) -> SkillInstallResponse:
async def install_skill(request: SkillInstallRequest, config: AppConfig = Depends(get_config)) -> SkillInstallResponse:
try:
skill_file_path = resolve_thread_virtual_path(request.thread_id, request.path)
result = install_skill_from_archive(skill_file_path)
await refresh_skills_system_prompt_cache_async(app_config)
result = await get_or_new_skill_storage(app_config=config).ainstall_skill_from_archive(skill_file_path)
await refresh_skills_system_prompt_cache_async()
return SkillInstallResponse(**result)
except FileNotFoundError as e:
raise HTTPException(status_code=404, detail=str(e))
@@ -138,9 +126,9 @@ async def install_skill(request: SkillInstallRequest, app_config: AppConfig = De
@router.get("/skills/custom", response_model=SkillsListResponse, summary="List Custom Skills")
async def list_custom_skills(app_config: AppConfig = Depends(get_config)) -> SkillsListResponse:
async def list_custom_skills(config: AppConfig = Depends(get_config)) -> SkillsListResponse:
try:
skills = [skill for skill in load_skills(app_config, enabled_only=False) if skill.category == "custom"]
skills = [skill for skill in get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False) if skill.category == SkillCategory.CUSTOM]
return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
except Exception as e:
logger.error("Failed to list custom skills: %s", e, exc_info=True)
@@ -148,13 +136,14 @@ async def list_custom_skills(app_config: AppConfig = Depends(get_config)) -> Ski
@router.get("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Get Custom Skill Content")
async def get_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
async def get_custom_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
try:
skills = load_skills(app_config, enabled_only=False)
skill = next((s for s in skills if s.name == skill_name and s.category == "custom"), None)
skill_name = skill_name.replace("\r\n", "").replace("\n", "")
skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == skill_name and s.category == SkillCategory.CUSTOM), None)
if skill is None:
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name, app_config))
return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=get_or_new_skill_storage(app_config=config).read_custom_skill(skill_name))
except HTTPException:
raise
except Exception as e:
@@ -163,35 +152,31 @@ async def get_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_
@router.put("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Edit Custom Skill")
async def update_custom_skill(
skill_name: str,
request: CustomSkillUpdateRequest,
app_config: AppConfig = Depends(get_config),
) -> CustomSkillContentResponse:
async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
try:
ensure_custom_skill_is_editable(skill_name, app_config)
validate_skill_markdown_content(skill_name, request.content)
scan = await scan_skill_content(app_config, request.content, executable=False, location=f"{skill_name}/SKILL.md")
skill_name = skill_name.replace("\r\n", "").replace("\n", "")
storage = get_or_new_skill_storage(app_config=config)
storage.ensure_custom_skill_is_editable(skill_name)
storage.validate_skill_markdown_content(skill_name, request.content)
scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/{SKILL_MD_FILE}", app_config=config)
if scan.decision == "block":
raise HTTPException(status_code=400, detail=f"Security scan blocked the edit: {scan.reason}")
skill_file = get_custom_skill_dir(skill_name, app_config) / "SKILL.md"
prev_content = skill_file.read_text(encoding="utf-8")
atomic_write(skill_file, request.content)
append_history(
prev_content = storage.read_custom_skill(skill_name)
storage.write_custom_skill(skill_name, SKILL_MD_FILE, request.content)
storage.append_history(
skill_name,
{
"action": "human_edit",
"author": "human",
"thread_id": None,
"file_path": "SKILL.md",
"file_path": SKILL_MD_FILE,
"prev_content": prev_content,
"new_content": request.content,
"scanner": {"decision": scan.decision, "reason": scan.reason},
},
app_config,
)
await refresh_skills_system_prompt_cache_async(app_config)
return await get_custom_skill(skill_name, app_config)
await refresh_skills_system_prompt_cache_async()
return await get_custom_skill(skill_name, config)
except HTTPException:
raise
except FileNotFoundError as e:
@@ -204,31 +189,23 @@ async def update_custom_skill(
@router.delete("/skills/custom/{skill_name}", summary="Delete Custom Skill")
async def delete_custom_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> dict[str, bool]:
async def delete_custom_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> dict[str, bool]:
try:
ensure_custom_skill_is_editable(skill_name, app_config)
skill_dir = get_custom_skill_dir(skill_name, app_config)
prev_content = read_custom_skill_content(skill_name, app_config)
try:
append_history(
skill_name,
{
"action": "human_delete",
"author": "human",
"thread_id": None,
"file_path": "SKILL.md",
"prev_content": prev_content,
"new_content": None,
"scanner": {"decision": "allow", "reason": "Deletion requested."},
},
app_config,
)
except OSError as e:
if not isinstance(e, PermissionError) and e.errno not in {errno.EACCES, errno.EPERM, errno.EROFS}:
raise
logger.warning("Skipping delete history write for custom skill %s due to readonly/permission failure; continuing with skill directory removal: %s", skill_name, e)
shutil.rmtree(skill_dir)
await refresh_skills_system_prompt_cache_async(app_config)
skill_name = skill_name.replace("\r\n", "").replace("\n", "")
storage = get_or_new_skill_storage(app_config=config)
storage.delete_custom_skill(
skill_name,
history_meta={
"action": "human_delete",
"author": "human",
"thread_id": None,
"file_path": SKILL_MD_FILE,
"prev_content": None,
"new_content": None,
"scanner": {"decision": "allow", "reason": "Deletion requested."},
},
)
await refresh_skills_system_prompt_cache_async()
return {"success": True}
except FileNotFoundError as e:
raise HTTPException(status_code=404, detail=str(e))
@@ -240,11 +217,13 @@ async def delete_custom_skill(skill_name: str, app_config: AppConfig = Depends(g
@router.get("/skills/custom/{skill_name}/history", response_model=CustomSkillHistoryResponse, summary="Get Custom Skill History")
async def get_custom_skill_history(skill_name: str, app_config: AppConfig = Depends(get_config)) -> CustomSkillHistoryResponse:
async def get_custom_skill_history(skill_name: str, config: AppConfig = Depends(get_config)) -> CustomSkillHistoryResponse:
try:
if not custom_skill_exists(skill_name, app_config) and not get_skill_history_file(skill_name, app_config).exists():
skill_name = skill_name.replace("\r\n", "").replace("\n", "")
storage = get_or_new_skill_storage(app_config=config)
if not storage.custom_skill_exists(skill_name) and not storage.get_skill_history_file(skill_name).exists():
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
return CustomSkillHistoryResponse(history=read_history(skill_name, app_config))
return CustomSkillHistoryResponse(history=storage.read_history(skill_name))
except HTTPException:
raise
except Exception as e:
@@ -253,42 +232,39 @@ async def get_custom_skill_history(skill_name: str, app_config: AppConfig = Depe
@router.post("/skills/custom/{skill_name}/rollback", response_model=CustomSkillContentResponse, summary="Rollback Custom Skill")
async def rollback_custom_skill(
skill_name: str,
request: SkillRollbackRequest,
app_config: AppConfig = Depends(get_config),
) -> CustomSkillContentResponse:
async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
try:
if not custom_skill_exists(skill_name, app_config) and not get_skill_history_file(skill_name, app_config).exists():
storage = get_or_new_skill_storage(app_config=config)
if not storage.custom_skill_exists(skill_name) and not storage.get_skill_history_file(skill_name).exists():
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
history = read_history(skill_name, app_config)
history = storage.read_history(skill_name)
if not history:
raise HTTPException(status_code=400, detail=f"Custom skill '{skill_name}' has no history")
record = history[request.history_index]
target_content = record.get("prev_content")
if target_content is None:
raise HTTPException(status_code=400, detail="Selected history entry has no previous content to roll back to")
validate_skill_markdown_content(skill_name, target_content)
scan = await scan_skill_content(app_config, target_content, executable=False, location=f"{skill_name}/SKILL.md")
skill_file = get_custom_skill_file(skill_name, app_config)
storage.validate_skill_markdown_content(skill_name, target_content)
scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/{SKILL_MD_FILE}", app_config=config)
skill_file = storage.get_custom_skill_file(skill_name)
current_content = skill_file.read_text(encoding="utf-8") if skill_file.exists() else None
history_entry = {
"action": "rollback",
"author": "human",
"thread_id": None,
"file_path": "SKILL.md",
"file_path": SKILL_MD_FILE,
"prev_content": current_content,
"new_content": target_content,
"rollback_from_ts": record.get("ts"),
"scanner": {"decision": scan.decision, "reason": scan.reason},
}
if scan.decision == "block":
append_history(skill_name, history_entry, app_config)
storage.append_history(skill_name, history_entry)
raise HTTPException(status_code=400, detail=f"Rollback blocked by security scanner: {scan.reason}")
atomic_write(skill_file, target_content)
append_history(skill_name, history_entry, app_config)
await refresh_skills_system_prompt_cache_async(app_config)
return await get_custom_skill(skill_name, app_config)
storage.write_custom_skill(skill_name, SKILL_MD_FILE, target_content)
storage.append_history(skill_name, history_entry)
await refresh_skills_system_prompt_cache_async()
return await get_custom_skill(skill_name, config)
except HTTPException:
raise
except IndexError:
@@ -308,9 +284,10 @@ async def rollback_custom_skill(
summary="Get Skill Details",
description="Retrieve detailed information about a specific skill by its name.",
)
async def get_skill(skill_name: str, app_config: AppConfig = Depends(get_config)) -> SkillResponse:
async def get_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> SkillResponse:
try:
skills = load_skills(app_config, enabled_only=False)
skill_name = skill_name.replace("\r\n", "").replace("\n", "")
skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == skill_name), None)
if skill is None:
@@ -330,14 +307,10 @@ async def get_skill(skill_name: str, app_config: AppConfig = Depends(get_config)
summary="Update Skill",
description="Update a skill's enabled status by modifying the extensions_config.json file.",
)
async def update_skill(
skill_name: str,
request: SkillUpdateRequest,
http_request: Request,
app_config: AppConfig = Depends(get_config),
) -> SkillResponse:
async def update_skill(skill_name: str, request: SkillUpdateRequest, config: AppConfig = Depends(get_config)) -> SkillResponse:
try:
skills = load_skills(app_config, enabled_only=False)
skill_name = skill_name.replace("\r\n", "").replace("\n", "")
skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == skill_name), None)
if skill is None:
@@ -348,29 +321,22 @@ async def update_skill(
config_path = Path.cwd().parent / "extensions_config.json"
logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
# Do not mutate the frozen AppConfig in place. Compose the new skills
# state in a fresh dict, write to disk, and reload AppConfig below so
# every subsequent Depends(get_config) sees the refreshed snapshot.
ext = app_config.extensions
updated_skills = {name: {"enabled": skill_config.enabled} for name, skill_config in ext.skills.items()}
updated_skills[skill_name] = {"enabled": request.enabled}
extensions_config = get_extensions_config()
extensions_config.skills[skill_name] = SkillStateConfig(enabled=request.enabled)
config_data = {
"mcpServers": {name: server.model_dump() for name, server in ext.mcp_servers.items()},
"skills": updated_skills,
"mcpServers": {name: server.model_dump() for name, server in extensions_config.mcp_servers.items()},
"skills": {name: {"enabled": skill_config.enabled} for name, skill_config in extensions_config.skills.items()},
}
with open(config_path, "w", encoding="utf-8") as f:
json.dump(config_data, f, indent=2)
logger.info(f"Skills configuration updated and saved to: {config_path}")
# Reload AppConfig and swap ``app.state.config`` so subsequent
# ``Depends(get_config)`` sees the refreshed value.
reloaded = AppConfig.from_file()
http_request.app.state.config = reloaded
await refresh_skills_system_prompt_cache_async(reloaded)
reload_extensions_config()
await refresh_skills_system_prompt_cache_async()
skills = load_skills(reloaded, enabled_only=False)
skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
updated_skill = next((s for s in skills if s.name == skill_name), None)
if updated_skill is None:
+7 -2
View File
@@ -102,7 +102,12 @@ def _format_conversation(messages: list[SuggestionMessage]) -> str:
description="Generate short follow-up questions a user might ask next, based on recent conversation context.",
)
@require_permission("threads", "read", owner_check=True)
async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request: Request, app_config: AppConfig = Depends(get_config)) -> SuggestionsResponse:
async def generate_suggestions(
thread_id: str,
body: SuggestionsRequest,
request: Request,
config: AppConfig = Depends(get_config),
) -> SuggestionsResponse:
if not body.messages:
return SuggestionsResponse(suggestions=[])
@@ -124,7 +129,7 @@ async def generate_suggestions(thread_id: str, body: SuggestionsRequest, request
user_content = f"Conversation Context:\n{conversation}\n\nGenerate {n} follow-up questions"
try:
model = create_chat_model(name=body.model_name, thinking_enabled=False, app_config=app_config)
model = create_chat_model(name=body.model_name, thinking_enabled=False, app_config=config)
response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)], config={"run_name": "suggest_agent"})
raw = _extract_response_text(response.content)
suggestions = _parse_json_string_list(raw) or []
+11 -7
View File
@@ -54,7 +54,6 @@ class RunCreateRequest(BaseModel):
after_seconds: float | None = Field(default=None, description="Delayed execution")
if_not_exists: Literal["reject", "create"] = Field(default="create", description="Thread creation policy")
feedback_keys: list[str] | None = Field(default=None, description="LangSmith feedback keys")
follow_up_to_run_id: str | None = Field(default=None, description="Run ID this message follows up on. Auto-detected from latest successful run if not provided.")
class RunResponse(BaseModel):
@@ -312,11 +311,15 @@ async def list_thread_messages(
if i in last_ai_indices:
run_id = msg["run_id"]
fb = feedback_map.get(run_id)
msg["feedback"] = {
"feedback_id": fb["feedback_id"],
"rating": fb["rating"],
"comment": fb.get("comment"),
} if fb else None
msg["feedback"] = (
{
"feedback_id": fb["feedback_id"],
"rating": fb["rating"],
"comment": fb.get("comment"),
}
if fb
else None
)
else:
msg["feedback"] = None
@@ -339,7 +342,8 @@ async def list_run_messages(
"""
event_store = get_run_event_store(request)
rows = await event_store.list_messages_by_run(
thread_id, run_id,
thread_id,
run_id,
limit=limit + 1,
before_seq=before_seq,
after_seq=after_seq,
+23 -22
View File
@@ -13,12 +13,11 @@ matching the LangGraph Platform wire format expected by the
from __future__ import annotations
import logging
import re
import time
import uuid
from typing import Any
from fastapi import APIRouter, HTTPException, Request
from langgraph.checkpoint.base import empty_checkpoint
from pydantic import BaseModel, Field, field_validator
from app.gateway.authz import require_permission
@@ -27,6 +26,7 @@ from app.gateway.utils import sanitize_log_param
from deerflow.config.paths import Paths, get_paths
from deerflow.runtime import serialize_channel_values
from deerflow.runtime.user_context import get_effective_user_id
from deerflow.utils.time import coerce_iso, now_iso
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/threads", tags=["threads"])
@@ -234,7 +234,7 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
checkpointer = get_checkpointer(request)
thread_store = get_thread_store(request)
thread_id = body.thread_id or str(uuid.uuid4())
now = time.time()
now = now_iso()
# ``body.metadata`` is already stripped of server-reserved keys by
# ``ThreadCreateRequest._strip_reserved`` — see the model definition.
@@ -244,8 +244,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
return ThreadResponse(
thread_id=thread_id,
status=existing_record.get("status", "idle"),
created_at=str(existing_record.get("created_at", "")),
updated_at=str(existing_record.get("updated_at", "")),
created_at=coerce_iso(existing_record.get("created_at", "")),
updated_at=coerce_iso(existing_record.get("updated_at", "")),
metadata=existing_record.get("metadata", {}),
)
@@ -263,8 +263,6 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
# Write an empty checkpoint so state endpoints work immediately
config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
try:
from langgraph.checkpoint.base import empty_checkpoint
ckpt_metadata = {
"step": -1,
"source": "input",
@@ -282,8 +280,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
return ThreadResponse(
thread_id=thread_id,
status="idle",
created_at=str(now),
updated_at=str(now),
created_at=now,
updated_at=now,
metadata=body.metadata,
)
@@ -308,8 +306,11 @@ async def search_threads(body: ThreadSearchRequest, request: Request) -> list[Th
ThreadResponse(
thread_id=r["thread_id"],
status=r.get("status", "idle"),
created_at=r.get("created_at", ""),
updated_at=r.get("updated_at", ""),
# ``coerce_iso`` heals legacy unix-second values that
# ``MemoryThreadMetaStore`` historically wrote with ``time.time()``;
# SQL-backed rows already arrive as ISO strings and pass through.
created_at=coerce_iso(r.get("created_at", "")),
updated_at=coerce_iso(r.get("updated_at", "")),
metadata=r.get("metadata", {}),
values={"title": r["display_name"]} if r.get("display_name") else {},
interrupts={},
@@ -341,8 +342,8 @@ async def patch_thread(thread_id: str, body: ThreadPatchRequest, request: Reques
return ThreadResponse(
thread_id=thread_id,
status=record.get("status", "idle"),
created_at=str(record.get("created_at", "")),
updated_at=str(record.get("updated_at", "")),
created_at=coerce_iso(record.get("created_at", "")),
updated_at=coerce_iso(record.get("updated_at", "")),
metadata=record.get("metadata", {}),
)
@@ -382,8 +383,8 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
record = {
"thread_id": thread_id,
"status": "idle",
"created_at": ckpt_meta.get("created_at", ""),
"updated_at": ckpt_meta.get("updated_at", ckpt_meta.get("created_at", "")),
"created_at": coerce_iso(ckpt_meta.get("created_at", "")),
"updated_at": coerce_iso(ckpt_meta.get("updated_at", ckpt_meta.get("created_at", ""))),
"metadata": {k: v for k, v in ckpt_meta.items() if k not in ("created_at", "updated_at", "step", "source", "writes", "parents")},
}
@@ -397,8 +398,8 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
return ThreadResponse(
thread_id=thread_id,
status=status,
created_at=str(record.get("created_at", "")),
updated_at=str(record.get("updated_at", "")),
created_at=coerce_iso(record.get("created_at", "")),
updated_at=coerce_iso(record.get("updated_at", "")),
metadata=record.get("metadata", {}),
values=serialize_channel_values(channel_values),
)
@@ -449,10 +450,10 @@ async def get_thread_state(thread_id: str, request: Request) -> ThreadStateRespo
values=values,
next=next_tasks,
metadata=metadata,
checkpoint={"id": checkpoint_id, "ts": str(metadata.get("created_at", ""))},
checkpoint={"id": checkpoint_id, "ts": coerce_iso(metadata.get("created_at", ""))},
checkpoint_id=checkpoint_id,
parent_checkpoint_id=parent_checkpoint_id,
created_at=str(metadata.get("created_at", "")),
created_at=coerce_iso(metadata.get("created_at", "")),
tasks=tasks,
)
@@ -502,7 +503,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
channel_values.update(body.values)
checkpoint["channel_values"] = channel_values
metadata["updated_at"] = time.time()
metadata["updated_at"] = now_iso()
if body.as_node:
metadata["source"] = "update"
@@ -543,7 +544,7 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
next=[],
metadata=metadata,
checkpoint_id=new_checkpoint_id,
created_at=str(metadata.get("created_at", "")),
created_at=coerce_iso(metadata.get("created_at", "")),
)
@@ -610,7 +611,7 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
parent_checkpoint_id=parent_id,
metadata=user_meta,
values=values,
created_at=str(metadata.get("created_at", "")),
created_at=coerce_iso(metadata.get("created_at", "")),
next=next_tasks,
)
)
+144 -18
View File
@@ -5,7 +5,7 @@ import os
import stat
from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile
from pydantic import BaseModel
from pydantic import BaseModel, Field
from app.gateway.authz import require_permission
from app.gateway.deps import get_config
@@ -15,12 +15,14 @@ from deerflow.runtime.user_context import get_effective_user_id
from deerflow.sandbox.sandbox_provider import SandboxProvider, get_sandbox_provider
from deerflow.uploads.manager import (
PathTraversalError,
UnsafeUploadPathError,
delete_file_safe,
enrich_file_listing,
ensure_uploads_dir,
get_uploads_dir,
list_files_in_dir,
normalize_filename,
open_upload_file_no_symlink,
upload_artifact_url,
upload_virtual_path,
)
@@ -30,6 +32,11 @@ logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/threads/{thread_id}/uploads", tags=["uploads"])
UPLOAD_CHUNK_SIZE = 8192
DEFAULT_MAX_FILES = 10
DEFAULT_MAX_FILE_SIZE = 50 * 1024 * 1024
DEFAULT_MAX_TOTAL_SIZE = 100 * 1024 * 1024
class UploadResponse(BaseModel):
"""Response model for file upload."""
@@ -37,6 +44,15 @@ class UploadResponse(BaseModel):
success: bool
files: list[dict[str, str]]
message: str
skipped_files: list[str] = Field(default_factory=list)
class UploadLimits(BaseModel):
"""Application-level upload limits exposed to clients."""
max_files: int
max_file_size: int
max_total_size: int
def _make_file_sandbox_writable(file_path: os.PathLike[str] | str) -> None:
@@ -69,6 +85,72 @@ def _get_uploads_config_value(app_config: AppConfig, key: str, default: object)
return getattr(uploads_cfg, key, default)
def _get_upload_limit(app_config: AppConfig, key: str, default: int, *, legacy_key: str | None = None) -> int:
try:
value = _get_uploads_config_value(app_config, key, None)
if value is None and legacy_key is not None:
value = _get_uploads_config_value(app_config, legacy_key, None)
if value is None:
value = default
limit = int(value)
if limit <= 0:
raise ValueError
return limit
except Exception:
logger.warning("Invalid uploads.%s value; falling back to %d", key, default)
return default
def _get_upload_limits(app_config: AppConfig) -> UploadLimits:
return UploadLimits(
max_files=_get_upload_limit(app_config, "max_files", DEFAULT_MAX_FILES, legacy_key="max_file_count"),
max_file_size=_get_upload_limit(app_config, "max_file_size", DEFAULT_MAX_FILE_SIZE, legacy_key="max_single_file_size"),
max_total_size=_get_upload_limit(app_config, "max_total_size", DEFAULT_MAX_TOTAL_SIZE),
)
def _cleanup_uploaded_paths(paths: list[os.PathLike[str] | str]) -> None:
for path in reversed(paths):
try:
os.unlink(path)
except FileNotFoundError:
pass
except Exception:
logger.warning("Failed to clean up upload path after rejected request: %s", path, exc_info=True)
async def _write_upload_file_with_limits(
file: UploadFile,
*,
uploads_dir: os.PathLike[str] | str,
display_filename: str,
max_single_file_size: int,
max_total_size: int,
total_size: int,
) -> tuple[os.PathLike[str] | str, int, int]:
file_size = 0
file_path, fh = open_upload_file_no_symlink(uploads_dir, display_filename)
try:
while chunk := await file.read(UPLOAD_CHUNK_SIZE):
file_size += len(chunk)
total_size += len(chunk)
if file_size > max_single_file_size:
raise HTTPException(status_code=413, detail=f"File too large: {display_filename}")
if total_size > max_total_size:
raise HTTPException(status_code=413, detail="Total upload size too large")
fh.write(chunk)
except Exception:
fh.close()
try:
os.unlink(file_path)
except FileNotFoundError:
pass
raise
else:
fh.close()
return file_path, file_size, total_size
def _auto_convert_documents_enabled(app_config: AppConfig) -> bool:
"""Return whether automatic host-side document conversion is enabled.
@@ -85,31 +167,41 @@ def _auto_convert_documents_enabled(app_config: AppConfig) -> bool:
@router.post("", response_model=UploadResponse)
@require_permission("threads", "write", owner_check=True, require_existing=True)
@require_permission("threads", "write", owner_check=True, require_existing=False)
async def upload_files(
thread_id: str,
request: Request,
files: list[UploadFile] = File(...),
app_config: AppConfig = Depends(get_config),
config: AppConfig = Depends(get_config),
) -> UploadResponse:
"""Upload multiple files to a thread's uploads directory."""
if not files:
raise HTTPException(status_code=400, detail="No files provided")
limits = _get_upload_limits(config)
if len(files) > limits.max_files:
raise HTTPException(status_code=413, detail=f"Too many files: maximum is {limits.max_files}")
try:
uploads_dir = ensure_uploads_dir(thread_id)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id, user_id=get_effective_user_id())
uploaded_files = []
written_paths = []
sandbox_sync_targets = []
skipped_files = []
total_size = 0
sandbox_provider = get_sandbox_provider(app_config)
sandbox_provider = get_sandbox_provider()
sync_to_sandbox = not _uses_thread_data_mounts(sandbox_provider)
sandbox = None
if sync_to_sandbox:
sandbox_id = sandbox_provider.acquire(thread_id)
sandbox = sandbox_provider.get(sandbox_id)
auto_convert_documents = _auto_convert_documents_enabled(app_config)
if sandbox is None:
raise HTTPException(status_code=500, detail="Failed to acquire sandbox")
auto_convert_documents = _auto_convert_documents_enabled(config)
for file in files:
if not file.filename:
@@ -122,35 +214,40 @@ async def upload_files(
continue
try:
content = await file.read()
file_path = uploads_dir / safe_filename
file_path.write_bytes(content)
file_path, file_size, total_size = await _write_upload_file_with_limits(
file,
uploads_dir=uploads_dir,
display_filename=safe_filename,
max_single_file_size=limits.max_file_size,
max_total_size=limits.max_total_size,
total_size=total_size,
)
written_paths.append(file_path)
virtual_path = upload_virtual_path(safe_filename)
if sync_to_sandbox and sandbox is not None:
_make_file_sandbox_writable(file_path)
sandbox.update_file(virtual_path, content)
if sync_to_sandbox:
sandbox_sync_targets.append((file_path, virtual_path))
file_info = {
"filename": safe_filename,
"size": str(len(content)),
"size": str(file_size),
"path": str(sandbox_uploads / safe_filename),
"virtual_path": virtual_path,
"artifact_url": upload_artifact_url(thread_id, safe_filename),
}
logger.info(f"Saved file: {safe_filename} ({len(content)} bytes) to {file_info['path']}")
logger.info(f"Saved file: {safe_filename} ({file_size} bytes) to {file_info['path']}")
file_ext = file_path.suffix.lower()
if auto_convert_documents and file_ext in CONVERTIBLE_EXTENSIONS:
md_path = await convert_file_to_markdown(file_path)
if md_path:
written_paths.append(md_path)
md_virtual_path = upload_virtual_path(md_path.name)
if sync_to_sandbox and sandbox is not None:
_make_file_sandbox_writable(md_path)
sandbox.update_file(md_virtual_path, md_path.read_bytes())
if sync_to_sandbox:
sandbox_sync_targets.append((md_path, md_virtual_path))
file_info["markdown_file"] = md_path.name
file_info["markdown_path"] = str(sandbox_uploads / md_path.name)
@@ -159,17 +256,46 @@ async def upload_files(
uploaded_files.append(file_info)
except HTTPException as e:
_cleanup_uploaded_paths(written_paths)
raise e
except UnsafeUploadPathError as e:
logger.warning("Skipping upload with unsafe destination %s: %s", file.filename, e)
skipped_files.append(safe_filename)
continue
except Exception as e:
logger.error(f"Failed to upload {file.filename}: {e}")
_cleanup_uploaded_paths(written_paths)
raise HTTPException(status_code=500, detail=f"Failed to upload {file.filename}: {str(e)}")
if sync_to_sandbox:
for file_path, virtual_path in sandbox_sync_targets:
_make_file_sandbox_writable(file_path)
sandbox.update_file(virtual_path, file_path.read_bytes())
message = f"Successfully uploaded {len(uploaded_files)} file(s)"
if skipped_files:
message += f"; skipped {len(skipped_files)} unsafe file(s)"
return UploadResponse(
success=True,
success=not skipped_files,
files=uploaded_files,
message=f"Successfully uploaded {len(uploaded_files)} file(s)",
message=message,
skipped_files=skipped_files,
)
@router.get("/limits", response_model=UploadLimits)
@require_permission("threads", "read", owner_check=True)
async def get_upload_limits(
thread_id: str,
request: Request,
config: AppConfig = Depends(get_config),
) -> UploadLimits:
"""Return upload limits used by the gateway for this thread."""
return _get_upload_limits(config)
@router.get("/list", response_model=dict)
@require_permission("threads", "read", owner_check=True)
async def list_uploaded_files(thread_id: str, request: Request) -> dict:
+41 -37
View File
@@ -8,18 +8,16 @@ frames, and consuming stream bridge events. Router modules
from __future__ import annotations
import asyncio
import dataclasses
import json
import logging
import re
import time
from collections.abc import Mapping
from typing import Any
from fastapi import HTTPException, Request
from langchain_core.messages import HumanMessage
from app.gateway.deps import get_run_context, get_run_manager, get_run_store, get_stream_bridge
from app.gateway.deps import get_run_context, get_run_manager, get_stream_bridge
from app.gateway.utils import sanitize_log_param
from deerflow.runtime import (
END_SENTINEL,
@@ -100,6 +98,44 @@ def normalize_input(raw_input: dict[str, Any] | None) -> dict[str, Any]:
_DEFAULT_ASSISTANT_ID = "lead_agent"
# Whitelist of run-context keys that the langgraph-compat layer forwards from
# ``body.context`` into the run config. ``config["context"]`` exists in
# LangGraph >=0.6, but these values must be written to both ``configurable``
# (for legacy ``_get_runtime_config`` consumers) and ``context`` because
# LangGraph >=1.1.9 no longer makes ``ToolRuntime.context`` fall back to
# ``configurable`` for consumers like ``setup_agent``.
_CONTEXT_CONFIGURABLE_KEYS: frozenset[str] = frozenset(
{
"model_name",
"mode",
"thinking_enabled",
"reasoning_effort",
"is_plan_mode",
"subagent_enabled",
"max_concurrent_subagents",
"agent_name",
"is_bootstrap",
}
)
def merge_run_context_overrides(config: dict[str, Any], context: Mapping[str, Any] | None) -> None:
"""Merge whitelisted keys from ``body.context`` into both ``config['configurable']``
and ``config['context']`` so they are visible to legacy configurable readers and
to LangGraph ``ToolRuntime.context`` consumers (e.g. the ``setup_agent`` tool
see issue #2677)."""
if not context:
return
configurable = config.setdefault("configurable", {})
runtime_context = config.setdefault("context", {})
for key in _CONTEXT_CONFIGURABLE_KEYS:
if key in context:
if isinstance(configurable, dict):
configurable.setdefault(key, context[key])
if isinstance(runtime_context, dict):
runtime_context.setdefault(key, context[key])
def resolve_agent_factory(assistant_id: str | None):
"""Resolve the agent factory callable from config.
@@ -213,21 +249,6 @@ async def start_run(
disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_
# Resolve follow_up_to_run_id: explicit from request, or auto-detect from latest successful run
follow_up_to_run_id = getattr(body, "follow_up_to_run_id", None)
if follow_up_to_run_id is None:
run_store = get_run_store(request)
try:
recent_runs = await run_store.list_by_thread(thread_id, limit=1)
if recent_runs and recent_runs[0].get("status") == "success":
follow_up_to_run_id = recent_runs[0]["run_id"]
except Exception:
pass # Don't block run creation
# Enrich base context with per-run field
if follow_up_to_run_id:
run_ctx = dataclasses.replace(run_ctx, follow_up_to_run_id=follow_up_to_run_id)
try:
record = await run_mgr.create_or_reject(
thread_id,
@@ -236,7 +257,6 @@ async def start_run(
metadata=body.metadata or {},
kwargs={"input": body.input, "config": body.config},
multitask_strategy=body.multitask_strategy,
follow_up_to_run_id=follow_up_to_run_id,
)
except ConflictError as exc:
raise HTTPException(status_code=409, detail=str(exc)) from exc
@@ -263,27 +283,11 @@ async def start_run(
graph_input = normalize_input(body.input)
config = build_run_config(thread_id, body.config, body.metadata, assistant_id=body.assistant_id)
# Merge DeerFlow-specific context overrides into configurable.
# Merge DeerFlow-specific context overrides into both ``configurable`` and ``context``.
# The ``context`` field is a custom extension for the langgraph-compat layer
# that carries agent configuration (model_name, thinking_enabled, etc.).
# Only agent-relevant keys are forwarded; unknown keys (e.g. thread_id) are ignored.
context = getattr(body, "context", None)
if context:
_CONTEXT_CONFIGURABLE_KEYS = {
"model_name",
"mode",
"thinking_enabled",
"reasoning_effort",
"is_plan_mode",
"subagent_enabled",
"max_concurrent_subagents",
"agent_name",
"is_bootstrap",
}
configurable = config.setdefault("configurable", {})
for key in _CONTEXT_CONFIGURABLE_KEYS:
if key in context:
configurable.setdefault(key, context[key])
merge_run_context_overrides(config, getattr(body, "context", None))
stream_modes = normalize_stream_modes(body.stream_mode)
+16 -24
View File
@@ -34,50 +34,42 @@ _LOG_FMT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
_LOG_DATEFMT = "%Y-%m-%d %H:%M:%S"
def _logging_level_from_config(name: str) -> int:
"""Map ``config.yaml`` ``log_level`` string to a ``logging`` level constant."""
mapping = logging.getLevelNamesMapping()
return mapping.get((name or "info").strip().upper(), logging.INFO)
def _setup_logging(log_level: int = logging.INFO) -> None:
"""Route logs to ``debug.log`` using *log_level* for the initial root/file setup.
This configures the root logger and the ``debug.log`` file handler so logs do
not print on the interactive console. It is idempotent: any pre-existing
handlers on the root logger (e.g. installed by ``logging.basicConfig`` in
transitively imported modules) are removed so the debug session output only
lands in ``debug.log``.
def _setup_logging(log_level: str) -> None:
"""Send application logs to ``debug.log`` at *log_level*; do not print them on the console.
Idempotent: any pre-existing handlers on the root logger (e.g. installed by
``logging.basicConfig`` in transitively imported modules) are removed so the
debug session output only lands in ``debug.log``.
Note: later config-driven logging adjustments may change named logger
verbosity without raising the root logger or file-handler thresholds set
here, so the eventual contents of ``debug.log`` may not be filtered solely by
this function's ``log_level`` argument.
"""
level = _logging_level_from_config(log_level)
root = logging.root
for h in list(root.handlers):
root.removeHandler(h)
h.close()
root.setLevel(level)
root.setLevel(log_level)
file_handler = logging.FileHandler("debug.log", mode="a", encoding="utf-8")
file_handler.setLevel(level)
file_handler.setLevel(log_level)
file_handler.setFormatter(logging.Formatter(_LOG_FMT, datefmt=_LOG_DATEFMT))
root.addHandler(file_handler)
def _update_logging_level(log_level: str) -> None:
"""Update the root logger and existing handlers to *log_level*."""
level = _logging_level_from_config(log_level)
root = logging.root
root.setLevel(level)
for handler in root.handlers:
handler.setLevel(level)
async def main():
# Install file logging first so warnings emitted while loading config do not
# leak onto the interactive terminal via Python's lastResort handler.
_setup_logging("info")
_setup_logging()
from deerflow.config import get_app_config
from deerflow.config.app_config import apply_logging_level
app_config = get_app_config()
_update_logging_level(app_config.log_level)
apply_logging_level(app_config.log_level)
# Delay the rest of the deerflow imports until *after* logging is installed
# so that any import-time side effects (e.g. deerflow.agents starts a
+13 -6
View File
@@ -259,6 +259,8 @@ sandbox:
When you configure `sandbox.mounts`, DeerFlow exposes those `container_path` values in the agent prompt so the agent can discover and operate on mounted directories directly instead of assuming everything must live under `/mnt/user-data`.
For bare-metal Docker sandbox runs that use localhost, DeerFlow binds the sandbox HTTP port to `127.0.0.1` by default so it is not exposed on every host interface. Docker-outside-of-Docker deployments that connect through `host.docker.internal` keep the broad legacy bind for compatibility. Set `DEER_FLOW_SANDBOX_BIND_HOST` explicitly if your deployment needs a different bind address.
### Skills
Configure the skills directory for specialized workflows:
@@ -319,11 +321,16 @@ models:
- `DEEPSEEK_API_KEY` - DeepSeek API key
- `NOVITA_API_KEY` - Novita API key (OpenAI-compatible endpoint)
- `TAVILY_API_KEY` - Tavily search API key
- `DEER_FLOW_PROJECT_ROOT` - Project root for relative runtime paths
- `DEER_FLOW_CONFIG_PATH` - Custom config file path
- `DEER_FLOW_EXTENSIONS_CONFIG_PATH` - Custom extensions config file path
- `DEER_FLOW_HOME` - Runtime state directory (defaults to `.deer-flow` under the project root)
- `DEER_FLOW_SKILLS_PATH` - Skills directory when `skills.path` is omitted
- `GATEWAY_ENABLE_DOCS` - Set to `false` to disable Swagger UI (`/docs`), ReDoc (`/redoc`), and OpenAPI schema (`/openapi.json`) endpoints (default: `true`)
## Configuration Location
The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`), not in the backend directory.
The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`). Set `DEER_FLOW_PROJECT_ROOT` when the process may start from another working directory, or set `DEER_FLOW_CONFIG_PATH` to point at a specific file.
## Configuration Priority
@@ -331,12 +338,12 @@ DeerFlow searches for configuration in this order:
1. Path specified in code via `config_path` argument
2. Path from `DEER_FLOW_CONFIG_PATH` environment variable
3. `config.yaml` in current working directory (typically `backend/` when running)
4. `config.yaml` in parent directory (project root: `deer-flow/`)
3. `config.yaml` under `DEER_FLOW_PROJECT_ROOT`, or under the current working directory when `DEER_FLOW_PROJECT_ROOT` is unset
4. Legacy backend/repository-root locations for monorepo compatibility
## Best Practices
1. **Place `config.yaml` in project root** - Not in `backend/` directory
1. **Place `config.yaml` in project root** - Set `DEER_FLOW_PROJECT_ROOT` if the runtime starts elsewhere
2. **Never commit `config.yaml`** - It's already in `.gitignore`
3. **Use environment variables for secrets** - Don't hardcode API keys
4. **Keep `config.example.yaml` updated** - Document all new options
@@ -347,7 +354,7 @@ DeerFlow searches for configuration in this order:
### "Config file not found"
- Ensure `config.yaml` exists in the **project root** directory (`deer-flow/config.yaml`)
- The backend searches parent directory by default, so root location is preferred
- If the runtime starts outside the project root, set `DEER_FLOW_PROJECT_ROOT`
- Alternatively, set `DEER_FLOW_CONFIG_PATH` environment variable to custom location
### "Invalid API key"
@@ -357,7 +364,7 @@ DeerFlow searches for configuration in this order:
### "Skills not loading"
- Check that `deer-flow/skills/` directory exists
- Verify skills have valid `SKILL.md` files
- Check `skills.path` configuration if using custom path
- Check `skills.path` or `DEER_FLOW_SKILLS_PATH` if using a custom path
### "Docker sandbox fails to start"
- Ensure Docker is running
+20 -2
View File
@@ -22,6 +22,8 @@ POST /api/threads/{thread_id}/uploads
**请求体:** `multipart/form-data`
- `files`: 一个或多个文件
网关会在应用层限制上传规模,默认最多 10 个文件、单文件 50 MiB、单次请求总计 100 MiB。可通过 `config.yaml``uploads.max_files``uploads.max_file_size``uploads.max_total_size` 调整;前端会读取同一组限制并在选择文件时提示,超过限制时后端返回 `413 Payload Too Large`
**响应:**
```json
{
@@ -48,7 +50,23 @@ POST /api/threads/{thread_id}/uploads
- `virtual_path`: Agent 在沙箱中使用的虚拟路径
- `artifact_url`: 前端通过 HTTP 访问文件的 URL
### 2. 列出已上传文件
### 2. 查询上传限制
```
GET /api/threads/{thread_id}/uploads/limits
```
返回网关当前生效的上传限制,供前端在用户选择文件前提示和拦截。
**响应:**
```json
{
"max_files": 10,
"max_file_size": 52428800,
"max_total_size": 104857600
}
```
### 3. 列出已上传文件
```
GET /api/threads/{thread_id}/uploads/list
```
@@ -71,7 +89,7 @@ GET /api/threads/{thread_id}/uploads/list
}
```
### 3. 删除文件
### 4. 删除文件
```
DELETE /api/threads/{thread_id}/uploads/{filename}
```
-343
View File
@@ -1,343 +0,0 @@
# DeerFlow 后端拆分设计文档:Harness + App
> 状态:Draft
> 作者:DeerFlow Team
> 日期:2026-03-13
## 1. 背景与动机
DeerFlow 后端当前是一个单一 Python 包(`src.*`),包含了从底层 agent 编排到上层用户产品的所有代码。随着项目发展,这种结构带来了几个问题:
- **复用困难**:其他产品(CLI 工具、Slack bot、第三方集成)想用 agent 能力,必须依赖整个后端,包括 FastAPI、IM SDK 等不需要的依赖
- **职责模糊**:agent 编排逻辑和用户产品逻辑混在同一个 `src/` 下,边界不清晰
- **依赖膨胀**LangGraph Server 运行时不需要 FastAPI/uvicorn/Slack SDK,但当前必须安装全部依赖
本文档提出将后端拆分为两部分:**deerflow-harness**(可发布的 agent 框架包)和 **app**(不打包的用户产品代码)。
## 2. 核心概念
### 2.1 Harness(线束/框架层)
Harness 是 agent 的构建与编排框架,回答 **"如何构建和运行 agent"** 的问题:
- Agent 工厂与生命周期管理
- Middleware pipeline
- 工具系统(内置工具 + MCP + 社区工具)
- 沙箱执行环境
- 子 agent 委派
- 记忆系统
- 技能加载与注入
- 模型工厂
- 配置系统
**Harness 是一个可发布的 Python 包**(`deerflow-harness`),可以独立安装和使用。
**Harness 的设计原则**:对上层应用完全无感知。它不知道也不关心谁在调用它——可以是 Web App、CLI、Slack Bot、或者一个单元测试。
### 2.2 App(应用层)
App 是面向用户的产品代码,回答 **"如何将 agent 呈现给用户"** 的问题:
- Gateway APIFastAPI REST 接口)
- IM Channels(飞书、Slack、Telegram 集成)
- Custom Agent 的 CRUD 管理
- 文件上传/下载的 HTTP 接口
**App 不打包、不发布**,它是 DeerFlow 项目内部的应用代码,直接运行。
**App 依赖 Harness,但 Harness 不依赖 App。**
### 2.3 边界划分
| 模块 | 归属 | 说明 |
|------|------|------|
| `config/` | Harness | 配置系统是基础设施 |
| `reflection/` | Harness | 动态模块加载工具 |
| `utils/` | Harness | 通用工具函数 |
| `agents/` | Harness | Agent 工厂、middleware、state、memory |
| `subagents/` | Harness | 子 agent 委派系统 |
| `sandbox/` | Harness | 沙箱执行环境 |
| `tools/` | Harness | 工具注册与发现 |
| `mcp/` | Harness | MCP 协议集成 |
| `skills/` | Harness | 技能加载、解析、定义 schema |
| `models/` | Harness | LLM 模型工厂 |
| `community/` | Harness | 社区工具(tavily、jina 等) |
| `client.py` | Harness | 嵌入式 Python 客户端 |
| `gateway/` | App | FastAPI REST API |
| `channels/` | App | IM 平台集成 |
**关于 Custom Agents**agent 定义格式(`config.yaml` + `SOUL.md` schema)由 Harness 层的 `config/agents_config.py` 定义,但文件的存储、CRUD、发现机制由 App 层的 `gateway/routers/agents.py` 负责。
## 3. 目标架构
### 3.1 目录结构
```
backend/
├── packages/
│ └── harness/
│ ├── pyproject.toml # deerflow-harness 包定义
│ └── deerflow/ # Python 包根(import 前缀: deerflow.*
│ ├── __init__.py
│ ├── config/
│ ├── reflection/
│ ├── utils/
│ ├── agents/
│ │ ├── lead_agent/
│ │ ├── middlewares/
│ │ ├── memory/
│ │ ├── checkpointer/
│ │ └── thread_state.py
│ ├── subagents/
│ ├── sandbox/
│ ├── tools/
│ ├── mcp/
│ ├── skills/
│ ├── models/
│ ├── community/
│ └── client.py
├── app/ # 不打包(import 前缀: app.*
│ ├── __init__.py
│ ├── gateway/
│ │ ├── __init__.py
│ │ ├── app.py
│ │ ├── config.py
│ │ ├── path_utils.py
│ │ └── routers/
│ └── channels/
│ ├── __init__.py
│ ├── base.py
│ ├── manager.py
│ ├── service.py
│ ├── store.py
│ ├── message_bus.py
│ ├── feishu.py
│ ├── slack.py
│ └── telegram.py
├── pyproject.toml # uv workspace root
├── langgraph.json
├── tests/
├── docs/
└── Makefile
```
### 3.2 Import 规则
两个层使用不同的 import 前缀,职责边界一目了然:
```python
# ---------------------------------------------------------------
# Harness 内部互相引用(deerflow.* 前缀)
# ---------------------------------------------------------------
from deerflow.agents import make_lead_agent
from deerflow.models import create_chat_model
from deerflow.config import get_app_config
from deerflow.tools import get_available_tools
# ---------------------------------------------------------------
# App 内部互相引用(app.* 前缀)
# ---------------------------------------------------------------
from app.gateway.app import app
from app.gateway.routers.uploads import upload_files
from app.channels.service import start_channel_service
# ---------------------------------------------------------------
# App 调用 Harness(单向依赖,Harness 永远不 import app
# ---------------------------------------------------------------
from deerflow.agents import make_lead_agent
from deerflow.models import create_chat_model
from deerflow.skills import load_skills
from deerflow.config.extensions_config import get_extensions_config
```
**App 调用 Harness 示例 — Gateway 中启动 agent**
```python
# app/gateway/routers/chat.py
from deerflow.agents.lead_agent.agent import make_lead_agent
from deerflow.models import create_chat_model
from deerflow.config import get_app_config
async def create_chat_session(thread_id: str, model_name: str):
config = get_app_config()
model = create_chat_model(name=model_name)
agent = make_lead_agent(config=...)
# ... 使用 agent 处理用户消息
```
**App 调用 Harness 示例 — Channel 中查询 skills**
```python
# app/channels/manager.py
from deerflow.skills import load_skills
from deerflow.agents.memory.updater import get_memory_data
def handle_status_command():
skills = load_skills(enabled_only=True)
memory = get_memory_data()
return f"Skills: {len(skills)}, Memory facts: {len(memory.get('facts', []))}"
```
**禁止方向**:Harness 代码中绝不能出现 `from app.``import app.`
### 3.3 为什么 App 不打包
| 方面 | 打包(放 packages/ 下) | 不打包(放 backend/app/ |
|------|------------------------|--------------------------|
| 命名空间 | 需要 pkgutil `extend_path` 合并,或独立前缀 | 天然独立,`app.*` vs `deerflow.*` |
| 发布需求 | 没有——App 是项目内部代码 | 不需要 pyproject.toml |
| 复杂度 | 需要管理两个包的构建、版本、依赖声明 | 直接运行,零额外配置 |
| 运行方式 | `pip install deerflow-app` | `PYTHONPATH=. uvicorn app.gateway.app:app` |
App 的唯一消费者是 DeerFlow 项目自身,没有独立发布的需求。放在 `backend/app/` 下作为普通 Python 包,通过 `PYTHONPATH` 或 editable install 让 Python 找到即可。
### 3.4 依赖关系
```
┌─────────────────────────────────────┐
│ app/ (不打包,直接运行) │
│ ├── fastapi, uvicorn │
│ ├── slack-sdk, lark-oapi, ... │
│ └── import deerflow.* │
└──────────────┬──────────────────────┘
┌─────────────────────────────────────┐
│ deerflow-harness (可发布的包) │
│ ├── langgraph, langchain │
│ ├── markitdown, pydantic, ... │
│ └── 零 app 依赖 │
└─────────────────────────────────────┘
```
**依赖分类**
| 分类 | 依赖包 |
|------|--------|
| Harness only | agent-sandbox, langchain*, langgraph*, markdownify, markitdown, pydantic, pyyaml, readabilipy, tavily-python, firecrawl-py, tiktoken, ddgs, duckdb, httpx, kubernetes, dotenv |
| App only | fastapi, uvicorn, sse-starlette, python-multipart, lark-oapi, slack-sdk, python-telegram-bot, markdown-to-mrkdwn |
| Shared | langgraph-sdkchannels 用 HTTP client, pydantic, httpx |
### 3.5 Workspace 配置
`backend/pyproject.toml`workspace root):
```toml
[project]
name = "deer-flow"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = ["deerflow-harness"]
[dependency-groups]
dev = ["pytest>=8.0.0", "ruff>=0.14.11"]
# App 的额外依赖(fastapi 等)也声明在 workspace root,因为 app 不打包
app = ["fastapi", "uvicorn", "sse-starlette", "python-multipart"]
channels = ["lark-oapi", "slack-sdk", "python-telegram-bot"]
[tool.uv.workspace]
members = ["packages/harness"]
[tool.uv.sources]
deerflow-harness = { workspace = true }
```
## 4. 当前的跨层依赖问题
在拆分之前,需要先解决 `client.py` 中两处从 harness 到 app 的反向依赖:
### 4.1 `_validate_skill_frontmatter`
```python
# client.py — harness 导入了 app 层代码
from src.gateway.routers.skills import _validate_skill_frontmatter
```
**解决方案**:将该函数提取到 `deerflow/skills/validation.py`。这是一个纯逻辑函数(解析 YAML frontmatter、校验字段),与 FastAPI 无关。
### 4.2 `CONVERTIBLE_EXTENSIONS` + `convert_file_to_markdown`
```python
# client.py — harness 导入了 app 层代码
from src.gateway.routers.uploads import CONVERTIBLE_EXTENSIONS, convert_file_to_markdown
```
**解决方案**:将它们提取到 `deerflow/utils/file_conversion.py`。仅依赖 `markitdown` + `pathlib`,是通用工具函数。
## 5. 基础设施变更
### 5.1 LangGraph Server
LangGraph Server 只需要 harness 包。`langgraph.json` 更新:
```json
{
"dependencies": ["./packages/harness"],
"graphs": {
"lead_agent": "deerflow.agents:make_lead_agent"
},
"checkpointer": {
"path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
}
}
```
### 5.2 Gateway API
```bash
# serve.sh / Makefile
# PYTHONPATH 包含 backend/ 根目录,使 app.* 和 deerflow.* 都能被找到
PYTHONPATH=. uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
```
### 5.3 Nginx
无需变更(只做 URL 路由,不涉及 Python 模块路径)。
### 5.4 Docker
Dockerfile 中的 module 引用从 `src.` 改为 `deerflow.` / `app.``COPY` 命令需覆盖 `packages/``app/` 目录。
## 6. 实施计划
分 3 个 PR 递进执行:
### PR 1:提取共享工具函数(Low Risk)
1. 创建 `src/skills/validation.py`,从 `gateway/routers/skills.py` 提取 `_validate_skill_frontmatter`
2. 创建 `src/utils/file_conversion.py`,从 `gateway/routers/uploads.py` 提取文件转换逻辑
3. 更新 `client.py``gateway/routers/skills.py``gateway/routers/uploads.py` 的 import
4. 运行全部测试确认无回归
### PR 2Rename + 物理拆分(High Risk,原子操作)
1. 创建 `packages/harness/` 目录,创建 `pyproject.toml`
2. `git mv` 将 harness 相关模块从 `src/` 移入 `packages/harness/deerflow/`
3. `git mv` 将 app 相关模块从 `src/` 移入 `app/`
4. 全局替换 import
- harness 模块:`src.*``deerflow.*`(所有 `.py` 文件、`langgraph.json`、测试、文档)
- app 模块:`src.gateway.*``app.gateway.*``src.channels.*``app.channels.*`
5. 更新 workspace root `pyproject.toml`
6. 更新 `langgraph.json``Makefile``Dockerfile`
7. `uv sync` + 全部测试 + 手动验证服务启动
### PR 3:边界检查 + 文档(Low Risk)
1. 添加 lint 规则:检查 harness 不 import app 模块
2. 更新 `CLAUDE.md``README.md`
## 7. 风险与缓解
| 风险 | 影响 | 缓解措施 |
|------|------|----------|
| 全局 rename 误伤 | 字符串中的 `src` 被错误替换 | 正则精确匹配 `\bsrc\.`review diff |
| LangGraph Server 找不到模块 | 服务启动失败 | `langgraph.json``dependencies` 指向正确的 harness 包路径 |
| App 的 `PYTHONPATH` 缺失 | Gateway/Channel 启动 import 报错 | Makefile/Docker 统一设置 `PYTHONPATH=.` |
| `config.yaml` 中的 `use` 字段引用旧路径 | 运行时模块解析失败 | `config.yaml` 中的 `use` 字段同步更新为 `deerflow.*` |
| 测试中 `sys.path` 混乱 | 测试失败 | 用 editable install`uv sync`)确保 deerflow 可导入,`conftest.py` 中添加 `app/``sys.path` |
## 8. 未来演进
- **独立发布**harness 可以发布到内部 PyPI,让其他项目直接 `pip install deerflow-harness`
- **插件化 App**:不同的 app(web、CLI、bot)可以各自独立,都依赖同一个 harness
- **更细粒度拆分**:如果 harness 内部模块继续增长,可以进一步拆分(如 `deerflow-sandbox``deerflow-mcp`
+14 -8
View File
@@ -23,6 +23,9 @@ DeerFlow uses a YAML configuration file that should be placed in the **project r
# Option A: Set environment variables (recommended)
export OPENAI_API_KEY="your-key-here"
# Optional: pin the project root when running from another directory
export DEER_FLOW_PROJECT_ROOT="/path/to/deer-flow"
# Option B: Edit config.yaml directly
vim config.yaml # or your preferred editor
```
@@ -35,17 +38,20 @@ DeerFlow uses a YAML configuration file that should be placed in the **project r
## Important Notes
- **Location**: `config.yaml` should be in `deer-flow/` (project root), not `deer-flow/backend/`
- **Location**: `config.yaml` should be in `deer-flow/` (project root)
- **Git**: `config.yaml` is automatically ignored by git (contains secrets)
- **Priority**: If both `backend/config.yaml` and `../config.yaml` exist, backend version takes precedence
- **Runtime root**: Set `DEER_FLOW_PROJECT_ROOT` if DeerFlow may start from outside the project root
- **Runtime data**: State defaults to `.deer-flow` under the project root; set `DEER_FLOW_HOME` to move it
- **Skills**: Skills default to `skills/` under the project root; set `DEER_FLOW_SKILLS_PATH` or `skills.path` to move them
## Configuration File Locations
The backend searches for `config.yaml` in this order:
1. `DEER_FLOW_CONFIG_PATH` environment variable (if set)
2. `backend/config.yaml` (current directory when running from backend/)
3. `deer-flow/config.yaml` (parent directory - **recommended location**)
1. Explicit `config_path` argument from code
2. `DEER_FLOW_CONFIG_PATH` environment variable (if set)
3. `config.yaml` under `DEER_FLOW_PROJECT_ROOT`, or the current working directory when `DEER_FLOW_PROJECT_ROOT` is unset
4. Legacy backend/repository-root locations for monorepo compatibility
**Recommended**: Place `config.yaml` in project root (`deer-flow/config.yaml`).
@@ -77,8 +83,8 @@ python -c "from deerflow.config.app_config import AppConfig; print(AppConfig.res
If it can't find the config:
1. Ensure you've copied `config.example.yaml` to `config.yaml`
2. Verify you're in the correct directory
3. Check the file exists: `ls -la ../config.yaml`
2. Verify you're in the project root, or set `DEER_FLOW_PROJECT_ROOT`
3. Check the file exists: `ls -la config.yaml`
### Permission denied
@@ -89,4 +95,4 @@ chmod 600 ../config.yaml # Protect sensitive configuration
## See Also
- [Configuration Guide](CONFIGURATION.md) - Detailed configuration options
- [Architecture Overview](../CLAUDE.md) - System architecture
- [Architecture Overview](../CLAUDE.md) - System architecture
+1 -1
View File
@@ -12,6 +12,6 @@
"path": "./app/gateway/langgraph_auth.py:auth"
},
"checkpointer": {
"path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
"path": "./packages/harness/deerflow/runtime/checkpointer/async_provider.py:make_checkpointer"
}
}
@@ -254,9 +254,11 @@ def _assemble_from_features(
from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
chain.append(ViewImageMiddleware())
from deerflow.tools.builtins import view_image_tool
extra_tools.append(view_image_tool)
if feat.sandbox is not False:
from deerflow.tools.builtins import view_image_tool
extra_tools.append(view_image_tool)
# --- [11] Subagent ---
if feat.subagent is not False:
@@ -3,7 +3,6 @@ import logging
from langchain.agents import create_agent
from langchain.agents.middleware import AgentMiddleware
from langchain_core.runnables import RunnableConfig
from langgraph.graph.state import CompiledStateGraph
from deerflow.agents.lead_agent.prompt import apply_prompt_template
from deerflow.agents.memory.summarization_hook import memory_flush_hook
@@ -19,8 +18,7 @@ from deerflow.agents.middlewares.tool_error_handling_middleware import build_lea
from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
from deerflow.agents.thread_state import ThreadState
from deerflow.config.agents_config import load_agent_config, validate_agent_name
from deerflow.config.app_config import AppConfig
from deerflow.config.deer_flow_context import DeerFlowContext
from deerflow.config.app_config import AppConfig, get_app_config
from deerflow.models import create_chat_model
logger = logging.getLogger(__name__)
@@ -35,8 +33,9 @@ def _get_runtime_config(config: RunnableConfig) -> dict:
return cfg
def _resolve_model_name(app_config: AppConfig, requested_model_name: str | None = None) -> str:
def _resolve_model_name(requested_model_name: str | None = None, *, app_config: AppConfig | None = None) -> str:
"""Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
app_config = app_config or get_app_config()
default_model_name = app_config.models[0].name if app_config.models else None
if default_model_name is None:
raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")
@@ -49,9 +48,10 @@ def _resolve_model_name(app_config: AppConfig, requested_model_name: str | None
return default_model_name
def _create_summarization_middleware(app_config: AppConfig) -> DeerFlowSummarizationMiddleware | None:
def _create_summarization_middleware(*, app_config: AppConfig | None = None) -> DeerFlowSummarizationMiddleware | None:
"""Create and configure the summarization middleware from config."""
config = app_config.summarization
resolved_app_config = app_config or get_app_config()
config = resolved_app_config.summarization
if not config.enabled:
return None
@@ -72,9 +72,9 @@ def _create_summarization_middleware(app_config: AppConfig) -> DeerFlowSummariza
# as middleware rather than lead_agent (SummarizationMiddleware is a
# LangChain built-in, so we tag the model at creation time).
if config.model_name:
model = create_chat_model(name=config.model_name, thinking_enabled=False, app_config=app_config)
model = create_chat_model(name=config.model_name, thinking_enabled=False, app_config=resolved_app_config)
else:
model = create_chat_model(thinking_enabled=False, app_config=app_config)
model = create_chat_model(thinking_enabled=False, app_config=resolved_app_config)
model = model.with_config(tags=["middleware:summarize"])
# Prepare kwargs
@@ -91,17 +91,13 @@ def _create_summarization_middleware(app_config: AppConfig) -> DeerFlowSummariza
kwargs["summary_prompt"] = config.summary_prompt
hooks: list[BeforeSummarizationHook] = []
if app_config.memory.enabled:
if resolved_app_config.memory.enabled:
hooks.append(memory_flush_hook)
# The logic below relies on two assumptions holding true: this factory is
# the sole entry point for DeerFlowSummarizationMiddleware, and the runtime
# config is not expected to change after startup.
try:
skills_container_path = app_config.skills.container_path or "/mnt/skills"
except Exception:
logger.exception("Failed to resolve skills container path; falling back to default")
skills_container_path = "/mnt/skills"
skills_container_path = resolved_app_config.skills.container_path or "/mnt/skills"
return DeerFlowSummarizationMiddleware(
**kwargs,
@@ -240,17 +236,16 @@ Being proactive with task management demonstrates thoroughness and ensures all r
# ToolErrorHandlingMiddleware should be before ClarificationMiddleware to convert tool exceptions to ToolMessages
# ClarificationMiddleware should be last to intercept clarification requests after model calls
def _build_middlewares(
app_config: AppConfig,
config: RunnableConfig,
*,
model_name: str | None,
agent_name: str | None = None,
custom_middlewares: list[AgentMiddleware] | None = None,
*,
app_config: AppConfig | None = None,
):
"""Build middleware chain based on runtime configuration.
Args:
app_config: Resolved application config.
config: Runtime configuration containing configurable options like is_plan_mode.
agent_name: If provided, MemoryMiddleware will use per-agent memory storage.
custom_middlewares: Optional list of custom middlewares to inject into the chain.
@@ -258,10 +253,11 @@ def _build_middlewares(
Returns:
List of middleware instances.
"""
middlewares = build_lead_runtime_middlewares(app_config=app_config, lazy_init=True)
resolved_app_config = app_config or get_app_config()
middlewares = build_lead_runtime_middlewares(app_config=resolved_app_config, lazy_init=True)
# Add summarization middleware if enabled
summarization_middleware = _create_summarization_middleware(app_config)
summarization_middleware = _create_summarization_middleware(app_config=resolved_app_config)
if summarization_middleware is not None:
middlewares.append(summarization_middleware)
@@ -273,23 +269,23 @@ def _build_middlewares(
middlewares.append(todo_list_middleware)
# Add TokenUsageMiddleware when token_usage tracking is enabled
if app_config.token_usage.enabled:
if resolved_app_config.token_usage.enabled:
middlewares.append(TokenUsageMiddleware())
# Add TitleMiddleware
middlewares.append(TitleMiddleware())
middlewares.append(TitleMiddleware(app_config=resolved_app_config))
# Add MemoryMiddleware (after TitleMiddleware)
middlewares.append(MemoryMiddleware(agent_name=agent_name))
middlewares.append(MemoryMiddleware(agent_name=agent_name, memory_config=resolved_app_config.memory))
# Add ViewImageMiddleware only if the current model supports vision.
# Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
model_config = app_config.get_model_config(model_name) if model_name else None
model_config = resolved_app_config.get_model_config(model_name) if model_name else None
if model_config is not None and model_config.supports_vision:
middlewares.append(ViewImageMiddleware())
# Add DeferredToolFilterMiddleware to hide deferred tool schemas from model binding
if app_config.tool_search.enabled:
if resolved_app_config.tool_search.enabled:
from deerflow.agents.middlewares.deferred_tool_filter_middleware import DeferredToolFilterMiddleware
middlewares.append(DeferredToolFilterMiddleware())
@@ -312,33 +308,20 @@ def _build_middlewares(
return middlewares
def make_lead_agent(
config: RunnableConfig,
app_config: AppConfig | None = None,
) -> CompiledStateGraph:
"""Build the lead agent from runtime config.
def make_lead_agent(config: RunnableConfig):
"""LangGraph graph factory; keep the signature compatible with LangGraph Server."""
runtime_config = _get_runtime_config(config)
runtime_app_config = runtime_config.get("app_config")
return _make_lead_agent(config, app_config=runtime_app_config or get_app_config())
Args:
config: LangGraph ``RunnableConfig`` carrying per-invocation options
(``thinking_enabled``, ``model_name``, ``is_plan_mode``, etc.).
app_config: Resolved application config. Required for in-process
entry points (DeerFlowClient, Gateway Worker). When omitted we
are being called via ``langgraph.json`` registration and reload
from disk the LangGraph Server bootstrap path has no other
way to thread the value.
"""
def _make_lead_agent(config: RunnableConfig, *, app_config: AppConfig):
# Lazy import to avoid circular dependency
from deerflow.tools import get_available_tools
from deerflow.tools.builtins import setup_agent
if app_config is None:
# LangGraph Server registers ``make_lead_agent`` via ``langgraph.json``
# and hands us only a ``RunnableConfig``. Reload config from disk
# here — it's a pure function, equivalent to the process-global the
# old code path would have read.
app_config = AppConfig.from_file()
cfg = _get_runtime_config(config)
resolved_app_config = app_config
thinking_enabled = cfg.get("thinking_enabled", True)
reasoning_effort = cfg.get("reasoning_effort", None)
@@ -354,9 +337,9 @@ def make_lead_agent(
agent_model_name = agent_config.model if agent_config and agent_config.model else None
# Final model name resolution: request → agent config → global default, with fallback for unknown names
model_name = _resolve_model_name(app_config, requested_model_name or agent_model_name)
model_name = _resolve_model_name(requested_model_name or agent_model_name, app_config=resolved_app_config)
model_config = app_config.get_model_config(model_name)
model_config = resolved_app_config.get_model_config(model_name)
if model_config is None:
raise ValueError("No chat model could be resolved. Please configure at least one model in config.yaml or provide a valid 'model_name'/'model' in the request.")
@@ -395,22 +378,34 @@ def make_lead_agent(
if is_bootstrap:
# Special bootstrap agent with minimal prompt for initial custom agent creation flow
return create_agent(
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=app_config),
tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=app_config) + [setup_agent],
middleware=_build_middlewares(app_config, config, model_name=model_name),
system_prompt=apply_prompt_template(app_config, subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=resolved_app_config),
tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=resolved_app_config) + [setup_agent],
middleware=_build_middlewares(config, model_name=model_name, app_config=resolved_app_config),
system_prompt=apply_prompt_template(
subagent_enabled=subagent_enabled,
max_concurrent_subagents=max_concurrent_subagents,
available_skills=set(["bootstrap"]),
app_config=resolved_app_config,
),
state_schema=ThreadState,
context_schema=DeerFlowContext,
)
# Default lead agent (unchanged behavior)
return create_agent(
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=app_config),
tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled, app_config=app_config),
middleware=_build_middlewares(app_config, config, model_name=model_name, agent_name=agent_name),
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, app_config=resolved_app_config),
tools=get_available_tools(
model_name=model_name,
groups=agent_config.tool_groups if agent_config else None,
subagent_enabled=subagent_enabled,
app_config=resolved_app_config,
),
middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name, app_config=resolved_app_config),
system_prompt=apply_prompt_template(
app_config, subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name, available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None
subagent_enabled=subagent_enabled,
max_concurrent_subagents=max_concurrent_subagents,
agent_name=agent_name,
available_skills=set(agent_config.skills) if agent_config and agent_config.skills is not None else None,
app_config=resolved_app_config,
),
state_schema=ThreadState,
context_schema=DeerFlowContext,
)
@@ -1,15 +1,20 @@
from __future__ import annotations
import asyncio
import logging
import threading
from datetime import datetime
from functools import lru_cache
from typing import TYPE_CHECKING
from deerflow.config.agents_config import load_agent_soul
from deerflow.config.app_config import AppConfig
from deerflow.skills import load_skills
from deerflow.skills.types import Skill
from deerflow.skills.storage import get_or_new_skill_storage
from deerflow.skills.types import Skill, SkillCategory
from deerflow.subagents import get_available_subagent_names
if TYPE_CHECKING:
from deerflow.config.app_config import AppConfig
logger = logging.getLogger(__name__)
_ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS = 5.0
@@ -20,20 +25,19 @@ _enabled_skills_refresh_version = 0
_enabled_skills_refresh_event = threading.Event()
def _load_enabled_skills_sync(app_config: AppConfig | None) -> list[Skill]:
return list(load_skills(app_config, enabled_only=True))
def _load_enabled_skills_sync() -> list[Skill]:
return list(get_or_new_skill_storage().load_skills(enabled_only=True))
def _start_enabled_skills_refresh_thread(app_config: AppConfig | None) -> None:
def _start_enabled_skills_refresh_thread() -> None:
threading.Thread(
target=_refresh_enabled_skills_cache_worker,
args=(app_config,),
name="deerflow-enabled-skills-loader",
daemon=True,
).start()
def _refresh_enabled_skills_cache_worker(app_config: AppConfig | None) -> None:
def _refresh_enabled_skills_cache_worker() -> None:
global _enabled_skills_cache, _enabled_skills_refresh_active
while True:
@@ -41,8 +45,8 @@ def _refresh_enabled_skills_cache_worker(app_config: AppConfig | None) -> None:
target_version = _enabled_skills_refresh_version
try:
skills = _load_enabled_skills_sync(app_config)
except (OSError, ImportError):
skills = _load_enabled_skills_sync()
except Exception:
logger.exception("Failed to load enabled skills for prompt injection")
skills = []
@@ -58,7 +62,7 @@ def _refresh_enabled_skills_cache_worker(app_config: AppConfig | None) -> None:
_enabled_skills_cache = None
def _ensure_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
def _ensure_enabled_skills_cache() -> threading.Event:
global _enabled_skills_refresh_active
with _enabled_skills_lock:
@@ -70,11 +74,11 @@ def _ensure_enabled_skills_cache(app_config: AppConfig | None) -> threading.Even
_enabled_skills_refresh_active = True
_enabled_skills_refresh_event.clear()
_start_enabled_skills_refresh_thread(app_config)
_start_enabled_skills_refresh_thread()
return _enabled_skills_refresh_event
def _invalidate_enabled_skills_cache(app_config: AppConfig | None) -> threading.Event:
def _invalidate_enabled_skills_cache() -> threading.Event:
global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
_get_cached_skills_prompt_section.cache_clear()
@@ -86,68 +90,56 @@ def _invalidate_enabled_skills_cache(app_config: AppConfig | None) -> threading.
return _enabled_skills_refresh_event
_enabled_skills_refresh_active = True
_start_enabled_skills_refresh_thread(app_config)
_start_enabled_skills_refresh_thread()
return _enabled_skills_refresh_event
def prime_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
_ensure_enabled_skills_cache(app_config)
def prime_enabled_skills_cache() -> None:
_ensure_enabled_skills_cache()
def warm_enabled_skills_cache(app_config: AppConfig | None = None, timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
if _ensure_enabled_skills_cache(app_config).wait(timeout=timeout_seconds):
def warm_enabled_skills_cache(timeout_seconds: float = _ENABLED_SKILLS_REFRESH_WAIT_TIMEOUT_SECONDS) -> bool:
if _ensure_enabled_skills_cache().wait(timeout=timeout_seconds):
return True
logger.warning("Timed out waiting %.1fs for enabled skills cache warm-up", timeout_seconds)
return False
def _get_enabled_skills(app_config: AppConfig | None = None):
def _get_enabled_skills():
with _enabled_skills_lock:
cached = _enabled_skills_cache
if cached is not None:
return list(cached)
_ensure_enabled_skills_cache(app_config)
_ensure_enabled_skills_cache()
return []
def _skill_mutability_label(category: str) -> str:
return "[custom, editable]" if category == "custom" else "[built-in]"
def _get_enabled_skills_for_config(app_config: AppConfig | None = None) -> list[Skill]:
"""Return enabled skills using the caller's config source.
When a concrete ``app_config`` is supplied, bypass the global enabled-skills
cache so the skill list and skill paths are resolved from the same config
object. This keeps request-scoped config injection consistent even while the
release branch still supports global fallback paths.
"""
if app_config is None:
return _get_enabled_skills()
return list(get_or_new_skill_storage(app_config=app_config).load_skills(enabled_only=True))
def clear_skills_system_prompt_cache(app_config: AppConfig | None = None) -> None:
_invalidate_enabled_skills_cache(app_config)
def _skill_mutability_label(category: SkillCategory | str) -> str:
return "[custom, editable]" if category == SkillCategory.CUSTOM else "[built-in]"
async def refresh_skills_system_prompt_cache_async(app_config: AppConfig | None = None) -> None:
await asyncio.to_thread(_invalidate_enabled_skills_cache(app_config).wait)
def clear_skills_system_prompt_cache() -> None:
_invalidate_enabled_skills_cache()
def _reset_skills_system_prompt_cache_state() -> None:
global _enabled_skills_cache, _enabled_skills_refresh_active, _enabled_skills_refresh_version
_get_cached_skills_prompt_section.cache_clear()
with _enabled_skills_lock:
_enabled_skills_cache = None
_enabled_skills_refresh_active = False
_enabled_skills_refresh_version = 0
_enabled_skills_refresh_event.clear()
def _refresh_enabled_skills_cache(app_config: AppConfig | None = None) -> None:
"""Backward-compatible test helper for direct synchronous reload."""
try:
skills = _load_enabled_skills_sync(app_config)
except Exception:
logger.exception("Failed to load enabled skills for prompt injection")
skills = []
with _enabled_skills_lock:
_enabled_skills_cache = skills
_enabled_skills_refresh_active = False
_enabled_skills_refresh_event.set()
async def refresh_skills_system_prompt_cache_async() -> None:
await asyncio.to_thread(_invalidate_enabled_skills_cache().wait)
def _build_skill_evolution_section(skill_evolution_enabled: bool) -> str:
@@ -166,7 +158,7 @@ Skip simple one-off tasks.
"""
def _build_available_subagents_description(available_names: list[str], bash_available: bool, app_config: AppConfig) -> str:
def _build_available_subagents_description(available_names: list[str], bash_available: bool, *, app_config: AppConfig | None = None) -> str:
"""Dynamically build subagent type descriptions from registry.
Mirrors Codex's pattern where agent_type_description is dynamically generated
@@ -188,7 +180,7 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
if name in builtin_descriptions:
lines.append(f"- **{name}**: {builtin_descriptions[name]}")
else:
config = get_subagent_config(name, app_config)
config = get_subagent_config(name, app_config=app_config)
if config is not None:
desc = config.description.split("\n")[0].strip() # First line only for brevity
lines.append(f"- **{name}**: {desc}")
@@ -196,23 +188,22 @@ def _build_available_subagents_description(available_names: list[str], bash_avai
return "\n".join(lines)
def _build_subagent_section(max_concurrent: int, app_config: AppConfig) -> str:
def _build_subagent_section(max_concurrent: int, *, app_config: AppConfig | None = None) -> str:
"""Build the subagent system prompt section with dynamic concurrency limit.
Args:
max_concurrent: Maximum number of concurrent subagent calls allowed per response.
app_config: Application config used to gate bash availability.
Returns:
Formatted subagent section string.
"""
n = max_concurrent
available_names = get_available_subagent_names(app_config)
available_names = get_available_subagent_names(app_config=app_config) if app_config is not None else get_available_subagent_names()
bash_available = "bash" in available_names
# Dynamically build subagent type descriptions from registry (aligned with Codex's
# agent_type_description pattern where all registered roles are listed in the tool spec).
available_subagents = _build_available_subagents_description(available_names, bash_available, app_config)
available_subagents = _build_available_subagents_description(available_names, bash_available, app_config=app_config)
direct_tool_examples = "bash, ls, read_file, web_search, etc." if bash_available else "ls, read_file, web_search, etc."
direct_execution_example = (
'# User asks: "Run the tests"\n# Thinking: Cannot decompose into parallel sub-tasks\n# → Execute directly\n\nbash("npm test") # Direct execution, not task()'
@@ -539,34 +530,44 @@ combined with a FastAPI gateway for REST API access [citation:FastAPI](https://f
"""
def _get_memory_context(app_config: AppConfig, agent_name: str | None = None) -> str:
def _get_memory_context(agent_name: str | None = None, *, app_config: AppConfig | None = None) -> str:
"""Get memory context for injection into system prompt.
Returns an empty string when memory is disabled or the stored memory file
cannot be read/parsed. A corrupt memory.json degrades the prompt to
no-memory; it never kills the agent.
Args:
agent_name: If provided, loads per-agent memory. If None, loads global memory.
app_config: Explicit application config. When provided, memory options
are read from this value instead of the global config singleton.
Returns:
Formatted memory context string wrapped in XML tags, or empty string if disabled.
"""
from deerflow.agents.memory import format_memory_for_injection, get_memory_data
from deerflow.runtime.user_context import get_effective_user_id
memory_config = app_config.memory
if not memory_config.enabled or not memory_config.injection_enabled:
return ""
try:
memory_data = get_memory_data(memory_config, agent_name, user_id=get_effective_user_id())
except (OSError, ValueError, UnicodeDecodeError):
logger.exception("Failed to load memory data for prompt injection")
return ""
from deerflow.agents.memory import format_memory_for_injection, get_memory_data
from deerflow.runtime.user_context import get_effective_user_id
memory_content = format_memory_for_injection(memory_data, max_tokens=memory_config.max_injection_tokens)
if not memory_content.strip():
return ""
if app_config is None:
from deerflow.config.memory_config import get_memory_config
return f"""<memory>
config = get_memory_config()
else:
config = app_config.memory
if not config.enabled or not config.injection_enabled:
return ""
memory_data = get_memory_data(agent_name, user_id=get_effective_user_id())
memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
if not memory_content.strip():
return ""
return f"""<memory>
{memory_content}
</memory>
"""
except Exception:
logger.exception("Failed to load memory context")
return ""
@lru_cache(maxsize=32)
@@ -601,12 +602,24 @@ You have access to skills that provide optimized workflows for specific tasks. E
</skill_system>"""
def get_skills_prompt_section(app_config: AppConfig, available_skills: set[str] | None = None) -> str:
def get_skills_prompt_section(available_skills: set[str] | None = None, *, app_config: AppConfig | None = None) -> str:
"""Generate the skills prompt section with available skills list."""
skills = _get_enabled_skills(app_config)
skills = _get_enabled_skills_for_config(app_config)
container_base_path = app_config.skills.container_path
skill_evolution_enabled = app_config.skill_evolution.enabled
if app_config is None:
try:
from deerflow.config import get_app_config
config = get_app_config()
container_base_path = config.skills.container_path
skill_evolution_enabled = config.skill_evolution.enabled
except Exception:
container_base_path = "/mnt/skills"
skill_evolution_enabled = False
else:
config = app_config
container_base_path = config.skills.container_path
skill_evolution_enabled = config.skill_evolution.enabled
if not skills and not skill_evolution_enabled:
return ""
@@ -630,7 +643,7 @@ def get_agent_soul(agent_name: str | None) -> str:
return ""
def get_deferred_tools_prompt_section(app_config: AppConfig) -> str:
def get_deferred_tools_prompt_section(*, app_config: AppConfig | None = None) -> str:
"""Generate <available-deferred-tools> block for the system prompt.
Lists only deferred tool names so the agent knows what exists
@@ -639,7 +652,17 @@ def get_deferred_tools_prompt_section(app_config: AppConfig) -> str:
"""
from deerflow.tools.builtins.tool_search import get_deferred_registry
if not app_config.tool_search.enabled:
if app_config is None:
try:
from deerflow.config import get_app_config
config = get_app_config()
except Exception:
return ""
else:
config = app_config
if not config.tool_search.enabled:
return ""
registry = get_deferred_registry()
@@ -650,9 +673,19 @@ def get_deferred_tools_prompt_section(app_config: AppConfig) -> str:
return f"<available-deferred-tools>\n{names}\n</available-deferred-tools>"
def _build_acp_section(app_config: AppConfig) -> str:
def _build_acp_section(*, app_config: AppConfig | None = None) -> str:
"""Build the ACP agent prompt section, only if ACP agents are configured."""
if not app_config.acp_agents:
if app_config is None:
try:
from deerflow.config.acp_config import get_acp_agents
agents = get_acp_agents()
except Exception:
return ""
else:
agents = getattr(app_config, "acp_agents", {}) or {}
if not agents:
return ""
return (
@@ -664,9 +697,20 @@ def _build_acp_section(app_config: AppConfig) -> str:
)
def _build_custom_mounts_section(app_config: AppConfig) -> str:
def _build_custom_mounts_section(*, app_config: AppConfig | None = None) -> str:
"""Build a prompt section for explicitly configured sandbox mounts."""
mounts = app_config.sandbox.mounts or []
if app_config is None:
try:
from deerflow.config import get_app_config
config = get_app_config()
except Exception:
logger.exception("Failed to load configured sandbox mounts for the lead-agent prompt")
return ""
else:
config = app_config
mounts = config.sandbox.mounts or []
if not mounts:
return ""
@@ -681,19 +725,19 @@ def _build_custom_mounts_section(app_config: AppConfig) -> str:
def apply_prompt_template(
app_config: AppConfig,
subagent_enabled: bool = False,
max_concurrent_subagents: int = 3,
*,
agent_name: str | None = None,
available_skills: set[str] | None = None,
app_config: AppConfig | None = None,
) -> str:
# Get memory context
memory_context = _get_memory_context(app_config, agent_name)
memory_context = _get_memory_context(agent_name, app_config=app_config)
# Include subagent section only if enabled (from runtime parameter)
n = max_concurrent_subagents
subagent_section = _build_subagent_section(n, app_config) if subagent_enabled else ""
subagent_section = _build_subagent_section(n, app_config=app_config) if subagent_enabled else ""
# Add subagent reminder to critical_reminders if enabled
subagent_reminder = (
@@ -714,14 +758,14 @@ def apply_prompt_template(
)
# Get skills section
skills_section = get_skills_prompt_section(app_config, available_skills)
skills_section = get_skills_prompt_section(available_skills, app_config=app_config)
# Get deferred tools section (tool_search)
deferred_tools_section = get_deferred_tools_prompt_section(app_config)
deferred_tools_section = get_deferred_tools_prompt_section(app_config=app_config)
# Build ACP agent section only if ACP agents are configured
acp_section = _build_acp_section(app_config)
custom_mounts_section = _build_custom_mounts_section(app_config)
acp_section = _build_acp_section(app_config=app_config)
custom_mounts_section = _build_custom_mounts_section(app_config=app_config)
acp_and_mounts_section = "\n".join(section for section in (acp_section, custom_mounts_section) if section)
# Format the prompt with dynamic skills and memory
@@ -7,17 +7,11 @@ from dataclasses import dataclass, field
from datetime import UTC, datetime
from typing import Any
from deerflow.config.app_config import AppConfig
from deerflow.config.memory_config import get_memory_config
logger = logging.getLogger(__name__)
# Module-level config pointer set by the middleware that owns the queue.
# The queue runs on a background Timer thread where ``Runtime`` and FastAPI
# request context are not accessible; the enqueuer (which does have runtime
# context) is responsible for plumbing ``AppConfig`` through ``add()``.
@dataclass
class ConversationContext:
"""Context for a conversation to be processed for memory update."""
@@ -37,21 +31,10 @@ class MemoryUpdateQueue:
This queue collects conversation contexts and processes them after
a configurable debounce period. Multiple conversations received within
the debounce window are batched together.
The queue captures an ``AppConfig`` reference at construction time and
reuses it for the MemoryUpdater it spawns. Callers must construct a
fresh queue when the config changes rather than reaching into a global.
"""
def __init__(self, app_config: AppConfig):
"""Initialize the memory update queue.
Args:
app_config: Application config. The queue reads its own
``memory`` section for debounce timing and hands the full
config to :class:`MemoryUpdater`.
"""
self._app_config = app_config
def __init__(self):
"""Initialize the memory update queue."""
self._queue: list[ConversationContext] = []
self._lock = threading.Lock()
self._timer: threading.Timer | None = None
@@ -66,8 +49,19 @@ class MemoryUpdateQueue:
correction_detected: bool = False,
reinforcement_detected: bool = False,
) -> None:
"""Add a conversation to the update queue."""
config = self._app_config.memory
"""Add a conversation to the update queue.
Args:
thread_id: The thread ID.
messages: The conversation messages.
agent_name: If provided, memory is stored per-agent. If None, uses global memory.
user_id: The user ID captured at enqueue time. Stored in ConversationContext so it
survives the threading.Timer boundary (ContextVar does not propagate across
raw threads).
correction_detected: Whether recent turns include an explicit correction signal.
reinforcement_detected: Whether recent turns include a positive reinforcement signal.
"""
config = get_memory_config()
if not config.enabled:
return
@@ -94,7 +88,7 @@ class MemoryUpdateQueue:
reinforcement_detected: bool = False,
) -> None:
"""Add a conversation and start processing immediately in the background."""
config = self._app_config.memory
config = get_memory_config()
if not config.enabled:
return
@@ -117,7 +111,7 @@ class MemoryUpdateQueue:
thread_id: str,
messages: list[Any],
agent_name: str | None,
user_id: str | None = None,
user_id: str | None,
correction_detected: bool,
reinforcement_detected: bool,
) -> None:
@@ -141,7 +135,7 @@ class MemoryUpdateQueue:
def _reset_timer(self) -> None:
"""Reset the debounce timer."""
config = self._app_config.memory
config = get_memory_config()
self._schedule_timer(config.debounce_seconds)
logger.debug("Memory update timer set for %ss", config.debounce_seconds)
@@ -181,7 +175,7 @@ class MemoryUpdateQueue:
logger.info("Processing %d queued memory updates", len(contexts_to_process))
try:
updater = MemoryUpdater(self._app_config)
updater = MemoryUpdater()
for context in contexts_to_process:
try:
@@ -253,35 +247,31 @@ class MemoryUpdateQueue:
return self._processing
# Queues keyed by ``id(AppConfig)`` so tests and multi-client setups with
# distinct configs do not share a debounce queue.
_memory_queues: dict[int, MemoryUpdateQueue] = {}
# Global singleton instance
_memory_queue: MemoryUpdateQueue | None = None
_queue_lock = threading.Lock()
def get_memory_queue(app_config: AppConfig) -> MemoryUpdateQueue:
"""Get or create the memory update queue for the given app config."""
key = id(app_config)
with _queue_lock:
queue = _memory_queues.get(key)
if queue is None:
queue = MemoryUpdateQueue(app_config)
_memory_queues[key] = queue
return queue
def get_memory_queue() -> MemoryUpdateQueue:
"""Get the global memory update queue singleton.
def reset_memory_queue(app_config: AppConfig | None = None) -> None:
"""Reset memory queue(s).
Pass an ``app_config`` to reset only its queue, or omit to reset all
(useful at test teardown).
Returns:
The memory update queue instance.
"""
global _memory_queue
with _queue_lock:
if app_config is not None:
queue = _memory_queues.pop(id(app_config), None)
if queue is not None:
queue.clear()
return
for queue in _memory_queues.values():
queue.clear()
_memory_queues.clear()
if _memory_queue is None:
_memory_queue = MemoryUpdateQueue()
return _memory_queue
def reset_memory_queue() -> None:
"""Reset the global memory queue.
This is useful for testing.
"""
global _memory_queue
with _queue_lock:
if _memory_queue is not None:
_memory_queue.clear()
_memory_queue = None
@@ -10,7 +10,7 @@ from pathlib import Path
from typing import Any
from deerflow.config.agents_config import AGENT_NAME_PATTERN
from deerflow.config.memory_config import MemoryConfig
from deerflow.config.memory_config import get_memory_config
from deerflow.config.paths import get_paths
logger = logging.getLogger(__name__)
@@ -62,15 +62,8 @@ class MemoryStorage(abc.ABC):
class FileMemoryStorage(MemoryStorage):
"""File-based memory storage provider."""
def __init__(self, memory_config: MemoryConfig):
"""Initialize the file memory storage.
Args:
memory_config: Memory configuration (storage_path etc.). Stored on
the instance so per-request lookups don't need to reach for
ambient state.
"""
self._memory_config = memory_config
def __init__(self):
"""Initialize the file memory storage."""
# Per-user/agent memory cache: keyed by (user_id, agent_name) tuple (None = global)
# Value: (memory_data, file_mtime)
self._memory_cache: dict[tuple[str | None, str | None], tuple[dict[str, Any], float | None]] = {}
@@ -90,11 +83,11 @@ class FileMemoryStorage(MemoryStorage):
def _get_memory_file_path(self, agent_name: str | None = None, *, user_id: str | None = None) -> Path:
"""Get the path to the memory file."""
config = self._memory_config
if user_id is not None:
if agent_name is not None:
self._validate_agent_name(agent_name)
return get_paths().user_agent_memory_file(user_id, agent_name)
config = get_memory_config()
if config.storage_path and Path(config.storage_path).is_absolute():
return Path(config.storage_path)
return get_paths().user_memory_file(user_id)
@@ -102,6 +95,7 @@ class FileMemoryStorage(MemoryStorage):
if agent_name is not None:
self._validate_agent_name(agent_name)
return get_paths().agent_memory_file(agent_name)
config = get_memory_config()
if config.storage_path:
p = Path(config.storage_path)
return p if p.is_absolute() else get_paths().base_dir / p
@@ -122,16 +116,20 @@ class FileMemoryStorage(MemoryStorage):
logger.warning("Failed to load memory file: %s", e)
return create_empty_memory()
@staticmethod
def _cache_key(agent_name: str | None = None, *, user_id: str | None = None) -> tuple[str | None, str | None]:
return (user_id, agent_name)
def load(self, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
"""Load memory data (cached with file modification time check)."""
file_path = self._get_memory_file_path(agent_name, user_id=user_id)
cache_key = self._cache_key(agent_name, user_id=user_id)
try:
current_mtime = file_path.stat().st_mtime if file_path.exists() else None
except OSError:
current_mtime = None
cache_key = (user_id, agent_name)
with self._cache_lock:
cached = self._memory_cache.get(cache_key)
if cached is not None and cached[1] == current_mtime:
@@ -148,13 +146,13 @@ class FileMemoryStorage(MemoryStorage):
"""Reload memory data from file, forcing cache invalidation."""
file_path = self._get_memory_file_path(agent_name, user_id=user_id)
memory_data = self._load_memory_from_file(agent_name, user_id=user_id)
cache_key = self._cache_key(agent_name, user_id=user_id)
try:
mtime = file_path.stat().st_mtime if file_path.exists() else None
except OSError:
mtime = None
cache_key = (user_id, agent_name)
with self._cache_lock:
self._memory_cache[cache_key] = (memory_data, mtime)
return memory_data
@@ -162,6 +160,7 @@ class FileMemoryStorage(MemoryStorage):
def save(self, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
"""Save memory data to file and update cache."""
file_path = self._get_memory_file_path(agent_name, user_id=user_id)
cache_key = self._cache_key(agent_name, user_id=user_id)
try:
file_path.parent.mkdir(parents=True, exist_ok=True)
@@ -181,7 +180,6 @@ class FileMemoryStorage(MemoryStorage):
except OSError:
mtime = None
cache_key = (user_id, agent_name)
with self._cache_lock:
self._memory_cache[cache_key] = (memory_data, mtime)
logger.info("Memory saved to %s", file_path)
@@ -191,31 +189,23 @@ class FileMemoryStorage(MemoryStorage):
return False
# Instances keyed by (storage_class_path, id(memory_config)) so tests can
# construct isolated storages and multi-client setups with different configs
# don't collide on a single process-wide singleton.
_storage_instances: dict[tuple[str, int], MemoryStorage] = {}
_storage_instance: MemoryStorage | None = None
_storage_lock = threading.Lock()
def get_memory_storage(memory_config: MemoryConfig) -> MemoryStorage:
"""Get the configured memory storage instance.
Caches one instance per ``(storage_class, memory_config)`` pair. In
single-config deployments this collapses to one instance; in multi-client
or test scenarios each config gets its own storage.
"""
key = (memory_config.storage_class, id(memory_config))
existing = _storage_instances.get(key)
if existing is not None:
return existing
def get_memory_storage() -> MemoryStorage:
"""Get the configured memory storage instance."""
global _storage_instance
if _storage_instance is not None:
return _storage_instance
with _storage_lock:
existing = _storage_instances.get(key)
if existing is not None:
return existing
if _storage_instance is not None:
return _storage_instance
config = get_memory_config()
storage_class_path = config.storage_class
storage_class_path = memory_config.storage_class
try:
module_path, class_name = storage_class_path.rsplit(".", 1)
import importlib
@@ -229,14 +219,13 @@ def get_memory_storage(memory_config: MemoryConfig) -> MemoryStorage:
if not issubclass(storage_class, MemoryStorage):
raise TypeError(f"Configured memory storage '{storage_class_path}' is not a subclass of MemoryStorage")
instance = storage_class(memory_config)
_storage_instance = storage_class()
except Exception as e:
logger.error(
"Failed to load memory storage %s, falling back to FileMemoryStorage: %s",
storage_class_path,
e,
)
instance = FileMemoryStorage(memory_config)
_storage_instance = FileMemoryStorage()
_storage_instances[key] = instance
return instance
return _storage_instance
@@ -5,19 +5,12 @@ from __future__ import annotations
from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
from deerflow.agents.memory.queue import get_memory_queue
from deerflow.agents.middlewares.summarization_middleware import SummarizationEvent
from deerflow.config.app_config import AppConfig
from deerflow.config.memory_config import get_memory_config
def memory_flush_hook(event: SummarizationEvent) -> None:
"""Flush messages about to be summarized into the memory queue.
Reads ``AppConfig`` from disk on every invocation. This hook is fired by
``SummarizationMiddleware`` which has no ergonomic way to thread an
explicit ``app_config`` through; ``AppConfig.from_file()`` is a pure load
so the cost is acceptable for this rare pre-summarization callback.
"""
app_config = AppConfig.from_file()
if not app_config.memory.enabled or not event.thread_id:
"""Flush messages about to be summarized into the memory queue."""
if not get_memory_config().enabled or not event.thread_id:
return
filtered_messages = filter_messages_for_memory(list(event.messages_to_summarize))
@@ -28,7 +21,7 @@ def memory_flush_hook(event: SummarizationEvent) -> None:
correction_detected = detect_correction(filtered_messages)
reinforcement_detected = not correction_detected and detect_reinforcement(filtered_messages)
queue = get_memory_queue(app_config)
queue = get_memory_queue()
queue.add_nowait(
thread_id=event.thread_id,
messages=filtered_messages,
@@ -9,7 +9,6 @@ import logging
import math
import re
import uuid
from collections.abc import Awaitable
from typing import Any
from deerflow.agents.memory.prompt import (
@@ -21,12 +20,17 @@ from deerflow.agents.memory.storage import (
get_memory_storage,
utc_now_iso_z,
)
from deerflow.config.app_config import AppConfig
from deerflow.config.memory_config import MemoryConfig
from deerflow.config.memory_config import get_memory_config
from deerflow.models import create_chat_model
logger = logging.getLogger(__name__)
# Thread pool for offloading sync memory updates when called from an async
# context. Unlike the previous asyncio.run() approach, this runs *sync*
# model.invoke() calls — no event loop is created, so the langchain async
# httpx client pool (globally cached via @lru_cache) is never touched and
# cross-loop connection reuse is impossible.
_SYNC_MEMORY_UPDATER_EXECUTOR = concurrent.futures.ThreadPoolExecutor(
max_workers=4,
thread_name_prefix="memory-updater-sync",
@@ -39,33 +43,45 @@ def _create_empty_memory() -> dict[str, Any]:
return create_empty_memory()
def _save_memory_to_file(memory_config: MemoryConfig, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
"""Save via the configured memory storage."""
return get_memory_storage(memory_config).save(memory_data, agent_name, user_id=user_id)
def _save_memory_to_file(memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> bool:
"""Backward-compatible wrapper around the configured memory storage save path."""
return get_memory_storage().save(memory_data, agent_name, user_id=user_id)
def get_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
def get_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
"""Get the current memory data via storage provider."""
return get_memory_storage(memory_config).load(agent_name, user_id=user_id)
return get_memory_storage().load(agent_name, user_id=user_id)
def reload_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
def reload_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
"""Reload memory data via storage provider."""
return get_memory_storage(memory_config).reload(agent_name, user_id=user_id)
return get_memory_storage().reload(agent_name, user_id=user_id)
def import_memory_data(memory_config: MemoryConfig, memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
"""Persist imported memory data via storage provider."""
storage = get_memory_storage(memory_config)
def import_memory_data(memory_data: dict[str, Any], agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
"""Persist imported memory data via storage provider.
Args:
memory_data: Full memory payload to persist.
agent_name: If provided, imports into per-agent memory.
user_id: If provided, scopes memory to a specific user.
Returns:
The saved memory data after storage normalization.
Raises:
OSError: If persisting the imported memory fails.
"""
storage = get_memory_storage()
if not storage.save(memory_data, agent_name, user_id=user_id):
raise OSError("Failed to save imported memory data")
return storage.load(agent_name, user_id=user_id)
def clear_memory_data(memory_config: MemoryConfig, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
def clear_memory_data(agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
"""Clear all stored memory data and persist an empty structure."""
cleared_memory = create_empty_memory()
if not _save_memory_to_file(memory_config, cleared_memory, agent_name, user_id=user_id):
if not _save_memory_to_file(cleared_memory, agent_name, user_id=user_id):
raise OSError("Failed to save cleared memory data")
return cleared_memory
@@ -78,7 +94,6 @@ def _validate_confidence(confidence: float) -> float:
def create_memory_fact(
memory_config: MemoryConfig,
content: str,
category: str = "context",
confidence: float = 0.5,
@@ -94,7 +109,7 @@ def create_memory_fact(
normalized_category = category.strip() or "context"
validated_confidence = _validate_confidence(confidence)
now = utc_now_iso_z()
memory_data = get_memory_data(memory_config, agent_name, user_id=user_id)
memory_data = get_memory_data(agent_name, user_id=user_id)
updated_memory = dict(memory_data)
facts = list(memory_data.get("facts", []))
facts.append(
@@ -109,15 +124,15 @@ def create_memory_fact(
)
updated_memory["facts"] = facts
if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
raise OSError("Failed to save memory data after creating fact")
return updated_memory
def delete_memory_fact(memory_config: MemoryConfig, fact_id: str, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
def delete_memory_fact(fact_id: str, agent_name: str | None = None, *, user_id: str | None = None) -> dict[str, Any]:
"""Delete a fact by its id and persist the updated memory data."""
memory_data = get_memory_data(memory_config, agent_name, user_id=user_id)
memory_data = get_memory_data(agent_name, user_id=user_id)
facts = memory_data.get("facts", [])
updated_facts = [fact for fact in facts if fact.get("id") != fact_id]
if len(updated_facts) == len(facts):
@@ -126,14 +141,13 @@ def delete_memory_fact(memory_config: MemoryConfig, fact_id: str, agent_name: st
updated_memory = dict(memory_data)
updated_memory["facts"] = updated_facts
if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
raise OSError(f"Failed to save memory data after deleting fact '{fact_id}'")
return updated_memory
def update_memory_fact(
memory_config: MemoryConfig,
fact_id: str,
content: str | None = None,
category: str | None = None,
@@ -143,7 +157,7 @@ def update_memory_fact(
user_id: str | None = None,
) -> dict[str, Any]:
"""Update an existing fact and persist the updated memory data."""
memory_data = get_memory_data(memory_config, agent_name, user_id=user_id)
memory_data = get_memory_data(agent_name, user_id=user_id)
updated_memory = dict(memory_data)
updated_facts: list[dict[str, Any]] = []
found = False
@@ -170,7 +184,7 @@ def update_memory_fact(
updated_memory["facts"] = updated_facts
if not _save_memory_to_file(memory_config, updated_memory, agent_name, user_id=user_id):
if not _save_memory_to_file(updated_memory, agent_name, user_id=user_id):
raise OSError(f"Failed to save memory data after updating fact '{fact_id}'")
return updated_memory
@@ -213,39 +227,6 @@ def _extract_text(content: Any) -> str:
return str(content)
def _run_async_update_sync(coro: Awaitable[bool]) -> bool:
"""Run an async memory update from sync code, including nested-loop contexts."""
handed_off = False
try:
try:
loop = asyncio.get_running_loop()
except RuntimeError:
loop = None
if loop is not None and loop.is_running():
future = _SYNC_MEMORY_UPDATER_EXECUTOR.submit(asyncio.run, coro)
handed_off = True
return future.result()
handed_off = True
return asyncio.run(coro)
except Exception:
if not handed_off:
close = getattr(coro, "close", None)
if callable(close):
try:
close()
except Exception:
logger.debug(
"Failed to close un-awaited memory update coroutine",
exc_info=True,
)
logger.exception("Failed to run async memory update from sync context")
return False
# Matches sentences that describe a file-upload *event* rather than general
# file-related work. Deliberately narrow to avoid removing legitimate facts
# such as "User works with CSV files" or "prefers PDF export".
@@ -295,25 +276,19 @@ def _fact_content_key(content: Any) -> str | None:
class MemoryUpdater:
"""Updates memory using LLM based on conversation context."""
def __init__(self, app_config: AppConfig, model_name: str | None = None):
def __init__(self, model_name: str | None = None):
"""Initialize the memory updater.
Args:
app_config: Application config (the updater needs both ``memory``
section for behavior and the full config for ``create_chat_model``).
model_name: Optional model name to use. If None, uses config or default.
"""
self._app_config = app_config
self._model_name = model_name
@property
def _memory_config(self) -> MemoryConfig:
return self._app_config.memory
def _get_model(self):
"""Get the model for memory updates."""
model_name = self._model_name or self._memory_config.model_name
return create_chat_model(name=model_name, thinking_enabled=False, app_config=self._app_config)
config = get_memory_config()
model_name = self._model_name or config.model_name
return create_chat_model(name=model_name, thinking_enabled=False)
def _build_correction_hint(
self,
@@ -349,11 +324,11 @@ class MemoryUpdater:
user_id: str | None = None,
) -> tuple[dict[str, Any], str] | None:
"""Load memory and build the update prompt for a conversation."""
config = self._memory_config
config = get_memory_config()
if not config.enabled or not messages:
return None
current_memory = get_memory_data(config, agent_name, user_id=user_id)
current_memory = get_memory_data(agent_name, user_id=user_id)
conversation_text = format_conversation_for_update(messages)
if not conversation_text.strip():
return None
@@ -389,7 +364,7 @@ class MemoryUpdater:
# cannot corrupt the still-cached original object reference.
updated_memory = self._apply_updates(copy.deepcopy(current_memory), update_data, thread_id)
updated_memory = _strip_upload_mentions_from_memory(updated_memory)
return get_memory_storage(self._memory_config).save(updated_memory, agent_name, user_id=user_id)
return get_memory_storage().save(updated_memory, agent_name, user_id=user_id)
async def aupdate_memory(
self,
@@ -400,10 +375,43 @@ class MemoryUpdater:
reinforcement_detected: bool = False,
user_id: str | None = None,
) -> bool:
"""Update memory asynchronously based on conversation messages."""
"""Update memory asynchronously by delegating to the sync path.
Uses ``asyncio.to_thread`` to run the *sync* ``model.invoke()`` path
in a worker thread so no second event loop is created and the
langchain async httpx client pool (shared with the lead agent) is
never touched. This eliminates the cross-loop connection-reuse bug
described in issue #2615.
"""
return await asyncio.to_thread(
self._do_update_memory_sync,
messages=messages,
thread_id=thread_id,
agent_name=agent_name,
correction_detected=correction_detected,
reinforcement_detected=reinforcement_detected,
user_id=user_id,
)
def _do_update_memory_sync(
self,
messages: list[Any],
thread_id: str | None = None,
agent_name: str | None = None,
correction_detected: bool = False,
reinforcement_detected: bool = False,
user_id: str | None = None,
) -> bool:
"""Pure-sync memory update using ``model.invoke()``.
Uses the *sync* LLM call path so no event loop is created. This
guarantees that the langchain provider's globally cached async
httpx ``AsyncClient`` / connection pool (the one shared with the
lead agent) is never touched no cross-loop connection reuse is
possible.
"""
try:
prepared = await asyncio.to_thread(
self._prepare_update_prompt,
prepared = self._prepare_update_prompt(
messages=messages,
agent_name=agent_name,
correction_detected=correction_detected,
@@ -415,9 +423,8 @@ class MemoryUpdater:
current_memory, prompt = prepared
model = self._get_model()
response = await model.ainvoke(prompt, config={"run_name": "memory_agent"})
return await asyncio.to_thread(
self._finalize_update,
response = model.invoke(prompt, config={"run_name": "memory_agent"})
return self._finalize_update(
current_memory=current_memory,
response_content=response.content,
thread_id=thread_id,
@@ -440,7 +447,16 @@ class MemoryUpdater:
reinforcement_detected: bool = False,
user_id: str | None = None,
) -> bool:
"""Synchronously update memory via the async updater path.
"""Synchronously update memory using the sync LLM path.
Uses ``model.invoke()`` (sync HTTP) which operates on a completely
separate connection pool from the async ``AsyncClient`` shared by
the lead agent. This eliminates the cross-loop connection-reuse
bug described in issue #2615.
When called from within a running event loop (e.g. from a LangGraph
node), the blocking sync call is offloaded to a thread pool so the
caller's loop is not blocked.
Args:
messages: List of conversation messages.
@@ -453,78 +469,35 @@ class MemoryUpdater:
Returns:
True if update was successful, False otherwise.
"""
config = self._memory_config
if not config.enabled:
return False
if not messages:
return False
try:
# Get current memory
current_memory = get_memory_data(config, agent_name, user_id=user_id)
loop = asyncio.get_running_loop()
except RuntimeError:
loop = None
# Format conversation for prompt
conversation_text = format_conversation_for_update(messages)
if not conversation_text.strip():
if loop is not None and loop.is_running():
try:
future = _SYNC_MEMORY_UPDATER_EXECUTOR.submit(
self._do_update_memory_sync,
messages=messages,
thread_id=thread_id,
agent_name=agent_name,
correction_detected=correction_detected,
reinforcement_detected=reinforcement_detected,
user_id=user_id,
)
return future.result()
except Exception:
logger.exception("Failed to offload memory update to executor")
return False
# Build prompt
correction_hint = ""
if correction_detected:
correction_hint = (
"IMPORTANT: Explicit correction signals were detected in this conversation. "
"Pay special attention to what the agent got wrong, what the user corrected, "
"and record the correct approach as a fact with category "
'"correction" and confidence >= 0.95 when appropriate.'
)
if reinforcement_detected:
reinforcement_hint = (
"IMPORTANT: Positive reinforcement signals were detected in this conversation. "
"The user explicitly confirmed the agent's approach was correct or helpful. "
"Record the confirmed approach, style, or preference as a fact with category "
'"preference" or "behavior" and confidence >= 0.9 when appropriate.'
)
correction_hint = (correction_hint + "\n" + reinforcement_hint).strip() if correction_hint else reinforcement_hint
prompt = MEMORY_UPDATE_PROMPT.format(
current_memory=json.dumps(current_memory, indent=2),
conversation=conversation_text,
correction_hint=correction_hint,
)
# Call LLM
model = self._get_model()
response = model.invoke(prompt)
response_text = _extract_text(response.content).strip()
# Parse response
# Remove markdown code blocks if present
if response_text.startswith("```"):
lines = response_text.split("\n")
response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
update_data = json.loads(response_text)
# Apply updates
updated_memory = self._apply_updates(current_memory, update_data, thread_id)
# Strip file-upload mentions from all summaries before saving.
# Uploaded files are session-scoped and won't exist in future sessions,
# so recording upload events in long-term memory causes the agent to
# try (and fail) to locate those files in subsequent conversations.
updated_memory = _strip_upload_mentions_from_memory(updated_memory)
# Save
return get_memory_storage(config).save(updated_memory, agent_name, user_id=user_id)
except json.JSONDecodeError as e:
logger.warning("Failed to parse LLM response for memory update: %s", e)
return False
except Exception as e:
logger.exception("Memory update failed: %s", e)
return False
return self._do_update_memory_sync(
messages=messages,
thread_id=thread_id,
agent_name=agent_name,
correction_detected=correction_detected,
reinforcement_detected=reinforcement_detected,
user_id=user_id,
)
def _apply_updates(
self,
@@ -542,7 +515,7 @@ class MemoryUpdater:
Returns:
Updated memory data.
"""
config = self._memory_config
config = get_memory_config()
now = utc_now_iso_z()
# Update user sections
@@ -70,20 +70,11 @@ class LLMErrorHandlingMiddleware(AgentMiddleware[AgentState]):
retry_base_delay_ms: int = 1000
retry_cap_delay_ms: int = 8000
circuit_failure_threshold: int = 5
circuit_recovery_timeout_sec: int = 60
def __init__(self, **kwargs: Any) -> None:
def __init__(self, *, app_config: AppConfig, **kwargs: Any) -> None:
super().__init__(**kwargs)
# Load Circuit Breaker configs from app config if available, fall back to defaults
try:
app_config = AppConfig.from_file()
self.circuit_failure_threshold = app_config.circuit_breaker.failure_threshold
self.circuit_recovery_timeout_sec = app_config.circuit_breaker.recovery_timeout_sec
except (FileNotFoundError, RuntimeError):
# Gracefully fall back to class defaults in test environments
pass
self.circuit_failure_threshold = app_config.circuit_breaker.failure_threshold
self.circuit_recovery_timeout_sec = app_config.circuit_breaker.recovery_timeout_sec
# Circuit Breaker state
self._circuit_lock = threading.Lock()
@@ -25,8 +25,6 @@ from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import HumanMessage
from langgraph.runtime import Runtime
from deerflow.config.deer_flow_context import DeerFlowContext
logger = logging.getLogger(__name__)
# Defaults — can be overridden via constructor
@@ -183,9 +181,12 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
self._tool_freq: dict[str, dict[str, int]] = defaultdict(lambda: defaultdict(int))
self._tool_freq_warned: dict[str, set[str]] = defaultdict(set)
def _get_thread_id(self, runtime: Runtime[DeerFlowContext]) -> str:
def _get_thread_id(self, runtime: Runtime) -> str:
"""Extract thread_id from runtime context for per-thread tracking."""
return runtime.context.thread_id or "default"
thread_id = runtime.context.get("thread_id") if runtime.context else None
if thread_id:
return thread_id
return "default"
def _evict_if_needed(self) -> None:
"""Evict least recently used threads if over the limit.
@@ -361,16 +362,16 @@ class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
# the conversation; injecting one mid-conversation crashes
# langchain_anthropic's _format_messages(). HumanMessage works
# with all providers. See #1299.
return {"messages": [HumanMessage(content=warning)]}
return {"messages": [HumanMessage(content=warning, name="loop_warning")]}
return None
@override
def after_model(self, state: AgentState, runtime: Runtime[DeerFlowContext]) -> dict | None:
def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._apply(state, runtime)
@override
async def aafter_model(self, state: AgentState, runtime: Runtime[DeerFlowContext]) -> dict | None:
async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._apply(state, runtime)
def reset(self, thread_id: str | None = None) -> None:
@@ -1,17 +1,21 @@
"""Middleware for memory mechanism."""
import logging
from typing import override
from typing import TYPE_CHECKING, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.config import get_config
from langgraph.runtime import Runtime
from deerflow.agents.memory.message_processing import detect_correction, detect_reinforcement, filter_messages_for_memory
from deerflow.agents.memory.queue import get_memory_queue
from deerflow.config.deer_flow_context import DeerFlowContext
from deerflow.config.memory_config import get_memory_config
from deerflow.runtime.user_context import get_effective_user_id
if TYPE_CHECKING:
from deerflow.config.memory_config import MemoryConfig
logger = logging.getLogger(__name__)
@@ -33,17 +37,20 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
state_schema = MemoryMiddlewareState
def __init__(self, agent_name: str | None = None):
def __init__(self, agent_name: str | None = None, *, memory_config: "MemoryConfig | None" = None):
"""Initialize the MemoryMiddleware.
Args:
agent_name: If provided, memory is stored per-agent. If None, uses global memory.
memory_config: Explicit memory config. When omitted, legacy global
config fallback is used.
"""
super().__init__()
self._agent_name = agent_name
self._memory_config = memory_config
@override
def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
"""Queue conversation for memory update after agent completes.
Args:
@@ -53,11 +60,15 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
Returns:
None (no state changes needed from this middleware).
"""
memory_config = runtime.context.app_config.memory
if not memory_config.enabled:
config = self._memory_config or get_memory_config()
if not config.enabled:
return None
thread_id = runtime.context.thread_id
# Get thread ID from runtime context first, then fall back to LangGraph's configurable metadata
thread_id = runtime.context.get("thread_id") if runtime.context else None
if thread_id is None:
config_data = get_config()
thread_id = config_data.get("configurable", {}).get("thread_id")
if not thread_id:
logger.debug("No thread_id in context, skipping memory update")
return None
@@ -86,7 +97,7 @@ class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
# threading.Timer fires on a different thread where ContextVar values are not
# propagated, so we must store user_id explicitly in ConversationContext.
user_id = get_effective_user_id()
queue = get_memory_queue(runtime.context.app_config)
queue = get_memory_queue()
queue.add(
thread_id=thread_id,
messages=filtered_messages,
@@ -5,11 +5,11 @@ from __future__ import annotations
import logging
from collections.abc import Collection
from dataclasses import dataclass
from typing import Any, Protocol, runtime_checkable
from typing import Any, Protocol, override, runtime_checkable
from langchain.agents import AgentState
from langchain.agents.middleware import SummarizationMiddleware
from langchain_core.messages import AIMessage, AnyMessage, RemoveMessage, ToolMessage
from langchain_core.messages import AIMessage, AnyMessage, HumanMessage, RemoveMessage, ToolMessage
from langgraph.config import get_config
from langgraph.graph.message import REMOVE_ALL_MESSAGES
from langgraph.runtime import Runtime
@@ -173,6 +173,13 @@ class DeerFlowSummarizationMiddleware(SummarizationMiddleware):
]
}
@override
def _build_new_messages(self, summary: str) -> list[HumanMessage]:
"""Override the base implementation to let the human message with the special name 'summary'.
And this message will be ignored to display in the frontend, but still can be used as context for the model.
"""
return [HumanMessage(content=f"Here is a summary of the conversation to date:\n\n{summary}", name="summary")]
def _partition_with_skill_rescue(
self,
messages: list[AnyMessage],
@@ -1,12 +1,14 @@
import logging
from datetime import UTC, datetime
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import HumanMessage
from langgraph.config import get_config
from langgraph.runtime import Runtime
from deerflow.agents.thread_state import ThreadDataState
from deerflow.config.deer_flow_context import DeerFlowContext
from deerflow.config.paths import Paths, get_paths
from deerflow.runtime.user_context import get_effective_user_id
@@ -77,10 +79,14 @@ class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
return self._get_thread_paths(thread_id, user_id=user_id)
@override
def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
thread_id = runtime.context.thread_id
def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime) -> dict | None:
context = runtime.context or {}
thread_id = context.get("thread_id")
if thread_id is None:
config = get_config()
thread_id = config.get("configurable", {}).get("thread_id")
if not thread_id:
if thread_id is None:
raise ValueError("Thread ID is required in runtime context or config.configurable")
user_id = get_effective_user_id()
@@ -93,8 +99,20 @@ class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
paths = self._create_thread_directories(thread_id, user_id=user_id)
logger.debug("Created thread data directories for thread %s", thread_id)
messages = list(state.get("messages", []))
last_message = messages[-1] if messages else None
if last_message and isinstance(last_message, HumanMessage):
messages[-1] = HumanMessage(
content=last_message.content,
id=last_message.id,
name=last_message.name or "user-input",
additional_kwargs={**last_message.additional_kwargs, "run_id": runtime.context.get("run_id"), "timestamp": datetime.now(UTC).isoformat()},
)
return {
"thread_data": {
**paths,
}
},
"messages": messages,
}
@@ -2,18 +2,20 @@
import logging
import re
from typing import Any, NotRequired, override
from typing import TYPE_CHECKING, Any, NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.config import get_config
from langgraph.runtime import Runtime
from deerflow.config.app_config import AppConfig
from deerflow.config.deer_flow_context import DeerFlowContext
from deerflow.config.title_config import TitleConfig
from deerflow.config.title_config import get_title_config
from deerflow.models import create_chat_model
if TYPE_CHECKING:
from deerflow.config.app_config import AppConfig
from deerflow.config.title_config import TitleConfig
logger = logging.getLogger(__name__)
@@ -28,6 +30,18 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
state_schema = TitleMiddlewareState
def __init__(self, *, app_config: "AppConfig | None" = None, title_config: "TitleConfig | None" = None):
super().__init__()
self._app_config = app_config
self._title_config = title_config
def _get_title_config(self):
if self._title_config is not None:
return self._title_config
if self._app_config is not None:
return self._app_config.title
return get_title_config()
def _normalize_content(self, content: object) -> str:
if isinstance(content, str):
return content
@@ -47,9 +61,10 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
return ""
def _should_generate_title(self, state: TitleMiddlewareState, title_config: TitleConfig) -> bool:
def _should_generate_title(self, state: TitleMiddlewareState) -> bool:
"""Check if we should generate a title for this thread."""
if not title_config.enabled:
config = self._get_title_config()
if not config.enabled:
return False
# Check if thread already has a title in state
@@ -68,11 +83,12 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
# Generate title after first complete exchange
return len(user_messages) == 1 and len(assistant_messages) >= 1
def _build_title_prompt(self, state: TitleMiddlewareState, title_config: TitleConfig) -> tuple[str, str]:
def _build_title_prompt(self, state: TitleMiddlewareState) -> tuple[str, str]:
"""Extract user/assistant messages and build the title prompt.
Returns (prompt_string, user_msg) so callers can use user_msg as fallback.
"""
config = self._get_title_config()
messages = state.get("messages", [])
user_msg_content = next((m.content for m in messages if m.type == "human"), "")
@@ -81,8 +97,8 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
user_msg = self._normalize_content(user_msg_content)
assistant_msg = self._strip_think_tags(self._normalize_content(assistant_msg_content))
prompt = title_config.prompt_template.format(
max_words=title_config.max_words,
prompt = config.prompt_template.format(
max_words=config.max_words,
user_msg=user_msg[:500],
assistant_msg=assistant_msg[:500],
)
@@ -92,15 +108,17 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
"""Remove <think>...</think> blocks emitted by reasoning models (e.g. minimax, DeepSeek-R1)."""
return re.sub(r"<think>[\s\S]*?</think>", "", text, flags=re.IGNORECASE).strip()
def _parse_title(self, content: object, title_config: TitleConfig) -> str:
def _parse_title(self, content: object) -> str:
"""Normalize model output into a clean title string."""
config = self._get_title_config()
title_content = self._normalize_content(content)
title_content = self._strip_think_tags(title_content)
title = title_content.strip().strip('"').strip("'")
return title[: title_config.max_chars] if len(title) > title_config.max_chars else title
return title[: config.max_chars] if len(title) > config.max_chars else title
def _fallback_title(self, user_msg: str, title_config: TitleConfig) -> str:
fallback_chars = min(title_config.max_chars, 50)
def _fallback_title(self, user_msg: str) -> str:
config = self._get_title_config()
fallback_chars = min(config.max_chars, 50)
if len(user_msg) > fallback_chars:
return user_msg[:fallback_chars].rstrip() + "..."
return user_msg if user_msg else "New Conversation"
@@ -116,42 +134,46 @@ class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
except Exception:
parent = {}
config = {**parent}
config["run_name"] = "title_agent"
config["tags"] = [*(config.get("tags") or []), "middleware:title"]
return config
def _generate_title_result(self, state: TitleMiddlewareState, title_config: TitleConfig) -> dict | None:
def _generate_title_result(self, state: TitleMiddlewareState) -> dict | None:
"""Generate a local fallback title without blocking on an LLM call."""
if not self._should_generate_title(state, title_config):
if not self._should_generate_title(state):
return None
_, user_msg = self._build_title_prompt(state, title_config)
return {"title": self._fallback_title(user_msg, title_config)}
_, user_msg = self._build_title_prompt(state)
return {"title": self._fallback_title(user_msg)}
async def _agenerate_title_result(self, state: TitleMiddlewareState, app_config: AppConfig) -> dict | None:
async def _agenerate_title_result(self, state: TitleMiddlewareState) -> dict | None:
"""Generate a title asynchronously and fall back locally on failure."""
title_config = app_config.title
if not self._should_generate_title(state, title_config):
if not self._should_generate_title(state):
return None
prompt, user_msg = self._build_title_prompt(state, title_config)
config = self._get_title_config()
prompt, user_msg = self._build_title_prompt(state)
try:
if title_config.model_name:
model = create_chat_model(name=title_config.model_name, thinking_enabled=False, app_config=app_config)
model_kwargs = {"thinking_enabled": False}
if self._app_config is not None:
model_kwargs["app_config"] = self._app_config
if config.model_name:
model = create_chat_model(name=config.model_name, **model_kwargs)
else:
model = create_chat_model(thinking_enabled=False, app_config=app_config)
model = create_chat_model(**model_kwargs)
response = await model.ainvoke(prompt, config=self._get_runnable_config())
title = self._parse_title(response.content, title_config)
title = self._parse_title(response.content)
if title:
return {"title": title}
except Exception:
logger.debug("Failed to generate async title; falling back to local title", exc_info=True)
return {"title": self._fallback_title(user_msg, title_config)}
return {"title": self._fallback_title(user_msg)}
@override
def after_model(self, state: TitleMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
return self._generate_title_result(state, runtime.context.app_config.title)
def after_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
return self._generate_title_result(state)
@override
async def aafter_model(self, state: TitleMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
return await self._agenerate_title_result(state, runtime.context.app_config)
async def aafter_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
return await self._agenerate_title_result(state)
@@ -1,31 +1,270 @@
"""Middleware for logging LLM token usage."""
"""Middleware for logging token usage and annotating step attribution."""
from __future__ import annotations
import logging
from typing import override
from collections import defaultdict
from typing import Any, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain.agents.middleware.todo import Todo
from langchain_core.messages import AIMessage
from langgraph.runtime import Runtime
logger = logging.getLogger(__name__)
TOKEN_USAGE_ATTRIBUTION_KEY = "token_usage_attribution"
def _string_arg(value: Any) -> str | None:
if isinstance(value, str):
normalized = value.strip()
return normalized or None
return None
def _normalize_todos(value: Any) -> list[Todo]:
if not isinstance(value, list):
return []
normalized: list[Todo] = []
for item in value:
if not isinstance(item, dict):
continue
todo: Todo = {}
content = _string_arg(item.get("content"))
status = item.get("status")
if content is not None:
todo["content"] = content
if status in {"pending", "in_progress", "completed"}:
todo["status"] = status
normalized.append(todo)
return normalized
def _todo_action_kind(previous: Todo | None, current: Todo) -> str:
status = current.get("status")
previous_content = previous.get("content") if previous else None
current_content = current.get("content")
if previous is None:
if status == "completed":
return "todo_complete"
if status == "in_progress":
return "todo_start"
return "todo_update"
if previous_content != current_content:
return "todo_update"
if status == "completed":
return "todo_complete"
if status == "in_progress":
return "todo_start"
return "todo_update"
def _build_todo_actions(previous_todos: list[Todo], next_todos: list[Todo]) -> list[dict[str, Any]]:
# This is the single source of truth for precise write_todos token
# attribution. The frontend intentionally falls back to a generic
# "Update to-do list" label when this metadata is missing or malformed.
previous_by_content: dict[str, list[tuple[int, Todo]]] = defaultdict(list)
matched_previous_indices: set[int] = set()
for index, todo in enumerate(previous_todos):
content = todo.get("content")
if isinstance(content, str) and content:
previous_by_content[content].append((index, todo))
actions: list[dict[str, Any]] = []
for index, todo in enumerate(next_todos):
content = todo.get("content")
if not isinstance(content, str) or not content:
continue
previous_match: Todo | None = None
content_matches = previous_by_content.get(content)
if content_matches:
while content_matches and content_matches[0][0] in matched_previous_indices:
content_matches.pop(0)
if content_matches:
previous_index, previous_match = content_matches.pop(0)
matched_previous_indices.add(previous_index)
if previous_match is None and index < len(previous_todos) and index not in matched_previous_indices:
previous_match = previous_todos[index]
matched_previous_indices.add(index)
if previous_match is not None:
previous_content = previous_match.get("content")
previous_status = previous_match.get("status")
if previous_content == content and previous_status == todo.get("status"):
continue
actions.append(
{
"kind": _todo_action_kind(previous_match, todo),
"content": content,
}
)
for index, todo in enumerate(previous_todos):
if index in matched_previous_indices:
continue
content = todo.get("content")
if not isinstance(content, str) or not content:
continue
actions.append(
{
"kind": "todo_remove",
"content": content,
}
)
return actions
def _describe_tool_call(tool_call: dict[str, Any], todos: list[Todo]) -> list[dict[str, Any]]:
name = _string_arg(tool_call.get("name")) or "unknown"
args = tool_call.get("args") if isinstance(tool_call.get("args"), dict) else {}
tool_call_id = _string_arg(tool_call.get("id"))
if name == "write_todos":
next_todos = _normalize_todos(args.get("todos"))
actions = _build_todo_actions(todos, next_todos)
if not actions:
return [
{
"kind": "tool",
"tool_name": name,
"tool_call_id": tool_call_id,
}
]
return [
{
**action,
"tool_call_id": tool_call_id,
}
for action in actions
]
if name == "task":
return [
{
"kind": "subagent",
"description": _string_arg(args.get("description")),
"subagent_type": _string_arg(args.get("subagent_type")),
"tool_call_id": tool_call_id,
}
]
if name in {"web_search", "image_search"}:
query = _string_arg(args.get("query"))
return [
{
"kind": "search",
"tool_name": name,
"query": query,
"tool_call_id": tool_call_id,
}
]
if name == "present_files":
return [
{
"kind": "present_files",
"tool_call_id": tool_call_id,
}
]
if name == "ask_clarification":
return [
{
"kind": "clarification",
"tool_call_id": tool_call_id,
}
]
return [
{
"kind": "tool",
"tool_name": name,
"description": _string_arg(args.get("description")),
"tool_call_id": tool_call_id,
}
]
def _infer_step_kind(message: AIMessage, actions: list[dict[str, Any]]) -> str:
if actions:
first_kind = actions[0].get("kind")
if len(actions) == 1 and first_kind in {"todo_start", "todo_complete", "todo_update", "todo_remove"}:
return "todo_update"
if len(actions) == 1 and first_kind == "subagent":
return "subagent_dispatch"
return "tool_batch"
if message.content:
return "final_answer"
return "thinking"
def _build_attribution(message: AIMessage, todos: list[Todo]) -> dict[str, Any]:
tool_calls = getattr(message, "tool_calls", None) or []
actions: list[dict[str, Any]] = []
current_todos = list(todos)
for raw_tool_call in tool_calls:
if not isinstance(raw_tool_call, dict):
continue
described_actions = _describe_tool_call(raw_tool_call, current_todos)
actions.extend(described_actions)
if raw_tool_call.get("name") == "write_todos":
args = raw_tool_call.get("args") if isinstance(raw_tool_call.get("args"), dict) else {}
current_todos = _normalize_todos(args.get("todos"))
tool_call_ids: list[str] = []
for tool_call in tool_calls:
if not isinstance(tool_call, dict):
continue
tool_call_id = _string_arg(tool_call.get("id"))
if tool_call_id is not None:
tool_call_ids.append(tool_call_id)
return {
# Schema changes should remain additive where possible so older
# frontends can ignore unknown fields and fall back safely.
"version": 1,
"kind": _infer_step_kind(message, actions),
"shared_attribution": len(actions) > 1,
"tool_call_ids": tool_call_ids,
"actions": actions,
}
class TokenUsageMiddleware(AgentMiddleware):
"""Logs token usage from model response usage_metadata."""
"""Logs token usage from model responses and annotates the AI step."""
@override
def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._log_usage(state)
@override
async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._log_usage(state)
def _log_usage(self, state: AgentState) -> None:
def _apply(self, state: AgentState) -> dict | None:
messages = state.get("messages", [])
if not messages:
return None
last = messages[-1]
if not isinstance(last, AIMessage):
return None
usage = getattr(last, "usage_metadata", None)
if usage:
logger.info(
@@ -34,4 +273,22 @@ class TokenUsageMiddleware(AgentMiddleware):
usage.get("output_tokens", "?"),
usage.get("total_tokens", "?"),
)
return None
todos = state.get("todos") or []
attribution = _build_attribution(last, todos if isinstance(todos, list) else [])
additional_kwargs = dict(getattr(last, "additional_kwargs", {}) or {})
if additional_kwargs.get(TOKEN_USAGE_ATTRIBUTION_KEY) == attribution:
return None
additional_kwargs[TOKEN_USAGE_ATTRIBUTION_KEY] = attribution
updated_msg = last.model_copy(update={"additional_kwargs": additional_kwargs})
return {"messages": [updated_msg]}
@override
def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._apply(state)
@override
async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._apply(state)
@@ -1,10 +1,8 @@
"""Tool error handling middleware and shared runtime middleware builders."""
from __future__ import annotations
import logging
from collections.abc import Awaitable, Callable
from typing import TYPE_CHECKING, override
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
@@ -13,8 +11,7 @@ from langgraph.errors import GraphBubbleUp
from langgraph.prebuilt.tool_node import ToolCallRequest
from langgraph.types import Command
if TYPE_CHECKING:
from deerflow.config.app_config import AppConfig
from deerflow.config.app_config import AppConfig
logger = logging.getLogger(__name__)
@@ -72,7 +69,7 @@ class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):
def _build_runtime_middlewares(
*,
app_config: "AppConfig",
app_config: AppConfig,
include_uploads: bool,
include_dangling_tool_call_patch: bool,
lazy_init: bool = True,
@@ -97,7 +94,7 @@ def _build_runtime_middlewares(
middlewares.append(DanglingToolCallMiddleware())
middlewares.append(LLMErrorHandlingMiddleware())
middlewares.append(LLMErrorHandlingMiddleware(app_config=app_config))
# Guardrail middleware (if configured)
guardrails_config = app_config.guardrails
@@ -129,7 +126,7 @@ def _build_runtime_middlewares(
return middlewares
def build_lead_runtime_middlewares(*, app_config: "AppConfig", lazy_init: bool = True) -> list[AgentMiddleware]:
def build_lead_runtime_middlewares(*, app_config: AppConfig, lazy_init: bool = True) -> list[AgentMiddleware]:
"""Middlewares shared by lead agent runtime before lead-only middlewares."""
return _build_runtime_middlewares(
app_config=app_config,
@@ -139,10 +136,32 @@ def build_lead_runtime_middlewares(*, app_config: "AppConfig", lazy_init: bool =
)
def build_subagent_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentMiddleware]:
def build_subagent_runtime_middlewares(
*,
app_config: AppConfig | None = None,
model_name: str | None = None,
lazy_init: bool = True,
) -> list[AgentMiddleware]:
"""Middlewares shared by subagent runtime before subagent-only middlewares."""
return _build_runtime_middlewares(
if app_config is None:
from deerflow.config import get_app_config
app_config = get_app_config()
middlewares = _build_runtime_middlewares(
app_config=app_config,
include_uploads=False,
include_dangling_tool_call_patch=True,
lazy_init=lazy_init,
)
if model_name is None and app_config.models:
model_name = app_config.models[0].name
model_config = app_config.get_model_config(model_name) if model_name else None
if model_config is not None and model_config.supports_vision:
from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
middlewares.append(ViewImageMiddleware())
return middlewares
@@ -9,7 +9,6 @@ from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import HumanMessage
from langgraph.runtime import Runtime
from deerflow.config.deer_flow_context import DeerFlowContext
from deerflow.config.paths import Paths, get_paths
from deerflow.runtime.user_context import get_effective_user_id
from deerflow.utils.file_conversion import extract_outline
@@ -186,7 +185,7 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
return files if files else None
@override
def before_agent(self, state: UploadsMiddlewareState, runtime: Runtime[DeerFlowContext]) -> dict | None:
def before_agent(self, state: UploadsMiddlewareState, runtime: Runtime) -> dict | None:
"""Inject uploaded files information before agent execution.
New files come from the current message's additional_kwargs.files.
@@ -215,7 +214,14 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
return None
# Resolve uploads directory for existence checks
thread_id = runtime.context.thread_id
thread_id = (runtime.context or {}).get("thread_id")
if thread_id is None:
try:
from langgraph.config import get_config
thread_id = get_config().get("configurable", {}).get("thread_id")
except RuntimeError:
pass # get_config() raises outside a runnable context (e.g. unit tests)
uploads_dir = self._paths.sandbox_uploads_dir(thread_id, user_id=get_effective_user_id()) if thread_id else None
# Get newly uploaded files from the current message's additional_kwargs.files
@@ -277,6 +283,7 @@ class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
updated_message = HumanMessage(
content=updated_content,
id=last_message.id,
name=last_message.name,
additional_kwargs=last_message.additional_kwargs,
)
+142 -92
View File
@@ -36,13 +36,12 @@ from deerflow.agents.lead_agent.agent import _build_middlewares
from deerflow.agents.lead_agent.prompt import apply_prompt_template
from deerflow.agents.thread_state import ThreadState
from deerflow.config.agents_config import AGENT_NAME_PATTERN
from deerflow.config.app_config import AppConfig
from deerflow.config.deer_flow_context import DeerFlowContext
from deerflow.config.extensions_config import ExtensionsConfig
from deerflow.config.app_config import get_app_config, reload_app_config
from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
from deerflow.config.paths import get_paths
from deerflow.models import create_chat_model
from deerflow.runtime.user_context import get_effective_user_id
from deerflow.skills.installer import install_skill_from_archive
from deerflow.skills.storage import get_or_new_skill_storage
from deerflow.uploads.manager import (
claim_unique_filename,
delete_file_safe,
@@ -117,7 +116,6 @@ class DeerFlowClient:
config_path: str | None = None,
checkpointer=None,
*,
config: AppConfig | None = None,
model_name: str | None = None,
thinking_enabled: bool = True,
subagent_enabled: bool = False,
@@ -132,14 +130,9 @@ class DeerFlowClient:
Args:
config_path: Path to config.yaml. Uses default resolution if None.
Ignored when ``config`` is provided.
checkpointer: LangGraph checkpointer instance for state persistence.
Required for multi-turn conversations on the same thread_id.
Without a checkpointer, each call is stateless.
config: Optional pre-constructed AppConfig. When provided, it takes
precedence over ``config_path`` and no file is read. Enables
multi-client isolation: two clients with different configs can
coexist in the same process without touching process-global state.
model_name: Override the default model name from config.
thinking_enabled: Enable model's extended thinking.
subagent_enabled: Enable subagent delegation.
@@ -148,18 +141,9 @@ class DeerFlowClient:
available_skills: Optional set of skill names to make available. If None (default), all scanned skills are available.
middlewares: Optional list of custom middlewares to inject into the agent.
"""
# Constructor-captured config: the client owns its AppConfig for its lifetime.
# Multiple clients with different configs do not contend.
#
# Priority: explicit ``config=`` > explicit ``config_path=`` > ``AppConfig.from_file()``
# with default path resolution. There is no ambient global fallback; if
# config.yaml cannot be located, ``from_file`` raises loudly.
if config is not None:
self._app_config = config
elif config_path is not None:
self._app_config = AppConfig.from_file(config_path)
else:
self._app_config = AppConfig.from_file()
if config_path is not None:
reload_app_config(config_path)
self._app_config = get_app_config()
if agent_name is not None and not AGENT_NAME_PATTERN.match(agent_name):
raise ValueError(f"Invalid agent name '{agent_name}'. Must match pattern: {AGENT_NAME_PATTERN.pattern}")
@@ -187,15 +171,6 @@ class DeerFlowClient:
self._agent = None
self._agent_config_key = None
def _reload_config(self) -> None:
"""Reload config from file and refresh the cached reference.
Only the client's own ``_app_config`` is rebuilt. Other clients
and the process-global are untouched, so multi-client coexistence
survives reload.
"""
self._app_config = AppConfig.from_file()
# ------------------------------------------------------------------
# Internal helpers
# ------------------------------------------------------------------
@@ -253,11 +228,10 @@ class DeerFlowClient:
max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
kwargs: dict[str, Any] = {
"model": create_chat_model(name=model_name, thinking_enabled=thinking_enabled, app_config=self._app_config),
"model": create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
"tools": self._get_tools(model_name=model_name, subagent_enabled=subagent_enabled),
"middleware": _build_middlewares(self._app_config, config, model_name=model_name, agent_name=self._agent_name, custom_middlewares=self._middlewares),
"middleware": _build_middlewares(config, model_name=model_name, agent_name=self._agent_name, custom_middlewares=self._middlewares),
"system_prompt": apply_prompt_template(
self._app_config,
subagent_enabled=subagent_enabled,
max_concurrent_subagents=max_concurrent_subagents,
agent_name=self._agent_name,
@@ -269,7 +243,7 @@ class DeerFlowClient:
if checkpointer is None:
from deerflow.runtime.checkpointer import get_checkpointer
checkpointer = get_checkpointer(self._app_config)
checkpointer = get_checkpointer()
if checkpointer is not None:
kwargs["checkpointer"] = checkpointer
@@ -277,11 +251,12 @@ class DeerFlowClient:
self._agent_config_key = key
logger.info("Agent created: agent_name=%s, model=%s, thinking=%s", self._agent_name, model_name, thinking_enabled)
def _get_tools(self, *, model_name: str | None, subagent_enabled: bool):
@staticmethod
def _get_tools(*, model_name: str | None, subagent_enabled: bool):
"""Lazy import to avoid circular dependency at module level."""
from deerflow.tools import get_available_tools
return get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled, app_config=self._app_config)
return get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled)
@staticmethod
def _serialize_tool_calls(tool_calls) -> list[dict]:
@@ -289,25 +264,35 @@ class DeerFlowClient:
return [{"name": tc["name"], "args": tc["args"], "id": tc.get("id")} for tc in tool_calls]
@staticmethod
def _ai_text_event(msg_id: str | None, text: str, usage: dict | None) -> "StreamEvent":
"""Build a ``messages-tuple`` AI text event, attaching usage when present."""
def _serialize_additional_kwargs(msg) -> dict[str, Any] | None:
"""Copy message additional_kwargs when present."""
additional_kwargs = getattr(msg, "additional_kwargs", None)
if isinstance(additional_kwargs, dict) and additional_kwargs:
return dict(additional_kwargs)
return None
@staticmethod
def _ai_text_event(msg_id: str | None, text: str, usage: dict | None, additional_kwargs: dict[str, Any] | None = None) -> "StreamEvent":
"""Build a ``messages-tuple`` AI text event."""
data: dict[str, Any] = {"type": "ai", "content": text, "id": msg_id}
if usage:
data["usage_metadata"] = usage
if additional_kwargs:
data["additional_kwargs"] = additional_kwargs
return StreamEvent(type="messages-tuple", data=data)
@staticmethod
def _ai_tool_calls_event(msg_id: str | None, tool_calls) -> "StreamEvent":
def _ai_tool_calls_event(msg_id: str | None, tool_calls, additional_kwargs: dict[str, Any] | None = None) -> "StreamEvent":
"""Build a ``messages-tuple`` AI tool-calls event."""
return StreamEvent(
type="messages-tuple",
data={
"type": "ai",
"content": "",
"id": msg_id,
"tool_calls": DeerFlowClient._serialize_tool_calls(tool_calls),
},
)
data: dict[str, Any] = {
"type": "ai",
"content": "",
"id": msg_id,
"tool_calls": DeerFlowClient._serialize_tool_calls(tool_calls),
}
if additional_kwargs:
data["additional_kwargs"] = additional_kwargs
return StreamEvent(type="messages-tuple", data=data)
@staticmethod
def _tool_message_event(msg: ToolMessage) -> "StreamEvent":
@@ -332,19 +317,30 @@ class DeerFlowClient:
d["tool_calls"] = DeerFlowClient._serialize_tool_calls(msg.tool_calls)
if getattr(msg, "usage_metadata", None):
d["usage_metadata"] = msg.usage_metadata
if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
d["additional_kwargs"] = additional_kwargs
return d
if isinstance(msg, ToolMessage):
return {
d = {
"type": "tool",
"content": DeerFlowClient._extract_text(msg.content),
"name": getattr(msg, "name", None),
"tool_call_id": getattr(msg, "tool_call_id", None),
"id": getattr(msg, "id", None),
}
if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
d["additional_kwargs"] = additional_kwargs
return d
if isinstance(msg, HumanMessage):
return {"type": "human", "content": msg.content, "id": getattr(msg, "id", None)}
d = {"type": "human", "content": msg.content, "id": getattr(msg, "id", None)}
if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
d["additional_kwargs"] = additional_kwargs
return d
if isinstance(msg, SystemMessage):
return {"type": "system", "content": msg.content, "id": getattr(msg, "id", None)}
d = {"type": "system", "content": msg.content, "id": getattr(msg, "id", None)}
if additional_kwargs := DeerFlowClient._serialize_additional_kwargs(msg):
d["additional_kwargs"] = additional_kwargs
return d
return {"type": "unknown", "content": str(msg), "id": getattr(msg, "id", None)}
@staticmethod
@@ -402,7 +398,7 @@ class DeerFlowClient:
if checkpointer is None:
from deerflow.runtime.checkpointer.provider import get_checkpointer
checkpointer = get_checkpointer(self._app_config)
checkpointer = get_checkpointer()
thread_info_map = {}
@@ -457,7 +453,7 @@ class DeerFlowClient:
if checkpointer is None:
from deerflow.runtime.checkpointer.provider import get_checkpointer
checkpointer = get_checkpointer(self._app_config)
checkpointer = get_checkpointer()
config = {"configurable": {"thread_id": thread_id}}
checkpoints = []
@@ -567,6 +563,7 @@ class DeerFlowClient:
- type="messages-tuple" data={"type": "ai", "content": <delta>, "id": str}
- type="messages-tuple" data={"type": "ai", "content": <delta>, "id": str, "usage_metadata": {...}}
- type="messages-tuple" data={"type": "ai", "content": "", "id": str, "tool_calls": [...]}
- type="messages-tuple" data={"type": "ai", "content": "", "id": str, "additional_kwargs": {...}}
- type="messages-tuple" data={"type": "tool", "content": str, "name": str, "tool_call_id": str, "id": str}
- type="end" data={"usage": {"input_tokens": int, "output_tokens": int, "total_tokens": int}}
"""
@@ -577,7 +574,9 @@ class DeerFlowClient:
self._ensure_agent(config)
state: dict[str, Any] = {"messages": [HumanMessage(content=message)]}
context = DeerFlowContext(app_config=self._app_config, thread_id=thread_id, agent_name=self._agent_name)
context = {"thread_id": thread_id}
if self._agent_name:
context["agent_name"] = self._agent_name
seen_ids: set[str] = set()
# Cross-mode handoff: ids already streamed via LangGraph ``messages``
@@ -587,6 +586,7 @@ class DeerFlowClient:
# in both the final ``messages`` chunk and the values snapshot —
# count it only on whichever arrives first.
counted_usage_ids: set[str] = set()
sent_additional_kwargs_by_id: dict[str, dict[str, Any]] = {}
cumulative_usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}
def _account_usage(msg_id: str | None, usage: Any) -> dict | None:
@@ -616,6 +616,20 @@ class DeerFlowClient:
"total_tokens": total_tokens,
}
def _unsent_additional_kwargs(msg_id: str | None, additional_kwargs: dict[str, Any] | None) -> dict[str, Any] | None:
if not additional_kwargs:
return None
if not msg_id:
return additional_kwargs
sent = sent_additional_kwargs_by_id.setdefault(msg_id, {})
delta = {key: value for key, value in additional_kwargs.items() if sent.get(key) != value}
if not delta:
return None
sent.update(delta)
return delta
for item in self._agent.stream(
state,
config=config,
@@ -643,17 +657,31 @@ class DeerFlowClient:
if isinstance(msg_chunk, AIMessage):
text = self._extract_text(msg_chunk.content)
additional_kwargs = self._serialize_additional_kwargs(msg_chunk)
counted_usage = _account_usage(msg_id, msg_chunk.usage_metadata)
sent_additional_kwargs = False
if text:
if msg_id:
streamed_ids.add(msg_id)
yield self._ai_text_event(msg_id, text, counted_usage)
additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
yield self._ai_text_event(
msg_id,
text,
counted_usage,
additional_kwargs_delta,
)
sent_additional_kwargs = bool(additional_kwargs_delta)
if msg_chunk.tool_calls:
if msg_id:
streamed_ids.add(msg_id)
yield self._ai_tool_calls_event(msg_id, msg_chunk.tool_calls)
additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
yield self._ai_tool_calls_event(
msg_id,
msg_chunk.tool_calls,
additional_kwargs_delta,
)
elif isinstance(msg_chunk, ToolMessage):
if msg_id:
@@ -676,17 +704,45 @@ class DeerFlowClient:
if msg_id and msg_id in streamed_ids:
if isinstance(msg, AIMessage):
_account_usage(msg_id, getattr(msg, "usage_metadata", None))
additional_kwargs = self._serialize_additional_kwargs(msg)
additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
if additional_kwargs_delta:
# Metadata-only follow-up: ``messages-tuple`` has no
# dedicated attribution event, so clients should
# merge this empty-content AI event by message id
# and ignore it for text rendering.
yield self._ai_text_event(msg_id, "", None, additional_kwargs_delta)
continue
if isinstance(msg, AIMessage):
counted_usage = _account_usage(msg_id, msg.usage_metadata)
additional_kwargs = self._serialize_additional_kwargs(msg)
sent_additional_kwargs = False
if msg.tool_calls:
yield self._ai_tool_calls_event(msg_id, msg.tool_calls)
additional_kwargs_delta = _unsent_additional_kwargs(msg_id, additional_kwargs)
yield self._ai_tool_calls_event(
msg_id,
msg.tool_calls,
additional_kwargs_delta,
)
sent_additional_kwargs = bool(additional_kwargs_delta)
text = self._extract_text(msg.content)
if text:
yield self._ai_text_event(msg_id, text, counted_usage)
additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
yield self._ai_text_event(
msg_id,
text,
counted_usage,
additional_kwargs_delta,
)
elif msg_id:
additional_kwargs_delta = None if sent_additional_kwargs else _unsent_additional_kwargs(msg_id, additional_kwargs)
if not additional_kwargs_delta:
continue
# See the metadata-only follow-up convention above.
yield self._ai_text_event(msg_id, "", None, additional_kwargs_delta)
elif isinstance(msg, ToolMessage):
yield self._tool_message_event(msg)
@@ -775,8 +831,6 @@ class DeerFlowClient:
Dict with "skills" key containing list of skill info dicts,
matching the Gateway API ``SkillsListResponse`` schema.
"""
from deerflow.skills.loader import load_skills
return {
"skills": [
{
@@ -786,7 +840,7 @@ class DeerFlowClient:
"category": s.category,
"enabled": s.enabled,
}
for s in load_skills(self._app_config, enabled_only=enabled_only)
for s in get_or_new_skill_storage().load_skills(enabled_only=enabled_only)
]
}
@@ -798,19 +852,19 @@ class DeerFlowClient:
"""
from deerflow.agents.memory.updater import get_memory_data
return get_memory_data(self._app_config.memory, user_id=get_effective_user_id())
return get_memory_data(user_id=get_effective_user_id())
def export_memory(self) -> dict:
"""Export current memory data for backup or transfer."""
from deerflow.agents.memory.updater import get_memory_data
return get_memory_data(self._app_config.memory, user_id=get_effective_user_id())
return get_memory_data(user_id=get_effective_user_id())
def import_memory(self, memory_data: dict) -> dict:
"""Import and persist full memory data."""
from deerflow.agents.memory.updater import import_memory_data
return import_memory_data(self._app_config.memory, memory_data, user_id=get_effective_user_id())
return import_memory_data(memory_data, user_id=get_effective_user_id())
def get_model(self, name: str) -> dict | None:
"""Get a specific model's configuration by name.
@@ -845,8 +899,8 @@ class DeerFlowClient:
Dict with "mcp_servers" key mapping server name to config,
matching the Gateway API ``McpConfigResponse`` schema.
"""
ext = self._app_config.extensions
return {"mcp_servers": {name: server.model_dump() for name, server in ext.mcp_servers.items()}}
config = get_extensions_config()
return {"mcp_servers": {name: server.model_dump() for name, server in config.mcp_servers.items()}}
def update_mcp_config(self, mcp_servers: dict[str, dict]) -> dict:
"""Update MCP server configurations.
@@ -868,19 +922,18 @@ class DeerFlowClient:
if config_path is None:
raise FileNotFoundError("Cannot locate extensions_config.json. Set DEER_FLOW_EXTENSIONS_CONFIG_PATH or ensure it exists in the project root.")
current_ext = self._app_config.extensions
current_config = get_extensions_config()
config_data = {
"mcpServers": mcp_servers,
"skills": {name: {"enabled": skill.enabled} for name, skill in current_ext.skills.items()},
"skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
}
self._atomic_write_json(config_path, config_data)
self._agent = None
self._agent_config_key = None
self._reload_config()
reloaded = self._app_config.extensions
reloaded = reload_extensions_config()
return {"mcp_servers": {name: server.model_dump() for name, server in reloaded.mcp_servers.items()}}
# ------------------------------------------------------------------
@@ -896,9 +949,9 @@ class DeerFlowClient:
Returns:
Skill info dict, or None if not found.
"""
from deerflow.skills.loader import load_skills
from deerflow.skills.storage import get_or_new_skill_storage
skill = next((s for s in load_skills(self._app_config, enabled_only=False) if s.name == name), None)
skill = next((s for s in get_or_new_skill_storage().load_skills(enabled_only=False) if s.name == name), None)
if skill is None:
return None
return {
@@ -923,9 +976,9 @@ class DeerFlowClient:
ValueError: If the skill is not found.
OSError: If the config file cannot be written.
"""
from deerflow.skills.loader import load_skills
from deerflow.skills.storage import get_or_new_skill_storage
skills = load_skills(self._app_config, enabled_only=False)
skills = get_or_new_skill_storage().load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == name), None)
if skill is None:
raise ValueError(f"Skill '{name}' not found")
@@ -934,25 +987,21 @@ class DeerFlowClient:
if config_path is None:
raise FileNotFoundError("Cannot locate extensions_config.json. Set DEER_FLOW_EXTENSIONS_CONFIG_PATH or ensure it exists in the project root.")
# Do not mutate self._app_config (frozen value). Compose the new
# skills state in a fresh dict, write it to disk, and let _reload_config()
# below rebuild AppConfig from the updated file.
ext = self._app_config.extensions
new_skills = {n: {"enabled": sc.enabled} for n, sc in ext.skills.items()}
new_skills[name] = {"enabled": enabled}
extensions_config = get_extensions_config()
extensions_config.skills[name] = SkillStateConfig(enabled=enabled)
config_data = {
"mcpServers": {n: s.model_dump() for n, s in ext.mcp_servers.items()},
"skills": new_skills,
"mcpServers": {n: s.model_dump() for n, s in extensions_config.mcp_servers.items()},
"skills": {n: {"enabled": sc.enabled} for n, sc in extensions_config.skills.items()},
}
self._atomic_write_json(config_path, config_data)
self._agent = None
self._agent_config_key = None
self._reload_config()
reload_extensions_config()
updated = next((s for s in load_skills(self._app_config, enabled_only=False) if s.name == name), None)
updated = next((s for s in get_or_new_skill_storage().load_skills(enabled_only=False) if s.name == name), None)
if updated is None:
raise RuntimeError(f"Skill '{name}' disappeared after update")
return {
@@ -976,7 +1025,7 @@ class DeerFlowClient:
FileNotFoundError: If the file does not exist.
ValueError: If the file is invalid.
"""
return install_skill_from_archive(skill_path)
return get_or_new_skill_storage().install_skill_from_archive(skill_path)
# ------------------------------------------------------------------
# Public API — memory management
@@ -990,25 +1039,25 @@ class DeerFlowClient:
"""
from deerflow.agents.memory.updater import reload_memory_data
return reload_memory_data(self._app_config.memory, user_id=get_effective_user_id())
return reload_memory_data(user_id=get_effective_user_id())
def clear_memory(self) -> dict:
"""Clear all persisted memory data."""
from deerflow.agents.memory.updater import clear_memory_data
return clear_memory_data(self._app_config.memory, user_id=get_effective_user_id())
return clear_memory_data(user_id=get_effective_user_id())
def create_memory_fact(self, content: str, category: str = "context", confidence: float = 0.5) -> dict:
"""Create a single fact manually."""
from deerflow.agents.memory.updater import create_memory_fact
return create_memory_fact(self._app_config.memory, content=content, category=category, confidence=confidence)
return create_memory_fact(content=content, category=category, confidence=confidence)
def delete_memory_fact(self, fact_id: str) -> dict:
"""Delete a single fact from memory by fact id."""
from deerflow.agents.memory.updater import delete_memory_fact
return delete_memory_fact(self._app_config.memory, fact_id)
return delete_memory_fact(fact_id)
def update_memory_fact(
self,
@@ -1021,7 +1070,6 @@ class DeerFlowClient:
from deerflow.agents.memory.updater import update_memory_fact
return update_memory_fact(
self._app_config.memory,
fact_id=fact_id,
content=content,
category=category,
@@ -1034,7 +1082,9 @@ class DeerFlowClient:
Returns:
Memory config dict.
"""
config = self._app_config.memory
from deerflow.config.memory_config import get_memory_config
config = get_memory_config()
return {
"enabled": config.enabled,
"storage_path": config.storage_path,
@@ -48,6 +48,12 @@ class AioSandbox(Sandbox):
self._home_dir = context.home_dir
return self._home_dir
# Default no_change_timeout for exec_command (seconds). Matches the
# client-level timeout so that long-running commands which produce no
# output are not prematurely terminated by the sandbox's built-in 120 s
# default.
_DEFAULT_NO_CHANGE_TIMEOUT = 600
def execute_command(self, command: str) -> str:
"""Execute a shell command in the sandbox.
@@ -66,13 +72,13 @@ class AioSandbox(Sandbox):
"""
with self._lock:
try:
result = self._client.shell.exec_command(command=command)
result = self._client.shell.exec_command(command=command, no_change_timeout=self._DEFAULT_NO_CHANGE_TIMEOUT)
output = result.data.output if result.data else ""
if output and _ERROR_OBSERVATION_SIGNATURE in output:
logger.warning("ErrorObservation detected in sandbox output, retrying with a fresh session")
fresh_id = str(uuid.uuid4())
result = self._client.shell.exec_command(command=command, id=fresh_id)
result = self._client.shell.exec_command(command=command, id=fresh_id, no_change_timeout=self._DEFAULT_NO_CHANGE_TIMEOUT)
output = result.data.output if result.data else ""
return output if output else "(no output)"
@@ -108,7 +114,7 @@ class AioSandbox(Sandbox):
"""
with self._lock:
try:
result = self._client.shell.exec_command(command=f"find {shlex.quote(path)} -maxdepth {max_depth} -type f -o -type d 2>/dev/null | head -500")
result = self._client.shell.exec_command(command=f"find {shlex.quote(path)} -maxdepth {max_depth} -type f -o -type d 2>/dev/null | head -500", no_change_timeout=self._DEFAULT_NO_CHANGE_TIMEOUT)
output = result.data.output if result.data else ""
if output:
return [line.strip() for line in output.strip().split("\n") if line.strip()]
@@ -25,7 +25,7 @@ except ImportError: # pragma: no cover - Windows fallback
fcntl = None # type: ignore[assignment]
import msvcrt
from deerflow.config.app_config import AppConfig
from deerflow.config import get_app_config
from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
from deerflow.runtime.user_context import get_effective_user_id
from deerflow.sandbox.sandbox import Sandbox
@@ -90,8 +90,7 @@ class AioSandboxProvider(SandboxProvider):
API_KEY: $MY_API_KEY
"""
def __init__(self, app_config: "AppConfig"):
self._app_config = app_config
def __init__(self):
self._lock = threading.Lock()
self._sandboxes: dict[str, AioSandbox] = {} # sandbox_id -> AioSandbox instance
self._sandbox_infos: dict[str, SandboxInfo] = {} # sandbox_id -> SandboxInfo (for destroy)
@@ -160,7 +159,8 @@ class AioSandboxProvider(SandboxProvider):
def _load_config(self) -> dict:
"""Load sandbox configuration from app config."""
sandbox_config = self._app_config.sandbox
config = get_app_config()
sandbox_config = config.sandbox
idle_timeout = getattr(sandbox_config, "idle_timeout", None)
replicas = getattr(sandbox_config, "replicas", None)
@@ -283,15 +283,17 @@ class AioSandboxProvider(SandboxProvider):
(paths.host_acp_workspace_dir(thread_id, user_id=user_id), "/mnt/acp-workspace", True),
]
def _get_skills_mount(self) -> tuple[str, str, bool] | None:
@staticmethod
def _get_skills_mount() -> tuple[str, str, bool] | None:
"""Get the skills directory mount configuration.
Mount source uses DEER_FLOW_HOST_SKILLS_PATH when running inside Docker (DooD)
so the host Docker daemon can resolve the path.
"""
try:
skills_path = self._app_config.skills.get_skills_path()
container_path = self._app_config.skills.container_path
config = get_app_config()
skills_path = config.skills.get_skills_path()
container_path = config.skills.container_path
if skills_path.exists():
# When running inside Docker with DooD, use host-side skills path.
@@ -9,6 +9,7 @@ from __future__ import annotations
import json
import logging
import os
import shlex
import subprocess
from datetime import datetime
@@ -86,6 +87,88 @@ def _format_container_mount(runtime: str, host_path: str, container_path: str, r
return ["-v", mount_spec]
def _redact_container_command_for_log(cmd: list[str]) -> list[str]:
"""Return a Docker/Container command with environment values redacted."""
redacted: list[str] = []
redact_next_env = False
for arg in cmd:
if redact_next_env:
if "=" in arg:
key = arg.split("=", 1)[0]
redacted.append(f"{key}=<redacted>" if key else "<redacted>")
else:
redacted.append(arg)
redact_next_env = False
continue
if arg in {"-e", "--env"}:
redacted.append(arg)
redact_next_env = True
continue
if arg.startswith("--env="):
value = arg.removeprefix("--env=")
if "=" in value:
key = value.split("=", 1)[0]
redacted.append(f"--env={key}=<redacted>" if key else "--env=<redacted>")
else:
redacted.append(arg)
continue
redacted.append(arg)
return redacted
def _format_container_command_for_log(cmd: list[str]) -> str:
if os.name == "nt":
return subprocess.list2cmdline(cmd)
return shlex.join(cmd)
def _normalize_sandbox_host(host: str) -> str:
return host.strip().lower()
def _is_ipv6_loopback_sandbox_host(host: str) -> bool:
return _normalize_sandbox_host(host) in {"::1", "[::1]"}
def _is_loopback_sandbox_host(host: str) -> bool:
return _normalize_sandbox_host(host) in {"", "localhost", "127.0.0.1", "::1", "[::1]"}
def _resolve_docker_bind_host(sandbox_host: str | None = None, bind_host: str | None = None) -> str:
"""Choose the host interface for legacy Docker ``-p`` sandbox publishing.
Bare-metal/local runs talk to sandboxes through localhost and should not
expose the sandbox HTTP API on every host interface. Docker-outside-of-
Docker deployments commonly use ``host.docker.internal`` from another
container; keep their legacy broad bind unless operators opt into a
narrower bind with ``DEER_FLOW_SANDBOX_BIND_HOST``. When operators choose
an IPv6 loopback sandbox host, bind Docker to IPv6 loopback as well so the
advertised sandbox URL and published socket use the same address family.
"""
explicit_bind = bind_host if bind_host is not None else os.environ.get("DEER_FLOW_SANDBOX_BIND_HOST")
if explicit_bind is not None:
explicit_bind = explicit_bind.strip()
if explicit_bind:
logger.debug("Docker sandbox bind: %s (explicit bind host override)", explicit_bind)
return explicit_bind
host = sandbox_host if sandbox_host is not None else os.environ.get("DEER_FLOW_SANDBOX_HOST", "localhost")
if _is_ipv6_loopback_sandbox_host(host):
logger.debug("Docker sandbox bind: [::1] (IPv6 loopback sandbox host)")
return "[::1]"
if _is_loopback_sandbox_host(host):
logger.debug("Docker sandbox bind: 127.0.0.1 (loopback default)")
return "127.0.0.1"
logger.debug("Docker sandbox bind: 0.0.0.0 (non-loopback sandbox host compatibility)")
return "0.0.0.0"
class LocalContainerBackend(SandboxBackend):
"""Backend that manages sandbox containers locally using Docker or Apple Container.
@@ -424,12 +507,17 @@ class LocalContainerBackend(SandboxBackend):
if self._runtime == "docker":
cmd.extend(["--security-opt", "seccomp=unconfined"])
if self._runtime == "docker":
port_mapping = f"{_resolve_docker_bind_host()}:{port}:8080"
else:
port_mapping = f"{port}:8080"
cmd.extend(
[
"--rm",
"-d",
"-p",
f"{port}:8080",
port_mapping,
"--name",
container_name,
]
@@ -464,7 +552,8 @@ class LocalContainerBackend(SandboxBackend):
cmd.append(self._image)
logger.info(f"Starting container using {self._runtime}: {' '.join(cmd)}")
log_cmd = _format_container_command_for_log(_redact_container_command_for_log(cmd))
logger.info(f"Starting container using {self._runtime}: {log_cmd}")
try:
result = subprocess.run(cmd, capture_output=True, text=True, check=True)
@@ -5,9 +5,9 @@ Web Search Tool - Search the web using DuckDuckGo (no API key required).
import json
import logging
from langchain.tools import ToolRuntime, tool
from langchain.tools import tool
from deerflow.config.deer_flow_context import resolve_context
from deerflow.config import get_app_config
logger = logging.getLogger(__name__)
@@ -55,7 +55,6 @@ def _search_text(
@tool("web_search", parse_docstring=True)
def web_search_tool(
query: str,
runtime: ToolRuntime,
max_results: int = 5,
) -> str:
"""Search the web for information. Use this tool to find current information, news, articles, and facts from the internet.
@@ -64,11 +63,11 @@ def web_search_tool(
query: Search keywords describing what you want to find. Be specific for better results.
max_results: Maximum number of results to return. Default is 5.
"""
tool_config = resolve_context(runtime).app_config.get_tool_config("web_search")
config = get_app_config().get_tool_config("web_search")
# Override max_results from config if set
if tool_config is not None and "max_results" in tool_config.model_extra:
max_results = tool_config.model_extra.get("max_results", max_results)
if config is not None and "max_results" in config.model_extra:
max_results = config.model_extra.get("max_results", max_results)
results = _search_text(
query=query,
@@ -1,39 +1,37 @@
import json
from exa_py import Exa
from langchain.tools import ToolRuntime, tool
from langchain.tools import tool
from deerflow.config.app_config import AppConfig
from deerflow.config.deer_flow_context import resolve_context
from deerflow.config import get_app_config
def _get_exa_client(app_config: AppConfig, tool_name: str = "web_search") -> Exa:
tool_config = app_config.get_tool_config(tool_name)
def _get_exa_client(tool_name: str = "web_search") -> Exa:
config = get_app_config().get_tool_config(tool_name)
api_key = None
if tool_config is not None and "api_key" in tool_config.model_extra:
api_key = tool_config.model_extra.get("api_key")
if config is not None and "api_key" in config.model_extra:
api_key = config.model_extra.get("api_key")
return Exa(api_key=api_key)
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str, runtime: ToolRuntime) -> str:
def web_search_tool(query: str) -> str:
"""Search the web.
Args:
query: The query to search for.
"""
try:
app_config = resolve_context(runtime).app_config
tool_config = app_config.get_tool_config("web_search")
config = get_app_config().get_tool_config("web_search")
max_results = 5
search_type = "auto"
contents_max_characters = 1000
if tool_config is not None:
max_results = tool_config.model_extra.get("max_results", max_results)
search_type = tool_config.model_extra.get("search_type", search_type)
contents_max_characters = tool_config.model_extra.get("contents_max_characters", contents_max_characters)
if config is not None:
max_results = config.model_extra.get("max_results", max_results)
search_type = config.model_extra.get("search_type", search_type)
contents_max_characters = config.model_extra.get("contents_max_characters", contents_max_characters)
client = _get_exa_client(app_config)
client = _get_exa_client()
res = client.search(
query,
type=search_type,
@@ -56,7 +54,7 @@ def web_search_tool(query: str, runtime: ToolRuntime) -> str:
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -67,7 +65,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
url: The URL to fetch the contents of.
"""
try:
client = _get_exa_client(resolve_context(runtime).app_config, "web_fetch")
client = _get_exa_client("web_fetch")
res = client.get_contents([url], text={"max_characters": 4096})
if res.results:
@@ -1,35 +1,33 @@
import json
from firecrawl import FirecrawlApp
from langchain.tools import ToolRuntime, tool
from langchain.tools import tool
from deerflow.config.app_config import AppConfig
from deerflow.config.deer_flow_context import resolve_context
from deerflow.config import get_app_config
def _get_firecrawl_client(app_config: AppConfig, tool_name: str = "web_search") -> FirecrawlApp:
tool_config = app_config.get_tool_config(tool_name)
def _get_firecrawl_client(tool_name: str = "web_search") -> FirecrawlApp:
config = get_app_config().get_tool_config(tool_name)
api_key = None
if tool_config is not None and "api_key" in tool_config.model_extra:
api_key = tool_config.model_extra.get("api_key")
if config is not None and "api_key" in config.model_extra:
api_key = config.model_extra.get("api_key")
return FirecrawlApp(api_key=api_key) # type: ignore[arg-type]
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str, runtime: ToolRuntime) -> str:
def web_search_tool(query: str) -> str:
"""Search the web.
Args:
query: The query to search for.
"""
try:
app_config = resolve_context(runtime).app_config
tool_config = app_config.get_tool_config("web_search")
config = get_app_config().get_tool_config("web_search")
max_results = 5
if tool_config is not None:
max_results = tool_config.model_extra.get("max_results", max_results)
if config is not None:
max_results = config.model_extra.get("max_results", max_results)
client = _get_firecrawl_client(app_config, "web_search")
client = _get_firecrawl_client("web_search")
result = client.search(query, limit=max_results)
# result.web contains list of SearchResultWeb objects
@@ -49,7 +47,7 @@ def web_search_tool(query: str, runtime: ToolRuntime) -> str:
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -60,8 +58,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
url: The URL to fetch the contents of.
"""
try:
app_config = resolve_context(runtime).app_config
client = _get_firecrawl_client(app_config, "web_fetch")
client = _get_firecrawl_client("web_fetch")
result = client.scrape(url, formats=["markdown"])
markdown_content = result.markdown or ""
@@ -5,9 +5,9 @@ Image Search Tool - Search images using DuckDuckGo for reference in image genera
import json
import logging
from langchain.tools import ToolRuntime, tool
from langchain.tools import tool
from deerflow.config.deer_flow_context import resolve_context
from deerflow.config import get_app_config
logger = logging.getLogger(__name__)
@@ -77,7 +77,6 @@ def _search_images(
@tool("image_search", parse_docstring=True)
def image_search_tool(
query: str,
runtime: ToolRuntime,
max_results: int = 5,
size: str | None = None,
type_image: str | None = None,
@@ -100,11 +99,11 @@ def image_search_tool(
type_image: Image type filter. Options: "photo", "clipart", "gif", "transparent", "line". Use "photo" for realistic references.
layout: Layout filter. Options: "Square", "Tall", "Wide". Choose based on your generation needs.
"""
tool_config = resolve_context(runtime).app_config.get_tool_config("image_search")
config = get_app_config().get_tool_config("image_search")
# Override max_results from config if set
if tool_config is not None and "max_results" in tool_config.model_extra:
max_results = tool_config.model_extra.get("max_results", max_results)
if config is not None and "max_results" in config.model_extra:
max_results = config.model_extra.get("max_results", max_results)
results = _search_images(
query=query,
@@ -1,7 +1,6 @@
from langchain.tools import ToolRuntime, tool
from langchain.tools import tool
from deerflow.config.app_config import AppConfig
from deerflow.config.deer_flow_context import resolve_context
from deerflow.config import get_app_config
from deerflow.utils.readability import ReadabilityExtractor
from .infoquest_client import InfoQuestClient
@@ -9,13 +8,13 @@ from .infoquest_client import InfoQuestClient
readability_extractor = ReadabilityExtractor()
def _get_infoquest_client(app_config: AppConfig) -> InfoQuestClient:
search_config = app_config.get_tool_config("web_search")
def _get_infoquest_client() -> InfoQuestClient:
search_config = get_app_config().get_tool_config("web_search")
search_time_range = -1
if search_config is not None and "search_time_range" in search_config.model_extra:
search_time_range = search_config.model_extra.get("search_time_range")
fetch_config = app_config.get_tool_config("web_fetch")
fetch_config = get_app_config().get_tool_config("web_fetch")
fetch_time = -1
if fetch_config is not None and "fetch_time" in fetch_config.model_extra:
fetch_time = fetch_config.model_extra.get("fetch_time")
@@ -26,7 +25,7 @@ def _get_infoquest_client(app_config: AppConfig) -> InfoQuestClient:
if fetch_config is not None and "navigation_timeout" in fetch_config.model_extra:
navigation_timeout = fetch_config.model_extra.get("navigation_timeout")
image_search_config = app_config.get_tool_config("image_search")
image_search_config = get_app_config().get_tool_config("image_search")
image_search_time_range = -1
if image_search_config is not None and "image_search_time_range" in image_search_config.model_extra:
image_search_time_range = image_search_config.model_extra.get("image_search_time_range")
@@ -45,18 +44,19 @@ def _get_infoquest_client(app_config: AppConfig) -> InfoQuestClient:
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str, runtime: ToolRuntime) -> str:
def web_search_tool(query: str) -> str:
"""Search the web.
Args:
query: The query to search for.
"""
client = _get_infoquest_client(resolve_context(runtime).app_config)
client = _get_infoquest_client()
return client.web_search(query)
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -66,7 +66,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
Args:
url: The URL to fetch the contents of.
"""
client = _get_infoquest_client(resolve_context(runtime).app_config)
client = _get_infoquest_client()
result = client.fetch(url)
if result.startswith("Error: "):
return result
@@ -75,7 +75,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
@tool("image_search", parse_docstring=True)
def image_search_tool(query: str, runtime: ToolRuntime) -> str:
def image_search_tool(query: str) -> str:
"""Search for images online. Use this tool BEFORE image generation to find reference images for characters, portraits, objects, scenes, or any content requiring visual accuracy.
**When to use:**
@@ -89,5 +89,5 @@ def image_search_tool(query: str, runtime: ToolRuntime) -> str:
Args:
query: The query to search for images.
"""
client = _get_infoquest_client(resolve_context(runtime).app_config)
client = _get_infoquest_client()
return client.image_search(query)
@@ -1,16 +1,16 @@
import asyncio
from langchain.tools import ToolRuntime, tool
from langchain.tools import tool
from deerflow.community.jina_ai.jina_client import JinaClient
from deerflow.config.deer_flow_context import resolve_context
from deerflow.config import get_app_config
from deerflow.utils.readability import ReadabilityExtractor
readability_extractor = ReadabilityExtractor()
@tool("web_fetch", parse_docstring=True)
async def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
async def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -22,9 +22,9 @@ async def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
"""
jina_client = JinaClient()
timeout = 10
tool_config = resolve_context(runtime).app_config.get_tool_config("web_fetch")
if tool_config is not None and "timeout" in tool_config.model_extra:
timeout = tool_config.model_extra.get("timeout")
config = get_app_config().get_tool_config("web_fetch")
if config is not None and "timeout" in config.model_extra:
timeout = config.model_extra.get("timeout")
html_content = await jina_client.crawl(url, return_format="html", timeout=timeout)
if isinstance(html_content, str) and html_content.startswith("Error:"):
return html_content
@@ -0,0 +1,3 @@
from .tools import web_search_tool
__all__ = ["web_search_tool"]
@@ -0,0 +1,95 @@
"""
Web Search Tool - Search the web using Serper (Google Search API).
Serper provides real-time Google Search results via a JSON API.
An API key is required. Sign up at https://serper.dev to get one.
"""
import json
import logging
import os
import httpx
from langchain.tools import tool
from deerflow.config import get_app_config
logger = logging.getLogger(__name__)
_SERPER_ENDPOINT = "https://google.serper.dev/search"
_api_key_warned = False
def _get_api_key() -> str | None:
config = get_app_config().get_tool_config("web_search")
if config is not None:
api_key = config.model_extra.get("api_key")
if isinstance(api_key, str) and api_key.strip():
return api_key
return os.getenv("SERPER_API_KEY")
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str, max_results: int = 5) -> str:
"""Search the web for information using Google Search via Serper.
Args:
query: Search keywords describing what you want to find. Be specific for better results.
max_results: Maximum number of search results to return. Default is 5.
"""
global _api_key_warned
config = get_app_config().get_tool_config("web_search")
if config is not None and "max_results" in config.model_extra:
max_results = config.model_extra.get("max_results", max_results)
api_key = _get_api_key()
if not api_key:
if not _api_key_warned:
_api_key_warned = True
logger.warning("Serper API key is not set. Set SERPER_API_KEY in your environment or provide api_key in config.yaml. Sign up at https://serper.dev")
return json.dumps(
{"error": "SERPER_API_KEY is not configured", "query": query},
ensure_ascii=False,
)
headers = {
"X-API-KEY": api_key,
"Content-Type": "application/json",
}
payload = {"q": query, "num": max_results}
try:
with httpx.Client(timeout=30) as client:
response = client.post(_SERPER_ENDPOINT, headers=headers, json=payload)
response.raise_for_status()
data = response.json()
except httpx.HTTPStatusError as e:
logger.error(f"Serper API returned HTTP {e.response.status_code}: {e.response.text}")
return json.dumps(
{"error": f"Serper API error: HTTP {e.response.status_code}", "query": query},
ensure_ascii=False,
)
except Exception as e:
logger.error(f"Serper search failed: {type(e).__name__}: {e}")
return json.dumps({"error": str(e), "query": query}, ensure_ascii=False)
organic = data.get("organic", [])
if not organic:
return json.dumps({"error": "No results found", "query": query}, ensure_ascii=False)
normalized_results = [
{
"title": r.get("title", ""),
"url": r.get("link", ""),
"content": r.get("snippet", ""),
}
for r in organic[:max_results]
]
output = {
"query": query,
"total_results": len(normalized_results),
"results": normalized_results,
}
return json.dumps(output, indent=2, ensure_ascii=False)
@@ -1,34 +1,32 @@
import json
from langchain.tools import ToolRuntime, tool
from langchain.tools import tool
from tavily import TavilyClient
from deerflow.config.app_config import AppConfig
from deerflow.config.deer_flow_context import resolve_context
from deerflow.config import get_app_config
def _get_tavily_client(app_config: AppConfig) -> TavilyClient:
tool_config = app_config.get_tool_config("web_search")
def _get_tavily_client() -> TavilyClient:
config = get_app_config().get_tool_config("web_search")
api_key = None
if tool_config is not None and "api_key" in tool_config.model_extra:
api_key = tool_config.model_extra.get("api_key")
if config is not None and "api_key" in config.model_extra:
api_key = config.model_extra.get("api_key")
return TavilyClient(api_key=api_key)
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str, runtime: ToolRuntime) -> str:
def web_search_tool(query: str) -> str:
"""Search the web.
Args:
query: The query to search for.
"""
app_config = resolve_context(runtime).app_config
tool_config = app_config.get_tool_config("web_search")
config = get_app_config().get_tool_config("web_search")
max_results = 5
if tool_config is not None and "max_results" in tool_config.model_extra:
max_results = tool_config.model_extra.get("max_results")
if config is not None and "max_results" in config.model_extra:
max_results = config.model_extra.get("max_results")
client = _get_tavily_client(app_config)
client = _get_tavily_client()
res = client.search(query, max_results=max_results)
normalized_results = [
{
@@ -43,7 +41,7 @@ def web_search_tool(query: str, runtime: ToolRuntime) -> str:
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
@@ -53,8 +51,7 @@ def web_fetch_tool(url: str, runtime: ToolRuntime) -> str:
Args:
url: The URL to fetch the contents of.
"""
app_config = resolve_context(runtime).app_config
client = _get_tavily_client(app_config)
client = _get_tavily_client()
res = client.extract([url])
if "failed_results" in res and len(res["failed_results"]) > 0:
return f"Error: {res['failed_results'][0]['error']}"
@@ -1,6 +1,6 @@
from .app_config import AppConfig
from .extensions_config import ExtensionsConfig
from .memory_config import MemoryConfig
from .app_config import get_app_config
from .extensions_config import ExtensionsConfig, get_extensions_config
from .memory_config import MemoryConfig, get_memory_config
from .paths import Paths, get_paths
from .skill_evolution_config import SkillEvolutionConfig
from .skills_config import SkillsConfig
@@ -13,16 +13,18 @@ from .tracing_config import (
)
__all__ = [
"AppConfig",
"ExtensionsConfig",
"MemoryConfig",
"Paths",
"get_app_config",
"SkillEvolutionConfig",
"SkillsConfig",
"get_enabled_tracing_providers",
"get_explicitly_enabled_tracing_providers",
"Paths",
"get_paths",
"SkillsConfig",
"ExtensionsConfig",
"get_extensions_config",
"MemoryConfig",
"get_memory_config",
"get_tracing_config",
"get_explicitly_enabled_tracing_providers",
"get_enabled_tracing_providers",
"is_tracing_enabled",
"validate_enabled_tracing_providers",
]
@@ -1,13 +1,16 @@
"""ACP (Agent Client Protocol) agent configuration loaded from config.yaml."""
from pydantic import BaseModel, ConfigDict, Field
import logging
from collections.abc import Mapping
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
class ACPAgentConfig(BaseModel):
"""Configuration for a single ACP-compatible agent."""
model_config = ConfigDict(frozen=True)
command: str = Field(description="Command to launch the ACP agent subprocess")
args: list[str] = Field(default_factory=list, description="Additional command arguments")
env: dict[str, str] = Field(default_factory=dict, description="Environment variables to inject into the agent subprocess. Values starting with $ are resolved from host environment variables.")
@@ -21,3 +24,28 @@ class ACPAgentConfig(BaseModel):
"are denied — the agent must be configured to operate without requesting permissions."
),
)
_acp_agents: dict[str, ACPAgentConfig] = {}
def get_acp_agents() -> dict[str, ACPAgentConfig]:
"""Get the currently configured ACP agents.
Returns:
Mapping of agent name -> ACPAgentConfig. Empty dict if no ACP agents are configured.
"""
return _acp_agents
def load_acp_config_from_dict(config_dict: Mapping[str, Mapping[str, object]] | None) -> None:
"""Load ACP agent configuration from a dictionary (typically from config.yaml).
Args:
config_dict: Mapping of agent name -> config fields.
"""
global _acp_agents
if config_dict is None:
config_dict = {}
_acp_agents = {name: ACPAgentConfig(**cfg) for name, cfg in config_dict.items()}
logger.info("ACP config loaded: %d agent(s): %s", len(_acp_agents), list(_acp_agents.keys()))
@@ -1,14 +1,32 @@
"""Configuration for the custom agents management API."""
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
class AgentsApiConfig(BaseModel):
"""Configuration for custom-agent and user-profile management routes."""
model_config = ConfigDict(frozen=True)
enabled: bool = Field(
default=False,
description=("Whether to expose the custom-agent management API over HTTP. When disabled, the gateway rejects read/write access to custom agent SOUL.md, config, and USER.md prompt-management routes."),
)
_agents_api_config: AgentsApiConfig = AgentsApiConfig()
def get_agents_api_config() -> AgentsApiConfig:
"""Get the current agents API configuration."""
return _agents_api_config
def set_agents_api_config(config: AgentsApiConfig) -> None:
"""Set the agents API configuration."""
global _agents_api_config
_agents_api_config = config
def load_agents_api_config_from_dict(config_dict: dict) -> None:
"""Load agents API configuration from a dictionary."""
global _agents_api_config
_agents_api_config = AgentsApiConfig(**config_dict)
@@ -5,7 +5,7 @@ import re
from typing import Any
import yaml
from pydantic import BaseModel, ConfigDict
from pydantic import BaseModel
from deerflow.config.paths import get_paths
@@ -29,8 +29,6 @@ def validate_agent_name(name: str | None) -> str | None:
class AgentConfig(BaseModel):
"""Configuration for a custom agent."""
model_config = ConfigDict(frozen=True)
name: str
description: str = ""
model: str | None = None
@@ -1,7 +1,6 @@
from __future__ import annotations
import logging
import os
from contextvars import ContextVar
from pathlib import Path
from typing import Any, Self
@@ -9,31 +8,38 @@ import yaml
from dotenv import load_dotenv
from pydantic import BaseModel, ConfigDict, Field
from deerflow.config.acp_config import ACPAgentConfig
from deerflow.config.agents_api_config import AgentsApiConfig
from deerflow.config.checkpointer_config import CheckpointerConfig
from deerflow.config.acp_config import ACPAgentConfig, load_acp_config_from_dict
from deerflow.config.agents_api_config import AgentsApiConfig, load_agents_api_config_from_dict
from deerflow.config.checkpointer_config import CheckpointerConfig, load_checkpointer_config_from_dict
from deerflow.config.database_config import DatabaseConfig
from deerflow.config.extensions_config import ExtensionsConfig
from deerflow.config.guardrails_config import GuardrailsConfig
from deerflow.config.memory_config import MemoryConfig
from deerflow.config.guardrails_config import GuardrailsConfig, load_guardrails_config_from_dict
from deerflow.config.memory_config import MemoryConfig, load_memory_config_from_dict
from deerflow.config.model_config import ModelConfig
from deerflow.config.run_events_config import RunEventsConfig
from deerflow.config.runtime_paths import existing_project_file
from deerflow.config.sandbox_config import SandboxConfig
from deerflow.config.skill_evolution_config import SkillEvolutionConfig
from deerflow.config.skills_config import SkillsConfig
from deerflow.config.stream_bridge_config import StreamBridgeConfig
from deerflow.config.subagents_config import SubagentsAppConfig
from deerflow.config.summarization_config import SummarizationConfig
from deerflow.config.title_config import TitleConfig
from deerflow.config.stream_bridge_config import StreamBridgeConfig, load_stream_bridge_config_from_dict
from deerflow.config.subagents_config import SubagentsAppConfig, load_subagents_config_from_dict
from deerflow.config.summarization_config import SummarizationConfig, load_summarization_config_from_dict
from deerflow.config.title_config import TitleConfig, load_title_config_from_dict
from deerflow.config.token_usage_config import TokenUsageConfig
from deerflow.config.tool_config import ToolConfig, ToolGroupConfig
from deerflow.config.tool_search_config import ToolSearchConfig
from deerflow.config.tool_search_config import ToolSearchConfig, load_tool_search_config_from_dict
load_dotenv()
logger = logging.getLogger(__name__)
CONFIG_FILE_DATABASE_DEFAULTS = {
"backend": "sqlite",
"sqlite_dir": ".deer-flow/data",
}
class CircuitBreakerConfig(BaseModel):
"""Configuration for the LLM Circuit Breaker."""
@@ -41,17 +47,41 @@ class CircuitBreakerConfig(BaseModel):
recovery_timeout_sec: int = Field(default=60, description="Time in seconds before attempting to recover the circuit")
def _default_config_candidates() -> tuple[Path, ...]:
"""Return deterministic config.yaml locations without relying on cwd."""
def _legacy_config_candidates() -> tuple[Path, ...]:
"""Return source-tree config.yaml locations for monorepo compatibility."""
backend_dir = Path(__file__).resolve().parents[4]
repo_root = backend_dir.parent
return (backend_dir / "config.yaml", repo_root / "config.yaml")
def logging_level_from_config(name: str | None) -> int:
"""Map ``config.yaml`` ``log_level`` string to a :mod:`logging` level constant."""
mapping = logging.getLevelNamesMapping()
return mapping.get((name or "info").strip().upper(), logging.INFO)
def apply_logging_level(name: str | None) -> None:
"""Resolve *name* to a logging level and apply it to the ``deerflow``/``app`` logger hierarchies.
Only the ``deerflow`` and ``app`` logger levels are changed so that
third-party library verbosity (e.g. uvicorn, sqlalchemy) is not
affected. Root handler levels are lowered (never raised) so that
messages from the configured loggers can propagate through without
being filtered, while preserving handler thresholds that may be
intentionally restrictive for third-party log output.
"""
level = logging_level_from_config(name)
for logger_name in ("deerflow", "app"):
logging.getLogger(logger_name).setLevel(level)
for handler in logging.root.handlers:
if level < handler.level:
handler.setLevel(level)
class AppConfig(BaseModel):
"""Config for the DeerFlow application"""
log_level: str = Field(default="info", description="Logging level for deerflow modules (debug/info/warning/error)")
log_level: str = Field(default="info", description="Logging level for deerflow and app modules (debug/info/warning/error); third-party libraries are not affected")
token_usage: TokenUsageConfig = Field(default_factory=TokenUsageConfig, description="Token usage tracking configuration")
models: list[ModelConfig] = Field(default_factory=list, description="Available models")
sandbox: SandboxConfig = Field(description="Sandbox configuration")
@@ -65,15 +95,15 @@ class AppConfig(BaseModel):
summarization: SummarizationConfig = Field(default_factory=SummarizationConfig, description="Conversation summarization configuration")
memory: MemoryConfig = Field(default_factory=MemoryConfig, description="Memory subsystem configuration")
agents_api: AgentsApiConfig = Field(default_factory=AgentsApiConfig, description="Custom-agent management API configuration")
acp_agents: dict[str, ACPAgentConfig] = Field(default_factory=dict, description="ACP-compatible agent configuration")
subagents: SubagentsAppConfig = Field(default_factory=SubagentsAppConfig, description="Subagent runtime configuration")
guardrails: GuardrailsConfig = Field(default_factory=GuardrailsConfig, description="Guardrail middleware configuration")
circuit_breaker: CircuitBreakerConfig = Field(default_factory=CircuitBreakerConfig, description="LLM circuit breaker configuration")
model_config = ConfigDict(extra="allow")
database: DatabaseConfig = Field(default_factory=DatabaseConfig, description="Unified database backend configuration")
run_events: RunEventsConfig = Field(default_factory=RunEventsConfig, description="Run event storage configuration")
model_config = ConfigDict(extra="allow", frozen=True)
checkpointer: CheckpointerConfig | None = Field(default=None, description="Checkpointer configuration")
stream_bridge: StreamBridgeConfig | None = Field(default=None, description="Stream bridge configuration")
acp_agents: dict[str, ACPAgentConfig] = Field(default_factory=dict, description="ACP agent configurations keyed by agent name")
@classmethod
def resolve_config_path(cls, config_path: str | None = None) -> Path:
@@ -82,7 +112,8 @@ class AppConfig(BaseModel):
Priority:
1. If provided `config_path` argument, use it.
2. If provided `DEER_FLOW_CONFIG_PATH` environment variable, use it.
3. Otherwise, search deterministic backend/repository-root defaults from `_default_config_candidates()`.
3. Otherwise, search the caller project root.
4. Finally, search legacy backend/repository-root defaults for monorepo compatibility.
"""
if config_path:
path = Path(config_path)
@@ -95,10 +126,14 @@ class AppConfig(BaseModel):
raise FileNotFoundError(f"Config file specified by environment variable `DEER_FLOW_CONFIG_PATH` not found at {path}")
return path
else:
for path in _default_config_candidates():
project_config = existing_project_file(("config.yaml",))
if project_config is not None:
return project_config
for path in _legacy_config_candidates():
if path.exists():
return path
raise FileNotFoundError("`config.yaml` file not found at the default backend or repository root locations")
raise FileNotFoundError("`config.yaml` file not found in the project root or legacy backend/repository root locations")
@classmethod
def from_file(cls, config_path: str | None = None) -> Self:
@@ -120,6 +155,50 @@ class AppConfig(BaseModel):
cls._check_config_version(config_data, resolved_path)
config_data = cls.resolve_env_variables(config_data)
cls._apply_database_defaults(config_data)
# Load title config if present
if "title" in config_data:
load_title_config_from_dict(config_data["title"])
# Load summarization config if present
if "summarization" in config_data:
load_summarization_config_from_dict(config_data["summarization"])
# Load memory config if present
if "memory" in config_data:
load_memory_config_from_dict(config_data["memory"])
# Always refresh agents API config so removed config sections reset
# singleton-backed state to its default/disabled values on reload.
load_agents_api_config_from_dict(config_data.get("agents_api") or {})
# Load subagents config if present
if "subagents" in config_data:
load_subagents_config_from_dict(config_data["subagents"])
# Load tool_search config if present
if "tool_search" in config_data:
load_tool_search_config_from_dict(config_data["tool_search"])
# Load guardrails config if present
if "guardrails" in config_data:
load_guardrails_config_from_dict(config_data["guardrails"])
# Load circuit_breaker config if present
if "circuit_breaker" in config_data:
config_data["circuit_breaker"] = config_data["circuit_breaker"]
# Load checkpointer config if present
if "checkpointer" in config_data:
load_checkpointer_config_from_dict(config_data["checkpointer"])
# Load stream bridge config if present
if "stream_bridge" in config_data:
load_stream_bridge_config_from_dict(config_data["stream_bridge"])
# Always refresh ACP agent config so removed entries do not linger across reloads.
load_acp_config_from_dict(config_data.get("acp_agents", {}))
# Load extensions config separately (it's in a different file)
extensions_config = ExtensionsConfig.from_file()
@@ -128,6 +207,18 @@ class AppConfig(BaseModel):
result = cls.model_validate(config_data)
return result
@classmethod
def _apply_database_defaults(cls, config_data: dict[str, Any]) -> None:
"""Apply config.yaml defaults for persistence when the section is absent."""
database_config = config_data.get("database")
if database_config is None:
database_config = {}
config_data["database"] = database_config
if not isinstance(database_config, dict):
return
for key, value in CONFIG_FILE_DATABASE_DEFAULTS.items():
database_config.setdefault(key, value)
@classmethod
def _check_config_version(cls, config_data: dict, config_path: Path) -> None:
"""Check if the user's config.yaml is outdated compared to config.example.yaml.
@@ -231,8 +322,133 @@ class AppConfig(BaseModel):
"""
return next((group for group in self.tool_groups if group.name == name), None)
# AppConfig is a pure value object: construct with ``from_file()``, pass around.
# Composition roots that hold the resolved instance:
# - Gateway: ``app.state.config`` via ``Depends(get_config)``
# - Client: ``DeerFlowClient._app_config``
# - Agent run: ``Runtime[DeerFlowContext].context.app_config``
# Compatibility singleton layer for code paths that have not yet been
# migrated to explicit ``AppConfig`` threading. New composition roots should
# prefer constructing ``AppConfig`` once and passing it down directly.
_app_config: AppConfig | None = None
_app_config_path: Path | None = None
_app_config_mtime: float | None = None
_app_config_is_custom = False
_current_app_config: ContextVar[AppConfig | None] = ContextVar("deerflow_current_app_config", default=None)
_current_app_config_stack: ContextVar[tuple[AppConfig | None, ...]] = ContextVar("deerflow_current_app_config_stack", default=())
def _get_config_mtime(config_path: Path) -> float | None:
"""Get the modification time of a config file if it exists."""
try:
return config_path.stat().st_mtime
except OSError:
return None
def _load_and_cache_app_config(config_path: str | None = None) -> AppConfig:
"""Load config from disk and refresh cache metadata."""
global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
resolved_path = AppConfig.resolve_config_path(config_path)
_app_config = AppConfig.from_file(str(resolved_path))
_app_config_path = resolved_path
_app_config_mtime = _get_config_mtime(resolved_path)
_app_config_is_custom = False
return _app_config
def get_app_config() -> AppConfig:
"""Get the DeerFlow config instance.
Returns a cached singleton instance and automatically reloads it when the
underlying config file path or modification time changes. Use
`reload_app_config()` to force a reload, or `reset_app_config()` to clear
the cache.
"""
global _app_config, _app_config_path, _app_config_mtime
runtime_override = _current_app_config.get()
if runtime_override is not None:
return runtime_override
if _app_config is not None and _app_config_is_custom:
return _app_config
resolved_path = AppConfig.resolve_config_path()
current_mtime = _get_config_mtime(resolved_path)
should_reload = _app_config is None or _app_config_path != resolved_path or _app_config_mtime != current_mtime
if should_reload:
if _app_config_path == resolved_path and _app_config_mtime is not None and current_mtime is not None and _app_config_mtime != current_mtime:
logger.info(
"Config file has been modified (mtime: %s -> %s), reloading AppConfig",
_app_config_mtime,
current_mtime,
)
_load_and_cache_app_config(str(resolved_path))
return _app_config
def reload_app_config(config_path: str | None = None) -> AppConfig:
"""Reload the config from file and update the cached instance.
This is useful when the config file has been modified and you want
to pick up the changes without restarting the application.
Args:
config_path: Optional path to config file. If not provided,
uses the default resolution strategy.
Returns:
The newly loaded AppConfig instance.
"""
return _load_and_cache_app_config(config_path)
def reset_app_config() -> None:
"""Reset the cached config instance.
This clears the singleton cache, causing the next call to
`get_app_config()` to reload from file. Useful for testing
or when switching between different configurations.
"""
global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
_app_config = None
_app_config_path = None
_app_config_mtime = None
_app_config_is_custom = False
def set_app_config(config: AppConfig) -> None:
"""Set a custom config instance.
This allows injecting a custom or mock config for testing purposes.
Args:
config: The AppConfig instance to use.
"""
global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
_app_config = config
_app_config_path = None
_app_config_mtime = None
_app_config_is_custom = True
def peek_current_app_config() -> AppConfig | None:
"""Return the runtime-scoped AppConfig override, if one is active."""
return _current_app_config.get()
def push_current_app_config(config: AppConfig) -> None:
"""Push a runtime-scoped AppConfig override for the current execution context."""
stack = _current_app_config_stack.get()
_current_app_config_stack.set(stack + (_current_app_config.get(),))
_current_app_config.set(config)
def pop_current_app_config() -> None:
"""Pop the latest runtime-scoped AppConfig override for the current execution context."""
stack = _current_app_config_stack.get()
if not stack:
_current_app_config.set(None)
return
previous = stack[-1]
_current_app_config_stack.set(stack[:-1])
_current_app_config.set(previous)
@@ -2,7 +2,7 @@
from typing import Literal
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
CheckpointerType = Literal["memory", "sqlite", "postgres"]
@@ -10,8 +10,6 @@ CheckpointerType = Literal["memory", "sqlite", "postgres"]
class CheckpointerConfig(BaseModel):
"""Configuration for LangGraph state persistence checkpointer."""
model_config = ConfigDict(frozen=True)
type: CheckpointerType = Field(
description="Checkpointer backend type. "
"'memory' is in-process only (lost on restart). "
@@ -25,3 +23,24 @@ class CheckpointerConfig(BaseModel):
"For sqlite, use a file path like '.deer-flow/checkpoints.db' or ':memory:' for in-memory. "
"For postgres, use a DSN like 'postgresql://user:pass@localhost:5432/db'.",
)
# Global configuration instance — None means no checkpointer is configured.
_checkpointer_config: CheckpointerConfig | None = None
def get_checkpointer_config() -> CheckpointerConfig | None:
"""Get the current checkpointer configuration, or None if not configured."""
return _checkpointer_config
def set_checkpointer_config(config: CheckpointerConfig | None) -> None:
"""Set the checkpointer configuration."""
global _checkpointer_config
_checkpointer_config = config
def load_checkpointer_config_from_dict(config_dict: dict) -> None:
"""Load checkpointer configuration from a dictionary."""
global _checkpointer_config
_checkpointer_config = CheckpointerConfig(**config_dict)
@@ -34,11 +34,10 @@ from __future__ import annotations
import os
from typing import Literal
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
class DatabaseConfig(BaseModel):
model_config = ConfigDict(frozen=True)
backend: Literal["memory", "sqlite", "postgres"] = Field(
default="memory",
description=("Storage backend for both checkpointer and application data. 'memory' for development (no persistence across restarts), 'sqlite' for single-node deployment, 'postgres' for production multi-node deployment."),
@@ -1,55 +0,0 @@
"""Per-invocation context for DeerFlow agent execution.
Injected via LangGraph Runtime. Middleware and tools access this
via Runtime[DeerFlowContext] parameters, through resolve_context().
"""
from __future__ import annotations
import logging
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from deerflow.config.app_config import AppConfig
logger = logging.getLogger(__name__)
@dataclass(frozen=True)
class DeerFlowContext:
"""Typed, immutable, per-invocation context injected via LangGraph Runtime.
Fields are all known at run start and never change during execution.
Mutable runtime state (e.g. sandbox_id) flows through ThreadState, not here.
"""
app_config: AppConfig
thread_id: str
agent_name: str | None = None
def resolve_context(runtime: Any) -> DeerFlowContext:
"""Return the typed DeerFlowContext that the runtime carries.
Gateway mode (``DeerFlowClient``, ``run_agent``) always attaches a typed
``DeerFlowContext`` via ``agent.astream(context=...)``; the LangGraph
Server path uses ``langgraph.json`` registration where the top-level
``make_lead_agent`` loads ``AppConfig`` from disk itself, so we still
arrive here with a typed context.
Only the dict/None shapes that legacy tests used to exercise would fall
through this function; we now reject them loudly instead of papering
over the missing context with an ambient ``AppConfig`` lookup.
"""
ctx = getattr(runtime, "context", None)
if isinstance(ctx, DeerFlowContext):
return ctx
raise RuntimeError(
"resolve_context: runtime.context is not a DeerFlowContext "
"(got type %s). Every entry point must attach one at invoke time — "
"Gateway/Client via agent.astream(context=DeerFlowContext(...)), "
"LangGraph Server via the make_lead_agent boundary that loads "
"AppConfig.from_file()." % type(ctx).__name__
)
@@ -7,12 +7,12 @@ from typing import Any, Literal
from pydantic import BaseModel, ConfigDict, Field
from deerflow.config.runtime_paths import existing_project_file
class McpOAuthConfig(BaseModel):
"""OAuth configuration for an MCP server (HTTP/SSE transports)."""
model_config = ConfigDict(extra="allow", frozen=True)
enabled: bool = Field(default=True, description="Whether OAuth token injection is enabled")
token_url: str = Field(description="OAuth token endpoint URL")
grant_type: Literal["client_credentials", "refresh_token"] = Field(
@@ -30,13 +30,12 @@ class McpOAuthConfig(BaseModel):
default_token_type: str = Field(default="Bearer", description="Default token type when missing in token response")
refresh_skew_seconds: int = Field(default=60, description="Refresh token this many seconds before expiry")
extra_token_params: dict[str, str] = Field(default_factory=dict, description="Additional form params sent to token endpoint")
model_config = ConfigDict(extra="allow")
class McpServerConfig(BaseModel):
"""Configuration for a single MCP server."""
model_config = ConfigDict(extra="allow", frozen=True)
enabled: bool = Field(default=True, description="Whether this MCP server is enabled")
type: str = Field(default="stdio", description="Transport type: 'stdio', 'sse', or 'http'")
command: str | None = Field(default=None, description="Command to execute to start the MCP server (for stdio type)")
@@ -46,13 +45,12 @@ class McpServerConfig(BaseModel):
headers: dict[str, str] = Field(default_factory=dict, description="HTTP headers to send (for sse or http type)")
oauth: McpOAuthConfig | None = Field(default=None, description="OAuth configuration (for sse or http type)")
description: str = Field(default="", description="Human-readable description of what this MCP server provides")
model_config = ConfigDict(extra="allow")
class SkillStateConfig(BaseModel):
"""Configuration for a single skill's state."""
model_config = ConfigDict(frozen=True)
enabled: bool = Field(default=True, description="Whether this skill is enabled")
@@ -68,7 +66,7 @@ class ExtensionsConfig(BaseModel):
default_factory=dict,
description="Map of skill name to state configuration",
)
model_config = ConfigDict(extra="allow", frozen=True, populate_by_name=True)
model_config = ConfigDict(extra="allow", populate_by_name=True)
@classmethod
def resolve_config_path(cls, config_path: str | None = None) -> Path | None:
@@ -77,8 +75,8 @@ class ExtensionsConfig(BaseModel):
Priority:
1. If provided `config_path` argument, use it.
2. If provided `DEER_FLOW_EXTENSIONS_CONFIG_PATH` environment variable, use it.
3. Otherwise, check for `extensions_config.json` in the current directory, then in the parent directory.
4. For backward compatibility, also check for `mcp_config.json` if `extensions_config.json` is not found.
3. Otherwise, search the caller project root for `extensions_config.json`, then `mcp_config.json`.
4. For backward compatibility, also search legacy backend/repository-root defaults.
5. If not found, return None (extensions are optional).
Args:
@@ -87,8 +85,9 @@ class ExtensionsConfig(BaseModel):
Resolution order:
1. If provided `config_path` argument, use it.
2. If provided `DEER_FLOW_EXTENSIONS_CONFIG_PATH` environment variable, use it.
3. Otherwise, search backend/repository-root defaults for
3. Otherwise, search the caller project root for
`extensions_config.json`, then legacy `mcp_config.json`.
4. Finally, search backend/repository-root defaults for monorepo compatibility.
Returns:
Path to the extensions config file if found, otherwise None.
@@ -104,6 +103,10 @@ class ExtensionsConfig(BaseModel):
raise FileNotFoundError(f"Extensions config file specified by environment variable `DEER_FLOW_EXTENSIONS_CONFIG_PATH` not found at {path}")
return path
else:
project_config = existing_project_file(("extensions_config.json", "mcp_config.json"))
if project_config is not None:
return project_config
backend_dir = Path(__file__).resolve().parents[4]
repo_root = backend_dir.parent
for path in (
@@ -199,3 +202,62 @@ class ExtensionsConfig(BaseModel):
# Default to enable for public & custom skill
return skill_category in ("public", "custom")
return skill_config.enabled
_extensions_config: ExtensionsConfig | None = None
def get_extensions_config() -> ExtensionsConfig:
"""Get the extensions config instance.
Returns a cached singleton instance. Use `reload_extensions_config()` to reload
from file, or `reset_extensions_config()` to clear the cache.
Returns:
The cached ExtensionsConfig instance.
"""
global _extensions_config
if _extensions_config is None:
_extensions_config = ExtensionsConfig.from_file()
return _extensions_config
def reload_extensions_config(config_path: str | None = None) -> ExtensionsConfig:
"""Reload the extensions config from file and update the cached instance.
This is useful when the config file has been modified and you want
to pick up the changes without restarting the application.
Args:
config_path: Optional path to extensions config file. If not provided,
uses the default resolution strategy.
Returns:
The newly loaded ExtensionsConfig instance.
"""
global _extensions_config
_extensions_config = ExtensionsConfig.from_file(config_path)
return _extensions_config
def reset_extensions_config() -> None:
"""Reset the cached extensions config instance.
This clears the singleton cache, causing the next call to
`get_extensions_config()` to reload from file. Useful for testing
or when switching between different configurations.
"""
global _extensions_config
_extensions_config = None
def set_extensions_config(config: ExtensionsConfig) -> None:
"""Set a custom extensions config instance.
This allows injecting a custom or mock config for testing purposes.
Args:
config: The ExtensionsConfig instance to use.
"""
global _extensions_config
_extensions_config = config
@@ -1,13 +1,11 @@
"""Configuration for pre-tool-call authorization."""
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
class GuardrailProviderConfig(BaseModel):
"""Configuration for a guardrail provider."""
model_config = ConfigDict(frozen=True)
use: str = Field(description="Class path (e.g. 'deerflow.guardrails.builtin:AllowlistProvider')")
config: dict = Field(default_factory=dict, description="Provider-specific settings passed as kwargs")
@@ -20,9 +18,31 @@ class GuardrailsConfig(BaseModel):
agent's passport reference, and returns an allow/deny decision.
"""
model_config = ConfigDict(frozen=True)
enabled: bool = Field(default=False, description="Enable guardrail middleware")
fail_closed: bool = Field(default=True, description="Block tool calls if provider errors")
passport: str | None = Field(default=None, description="OAP passport path or hosted agent ID")
provider: GuardrailProviderConfig | None = Field(default=None, description="Guardrail provider configuration")
_guardrails_config: GuardrailsConfig | None = None
def get_guardrails_config() -> GuardrailsConfig:
"""Get the guardrails config, returning defaults if not loaded."""
global _guardrails_config
if _guardrails_config is None:
_guardrails_config = GuardrailsConfig()
return _guardrails_config
def load_guardrails_config_from_dict(data: dict) -> GuardrailsConfig:
"""Load guardrails config from a dict (called during AppConfig loading)."""
global _guardrails_config
_guardrails_config = GuardrailsConfig.model_validate(data)
return _guardrails_config
def reset_guardrails_config() -> None:
"""Reset the cached config instance. Used in tests to prevent singleton leaks."""
global _guardrails_config
_guardrails_config = None
@@ -1,13 +1,11 @@
"""Configuration for memory mechanism."""
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
class MemoryConfig(BaseModel):
"""Configuration for global memory mechanism."""
model_config = ConfigDict(frozen=True)
enabled: bool = Field(
default=True,
description="Whether to enable memory mechanism",
@@ -62,3 +60,24 @@ class MemoryConfig(BaseModel):
le=8000,
description="Maximum tokens to use for memory injection",
)
# Global configuration instance
_memory_config: MemoryConfig = MemoryConfig()
def get_memory_config() -> MemoryConfig:
"""Get the current memory configuration."""
return _memory_config
def set_memory_config(config: MemoryConfig) -> None:
"""Set the memory configuration."""
global _memory_config
_memory_config = config
def load_memory_config_from_dict(config_dict: dict) -> None:
"""Load memory configuration from a dictionary."""
global _memory_config
_memory_config = MemoryConfig(**config_dict)
@@ -12,7 +12,7 @@ class ModelConfig(BaseModel):
description="Class path of the model provider(e.g. langchain_openai.ChatOpenAI)",
)
model: str = Field(..., description="Model name")
model_config = ConfigDict(extra="allow", frozen=True)
model_config = ConfigDict(extra="allow")
use_responses_api: bool | None = Field(
default=None,
description="Whether to route OpenAI ChatOpenAI calls through the /v1/responses API",
@@ -3,6 +3,8 @@ import re
import shutil
from pathlib import Path, PureWindowsPath
from deerflow.config.runtime_paths import runtime_home
# Virtual path prefix seen by agents inside the sandbox
VIRTUAL_PATH_PREFIX = "/mnt/user-data"
@@ -11,9 +13,8 @@ _SAFE_USER_ID_RE = re.compile(r"^[A-Za-z0-9_\-]+$")
def _default_local_base_dir() -> Path:
"""Return the repo-local DeerFlow state directory without relying on cwd."""
backend_dir = Path(__file__).resolve().parents[4]
return backend_dir / ".deer-flow"
"""Return the caller project's writable DeerFlow state directory."""
return runtime_home()
def _validate_thread_id(thread_id: str) -> str:
@@ -81,7 +82,7 @@ class Paths:
BaseDir resolution (in priority order):
1. Constructor argument `base_dir`
2. DEER_FLOW_HOME environment variable
3. Repo-local fallback derived from this module path: `{backend_dir}/.deer-flow`
3. Caller project fallback: `{project_root}/.deer-flow`
"""
def __init__(self, base_dir: str | Path | None = None) -> None:
@@ -15,11 +15,10 @@ from __future__ import annotations
from typing import Literal
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
class RunEventsConfig(BaseModel):
model_config = ConfigDict(frozen=True)
backend: Literal["memory", "db", "jsonl"] = Field(
default="memory",
description="Storage backend for run events. 'memory' for development (no persistence), 'db' for production (SQL queries), 'jsonl' for lightweight single-node persistence.",
@@ -0,0 +1,41 @@
"""Runtime path resolution for standalone harness usage."""
import os
from pathlib import Path
def project_root() -> Path:
"""Return the caller project root for runtime-owned files."""
if env_root := os.getenv("DEER_FLOW_PROJECT_ROOT"):
root = Path(env_root).resolve()
if not root.exists():
raise ValueError(f"DEER_FLOW_PROJECT_ROOT is set to '{env_root}', but the resolved path '{root}' does not exist.")
if not root.is_dir():
raise ValueError(f"DEER_FLOW_PROJECT_ROOT is set to '{env_root}', but the resolved path '{root}' is not a directory.")
return root
return Path.cwd().resolve()
def runtime_home() -> Path:
"""Return the writable DeerFlow state directory."""
if env_home := os.getenv("DEER_FLOW_HOME"):
return Path(env_home).resolve()
return project_root() / ".deer-flow"
def resolve_path(value: str | os.PathLike[str], *, base: Path | None = None) -> Path:
"""Resolve absolute paths as-is and relative paths against the project root."""
path = Path(value)
if not path.is_absolute():
path = (base or project_root()) / path
return path.resolve()
def existing_project_file(names: tuple[str, ...]) -> Path | None:
"""Return the first existing named file under the project root."""
root = project_root()
for name in names:
candidate = root / name
if candidate.is_file():
return candidate
return None
@@ -4,8 +4,6 @@ from pydantic import BaseModel, ConfigDict, Field
class VolumeMountConfig(BaseModel):
"""Configuration for a volume mount."""
model_config = ConfigDict(frozen=True)
host_path: str = Field(..., description="Path on the host machine")
container_path: str = Field(..., description="Path inside the container")
read_only: bool = Field(default=False, description="Whether the mount is read-only")
@@ -82,4 +80,4 @@ class SandboxConfig(BaseModel):
description="Maximum characters to keep from ls tool output. Output exceeding this limit is head-truncated. Set to 0 to disable truncation.",
)
model_config = ConfigDict(extra="allow", frozen=True)
model_config = ConfigDict(extra="allow")
@@ -1,11 +1,9 @@
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
class SkillEvolutionConfig(BaseModel):
"""Configuration for agent-managed skill evolution."""
model_config = ConfigDict(frozen=True)
enabled: bool = Field(
default=False,
description="Whether the agent can create and modify skills under skills/custom.",
@@ -1,21 +1,28 @@
import os
from pathlib import Path
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
from deerflow.config.runtime_paths import project_root, resolve_path
def _default_repo_root() -> Path:
"""Resolve the repo root without relying on the current working directory."""
return Path(__file__).resolve().parents[5]
def _legacy_skills_candidates() -> tuple[Path, ...]:
"""Return source-tree skills locations for monorepo compatibility."""
backend_dir = Path(__file__).resolve().parents[4]
repo_root = backend_dir.parent
return (repo_root / "skills",)
class SkillsConfig(BaseModel):
"""Configuration for skills system"""
model_config = ConfigDict(frozen=True)
use: str = Field(
default="deerflow.skills.storage.local_skill_storage:LocalSkillStorage",
description="Class path of the SkillStorage implementation.",
)
path: str | None = Field(
default=None,
description="Path to skills directory. If not specified, defaults to ../skills relative to backend directory",
description=("Path to skills directory. If not specified, defaults to `skills` under the caller project root, falling back to the legacy repo-root location for monorepo compatibility."),
)
container_path: str = Field(
default="/mnt/skills",
@@ -26,21 +33,30 @@ class SkillsConfig(BaseModel):
"""
Get the resolved skills directory path.
Returns:
Path to the skills directory
Resolution order:
1. Explicit ``path`` field
2. ``DEER_FLOW_SKILLS_PATH`` environment variable
3. ``skills`` under the caller project root (``project_root()``)
4. Legacy repo-root candidates for monorepo compatibility (``_legacy_skills_candidates``)
When none of (3) or (4) exist on disk, the project-root default is returned so callers
can still surface a stable "no skills" location without raising.
"""
if self.path:
# Use configured path (can be absolute or relative)
path = Path(self.path)
if not path.is_absolute():
# If relative, resolve from the repo root for deterministic behavior.
path = _default_repo_root() / path
return path.resolve()
else:
# Default: ../skills relative to backend directory
from deerflow.skills.loader import get_skills_root_path
# Use configured path (can be absolute or relative to project root)
return resolve_path(self.path)
if env_path := os.getenv("DEER_FLOW_SKILLS_PATH"):
return resolve_path(env_path)
return get_skills_root_path()
project_default = project_root() / "skills"
if project_default.is_dir():
return project_default
for candidate in _legacy_skills_candidates():
if candidate.is_dir():
return candidate
return project_default
def get_skill_container_path(self, skill_name: str, category: str = "public") -> str:
"""
@@ -2,7 +2,7 @@
from typing import Literal
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
StreamBridgeType = Literal["memory", "redis"]
@@ -10,8 +10,6 @@ StreamBridgeType = Literal["memory", "redis"]
class StreamBridgeConfig(BaseModel):
"""Configuration for the stream bridge that connects agent workers to SSE endpoints."""
model_config = ConfigDict(frozen=True)
type: StreamBridgeType = Field(
default="memory",
description="Stream bridge backend type. 'memory' uses in-process asyncio.Queue (single-process only). 'redis' uses Redis Streams (planned for Phase 2, not yet implemented).",
@@ -24,3 +22,25 @@ class StreamBridgeConfig(BaseModel):
default=256,
description="Maximum number of events buffered per run in the memory bridge.",
)
# Global configuration instance — None means no stream bridge is configured
# (falls back to memory with defaults).
_stream_bridge_config: StreamBridgeConfig | None = None
def get_stream_bridge_config() -> StreamBridgeConfig | None:
"""Get the current stream bridge configuration, or None if not configured."""
return _stream_bridge_config
def set_stream_bridge_config(config: StreamBridgeConfig | None) -> None:
"""Set the stream bridge configuration."""
global _stream_bridge_config
_stream_bridge_config = config
def load_stream_bridge_config_from_dict(config_dict: dict) -> None:
"""Load stream bridge configuration from a dictionary."""
global _stream_bridge_config
_stream_bridge_config = StreamBridgeConfig(**config_dict)
@@ -1,13 +1,15 @@
"""Configuration for the subagent system loaded from config.yaml."""
from pydantic import BaseModel, ConfigDict, Field
import logging
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
class SubagentOverrideConfig(BaseModel):
"""Per-agent configuration overrides."""
model_config = ConfigDict(frozen=True)
timeout_seconds: int | None = Field(
default=None,
ge=1,
@@ -69,8 +71,6 @@ class CustomSubagentConfig(BaseModel):
class SubagentsAppConfig(BaseModel):
"""Configuration for the subagent system."""
model_config = ConfigDict(frozen=True)
timeout_seconds: int = Field(
default=900,
ge=1,
@@ -140,3 +140,48 @@ class SubagentsAppConfig(BaseModel):
if override is not None and override.skills is not None:
return override.skills
return None
_subagents_config: SubagentsAppConfig = SubagentsAppConfig()
def get_subagents_app_config() -> SubagentsAppConfig:
"""Get the current subagents configuration."""
return _subagents_config
def load_subagents_config_from_dict(config_dict: dict) -> None:
"""Load subagents configuration from a dictionary."""
global _subagents_config
_subagents_config = SubagentsAppConfig(**config_dict)
overrides_summary = {}
for name, override in _subagents_config.agents.items():
parts = []
if override.timeout_seconds is not None:
parts.append(f"timeout={override.timeout_seconds}s")
if override.max_turns is not None:
parts.append(f"max_turns={override.max_turns}")
if override.model is not None:
parts.append(f"model={override.model}")
if override.skills is not None:
parts.append(f"skills={override.skills}")
if parts:
overrides_summary[name] = ", ".join(parts)
custom_agents_names = list(_subagents_config.custom_agents.keys())
if overrides_summary or custom_agents_names:
logger.info(
"Subagents config loaded: default timeout=%ss, default max_turns=%s, per-agent overrides=%s, custom_agents=%s",
_subagents_config.timeout_seconds,
_subagents_config.max_turns,
overrides_summary or "none",
custom_agents_names or "none",
)
else:
logger.info(
"Subagents config loaded: default timeout=%ss, default max_turns=%s, no per-agent overrides",
_subagents_config.timeout_seconds,
_subagents_config.max_turns,
)
@@ -2,7 +2,7 @@
from typing import Literal
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
ContextSizeType = Literal["fraction", "tokens", "messages"]
@@ -10,8 +10,6 @@ ContextSizeType = Literal["fraction", "tokens", "messages"]
class ContextSize(BaseModel):
"""Context size specification for trigger or keep parameters."""
model_config = ConfigDict(frozen=True)
type: ContextSizeType = Field(description="Type of context size specification")
value: int | float = Field(description="Value for the context size specification")
@@ -23,8 +21,6 @@ class ContextSize(BaseModel):
class SummarizationConfig(BaseModel):
"""Configuration for automatic conversation summarization."""
model_config = ConfigDict(frozen=True)
enabled: bool = Field(
default=False,
description="Whether to enable automatic conversation summarization",
@@ -74,3 +70,24 @@ class SummarizationConfig(BaseModel):
default_factory=lambda: ["read_file", "read", "view", "cat"],
description="Tool names treated as skill file reads when preserving recently-loaded skills across summarization.",
)
# Global configuration instance
_summarization_config: SummarizationConfig = SummarizationConfig()
def get_summarization_config() -> SummarizationConfig:
"""Get the current summarization configuration."""
return _summarization_config
def set_summarization_config(config: SummarizationConfig) -> None:
"""Set the summarization configuration."""
global _summarization_config
_summarization_config = config
def load_summarization_config_from_dict(config_dict: dict) -> None:
"""Load summarization configuration from a dictionary."""
global _summarization_config
_summarization_config = SummarizationConfig(**config_dict)
@@ -1,13 +1,11 @@
"""Configuration for automatic thread title generation."""
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
class TitleConfig(BaseModel):
"""Configuration for automatic thread title generation."""
model_config = ConfigDict(frozen=True)
enabled: bool = Field(
default=True,
description="Whether to enable automatic title generation",
@@ -32,3 +30,24 @@ class TitleConfig(BaseModel):
default=("Generate a concise title (max {max_words} words) for this conversation.\nUser: {user_msg}\nAssistant: {assistant_msg}\n\nReturn ONLY the title, no quotes, no explanation."),
description="Prompt template for title generation",
)
# Global configuration instance
_title_config: TitleConfig = TitleConfig()
def get_title_config() -> TitleConfig:
"""Get the current title configuration."""
return _title_config
def set_title_config(config: TitleConfig) -> None:
"""Set the title configuration."""
global _title_config
_title_config = config
def load_title_config_from_dict(config_dict: dict) -> None:
"""Load title configuration from a dictionary."""
global _title_config
_title_config = TitleConfig(**config_dict)
@@ -1,9 +1,7 @@
from pydantic import BaseModel, ConfigDict, Field
from pydantic import BaseModel, Field
class TokenUsageConfig(BaseModel):
"""Configuration for token usage tracking."""
model_config = ConfigDict(frozen=True)
enabled: bool = Field(default=False, description="Enable token usage tracking middleware")

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