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Author SHA1 Message Date
rayhpeng e9f3ee73c2 Merge remote-tracking branch 'origin/rayhpeng/persistence-scaffold' into rayhpeng/persistence-scaffold 2026-04-07 10:58:31 +08:00
rayhpeng 439c10d6f2 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>
2026-04-07 10:56:03 +08:00
rayhpeng 6f155d3b4b 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>
2026-04-07 10:42:26 +08:00
rayhpeng d25c8d371f 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>
2026-04-07 10:32:40 +08:00
rayhpeng c2a1e832a7 Merge remote-tracking branch 'origin/main' into rayhpeng/persistence-scaffold 2026-04-07 09:52:56 +08:00
rayhpeng b62945041f Merge branch 'main' into rayhpeng/persistence-scaffold 2026-04-07 09:44:38 +08:00
rayhpeng c89446ff0a Merge branch 'main' into rayhpeng/persistence-scaffold
# Conflicts:
#	config.example.yaml
2026-04-06 22:16:42 +08:00
rayhpeng 11dcf48596 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>
2026-04-06 22:11:04 +08:00
rayhpeng cfb167c702 Merge remote-tracking branch 'origin/rayhpeng/persistence-scaffold' into rayhpeng/persistence-scaffold 2026-04-06 21:48:34 +08:00
rayhpeng 5ead75d289 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>
2026-04-06 21:46:54 +08:00
rayhpeng 3048644169 Merge branch 'main' into rayhpeng/persistence-scaffold 2026-04-06 21:41:05 +08:00
rayhpeng 0ecc2f954c 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>
2026-04-06 21:24:05 +08:00
rayhpeng 29547c0ee4 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>
2026-04-06 17:45:41 +08:00
rayhpeng 51c68db376 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>
2026-04-06 17:30:00 +08:00
rayhpeng a5831d3abf Merge branch 'main' into rayhpeng/persistence-scaffold
# Conflicts:
#	backend/tests/test_model_factory.py
2026-04-06 17:11:49 +08:00
rayhpeng ddd8613520 Merge remote-tracking branch 'origin/rayhpeng/persistence-scaffold' into rayhpeng/persistence-scaffold 2026-04-06 11:44:42 +08:00
rayhpeng d592a98452 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>
2026-04-06 11:24:29 +08:00
rayhpeng 0af0ae7fbb 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>
2026-04-06 11:20:34 +08:00
rayhpeng 332fb18b34 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>
2026-04-06 11:09:42 +08:00
rayhpeng eba6810a44 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>
2026-04-06 10:59:47 +08:00
rayhpeng e4e4320af5 Merge branch 'main' into rayhpeng/persistence-scaffold 2026-04-06 10:22:53 +08:00
rayhpeng 8746a2bcd9 Merge remote-tracking branch 'origin/rayhpeng/persistence-scaffold' into rayhpeng/persistence-scaffold 2026-04-05 23:51:36 +08:00
rayhpeng 3f00a22df3 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>
2026-04-05 23:46:35 +08:00
rayhpeng 07954cf9d2 style: apply ruff format to persistence and runtime files
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-05 23:44:48 +08:00
rayhpeng 107b3143c3 Merge branch 'main' into rayhpeng/persistence-scaffold
# Conflicts:
#	backend/Dockerfile
#	backend/uv.lock
2026-04-05 23:40:49 +08:00
rayhpeng b94383c93a 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>
2026-04-05 22:49:26 +08:00
rayhpeng 32f69674a5 chore: update uv.lock
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-05 22:12:41 +08:00
rayhpeng fc4e3a52d4 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>
2026-04-05 22:02:50 +08:00
rayhpeng 7fdf9cad99 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>
2026-04-05 21:52:05 +08:00
rayhpeng 8a6ed365aa 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>
2026-04-04 21:43:04 +08:00
rayhpeng cef83878d4 fix: remove duplicate optional-dependencies header in pyproject.toml
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-04 21:34:36 +08:00
rayhpeng 4737fc3aa9 Merge branch 'main' into rayhpeng/persistence-scaffold
# Conflicts:
#	.env.example
#	backend/packages/harness/deerflow/agents/middlewares/title_middleware.py
2026-04-04 21:28:07 +08:00
rayhpeng b55a9c8d28 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>
2026-04-04 21:23:32 +08:00
rayhpeng 35001c7c73 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>
2026-04-04 21:07:21 +08:00
rayhpeng 52e7acafee 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>
2026-04-04 20:52:27 +08:00
rayhpeng 2d135aad0f test(events): add full run sequence integration test for OpenAI content format
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-04 09:49:14 +08:00
rayhpeng fdac5d5930 feat(events): add record_middleware method for middleware trace events
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-04 09:43:11 +08:00
rayhpeng 41745f1f2b 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>
2026-04-04 09:37:34 +08:00
rayhpeng 362226be6e feat(events): summary content uses OpenAI system message format
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-04 09:21:51 +08:00
rayhpeng 704f6a9209 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>
2026-04-04 09:18:32 +08:00
rayhpeng 8b1d569589 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>
2026-04-04 09:13:12 +08:00
rayhpeng db59dfa6fb feat(events): human_message content uses OpenAI user message format
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-04 09:07:34 +08:00
rayhpeng 17c8dbd9aa fix(converters): handle empty list content as null, clean up test
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-04 09:04:37 +08:00
rayhpeng bfbb3e1b8d 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>
2026-04-04 09:00:12 +08:00
rayhpeng 74dc663c23 fix(events): use metadata flag instead of heuristic for dict content detection
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-04 08:56:13 +08:00
rayhpeng 17eb509dbd 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>
2026-04-04 08:49:25 +08:00
rayhpeng b92ddafd4b 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>
2026-04-03 17:26:11 +08:00
rayhpeng e5b01d7e74 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>
2026-04-03 17:25:22 +08:00
rayhpeng e362aaefbd 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>
2026-04-03 17:25:03 +08:00
rayhpeng 3b4622a26f 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>
2026-04-03 17:24:43 +08:00
rayhpeng 14c5f4b798 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>
2026-04-03 11:31:22 +08:00
rayhpeng 2e4cb5c6a9 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>
2026-04-02 19:36:15 +08:00
rayhpeng 5cb0471af5 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>
2026-04-02 19:10:11 +08:00
rayhpeng e3179cd54d 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>
2026-04-02 19:03:38 +08:00
rayhpeng 23eacf9533 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>
2026-04-02 14:23:13 +08:00
rayhpeng 1ff6b5f7ab 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>
2026-04-01 15:50:06 +08:00
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---
name: smoke-test
description: End-to-end smoke test skill for DeerFlow. Guides through: 1) Pulling latest code, 2) Docker OR Local installation and deployment (user preference, default to Local if Docker network issues), 3) Service availability verification, 4) Health check, 5) Final test report. Use when the user says "run smoke test", "smoke test deployment", "verify installation", "test service availability", "end-to-end test", or similar.
---
# DeerFlow Smoke Test Skill
This skill guides the Agent through DeerFlow's full end-to-end smoke test workflow, including code updates, deployment (supporting both Docker and local installation modes), service availability verification, and health checks.
## Deployment Mode Selection
This skill supports two deployment modes:
- **Local installation mode** (recommended, especially when network issues occur) - Run all services directly on the local machine
- **Docker mode** - Run all services inside Docker containers
**Selection strategy**:
- If the user explicitly asks for Docker mode, use Docker
- If network issues occur (such as slow image pulls), automatically switch to local mode
- Default to local mode whenever possible
## Structure
```
smoke-test/
├── SKILL.md ← You are here - core workflow and logic
├── scripts/
│ ├── check_docker.sh ← Check the Docker environment
│ ├── check_local_env.sh ← Check local environment dependencies
│ ├── frontend_check.sh ← Frontend page smoke check
│ ├── pull_code.sh ← Pull the latest code
│ ├── deploy_docker.sh ← Docker deployment
│ ├── deploy_local.sh ← Local deployment
│ └── health_check.sh ← Service health check
├── references/
│ ├── SOP.md ← Standard operating procedure
│ └── troubleshooting.md ← Troubleshooting guide
└── templates/
├── report.local.template.md ← Local mode smoke test report template
└── report.docker.template.md ← Docker mode smoke test report template
```
## Standard Operating Procedure (SOP)
### Phase 1: Code Update Check
1. **Confirm current directory** - Verify that the current working directory is the DeerFlow project root
2. **Check Git status** - See whether there are uncommitted changes
3. **Pull the latest code** - Use `git pull origin main` to get the latest updates
4. **Confirm code update** - Verify that the latest code was pulled successfully
### Phase 2: Deployment Mode Selection and Environment Check
**Choose deployment mode**:
- Ask for user preference, or choose automatically based on network conditions
- Default to local installation mode
**Local mode environment check**:
1. **Check Node.js version** - Requires 22+
2. **Check pnpm** - Package manager
3. **Check uv** - Python package manager
4. **Check nginx** - Reverse proxy
5. **Check required ports** - Confirm that ports 2026, 3000, and 8001 are not occupied
**Docker mode environment check** (if Docker is selected):
1. **Check whether Docker is installed** - Run `docker --version`
2. **Check Docker daemon status** - Run `docker info`
3. **Check Docker Compose availability** - Run `docker compose version`
4. **Check required ports** - Confirm that port 2026 is not occupied
### Phase 3: Configuration Preparation
1. **Check whether config.yaml exists**
- If it does not exist, run `make config` to generate it
- If it already exists, check whether it needs an upgrade with `make config-upgrade`
2. **Check the .env file**
- Verify that required environment variables are configured
- Especially model API keys such as `OPENAI_API_KEY`
### Phase 4: Deployment Execution
**Local mode deployment**:
1. **Check dependencies** - Run `make check`
2. **Install dependencies** - Run `make install`
3. **(Optional) Pre-pull the sandbox image** - If needed, run `make setup-sandbox`
4. **Start services** - Run `make dev-daemon` (background mode, recommended) or `make dev` (foreground mode)
5. **Wait for startup** - Give all services enough time to start completely (90-120 seconds recommended)
**Docker mode deployment** (if Docker is selected):
1. **Initialize Docker environment** - Run `make docker-init`
2. **Start Docker services** - Run `make docker-start`
3. **Wait for startup** - Give all containers enough time to start completely (60 seconds recommended)
### Phase 5: Service Health Check
**Local mode health check**:
1. **Check process status** - Confirm that Gateway, Frontend, and Nginx processes are all running
2. **Check frontend service** - Visit `http://localhost:2026` and verify that the page loads
3. **Check API Gateway** - Verify the `http://localhost:2026/health` endpoint
4. **Check LangGraph-compatible API** - Verify the `/api/langgraph/*` route exposed by Gateway
5. **Frontend route smoke check** - Run `bash .agent/skills/smoke-test/scripts/frontend_check.sh` to verify key routes under `/workspace`
**Docker mode health check** (when using Docker):
1. **Check container status** - Run `docker ps` and confirm that all containers are running
2. **Check frontend service** - Visit `http://localhost:2026` and verify that the page loads
3. **Check API Gateway** - Verify the `http://localhost:2026/health` endpoint
4. **Check LangGraph-compatible API** - Verify the `/api/langgraph/*` route exposed by Gateway
5. **Frontend route smoke check** - Run `bash .agent/skills/smoke-test/scripts/frontend_check.sh` to verify key routes under `/workspace`
### Optional Functional Verification
1. **List available models** - Verify that model configuration loads correctly
2. **List available skills** - Verify that the skill directory is mounted correctly
3. **Simple chat test** - Send a simple message to verify the end-to-end flow
### Phase 6: Generate Test Report
1. **Collect all test results** - Summarize execution status for each phase
2. **Record encountered issues** - If anything fails, record the error details
3. **Generate the final report** - Use the template that matches the selected deployment mode to create the complete test report, including overall conclusion, detailed key test cases, and explicit frontend page / route results
4. **Provide follow-up recommendations** - Offer suggestions based on the test results
## Execution Rules
- **Follow the sequence** - Execute strictly in the order described above
- **Idempotency** - Every step should be safe to repeat
- **Error handling** - If a step fails, stop and report the issue, then provide troubleshooting suggestions
- **Detailed logging** - Record the execution result and status of each step
- **User confirmation** - Ask for confirmation before potentially risky operations such as overwriting config
- **Mode preference** - Prefer local mode to avoid network-related issues
- **Template requirement** - The final report must use the matching template under `templates/`; do not output a free-form summary instead of the template-based report
- **Report clarity** - The execution summary must include the overall pass/fail conclusion plus per-case result explanations, and frontend smoke check results must be listed explicitly in the report
- **Optional phase handling** - If functional verification is not executed, do not present it as a separate skipped phase in the final report
## Known Acceptable Warnings
The following warnings can appear during smoke testing and do not block a successful result:
- Feishu/Lark SSL errors in Gateway logs (certificate verification failure) can be ignored if that channel is not enabled
- Warnings in Gateway logs about missing methods in the custom checkpointer, such as `adelete_for_runs` or `aprune`, do not affect the core functionality
## Key Tools
Use the following tools during execution:
1. **bash** - Run shell commands
2. **present_file** - Show generated reports and important files
3. **task_tool** - Organize complex steps with subtasks when needed
## Success Criteria
Smoke test pass criteria (local mode):
- [x] Latest code is pulled successfully
- [x] Local environment check passes (Node.js 22+, pnpm, uv, nginx)
- [x] Configuration files are set up correctly
- [x] `make check` passes
- [x] `make install` completes successfully
- [x] `make dev` starts successfully
- [x] All service processes run normally
- [x] Frontend page is accessible
- [x] Frontend route smoke check passes (`/workspace` key routes)
- [x] API Gateway health check passes
- [x] Test report is generated completely
Smoke test pass criteria (Docker mode):
- [x] Latest code is pulled successfully
- [x] Docker environment check passes
- [x] Configuration files are set up correctly
- [x] `make docker-init` completes successfully
- [x] `make docker-start` completes successfully
- [x] All Docker containers run normally
- [x] Frontend page is accessible
- [x] Frontend route smoke check passes (`/workspace` key routes)
- [x] API Gateway health check passes
- [x] Test report is generated completely
## Read Reference Files
Before starting execution, read the following reference files:
1. `references/SOP.md` - Detailed step-by-step operating instructions
2. `references/troubleshooting.md` - Common issues and solutions
3. `templates/report.local.template.md` - Local mode test report template
4. `templates/report.docker.template.md` - Docker mode test report template
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# DeerFlow Smoke Test Standard Operating Procedure (SOP)
This document describes the detailed operating steps for each phase of the DeerFlow smoke test.
## Phase 1: Code Update Check
### 1.1 Confirm Current Directory
**Objective**: Verify that the current working directory is the DeerFlow project root.
**Steps**:
1. Run `pwd` to view the current working directory
2. Check whether the directory contains the following files/directories:
- `Makefile`
- `backend/`
- `frontend/`
- `config.example.yaml`
**Success Criteria**: The current directory contains all of the files/directories listed above.
---
### 1.2 Check Git Status
**Objective**: Check whether there are uncommitted changes.
**Steps**:
1. Run `git status`
2. Check whether the output includes "Changes not staged for commit" or "Untracked files"
**Notes**:
- If there are uncommitted changes, recommend that the user commit or stash them first to avoid conflicts while pulling
- If the user confirms that they want to continue, this step can be skipped
---
### 1.3 Pull the Latest Code
**Objective**: Fetch the latest code updates.
**Steps**:
1. Run `git fetch origin main`
2. Run `git pull origin main`
**Success Criteria**:
- The commands succeed without errors
- The output shows "Already up to date" or indicates that new commits were pulled successfully
---
### 1.4 Confirm Code Update
**Objective**: Verify that the latest code was pulled successfully.
**Steps**:
1. Run `git log -1 --oneline` to view the latest commit
2. Record the commit hash and message
---
## Phase 2: Deployment Mode Selection and Environment Check
### 2.1 Choose Deployment Mode
**Objective**: Decide whether to use local mode or Docker mode.
**Decision Flow**:
1. Prefer local mode first to avoid network-related issues
2. If the user explicitly requests Docker, use Docker
3. If Docker network issues occur, switch to local mode automatically
---
### 2.2 Local Mode Environment Check
**Objective**: Verify that local development environment dependencies are satisfied.
#### 2.2.1 Check Node.js Version
**Steps**:
1. If nvm is used, run `nvm use 22` to switch to Node 22+
2. Run `node --version`
**Success Criteria**: Version >= 22.x
**Failure Handling**:
- If the version is too low, ask the user to install/switch Node.js with nvm:
```bash
nvm install 22
nvm use 22
```
- Or install it from the official website: https://nodejs.org/
---
#### 2.2.2 Check pnpm
**Steps**:
1. Run `pnpm --version`
**Success Criteria**: The command returns pnpm version information.
**Failure Handling**:
- If pnpm is not installed, ask the user to install it with `npm install -g pnpm`
---
#### 2.2.3 Check uv
**Steps**:
1. Run `uv --version`
**Success Criteria**: The command returns uv version information.
**Failure Handling**:
- If uv is not installed, ask the user to install uv
---
#### 2.2.4 Check nginx
**Steps**:
1. Run `nginx -v`
**Success Criteria**: The command returns nginx version information.
**Failure Handling**:
- macOS: install with Homebrew using `brew install nginx`
- Linux: install using the system package manager
---
#### 2.2.5 Check Required Ports
**Steps**:
1. Run the following commands to check ports:
```bash
lsof -i :2026 # Main port
lsof -i :3000 # Frontend
lsof -i :8001 # Gateway
```
**Success Criteria**: All ports are free, or they are occupied only by DeerFlow-related processes.
**Failure Handling**:
- If a port is occupied, ask the user to stop the related process
---
### 2.3 Docker Mode Environment Check (If Docker Is Selected)
#### 2.3.1 Check Whether Docker Is Installed
**Steps**:
1. Run `docker --version`
**Success Criteria**: The command returns Docker version information, such as "Docker version 24.x.x".
---
#### 2.3.2 Check Docker Daemon Status
**Steps**:
1. Run `docker info`
**Success Criteria**: The command runs successfully and shows Docker system information.
**Failure Handling**:
- If it fails, ask the user to start Docker Desktop or the Docker service
---
#### 2.3.3 Check Docker Compose Availability
**Steps**:
1. Run `docker compose version`
**Success Criteria**: The command returns Docker Compose version information.
---
#### 2.3.4 Check Required Ports
**Steps**:
1. Run `lsof -i :2026` (macOS/Linux) or `netstat -ano | findstr :2026` (Windows)
**Success Criteria**: Port 2026 is free, or it is occupied only by a DeerFlow-related process.
**Failure Handling**:
- If the port is occupied by another process, ask the user to stop that process or change the configuration
---
## Phase 3: Configuration Preparation
### 3.1 Check config.yaml
**Steps**:
1. Check whether `config.yaml` exists
2. If it does not exist, run `make config`
3. If it already exists, consider running `make config-upgrade` to merge new fields
**Validation**:
- Check whether at least one model is configured in config.yaml
- Check whether the model configuration references the correct environment variables
---
### 3.2 Check the .env File
**Steps**:
1. Check whether the `.env` file exists
2. If it does not exist, copy it from `.env.example`
3. Check whether the following environment variables are configured:
- `OPENAI_API_KEY` (or other model API keys)
- Other required settings
---
## Phase 4: Deployment Execution
### 4.1 Local Mode Deployment
#### 4.1.1 Check Dependencies
**Steps**:
1. Run `make check`
**Description**: This command validates all required tools (Node.js 22+, pnpm, uv, nginx).
---
#### 4.1.2 Install Dependencies
**Steps**:
1. Run `make install`
**Description**: This command installs both backend and frontend dependencies.
**Notes**:
- This step may take some time
- If network issues cause failures, try using a closer or mirrored package registry
---
#### 4.1.3 (Optional) Pre-pull the Sandbox Image
**Steps**:
1. If Docker / Container sandbox is used, run `make setup-sandbox`
**Description**: This step is optional and not needed for local sandbox mode.
---
#### 4.1.4 Start Services
**Steps**:
1. Run `make dev-daemon` (background mode)
**Description**: This command starts all services (Gateway embedded runtime, Frontend, Nginx).
**Notes**:
- `make dev` runs in the foreground and stops with Ctrl+C
- `make dev-daemon` runs in the background
- Use `make stop` to stop services
---
#### 4.1.5 Wait for Services to Start
**Steps**:
1. Wait 90-120 seconds for all services to start completely
2. You can monitor startup progress by checking these log files:
- `logs/gateway.log`
- `logs/frontend.log`
- `logs/nginx.log`
---
### 4.2 Docker Mode Deployment (If Docker Is Selected)
#### 4.2.1 Initialize the Docker Environment
**Steps**:
1. Run `make docker-init`
**Description**: This command pulls the sandbox image if needed.
---
#### 4.2.2 Start Docker Services
**Steps**:
1. Run `make docker-start`
**Description**: This command builds and starts all required Docker containers.
---
#### 4.2.3 Wait for Services to Start
**Steps**:
1. Wait 60-90 seconds for all services to start completely
2. You can run `make docker-logs` to monitor startup progress
---
## Phase 5: Service Health Check
### 5.1 Local Mode Health Check
#### 5.1.1 Check Process Status
**Steps**:
1. Run the following command to check processes:
```bash
ps aux | grep -E "(uvicorn|next|nginx)" | grep -v grep
```
**Success Criteria**: Confirm that the following processes are running:
- Gateway (`uvicorn app.gateway.app:app`)
- Frontend (`next dev` or `next start`)
- Nginx (`nginx`)
---
#### 5.1.2 Check Frontend Service
**Steps**:
1. Use curl or a browser to visit `http://localhost:2026`
2. Verify that the page loads normally
**Example curl command**:
```bash
curl -I http://localhost:2026
```
**Success Criteria**: Returns an HTTP 200 status code.
---
#### 5.1.3 Check API Gateway
**Steps**:
1. Visit `http://localhost:2026/health`
**Example curl command**:
```bash
curl http://localhost:2026/health
```
**Success Criteria**: Returns health status JSON.
---
#### 5.1.4 Check LangGraph-compatible API
**Steps**:
1. Visit `http://localhost:2026/api/langgraph/assistants/lead_agent` to verify Gateway's LangGraph-compatible API route is reachable.
2. A `401` response is acceptable when authentication is enabled and no session cookie is provided.
---
### 5.2 Docker Mode Health Check (When Using Docker)
#### 5.2.1 Check Container Status
**Steps**:
1. Run `docker ps`
2. Confirm that the following containers are running:
- `deer-flow-nginx`
- `deer-flow-frontend`
- `deer-flow-gateway`
---
#### 5.2.2 Check Frontend Service
**Steps**:
1. Use curl or a browser to visit `http://localhost:2026`
2. Verify that the page loads normally
**Example curl command**:
```bash
curl -I http://localhost:2026
```
**Success Criteria**: Returns an HTTP 200 status code.
---
#### 5.2.3 Check API Gateway
**Steps**:
1. Visit `http://localhost:2026/health`
**Example curl command**:
```bash
curl http://localhost:2026/health
```
**Success Criteria**: Returns health status JSON.
---
#### 5.2.4 Check LangGraph-compatible API
**Steps**:
1. Visit `http://localhost:2026/api/langgraph/assistants/lead_agent` to verify Gateway's LangGraph-compatible API route is reachable.
2. A `401` response is acceptable when authentication is enabled and no session cookie is provided.
---
## Optional Functional Verification
### 6.1 List Available Models
**Steps**: Verify the model list through the API or UI.
---
### 6.2 List Available Skills
**Steps**: Verify the skill list through the API or UI.
---
### 6.3 Simple Chat Test
**Steps**: Send a simple message to test the complete workflow.
---
## Phase 6: Generate the Test Report
### 6.1 Collect Test Results
Summarize the execution status of each phase and record successful and failed items.
### 6.2 Record Issues
If anything fails, record detailed error information.
### 6.3 Generate the Report
Use the template to create a complete test report.
### 6.4 Provide Recommendations
Provide follow-up recommendations based on the test results.
@@ -1,593 +0,0 @@
# Troubleshooting Guide
This document lists common issues encountered during DeerFlow smoke testing and how to resolve them.
## Code Update Issues
### Issue: `git pull` Fails with a Merge Conflict Warning
**Symptoms**:
```
error: Your local changes to the following files would be overwritten by merge
```
**Solutions**:
1. Option A: Commit local changes first
```bash
git add .
git commit -m "Save local changes"
git pull origin main
```
2. Option B: Stash local changes
```bash
git stash
git pull origin main
git stash pop # Restore changes later if needed
```
3. Option C: Discard local changes (use with caution)
```bash
git reset --hard HEAD
git pull origin main
```
---
## Local Mode Environment Issues
### Issue: Node.js Version Is Too Old
**Symptoms**:
```
Node.js version is too old. Requires 22+, got x.x.x
```
**Solutions**:
1. Install or upgrade Node.js with nvm:
```bash
nvm install 22
nvm use 22
```
2. Or download and install it from the official website: https://nodejs.org/
3. Verify the version:
```bash
node --version
```
---
### Issue: pnpm Is Not Installed
**Symptoms**:
```
command not found: pnpm
```
**Solutions**:
1. Install pnpm with npm:
```bash
npm install -g pnpm
```
2. Or use the official installation script:
```bash
curl -fsSL https://get.pnpm.io/install.sh | sh -
```
3. Verify the installation:
```bash
pnpm --version
```
---
### Issue: uv Is Not Installed
**Symptoms**:
```
command not found: uv
```
**Solutions**:
1. Use the official installation script:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. macOS users can also install it with Homebrew:
```bash
brew install uv
```
3. Verify the installation:
```bash
uv --version
```
---
### Issue: nginx Is Not Installed
**Symptoms**:
```
command not found: nginx
```
**Solutions**:
1. macOS (Homebrew):
```bash
brew install nginx
```
2. Ubuntu/Debian:
```bash
sudo apt update
sudo apt install nginx
```
3. CentOS/RHEL:
```bash
sudo yum install nginx
```
4. Verify the installation:
```bash
nginx -v
```
---
### Issue: Port Is Already in Use
**Symptoms**:
```
Error: listen EADDRINUSE: address already in use :::2026
```
**Solutions**:
1. Find the process using the port:
```bash
lsof -i :2026 # macOS/Linux
netstat -ano | findstr :2026 # Windows
```
2. Stop that process:
```bash
kill -9 <PID> # macOS/Linux
taskkill /PID <PID> /F # Windows
```
3. Or stop DeerFlow services first:
```bash
make stop
```
---
## Local Mode Dependency Installation Issues
### Issue: `make install` Fails Due to Network Timeout
**Symptoms**:
Network timeouts or connection failures occur during dependency installation.
**Solutions**:
1. Configure pnpm to use a mirror registry:
```bash
pnpm config set registry https://registry.npmmirror.com
```
2. Configure uv to use a mirror registry:
```bash
uv pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
```
3. Retry the installation:
```bash
make install
```
---
### Issue: Python Dependency Installation Fails
**Symptoms**:
Errors occur during `uv sync`.
**Solutions**:
1. Clean the uv cache:
```bash
cd backend
uv cache clean
```
2. Resync dependencies:
```bash
cd backend
uv sync
```
3. View detailed error logs:
```bash
cd backend
uv sync --verbose
```
---
### Issue: Frontend Dependency Installation Fails
**Symptoms**:
Errors occur during `pnpm install`.
**Solutions**:
1. Clean the pnpm cache:
```bash
cd frontend
pnpm store prune
```
2. Remove node_modules and the lock file:
```bash
cd frontend
rm -rf node_modules pnpm-lock.yaml
```
3. Reinstall:
```bash
cd frontend
pnpm install
```
---
## Local Mode Service Startup Issues
### Issue: Services Exit Immediately After Startup
**Symptoms**:
Processes exit quickly after running `make dev-daemon`.
**Solutions**:
1. Check log files:
```bash
tail -f logs/gateway.log
tail -f logs/frontend.log
tail -f logs/nginx.log
```
2. Check whether config.yaml is configured correctly
3. Check environment variables in the .env file
4. Confirm that required ports are not occupied
5. Stop all services and restart:
```bash
make stop
make dev-daemon
```
---
### Issue: Nginx Fails to Start Because Temp Directories Do Not Exist
**Symptoms**:
```
nginx: [emerg] mkdir() "/opt/homebrew/var/run/nginx/client_body_temp" failed (2: No such file or directory)
```
**Solutions**:
Add local temp directory configuration to `docker/nginx/nginx.local.conf` so nginx uses the repository's temp directory.
Add the following at the beginning of the `http` block:
```nginx
client_body_temp_path temp/client_body_temp;
proxy_temp_path temp/proxy_temp;
fastcgi_temp_path temp/fastcgi_temp;
uwsgi_temp_path temp/uwsgi_temp;
scgi_temp_path temp/scgi_temp;
```
Note: The `temp/` directory under the repository root is created automatically by `make dev` or `make dev-daemon`.
---
### Issue: Nginx Fails to Start (General)
**Symptoms**:
The nginx process fails to start or reports an error.
**Solutions**:
1. Check the nginx configuration:
```bash
nginx -t -c docker/nginx/nginx.local.conf -p .
```
2. Check nginx logs:
```bash
tail -f logs/nginx.log
```
3. Ensure no other nginx process is running:
```bash
ps aux | grep nginx
```
4. If needed, stop existing nginx processes:
```bash
pkill -9 nginx
```
---
### Issue: Frontend Compilation Fails
**Symptoms**:
Compilation errors appear in `frontend.log`.
**Solutions**:
1. Check frontend logs:
```bash
tail -f logs/frontend.log
```
2. Check whether Node.js version is 22+
3. Reinstall frontend dependencies:
```bash
cd frontend
rm -rf node_modules .next
pnpm install
```
4. Restart services:
```bash
make stop
make dev-daemon
```
---
### Issue: Gateway Fails to Start
**Symptoms**:
Errors appear in `gateway.log`.
**Solutions**:
1. Check gateway logs:
```bash
tail -f logs/gateway.log
```
2. Check whether config.yaml exists and has valid formatting
3. Check whether Python dependencies are complete:
```bash
cd backend
uv sync
```
4. Confirm that the Gateway process is running normally.
---
## Docker-Related Issues
### Issue: Docker Commands Cannot Run
**Symptoms**:
```
Cannot connect to the Docker daemon
```
**Solutions**:
1. Confirm that Docker Desktop is running
2. macOS: check whether the Docker icon appears in the top menu bar
3. Linux: run `sudo systemctl start docker`
4. Run `docker info` again to verify
---
### Issue: `make docker-init` Fails to Pull the Image
**Symptoms**:
```
Error pulling image: connection refused
```
**Solutions**:
1. Check network connectivity
2. Configure a Docker image mirror if needed
3. Check whether a proxy is required
4. Switch to local installation mode if necessary (recommended)
---
## Configuration File Issues
### Issue: config.yaml Is Missing or Invalid
**Symptoms**:
```
Error: could not read config.yaml
```
**Solutions**:
1. Regenerate the configuration file:
```bash
make config
```
2. Check YAML syntax:
- Make sure indentation is correct (use 2 spaces)
- Make sure there are no tab characters
- Check that there is a space after each colon
3. Use a YAML validation tool to check the format
---
### Issue: Model API Key Is Not Configured
**Symptoms**:
After services start, API requests fail with authentication errors.
**Solutions**:
1. Edit the .env file and add the API key:
```bash
OPENAI_API_KEY=your-actual-api-key-here
```
2. Restart services (local mode):
```bash
make stop
make dev-daemon
```
3. Restart services (Docker mode):
```bash
make docker-stop
make docker-start
```
4. Confirm that the model configuration in config.yaml references the environment variable correctly
---
## Service Health Check Issues
### Issue: Frontend Page Is Not Accessible
**Symptoms**:
The browser shows a connection failure when visiting http://localhost:2026.
**Solutions** (local mode):
1. Confirm that the nginx process is running:
```bash
ps aux | grep nginx
```
2. Check nginx logs:
```bash
tail -f logs/nginx.log
```
3. Check firewall settings
**Solutions** (Docker mode):
1. Confirm that the nginx container is running:
```bash
docker ps | grep nginx
```
2. Check nginx logs:
```bash
cd docker && docker compose -p deer-flow-dev -f docker-compose-dev.yaml logs nginx
```
3. Check firewall settings
---
### Issue: API Gateway Health Check Fails
**Symptoms**:
Accessing `/health` returns an error or times out.
**Solutions** (local mode):
1. Check gateway logs:
```bash
tail -f logs/gateway.log
```
2. Confirm that config.yaml exists and has valid formatting
3. Check whether Python dependencies are complete
4. Confirm that the Gateway process is running normally.
**Solutions** (Docker mode):
1. Check gateway container logs:
```bash
make docker-logs-gateway
```
2. Confirm that config.yaml is mounted correctly
3. Check whether Python dependencies are complete
4. Confirm that the Gateway process is running normally.
---
## Common Diagnostic Commands
### Local Mode Diagnostics
#### View All Service Processes
```bash
ps aux | grep -E "(uvicorn|next|nginx)" | grep -v grep
```
#### View Service Logs
```bash
# View all logs
tail -f logs/*.log
# View specific service logs
tail -f logs/gateway.log
tail -f logs/frontend.log
tail -f logs/nginx.log
```
#### Stop All Services
```bash
make stop
```
#### Fully Reset the Local Environment
```bash
make stop
make clean
make config
make install
make dev-daemon
```
---
### Docker Mode Diagnostics
#### View All Container Status
```bash
docker ps -a
```
#### View Container Resource Usage
```bash
docker stats
```
#### Enter a Container for Debugging
```bash
docker exec -it deer-flow-gateway sh
```
#### Clean Up All DeerFlow-Related Containers and Images
```bash
make docker-stop
cd docker && docker compose -p deer-flow-dev -f docker-compose-dev.yaml down -v
```
#### Fully Reset the Docker Environment
```bash
make docker-stop
make clean
make config
make docker-init
make docker-start
```
---
## Get More Help
If the solutions above do not resolve the issue:
1. Check the GitHub issues for the project: https://github.com/bytedance/deer-flow/issues
2. Review the project documentation: README.md and the `backend/docs/` directory
3. Open a new issue and include detailed error logs
@@ -1,80 +0,0 @@
#!/usr/bin/env bash
set -e
echo "=========================================="
echo " Checking Docker Environment"
echo "=========================================="
echo ""
# Check whether Docker is installed
if command -v docker >/dev/null 2>&1; then
echo "✓ Docker is installed"
docker --version
else
echo "✗ Docker is not installed"
exit 1
fi
echo ""
# Check the Docker daemon
if docker info >/dev/null 2>&1; then
echo "✓ Docker daemon is running normally"
else
echo "✗ Docker daemon is not running"
echo " Please start Docker Desktop or the Docker service"
exit 1
fi
echo ""
# Check Docker Compose
if docker compose version >/dev/null 2>&1; then
echo "✓ Docker Compose is available"
docker compose version
else
echo "✗ Docker Compose is not available"
exit 1
fi
echo ""
# Check port 2026
if ! command -v lsof >/dev/null 2>&1; then
echo "✗ lsof is required to check whether port 2026 is available"
exit 1
fi
port_2026_usage="$(lsof -nP -iTCP:2026 -sTCP:LISTEN 2>/dev/null || true)"
if [ -n "$port_2026_usage" ]; then
echo "⚠ Port 2026 is already in use"
echo " Occupying process:"
echo "$port_2026_usage"
deerflow_process_found=0
while IFS= read -r pid; do
if [ -z "$pid" ]; then
continue
fi
process_command="$(ps -p "$pid" -o command= 2>/dev/null || true)"
case "$process_command" in
*[Dd]eer[Ff]low*|*[Dd]eerflow*|*[Nn]ginx*deerflow*|*deerflow/*[Nn]ginx*)
deerflow_process_found=1
;;
esac
done <<EOF
$(printf '%s\n' "$port_2026_usage" | awk 'NR > 1 {print $2}')
EOF
if [ "$deerflow_process_found" -eq 1 ]; then
echo "✓ Port 2026 is occupied by DeerFlow"
else
echo "✗ Port 2026 must be free before starting DeerFlow"
exit 1
fi
else
echo "✓ Port 2026 is available"
fi
echo ""
echo "=========================================="
echo " Docker Environment Check Complete"
echo "=========================================="
@@ -1,93 +0,0 @@
#!/usr/bin/env bash
set -e
echo "=========================================="
echo " Checking Local Development Environment"
echo "=========================================="
echo ""
all_passed=true
# Check Node.js
echo "1. Checking Node.js..."
if command -v node >/dev/null 2>&1; then
NODE_VERSION=$(node --version | sed 's/v//')
NODE_MAJOR=$(echo "$NODE_VERSION" | cut -d. -f1)
if [ "$NODE_MAJOR" -ge 22 ]; then
echo "✓ Node.js is installed (version: $NODE_VERSION)"
else
echo "✗ Node.js version is too old (current: $NODE_VERSION, required: 22+)"
all_passed=false
fi
else
echo "✗ Node.js is not installed"
all_passed=false
fi
echo ""
# Check pnpm
echo "2. Checking pnpm..."
if command -v pnpm >/dev/null 2>&1; then
echo "✓ pnpm is installed (version: $(pnpm --version))"
else
echo "✗ pnpm is not installed"
echo " Install command: npm install -g pnpm"
all_passed=false
fi
echo ""
# Check uv
echo "3. Checking uv..."
if command -v uv >/dev/null 2>&1; then
echo "✓ uv is installed (version: $(uv --version))"
else
echo "✗ uv is not installed"
all_passed=false
fi
echo ""
# Check nginx
echo "4. Checking nginx..."
if command -v nginx >/dev/null 2>&1; then
echo "✓ nginx is installed (version: $(nginx -v 2>&1))"
else
echo "✗ nginx is not installed"
echo " macOS: brew install nginx"
echo " Linux: install it with the system package manager"
all_passed=false
fi
echo ""
# Check ports
echo "5. Checking ports..."
if ! command -v lsof >/dev/null 2>&1; then
echo "✗ lsof is not installed, so port availability cannot be verified"
echo " Install lsof and rerun this check"
all_passed=false
else
for port in 2026 3000 8001; do
if lsof -i :$port >/dev/null 2>&1; then
echo "⚠ Port $port is already in use:"
lsof -i :$port | head -2
all_passed=false
else
echo "✓ Port $port is available"
fi
done
fi
echo ""
# Summary
echo "=========================================="
echo " Environment Check Summary"
echo "=========================================="
echo ""
if [ "$all_passed" = true ]; then
echo "✅ All environment checks passed!"
echo ""
echo "Next step: run make install to install dependencies"
exit 0
else
echo "❌ Some checks failed. Please fix the issues above first"
exit 1
fi
@@ -1,65 +0,0 @@
#!/usr/bin/env bash
set -e
echo "=========================================="
echo " Docker Deployment"
echo "=========================================="
echo ""
# Check config.yaml
if [ ! -f "config.yaml" ]; then
echo "config.yaml does not exist. Generating it..."
make config
echo ""
echo "⚠ Please edit config.yaml to configure your models and API keys"
echo " Then run this script again"
exit 1
else
echo "✓ config.yaml exists"
fi
echo ""
# Check the .env file
if [ ! -f ".env" ]; then
echo ".env does not exist. Copying it from the example..."
if [ -f ".env.example" ]; then
cp .env.example .env
echo "✓ Created the .env file"
else
echo "⚠ .env.example does not exist. Please create the .env file manually"
fi
else
echo "✓ .env file exists"
fi
echo ""
# Check the frontend .env file
if [ ! -f "frontend/.env" ]; then
echo "frontend/.env does not exist. Copying it from the example..."
if [ -f "frontend/.env.example" ]; then
cp frontend/.env.example frontend/.env
echo "✓ Created the frontend/.env file"
else
echo "⚠ frontend/.env.example does not exist. Please create frontend/.env manually"
fi
else
echo "✓ frontend/.env file exists"
fi
echo ""
# Initialize the Docker environment
echo "Initializing the Docker environment..."
make docker-init
echo ""
# Start Docker services
echo "Starting Docker services..."
make docker-start
echo ""
echo "=========================================="
echo " Deployment Complete"
echo "=========================================="
echo ""
echo "🌐 Access URL: http://localhost:2026"
echo "📋 View logs: make docker-logs"
echo "🛑 Stop services: make docker-stop"
@@ -1,62 +0,0 @@
#!/usr/bin/env bash
set -e
echo "=========================================="
echo " Local Mode Deployment"
echo "=========================================="
echo ""
# Check config.yaml
if [ ! -f "config.yaml" ]; then
echo "config.yaml does not exist. Generating it..."
make config
echo ""
echo "⚠ Please edit config.yaml to configure your models and API keys"
echo " Then run this script again"
exit 1
else
echo "✓ config.yaml exists"
fi
echo ""
# Check the .env file
if [ ! -f ".env" ]; then
echo ".env does not exist. Copying it from the example..."
if [ -f ".env.example" ]; then
cp .env.example .env
echo "✓ Created the .env file"
else
echo "⚠ .env.example does not exist. Please create the .env file manually"
fi
else
echo "✓ .env file exists"
fi
echo ""
# Check dependencies
echo "Checking dependencies..."
make check
echo ""
# Install dependencies
echo "Installing dependencies..."
make install
echo ""
# Start services
echo "Starting services (background mode)..."
make dev-daemon
echo ""
echo "=========================================="
echo " Deployment Complete"
echo "=========================================="
echo ""
echo "🌐 Access URL: http://localhost:2026"
echo "📋 View logs:"
echo " - logs/gateway.log"
echo " - logs/frontend.log"
echo " - logs/nginx.log"
echo "🛑 Stop services: make stop"
echo ""
echo "Please wait 90-120 seconds for all services to start completely, then run the health check"
@@ -1,70 +0,0 @@
#!/usr/bin/env bash
set +e
echo "=========================================="
echo " Frontend Page Smoke Check"
echo "=========================================="
echo ""
BASE_URL="${BASE_URL:-http://localhost:2026}"
DOC_PATH="${DOC_PATH:-/en/docs}"
all_passed=true
check_status() {
local name="$1"
local url="$2"
local expected_re="$3"
local status
status="$(curl -s -o /dev/null -w "%{http_code}" -L "$url")"
if echo "$status" | grep -Eq "$expected_re"; then
echo "$name ($url) -> $status"
else
echo "$name ($url) -> $status (expected: $expected_re)"
all_passed=false
fi
}
check_final_url() {
local name="$1"
local url="$2"
local expected_path_re="$3"
local effective
effective="$(curl -s -o /dev/null -w "%{url_effective}" -L "$url")"
if echo "$effective" | grep -Eq "$expected_path_re"; then
echo "$name redirect target -> $effective"
else
echo "$name redirect target -> $effective (expected path: $expected_path_re)"
all_passed=false
fi
}
echo "1. Checking entry pages..."
check_status "Landing page" "${BASE_URL}/" "200"
check_status "Workspace redirect" "${BASE_URL}/workspace" "200|301|302|307|308"
check_final_url "Workspace redirect" "${BASE_URL}/workspace" "/workspace/chats/"
echo ""
echo "2. Checking key workspace routes..."
check_status "New chat page" "${BASE_URL}/workspace/chats/new" "200"
check_status "Chats list page" "${BASE_URL}/workspace/chats" "200"
check_status "Agents gallery page" "${BASE_URL}/workspace/agents" "200"
echo ""
echo "3. Checking docs route (optional)..."
check_status "Docs page" "${BASE_URL}${DOC_PATH}" "200|404"
echo ""
echo "=========================================="
echo " Frontend Smoke Check Summary"
echo "=========================================="
echo ""
if [ "$all_passed" = true ]; then
echo "✅ Frontend smoke checks passed!"
exit 0
else
echo "❌ Frontend smoke checks failed"
exit 1
fi
@@ -1,124 +0,0 @@
#!/usr/bin/env bash
set +e
echo "=========================================="
echo " Service Health Check"
echo "=========================================="
echo ""
all_passed=true
mode="${SMOKE_TEST_MODE:-auto}"
summary_hint="make logs"
print_step() {
echo "$1"
}
check_http_status() {
local name="$1"
local url="$2"
local expected_re="$3"
local status
status="$(curl -s -o /dev/null -w "%{http_code}" "$url" 2>/dev/null)"
if echo "$status" | grep -Eq "$expected_re"; then
echo "$name is accessible ($url -> $status)"
else
echo "$name is not accessible ($url -> ${status:-000})"
all_passed=false
fi
}
check_listen_port() {
local name="$1"
local port="$2"
if lsof -nP -iTCP:"$port" -sTCP:LISTEN >/dev/null 2>&1; then
echo "$name is listening on port $port"
else
echo "$name is not listening on port $port"
all_passed=false
fi
}
docker_available() {
command -v docker >/dev/null 2>&1 && docker info >/dev/null 2>&1
}
detect_mode() {
case "$mode" in
local|docker)
echo "$mode"
return
;;
esac
if docker_available && docker ps --format "{{.Names}}" | grep -q "deer-flow"; then
echo "docker"
else
echo "local"
fi
}
mode="$(detect_mode)"
echo "Deployment mode: $mode"
echo ""
if [ "$mode" = "docker" ]; then
summary_hint="make docker-logs"
print_step "1. Checking container status..."
if docker ps --format "{{.Names}}" | grep -q "deer-flow"; then
echo "✓ Containers are running:"
docker ps --format " - {{.Names}} ({{.Status}})"
else
echo "✗ No DeerFlow-related containers are running"
all_passed=false
fi
else
summary_hint="logs/{gateway,frontend,nginx}.log"
print_step "1. Checking local service ports..."
check_listen_port "Nginx" 2026
check_listen_port "Frontend" 3000
check_listen_port "Gateway" 8001
fi
echo ""
echo "2. Waiting for services to fully start (30 seconds)..."
sleep 30
echo ""
echo "3. Checking frontend service..."
check_http_status "Frontend service" "http://localhost:2026" "200|301|302|307|308"
echo ""
echo "4. Checking API Gateway..."
health_response=$(curl -s http://localhost:2026/health 2>/dev/null)
if [ $? -eq 0 ] && [ -n "$health_response" ]; then
echo "✓ API Gateway health check passed"
echo " Response: $health_response"
else
echo "✗ API Gateway health check failed"
all_passed=false
fi
echo ""
echo "5. Checking LangGraph-compatible Gateway API..."
check_http_status "LangGraph-compatible Gateway API" "http://localhost:2026/api/langgraph/assistants/lead_agent" "200|401"
echo ""
echo "=========================================="
echo " Health Check Summary"
echo "=========================================="
echo ""
if [ "$all_passed" = true ]; then
echo "✅ All checks passed!"
echo ""
echo "🌐 Application URL: http://localhost:2026"
exit 0
else
echo "❌ Some checks failed"
echo ""
echo "Please review: $summary_hint"
exit 1
fi
@@ -1,49 +0,0 @@
#!/usr/bin/env bash
set -e
echo "=========================================="
echo " Pulling the Latest Code"
echo "=========================================="
echo ""
# Check whether the current directory is a Git repository
if [ ! -d ".git" ]; then
echo "✗ The current directory is not a Git repository"
exit 1
fi
# Check Git status
echo "Checking Git status..."
if git status --porcelain | grep -q .; then
echo "⚠ Uncommitted changes detected:"
git status --short
echo ""
echo "Please commit or stash your changes before continuing"
echo "Options:"
echo " 1. git add . && git commit -m 'Save changes'"
echo " 2. git stash (stash changes and restore them later)"
echo " 3. git reset --hard HEAD (discard local changes - use with caution)"
exit 1
else
echo "✓ Working tree is clean"
fi
echo ""
# Fetch remote updates
echo "Fetching remote updates..."
git fetch origin main
echo ""
# Pull the latest code
echo "Pulling the latest code..."
git pull origin main
echo ""
# Show the latest commit
echo "Latest commit:"
git log -1 --oneline
echo ""
echo "=========================================="
echo " Code Update Complete"
echo "=========================================="
@@ -1,179 +0,0 @@
# DeerFlow Smoke Test Report
**Test Date**: {{test_date}}
**Test Environment**: {{test_environment}}
**Deployment Mode**: Docker
**Test Version**: {{git_commit}}
---
## Execution Summary
| Metric | Status |
|------|------|
| Total Test Phases | 6 |
| Passed Phases | {{passed_stages}} |
| Failed Phases | {{failed_stages}} |
| Overall Conclusion | **{{overall_status}}** |
### Key Test Cases
| Case | Result | Details |
|------|--------|---------|
| Code update check | {{case_code_update}} | {{case_code_update_details}} |
| Environment check | {{case_env_check}} | {{case_env_check_details}} |
| Configuration preparation | {{case_config_prep}} | {{case_config_prep_details}} |
| Deployment | {{case_deploy}} | {{case_deploy_details}} |
| Health check | {{case_health_check}} | {{case_health_check_details}} |
| Frontend routes | {{case_frontend_routes_overall}} | {{case_frontend_routes_details}} |
---
## Detailed Test Results
### Phase 1: Code Update Check
- [x] Confirm current directory - {{status_dir_check}}
- [x] Check Git status - {{status_git_status}}
- [x] Pull latest code - {{status_git_pull}}
- [x] Confirm code update - {{status_git_verify}}
**Phase Status**: {{stage1_status}}
---
### Phase 2: Docker Environment Check
- [x] Docker version - {{status_docker_version}}
- [x] Docker daemon - {{status_docker_daemon}}
- [x] Docker Compose - {{status_docker_compose}}
- [x] Port check - {{status_port_check}}
**Phase Status**: {{stage2_status}}
---
### Phase 3: Configuration Preparation
- [x] config.yaml - {{status_config_yaml}}
- [x] .env file - {{status_env_file}}
- [x] Model configuration - {{status_model_config}}
**Phase Status**: {{stage3_status}}
---
### Phase 4: Docker Deployment
- [x] docker-init - {{status_docker_init}}
- [x] docker-start - {{status_docker_start}}
- [x] Service startup wait - {{status_wait_startup}}
**Phase Status**: {{stage4_status}}
---
### Phase 5: Service Health Check
- [x] Container status - {{status_containers}}
- [x] Frontend service - {{status_frontend}}
- [x] API Gateway - {{status_api_gateway}}
- [x] LangGraph-compatible Gateway API - {{status_langgraph}}
**Phase Status**: {{stage5_status}}
---
### Frontend Routes Smoke Results
| Route | Status | Details |
|-------|--------|---------|
| Landing `/` | {{landing_status}} | {{landing_details}} |
| Workspace redirect `/workspace` | {{workspace_redirect_status}} | target {{workspace_redirect_target}} |
| New chat `/workspace/chats/new` | {{new_chat_status}} | {{new_chat_details}} |
| Chats list `/workspace/chats` | {{chats_list_status}} | {{chats_list_details}} |
| Agents gallery `/workspace/agents` | {{agents_gallery_status}} | {{agents_gallery_details}} |
| Docs `{{docs_path}}` | {{docs_status}} | {{docs_details}} |
**Summary**: {{frontend_routes_summary}}
---
### Phase 6: Test Report Generation
- [x] Result summary - {{status_summary}}
- [x] Issue log - {{status_issues}}
- [x] Report generation - {{status_report}}
**Phase Status**: {{stage6_status}}
---
## Issue Log
### Issue 1
**Description**: {{issue1_description}}
**Severity**: {{issue1_severity}}
**Solution**: {{issue1_solution}}
---
## Environment Information
### Docker Version
```text
{{docker_version_output}}
```
### Git Information
```text
Repository: {{git_repo}}
Branch: {{git_branch}}
Commit: {{git_commit}}
Commit Message: {{git_commit_message}}
```
### Configuration Summary
- config.yaml exists: {{config_exists}}
- .env file exists: {{env_exists}}
- Number of configured models: {{model_count}}
---
## Container Status
| Container Name | Status | Uptime |
|----------|------|----------|
| deer-flow-nginx | {{nginx_status}} | {{nginx_uptime}} |
| deer-flow-frontend | {{frontend_status}} | {{frontend_uptime}} |
| deer-flow-gateway | {{gateway_status}} | {{gateway_uptime}} |
---
## Recommendations and Next Steps
### If the Test Passes
1. [ ] Visit http://localhost:2026 to start using DeerFlow
2. [ ] Configure your preferred model if it is not configured yet
3. [ ] Explore available skills
4. [ ] Refer to the documentation to learn more features
### If the Test Fails
1. [ ] Review references/troubleshooting.md for common solutions
2. [ ] Check Docker logs: `make docker-logs`
3. [ ] Verify configuration file format and content
4. [ ] If needed, fully reset the environment: `make clean && make config && make docker-init && make docker-start`
---
## Appendix
### Full Logs
{{full_logs}}
### Tester
{{tester_name}}
---
*Report generated at: {{report_time}}*
@@ -1,185 +0,0 @@
# DeerFlow Smoke Test Report
**Test Date**: {{test_date}}
**Test Environment**: {{test_environment}}
**Deployment Mode**: Local
**Test Version**: {{git_commit}}
---
## Execution Summary
| Metric | Status |
|------|------|
| Total Test Phases | 6 |
| Passed Phases | {{passed_stages}} |
| Failed Phases | {{failed_stages}} |
| Overall Conclusion | **{{overall_status}}** |
### Key Test Cases
| Case | Result | Details |
|------|--------|---------|
| Code update check | {{case_code_update}} | {{case_code_update_details}} |
| Environment check | {{case_env_check}} | {{case_env_check_details}} |
| Configuration preparation | {{case_config_prep}} | {{case_config_prep_details}} |
| Deployment | {{case_deploy}} | {{case_deploy_details}} |
| Health check | {{case_health_check}} | {{case_health_check_details}} |
| Frontend routes | {{case_frontend_routes_overall}} | {{case_frontend_routes_details}} |
---
## Detailed Test Results
### Phase 1: Code Update Check
- [x] Confirm current directory - {{status_dir_check}}
- [x] Check Git status - {{status_git_status}}
- [x] Pull latest code - {{status_git_pull}}
- [x] Confirm code update - {{status_git_verify}}
**Phase Status**: {{stage1_status}}
---
### Phase 2: Local Environment Check
- [x] Node.js version - {{status_node_version}}
- [x] pnpm - {{status_pnpm}}
- [x] uv - {{status_uv}}
- [x] nginx - {{status_nginx}}
- [x] Port check - {{status_port_check}}
**Phase Status**: {{stage2_status}}
---
### Phase 3: Configuration Preparation
- [x] config.yaml - {{status_config_yaml}}
- [x] .env file - {{status_env_file}}
- [x] Model configuration - {{status_model_config}}
**Phase Status**: {{stage3_status}}
---
### Phase 4: Local Deployment
- [x] make check - {{status_make_check}}
- [x] make install - {{status_make_install}}
- [x] make dev-daemon / make dev - {{status_local_start}}
- [x] Service startup wait - {{status_wait_startup}}
**Phase Status**: {{stage4_status}}
---
### Phase 5: Service Health Check
- [x] Process status - {{status_processes}}
- [x] Frontend service - {{status_frontend}}
- [x] API Gateway - {{status_api_gateway}}
- [x] LangGraph-compatible Gateway API - {{status_langgraph}}
**Phase Status**: {{stage5_status}}
---
### Frontend Routes Smoke Results
| Route | Status | Details |
|-------|--------|---------|
| Landing `/` | {{landing_status}} | {{landing_details}} |
| Workspace redirect `/workspace` | {{workspace_redirect_status}} | target {{workspace_redirect_target}} |
| New chat `/workspace/chats/new` | {{new_chat_status}} | {{new_chat_details}} |
| Chats list `/workspace/chats` | {{chats_list_status}} | {{chats_list_details}} |
| Agents gallery `/workspace/agents` | {{agents_gallery_status}} | {{agents_gallery_details}} |
| Docs `{{docs_path}}` | {{docs_status}} | {{docs_details}} |
**Summary**: {{frontend_routes_summary}}
---
### Phase 6: Test Report Generation
- [x] Result summary - {{status_summary}}
- [x] Issue log - {{status_issues}}
- [x] Report generation - {{status_report}}
**Phase Status**: {{stage6_status}}
---
## Issue Log
### Issue 1
**Description**: {{issue1_description}}
**Severity**: {{issue1_severity}}
**Solution**: {{issue1_solution}}
---
## Environment Information
### Local Dependency Versions
```text
Node.js: {{node_version_output}}
pnpm: {{pnpm_version_output}}
uv: {{uv_version_output}}
nginx: {{nginx_version_output}}
```
### Git Information
```text
Repository: {{git_repo}}
Branch: {{git_branch}}
Commit: {{git_commit}}
Commit Message: {{git_commit_message}}
```
### Configuration Summary
- config.yaml exists: {{config_exists}}
- .env file exists: {{env_exists}}
- Number of configured models: {{model_count}}
---
## Local Service Status
| Service | Status | Endpoint |
|---------|--------|----------|
| Nginx | {{nginx_status}} | {{nginx_endpoint}} |
| Frontend | {{frontend_status}} | {{frontend_endpoint}} |
| Gateway | {{gateway_status}} | {{gateway_endpoint}} |
| Gateway LangGraph API | {{langgraph_status}} | {{langgraph_endpoint}} |
---
## Recommendations and Next Steps
### If the Test Passes
1. [ ] Visit http://localhost:2026 to start using DeerFlow
2. [ ] Configure your preferred model if it is not configured yet
3. [ ] Explore available skills
4. [ ] Refer to the documentation to learn more features
### If the Test Fails
1. [ ] Review references/troubleshooting.md for common solutions
2. [ ] Check local logs: `logs/{gateway,frontend,nginx}.log`
3. [ ] Verify configuration file format and content
4. [ ] If needed, fully reset the environment: `make stop && make clean && make install && make dev-daemon`
---
## Appendix
### Full Logs
{{full_logs}}
### Tester
{{tester_name}}
---
*Report generated at: {{report_time}}*
+2 -29
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@@ -1,6 +1,3 @@
# 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
@@ -9,9 +6,8 @@ JINA_API_KEY=your-jina-api-key
# InfoQuest API Key
INFOQUEST_API_KEY=your-infoquest-api-key
# Browser CORS allowlist for split-origin or port-forwarded deployments (comma-separated exact origins).
# Leave unset when using the unified nginx endpoint, e.g. http://localhost:2026.
# GATEWAY_CORS_ORIGINS=http://localhost:3000,http://127.0.0.1:3000
# CORS Origins (comma-separated) - e.g., http://localhost:3000,http://localhost:3001
# CORS_ORIGINS=http://localhost:3000
# Optional:
# FIRECRAWL_API_KEY=your-firecrawl-api-key
@@ -21,7 +17,6 @@ INFOQUEST_API_KEY=your-infoquest-api-key
# DEEPSEEK_API_KEY=your-deepseek-api-key
# NOVITA_API_KEY=your-novita-api-key # OpenAI-compatible, see https://novita.ai
# MINIMAX_API_KEY=your-minimax-api-key # OpenAI-compatible, see https://platform.minimax.io
# STEPFUN_API_KEY=your-stepfun-api-key # OpenAI-compatible, see https://platform.stepfun.com
# VLLM_API_KEY=your-vllm-api-key # OpenAI-compatible
# FEISHU_APP_ID=your-feishu-app-id
# FEISHU_APP_SECRET=your-feishu-app-secret
@@ -29,7 +24,6 @@ INFOQUEST_API_KEY=your-infoquest-api-key
# SLACK_BOT_TOKEN=your-slack-bot-token
# SLACK_APP_TOKEN=your-slack-app-token
# TELEGRAM_BOT_TOKEN=your-telegram-bot-token
# DISCORD_BOT_TOKEN=your-discord-bot-token
# Enable LangSmith to monitor and debug your LLM calls, agent runs, and tool executions.
# LANGSMITH_TRACING=true
@@ -45,24 +39,3 @@ 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
# Shared internal Gateway auth token for multi-worker deployments.
# `make up` generates and persists this automatically; set it manually only
# when you run Gateway workers outside the bundled deploy script.
# DEER_FLOW_INTERNAL_AUTH_TOKEN=your-shared-internal-token
# ── Frontend SSR → Gateway wiring ─────────────────────────────────────────────
# The Next.js server uses these to reach the Gateway during SSR (auth checks,
# /api/* rewrites). They default to localhost values that match `make dev` and
# `make start`, so most local users do not need to set them.
#
# Override only when the Gateway is not on localhost:8001 (e.g. when the
# frontend and gateway run on different hosts, in containers with a service
# alias, or behind a different port). docker-compose already sets these.
# DEER_FLOW_INTERNAL_GATEWAY_BASE_URL=http://localhost:8001
# DEER_FLOW_TRUSTED_ORIGINS=http://localhost:3000,http://localhost:2026
-159
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@@ -1,159 +0,0 @@
name: 🐛 Bug report
description: Report something that isn't working so maintainers can reproduce and fix it.
title: "[bug] "
labels: ["bug"]
body:
- type: markdown
attributes:
value: |
Thanks for taking the time to file a bug. A clear, reproducible report is the
single biggest factor in how fast it gets fixed.
Please fill in every required field — especially **reproduction steps** and **logs**.
- type: checkboxes
id: preflight
attributes:
label: Before you start
options:
- label: I searched [existing issues](https://github.com/bytedance/deer-flow/issues?q=is%3Aissue) and this is not a duplicate.
required: true
- label: I can reproduce this on the latest `main`.
required: false
- type: input
id: summary
attributes:
label: Problem summary
description: One sentence describing the bug.
placeholder: e.g. make dev fails to start the gateway service
validations:
required: true
- type: dropdown
id: areas
attributes:
label: Affected area(s)
description: Which part of DeerFlow does this touch? Select all that apply.
multiple: true
options:
- Frontend (UI / Next.js)
- Backend API (gateway / endpoints / SSE)
- Agents / LangGraph (graph, prompts, langgraph.json)
- Sandbox / Docker
- Skills
- MCP
- Config / setup (make, config.yaml, env)
- Docs
- Not sure
validations:
required: true
- type: textarea
id: actual
attributes:
label: What happened?
description: The actual behavior. Include the key error lines verbatim.
placeholder: When I do X, I expected Y but I got Z.
validations:
required: true
- type: textarea
id: expected
attributes:
label: Expected behavior
placeholder: What did you expect to happen instead?
validations:
required: true
- type: textarea
id: reproduce
attributes:
label: Steps to reproduce
description: Exact commands and sequence. Minimal steps that reliably reproduce the problem.
placeholder: |
1. make check
2. make install
3. make dev
4. ...
validations:
required: true
- type: textarea
id: logs
attributes:
label: Relevant logs
description: Paste key lines from logs (for example `logs/gateway.log`, `logs/frontend.log`). Redact secrets.
render: shell
validations:
required: true
- type: dropdown
id: run_mode
attributes:
label: How are you running DeerFlow?
options:
- Local (make dev)
- Docker (make docker-start)
- CI
- Other
validations:
required: true
- type: dropdown
id: os
attributes:
label: Operating system
options:
- macOS
- Linux
- Windows
- Other
validations:
required: true
- type: input
id: platform_details
attributes:
label: Platform details
description: Architecture and shell, if relevant.
placeholder: e.g. arm64, zsh
- type: input
id: python_version
attributes:
label: Python version
placeholder: e.g. Python 3.12.9
- type: input
id: node_version
attributes:
label: Node.js version
placeholder: e.g. v22.11.0
- type: input
id: pnpm_version
attributes:
label: pnpm version
placeholder: e.g. 10.26.2
- type: input
id: uv_version
attributes:
label: uv version
placeholder: e.g. 0.7.20
- type: textarea
id: git_info
attributes:
label: Git state
description: Output of `git branch --show-current` and the latest commit SHA.
placeholder: |
branch: feature/my-branch
commit: abcdef1
- type: textarea
id: additional
attributes:
label: Additional context
description: Screenshots, related issues, config snippets (redacted), or anything else that helps triage.
-11
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@@ -1,11 +0,0 @@
blank_issues_enabled: false
contact_links:
- name: 💬 Questions & usage help
url: https://github.com/bytedance/deer-flow/discussions/categories/q-a
about: "How do I use X? Why does Y behave like that? Ask in Discussions — it gets answered faster and stays searchable."
- name: 💡 Ideas & proposals
url: https://github.com/bytedance/deer-flow/discussions/categories/ideas
about: Have a half-formed idea? Float it in Discussions before opening a formal feature request.
- name: 🔒 Report a security vulnerability
url: https://github.com/bytedance/deer-flow/security/policy
about: Do not open a public issue for security problems. Follow the security policy instead.
@@ -1,67 +0,0 @@
name: 💡 Feature request
description: Propose a new capability or an improvement to an existing one.
title: "[feat] "
labels: ["enhancement"]
body:
- type: markdown
attributes:
value: |
Thanks for the suggestion. For non-trivial features, please open a
[Discussion](https://github.com/bytedance/deer-flow/discussions/categories/ideas)
first to align on scope before writing code.
- type: checkboxes
id: preflight
attributes:
label: Before you start
options:
- label: I searched [existing issues](https://github.com/bytedance/deer-flow/issues?q=is%3Aissue) and this is not a duplicate.
required: true
- type: textarea
id: problem
attributes:
label: Problem / motivation
description: What problem does this solve? What is painful today, or what does it unblock?
placeholder: "I'm always frustrated when ..."
validations:
required: true
- type: textarea
id: solution
attributes:
label: Proposed solution
description: Describe the change from a user's / caller's perspective.
validations:
required: true
- type: dropdown
id: areas
attributes:
label: Affected area(s)
description: Which part of DeerFlow would this touch? Select all that apply.
multiple: true
options:
- Frontend (UI / Next.js)
- Backend API (gateway / endpoints / SSE)
- Agents / LangGraph (graph, prompts, langgraph.json)
- Sandbox / Docker
- Skills
- MCP
- Config / setup
- Docs
- Not sure
validations:
required: true
- type: textarea
id: alternatives
attributes:
label: Alternatives considered
description: Other approaches you weighed and why you discarded them.
- type: textarea
id: additional
attributes:
label: Additional context
description: Mockups, links, related issues, or anything else that helps.
@@ -0,0 +1,128 @@
name: Runtime Information
description: Report runtime/environment details to help reproduce an issue.
title: "[runtime] "
labels:
- needs-triage
body:
- type: markdown
attributes:
value: |
Thanks for sharing runtime details.
Complete this form so maintainers can quickly reproduce and diagnose the problem.
- type: input
id: summary
attributes:
label: Problem summary
description: Short summary of the issue.
placeholder: e.g. make dev fails to start gateway service
validations:
required: true
- type: textarea
id: expected
attributes:
label: Expected behavior
placeholder: What did you expect to happen?
validations:
required: true
- type: textarea
id: actual
attributes:
label: Actual behavior
placeholder: What happened instead? Include key error lines.
validations:
required: true
- type: dropdown
id: os
attributes:
label: Operating system
options:
- macOS
- Linux
- Windows
- Other
validations:
required: true
- type: input
id: platform_details
attributes:
label: Platform details
description: Add architecture and shell if relevant.
placeholder: e.g. arm64, zsh
- type: input
id: python_version
attributes:
label: Python version
placeholder: e.g. Python 3.12.9
- type: input
id: node_version
attributes:
label: Node.js version
placeholder: e.g. v23.11.0
- type: input
id: pnpm_version
attributes:
label: pnpm version
placeholder: e.g. 10.26.2
- type: input
id: uv_version
attributes:
label: uv version
placeholder: e.g. 0.7.20
- type: dropdown
id: run_mode
attributes:
label: How are you running DeerFlow?
options:
- Local (make dev)
- Docker (make docker-dev)
- CI
- Other
validations:
required: true
- type: textarea
id: reproduce
attributes:
label: Reproduction steps
description: Provide exact commands and sequence.
placeholder: |
1. make check
2. make install
3. make dev
4. ...
validations:
required: true
- type: textarea
id: logs
attributes:
label: Relevant logs
description: Paste key lines from logs (for example logs/gateway.log, logs/frontend.log).
render: shell
validations:
required: true
- type: textarea
id: git_info
attributes:
label: Git state
description: Share output of git branch and latest commit SHA.
placeholder: |
branch: feature/my-branch
commit: abcdef1
- type: textarea
id: additional
attributes:
label: Additional context
description: Add anything else that might help triage.
-119
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@@ -1,119 +0,0 @@
# Declarative label source of truth for DeerFlow.
#
# This file is the single source of truth for repository labels used by the
# auto-labeling workflows (.github/workflows/pr-labeler.yml, pr-triage.yml,
# issue-triage.yml). Auto-labelers can only apply labels that already exist,
# so every label referenced by a workflow MUST be declared here.
#
# Apply with: uv run --with pyyaml python scripts/sync_labels.py [--repo OWNER/NAME]
# CI keeps it in sync via .github/workflows/label-sync.yml (runs on changes here).
#
# Sync is additive/update-only: it creates or updates the labels listed below
# and never deletes labels that are not listed.
#
# Color = 6-digit hex without the leading '#'.
labels:
# ── Type ─────────────────────────────────────────────────────────────────
# Mostly GitHub defaults; declared here so colors/descriptions stay stable
# and so issue templates can rely on them existing.
- name: bug
color: d73a4a
description: Something isn't working
- name: enhancement
color: a2eeef
description: New feature or request
- name: documentation
color: 0075ca
description: Improvements or additions to documentation
- name: question
color: d876e3
description: Further information is requested
# ── Area (auto, by changed paths — see .github/labeler.yml) ───────────────
# Mirrors the "Surface area" section of the pull request template.
- name: "area:frontend"
color: c5def5
description: Next.js frontend under frontend/
- name: "area:backend"
color: c5def5
description: Gateway / runtime / core backend under backend/
- name: "area:agents"
color: c5def5
description: Agents, subagents, graph wiring, prompts, langgraph.json
- name: "area:sandbox"
color: c5def5
description: Sandboxed execution and docker/
- name: "area:skills"
color: c5def5
description: Skills under skills/ or the skills harness
- name: "area:mcp"
color: c5def5
description: Model Context Protocol integration
- name: "area:ci"
color: c5def5
description: GitHub Actions, CI config, repo tooling
- name: "area:docs"
color: c5def5
description: Documentation and Markdown only
- name: "area:deps"
color: c5def5
description: Dependency manifests / lockfiles
# ── Size (auto, by additions + deletions — see pr-triage.yml) ─────────────
- name: "size/XS"
color: "009900"
description: PR changes < 20 lines
- name: "size/S"
color: 77bb00
description: PR changes 20-100 lines
- name: "size/M"
color: eebb00
description: PR changes 100-300 lines
- name: "size/L"
color: ee9900
description: PR changes 300-700 lines
- name: "size/XL"
color: ee5500
description: PR changes 700+ lines
# ── Risk (auto, by changed paths — see pr-triage.yml) ─────────────────────
- name: "risk:low"
color: 0e8a16
description: "Low risk: docs / i18n / assets only"
- name: "risk:medium"
color: fbca04
description: "Medium risk: regular code changes"
- name: "risk:high"
color: b60205
description: "High risk: backend API, agents, sandbox, auth, deps, CI"
# ── Priority (manual) ─────────────────────────────────────────────────────
- name: P0
color: b60205
description: Critical priority
- name: P1
color: d93f0b
description: Major priority
- name: P2
color: e99695
description: Normal priority
# ── Status (auto + manual) ────────────────────────────────────────────────
- name: needs-triage
color: fef2c0
description: Awaiting maintainer triage
- name: needs-validation
color: d4c5f9
description: Touches front/back contract surface; needs real-path validation
- name: skip-validation
color: cccccc
description: "Maintainer override: do not auto-add needs-validation on this PR"
- name: reviewing
color: 5319e7
description: A maintainer is reviewing this PR
# ── Contributor ───────────────────────────────────────────────────────────
- name: first-time-contributor
color: c2e0c6
description: First contribution to this repository — be welcoming
-75
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@@ -1,75 +0,0 @@
<!-- Reference a related issue with #123. Use Fixes / Closes / Resolves to
auto-close it on merge. Delete this line if the PR doesn't reference an issue. -->
Fixes #
## Why
<!-- Why are you opening this PR? Cover two things:
- The trigger — what made you write this? A bug you hit, a feature you need,
tech debt, or a prod issue?
- The pain being addressed — user-facing problem, or what it unblocks.
For non-trivial features, please open an issue/discussion first to align on
scope before writing code. -->
## What changed
<!-- Describe the change from a user's / caller's perspective, not as a code diff. e.g.:
- "Settings now has a 'Custom endpoint' field, off by default"
- "Backend /api/chat gains a `stream` flag, defaults to false"
- "Default model changed from X to Y — existing users notice on first run" -->
## Surface area
<!-- Check every box that applies. Reviewers use this to scope the review. -->
- [ ] **Frontend UI** — page / component / setting / interaction under `frontend/`
- [ ] **Backend API** — endpoint / SSE event / request-response shape under `backend/app`
- [ ] **Agents / LangGraph** — agent node, graph wiring, `langgraph.json`, or prompt change
- [ ] **Sandbox**`docker/` or sandboxed execution
- [ ] **Skills** — change under `skills/`
- [ ] **Dependencies** — new/upgraded entry in `backend/pyproject.toml` or `frontend/package.json` (say what it buys us)
- [ ] **Default behavior change** — changes existing behavior without the user opting in (default model, default setting, data shape)
- [ ] **Docs / tests / CI only** — no runtime behavior change
## Screenshots / Recording
<!-- If you checked "Frontend UI", attach screenshots showing the entry point —
where users discover the change — not just the feature in isolation.
Before/after is best for behavior changes. Short GIFs welcome. -->
## Bug fix verification
<!-- Skip (delete) this section if this PR is not a bug fix.
Bugs should be encoded as a failing test that goes red before the fix.
Confirm:
- Test path that reproduces the bug:
- Did it go red on `main` and green on this branch? (yes / no)
- If a red test wasn't cheap to write, explain why and what you did instead. -->
## Validation
<!-- What you actually ran. Run at least the checks for the area you changed:
Backend: cd backend && make lint && make test
Frontend: cd frontend && pnpm format && pnpm lint && pnpm typecheck && BETTER_AUTH_SECRET=local-dev-secret pnpm build && make test
Frontend E2E (if you touched frontend/): cd frontend && make test-e2e -->
## AI assistance
<!-- DeerFlow is an AI project — most PRs here use AI coding tools, and that's
welcome. Disclosing it just helps reviewers calibrate how closely to read the
diff. Please fill all three; don't delete the section. -->
**Tool(s) used:** <!-- e.g. Claude Code, Cursor, GitHub Copilot, Codex, Windsurf, or "none" -->
**How you used it:** <!-- e.g. "generated the module from a spec", "autocomplete only",
"AI wrote tests, I wrote the impl". A prompt or conversation link is great too. -->
- [ ] I've read and understand every line of this change and take responsibility for it — it's not unreviewed AI output.
@@ -1,46 +0,0 @@
name: Backend Blocking IO
on:
push:
branches: ["main"]
paths:
- "backend/**"
- ".github/workflows/backend-blocking-io-tests.yml"
pull_request:
types: [opened, synchronize, reopened, ready_for_review]
paths:
- "backend/**"
- ".github/workflows/backend-blocking-io-tests.yml"
concurrency:
group: blocking-io-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
permissions:
contents: read
jobs:
backend-blocking-io:
if: github.event_name != 'pull_request' || github.event.pull_request.draft == false
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.12"
- name: Install uv
uses: astral-sh/setup-uv@v3
- name: Install backend dependencies
working-directory: backend
run: uv sync --group dev
- name: Run blocking IO regression tests
working-directory: backend
run: make test-blocking-io
-101
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@@ -1,101 +0,0 @@
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: ${{ steps.push.outputs.digest }}
push-to-registry: true
-63
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@@ -1,63 +0,0 @@
name: E2E Tests
on:
push:
branches: [ 'main' ]
paths:
- 'frontend/**'
- '.github/workflows/e2e-tests.yml'
pull_request:
types: [opened, synchronize, reopened, ready_for_review]
paths:
- 'frontend/**'
- '.github/workflows/e2e-tests.yml'
concurrency:
group: e2e-tests-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
permissions:
contents: read
jobs:
e2e-tests:
if: ${{ github.event_name != 'pull_request' || github.event.pull_request.draft == false }}
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout
uses: actions/checkout@v6
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: '22'
- name: Enable Corepack
run: corepack enable
- name: Use pinned pnpm version
run: corepack prepare pnpm@10.26.2 --activate
- name: Install frontend dependencies
working-directory: frontend
run: pnpm install --frozen-lockfile
- name: Install Playwright Chromium
working-directory: frontend
run: npx playwright install chromium --with-deps
- name: Run E2E tests
working-directory: frontend
run: pnpm exec playwright test
env:
SKIP_ENV_VALIDATION: '1'
- name: Upload Playwright report
uses: actions/upload-artifact@v4
if: ${{ !cancelled() }}
with:
name: playwright-report
path: frontend/playwright-report/
retention-days: 7
-43
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@@ -1,43 +0,0 @@
name: Frontend Unit Tests
on:
push:
branches: [ 'main' ]
pull_request:
types: [opened, synchronize, reopened, ready_for_review]
concurrency:
group: frontend-unit-tests-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
permissions:
contents: read
jobs:
frontend-unit-tests:
if: github.event.pull_request.draft == false
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- name: Checkout
uses: actions/checkout@v6
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: '22'
- name: Enable Corepack
run: corepack enable
- name: Use pinned pnpm version
run: corepack prepare pnpm@10.26.2 --activate
- name: Install frontend dependencies
working-directory: frontend
run: pnpm install --frozen-lockfile
- name: Run unit tests of frontend
working-directory: frontend
run: make test
-38
View File
@@ -1,38 +0,0 @@
name: Label Sync
# Keeps repository labels in sync with the declarative source of truth
# (.github/labels.yml). Runs whenever that file changes on main, and can be
# triggered manually. Additive/update-only — never deletes labels.
on:
push:
branches: [main]
paths:
- ".github/labels.yml"
- "scripts/sync_labels.py"
- ".github/workflows/label-sync.yml"
workflow_dispatch:
permissions:
contents: read
issues: write
concurrency:
group: label-sync
cancel-in-progress: false
jobs:
sync:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
- name: Install uv
uses: astral-sh/setup-uv@v7
- name: Sync labels
run: uv run --with pyyaml python scripts/sync_labels.py
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
GH_REPO: ${{ github.repository }}
-108
View File
@@ -1,108 +0,0 @@
name: Replay E2E (front-back contract)
# Guards the front-back contract via record/replay (no API key in CI):
# Layer 1 — backend golden: replay a recorded trace through the real gateway,
# assert the SSE event sequence matches the committed golden.
# Layer 2 — full-stack render: real Next.js frontend + real gateway (replay
# model) + Chromium; assert the replayed turns render in the browser.
# Triggered by changes on EITHER side of the contract so a backend change can no
# longer pass without the frontend-facing checks running.
on:
push:
branches: ["main"]
paths:
- "frontend/**"
- "backend/app/gateway/**"
- "backend/packages/harness/**"
- "backend/tests/fixtures/replay/**"
- "backend/tests/replay_provider.py"
- "backend/tests/_replay_fixture.py"
- "backend/tests/seed_runs_router.py"
- "backend/tests/test_replay_golden.py"
- "backend/scripts/run_replay_gateway.py"
- ".github/workflows/replay-e2e.yml"
pull_request:
types: [opened, synchronize, reopened, ready_for_review]
paths:
- "frontend/**"
- "backend/app/gateway/**"
- "backend/packages/harness/**"
- "backend/tests/fixtures/replay/**"
- "backend/tests/replay_provider.py"
- "backend/tests/_replay_fixture.py"
- "backend/tests/seed_runs_router.py"
- "backend/tests/test_replay_golden.py"
- "backend/scripts/run_replay_gateway.py"
- ".github/workflows/replay-e2e.yml"
concurrency:
group: replay-e2e-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
permissions:
contents: read
jobs:
backend-replay-golden:
name: Layer 1 — backend golden (no API key)
if: github.event_name != 'pull_request' || github.event.pull_request.draft == false
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- uses: actions/checkout@v6
- name: Set up Python
uses: actions/setup-python@v6
with:
python-version: "3.12"
- name: Install uv
uses: astral-sh/setup-uv@v7
- name: Install backend dependencies
working-directory: backend
run: uv sync --group dev
- name: Replay golden (backend SSE contract)
working-directory: backend
run: PYTHONPATH=. uv run pytest tests/test_replay_golden.py -v
fullstack-replay-render:
name: Layer 2 — full-stack render (no API key)
if: github.event_name != 'pull_request' || github.event.pull_request.draft == false
runs-on: ubuntu-latest
timeout-minutes: 25
steps:
- uses: actions/checkout@v6
- name: Set up Python
uses: actions/setup-python@v6
with:
python-version: "3.12"
- name: Install uv
uses: astral-sh/setup-uv@v7
- name: Install backend dependencies (replay gateway)
working-directory: backend
run: uv sync --group dev
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: "22"
- name: Enable Corepack
run: corepack enable
- name: Use pinned pnpm version
run: corepack prepare pnpm@10.26.2 --activate
- name: Install frontend dependencies
working-directory: frontend
run: pnpm install --frozen-lockfile
- name: Install Playwright Chromium
working-directory: frontend
run: npx playwright install chromium --with-deps
- name: Full-stack replay render (DOM assertions are the gate)
working-directory: frontend
run: pnpm exec playwright test -c playwright.real-backend.config.ts
- name: Upload report + render artifact
uses: actions/upload-artifact@v4
if: ${{ !cancelled() }}
with:
name: replay-render
path: |
frontend/playwright-report/
frontend/test-results/
retention-days: 7
-223
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@@ -1,223 +0,0 @@
name: Triage
# One workflow for all event-driven PR/issue labeling. Replaces the former
# pr-labeler / pr-triage / issue-triage workflows (and drops actions/labeler).
#
# Design notes:
# * All jobs are pure-metadata: they read changed-file lists / PR fields / the
# review payload via the API and write labels. PR code is NEVER checked out
# or executed, so pull_request_target is safe here.
# * Each job only reconciles labels in namespaces IT owns
# (area:* / size/* / risk:* / needs-validation). It never touches labels
# applied by maintainers or other tools (bug, priority, etc.). first-time-
# contributor and reviewing are add-only.
# * State is read LIVE (listFiles + listLabelsOnIssue) at run time, not from
# the (stale) event payload, so rapid synchronize events converge instead
# of thrashing.
on:
pull_request_target:
types: [opened, synchronize, reopened, ready_for_review]
pull_request_review:
types: [submitted]
issues:
types: [opened]
permissions:
contents: read
pull-requests: write
issues: write
jobs:
# ── PR: area / size / risk / needs-validation / first-time ─────────────────
pr-labels:
if: github.event_name == 'pull_request_target' && github.event.pull_request.draft == false
runs-on: ubuntu-latest
concurrency:
group: triage-pr-${{ github.event.pull_request.number }}
cancel-in-progress: true
steps:
- name: Apply PR labels from live state
uses: actions/github-script@v8
with:
script: |
const pr = context.payload.pull_request;
const { owner, repo } = context.repo;
const num = pr.number;
// ---- live changed files ----
const files = await github.paginate(github.rest.pulls.listFiles, {
owner, repo, pull_number: num, per_page: 100,
});
const paths = files.map(f => f.filename);
const m = (re) => paths.some(p => re.test(p));
// ---- area: replaces .github/labeler.yml (path -> area) ----
const AREA_RULES = [
['area:frontend', [/^frontend\//]],
['area:backend', [/^backend\/app\//, /^backend\/packages\/harness\/deerflow\/(runtime|persistence|config|tools|guardrails|tracing|models|utils|uploads)\//]],
['area:agents', [/^backend\/packages\/harness\/deerflow\/(agents|subagents|reflection)\//, /(^|\/)langgraph\.json$/, /^backend\/.*\/prompts\//]],
['area:sandbox', [/^docker\//, /^backend\/packages\/harness\/deerflow\/sandbox\//, /(^|\/)Dockerfile$/]],
['area:skills', [/^skills\//, /^backend\/packages\/harness\/deerflow\/skills\//, /^frontend\/src\/core\/skills\//]],
['area:mcp', [/^backend\/packages\/harness\/deerflow\/mcp\//, /^frontend\/src\/core\/mcp\//]],
['area:ci', [/^\.github\//, /^scripts\//]],
['area:docs', [/^docs\//, /\.mdx?$/]],
['area:deps', [/(^|\/)(pyproject\.toml|uv\.lock|package\.json|pnpm-lock\.yaml)$/]],
];
const areaLabels = AREA_RULES
.filter(([, res]) => res.some(re => m(re)))
.map(([label]) => label);
// ---- size: additions+deletions, excluding lockfiles/snapshots ----
const EXCLUDE_SIZE = /(^|\/)(uv\.lock|pnpm-lock\.yaml|package-lock\.json)$|\.snap$/;
const churn = files
.filter(f => !EXCLUDE_SIZE.test(f.filename))
.reduce((s, f) => s + (f.additions || 0) + (f.deletions || 0), 0);
const sizeLabel =
churn < 20 ? 'size/XS' :
churn < 100 ? 'size/S' :
churn < 300 ? 'size/M' :
churn < 700 ? 'size/L' : 'size/XL';
// ---- risk ----
const docsOnly = paths.length > 0 && paths.every(p =>
/\.(md|mdx|txt)$/i.test(p) || p.startsWith('docs/') ||
/\.(png|jpe?g|gif|svg|webp|ico)$/i.test(p));
const highRisk =
m(/^backend\/app\/gateway\//) ||
m(/^backend\/packages\/harness\/deerflow\/(agents|subagents|sandbox)\//) ||
m(/(^|\/)langgraph\.json$/) ||
m(/(^|\/)(auth|authz|security)/i) ||
m(/(pyproject\.toml|uv\.lock|package\.json|pnpm-lock\.yaml)$/) ||
m(/^docker\//) ||
m(/^\.github\/workflows\//);
const riskLabel = docsOnly ? 'risk:low' : (highRisk ? 'risk:high' : 'risk:medium');
// ---- needs-validation: front/back contract surface ----
const contract =
m(/^backend\/app\/gateway\//) ||
m(/^backend\/packages\/harness\/deerflow\/(agents|subagents)\//) ||
m(/(^|\/)langgraph\.json$/) ||
m(/^frontend\/src\/core\/(api|threads|messages)\//);
// ---- live current labels (NOT the stale event payload) ----
const current = (await github.paginate(github.rest.issues.listLabelsOnIssue, {
owner, repo, issue_number: num, per_page: 100,
})).map(l => l.name);
const hasSkip = current.includes('skip-validation');
// Reconcile ONLY namespaces we own; never touch others.
const owned = (n) =>
n.startsWith('area:') || n.startsWith('size/') ||
n.startsWith('risk:') || n === 'needs-validation';
const desired = new Set([...areaLabels, sizeLabel, riskLabel]);
if (contract && !hasSkip) desired.add('needs-validation');
const toRemove = current.filter(n => owned(n) && !desired.has(n));
const toAdd = [...desired].filter(n => !current.includes(n));
// first-time-contributor: add-only, on opened, real users only.
if (context.payload.action === 'opened' &&
pr.user.type === 'User' &&
['FIRST_TIME_CONTRIBUTOR', 'FIRST_TIMER'].includes(pr.author_association) &&
!current.includes('first-time-contributor')) {
toAdd.push('first-time-contributor');
}
for (const name of toRemove) {
try {
await github.rest.issues.removeLabel({ owner, repo, issue_number: num, name });
} catch (e) {
if (e.status !== 404) throw e;
}
}
if (toAdd.length) {
await github.rest.issues.addLabels({ owner, repo, issue_number: num, labels: toAdd });
}
core.info(`area=[${areaLabels.join(',')}] ${sizeLabel} ${riskLabel} churn=${churn} ` +
`validation=${desired.has('needs-validation')} ` +
`(+${toAdd.join(',') || '-'} / -${toRemove.join(',') || '-'})`);
# ── PR: reviewing label on a maintainer's human review ─────────────────────
reviewing:
if: github.event_name == 'pull_request_review'
runs-on: ubuntu-latest
concurrency:
group: triage-review-${{ github.event.pull_request.number }}
cancel-in-progress: false
steps:
- name: Add reviewing label for maintainer reviews
uses: actions/github-script@v8
with:
script: |
const { owner, repo } = context.repo;
const num = context.payload.pull_request.number;
const review = context.payload.review;
const assoc = review.author_association; // payload field; no API call
const type = review.user && review.user.type;
// author_association is NONE for every automated reviewer
// (Copilot, CodeRabbit, Codex, Sourcery, ...), so this allowlist
// drops them all without a denylist — and never calls the
// collaborators API that 404s on "Copilot is not a user".
// user.type === 'User' guards the rare bot-added-as-collaborator case.
if (!['OWNER', 'MEMBER', 'COLLABORATOR'].includes(assoc) || type !== 'User') {
core.info(`reviewer ${review.user && review.user.login} assoc=${assoc} type=${type}; skipping.`);
return;
}
const labels = (await github.paginate(github.rest.issues.listLabelsOnIssue, {
owner, repo, issue_number: num, per_page: 100,
})).map(l => l.name);
if (labels.includes('reviewing')) {
core.info('Already labeled reviewing; skipping.');
return;
}
try {
await github.rest.issues.addLabels({
owner, repo, issue_number: num, labels: ['reviewing'],
});
core.info('Added "reviewing".');
} catch (e) {
if (e.status === 403) core.info('No permission to label (expected on some fork PRs).');
else throw e;
}
# ── Issue: needs-triage on every new issue ────────────────────────────────
issue-triage:
if: github.event_name == 'issues'
runs-on: ubuntu-latest
concurrency:
group: triage-issue-${{ github.event.issue.number }}
cancel-in-progress: false
steps:
- name: Add needs-triage label
uses: actions/github-script@v8
with:
script: |
const { owner, repo } = context.repo;
const issue_number = context.payload.issue.number;
// Read live labels (not the event payload) so labels added at creation
// time via the API or by another automation are seen — consistent with
// the live-state reads in the PR jobs above.
const current = (await github.paginate(github.rest.issues.listLabelsOnIssue, {
owner, repo, issue_number, per_page: 100,
})).map(l => l.name);
if (current.includes('needs-triage')) {
core.info('Issue already has needs-triage; nothing to do.');
return;
}
// Self-heal: create the label if it does not exist yet.
try {
await github.rest.issues.createLabel({
owner, repo, name: 'needs-triage', color: 'fef2c0',
description: 'Awaiting maintainer triage',
});
} catch (e) {
if (e.status !== 422) throw e; // 422 = already exists
}
await github.rest.issues.addLabels({
owner, repo, issue_number, labels: ['needs-triage'],
});
core.info(`Added needs-triage to #${issue_number}.`);
-3
View File
@@ -40,7 +40,6 @@ coverage/
skills/custom/*
logs/
log/
debug.log
# Local git hooks (keep only on this machine, do not push)
.githooks/
@@ -56,7 +55,5 @@ web/
backend/Dockerfile.langgraph
config.yaml.bak
.playwright-mcp
/frontend/test-results/
/frontend/playwright-report/
.gstack/
.worktrees
-33
View File
@@ -1,33 +0,0 @@
repos:
# Backend: ruff lint + format via uv (uses the same ruff version as backend deps)
- repo: local
hooks:
- id: ruff
name: ruff lint
entry: bash -c 'cd backend && uv run ruff check --fix "${@/#backend\//}"' --
language: system
types_or: [python]
files: ^backend/
- id: ruff-format
name: ruff format
entry: bash -c 'cd backend && uv run ruff format "${@/#backend\//}"' --
language: system
types_or: [python]
files: ^backend/
# Frontend: eslint + prettier (must run from frontend/ for node_modules resolution)
- repo: local
hooks:
- id: frontend-eslint
name: eslint (frontend)
entry: bash -c 'cd frontend && npx eslint --fix "${@/#frontend\//}"' --
language: system
types_or: [javascript, tsx, ts]
files: ^frontend/
- id: frontend-prettier
name: prettier (frontend)
entry: bash -c 'cd frontend && npx prettier --write "${@/#frontend\//}"' --
language: system
files: ^frontend/
types_or: [javascript, tsx, ts, json, css]
-128
View File
@@ -1,128 +0,0 @@
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
willem.jiang@gmail.com.
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series
of actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within
the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.
+26 -52
View File
@@ -46,12 +46,12 @@ Docker provides a consistent, isolated environment with all dependencies pre-con
All services will start with hot-reload enabled:
- Frontend changes are automatically reloaded
- Backend changes trigger automatic restart
- Gateway-hosted LangGraph-compatible runtime supports hot-reload
- LangGraph server supports hot-reload
4. **Access the application**:
- Web Interface: http://localhost:2026
- API Gateway: http://localhost:2026/api/*
- LangGraph-compatible API: http://localhost:2026/api/langgraph/*
- LangGraph: http://localhost:2026/api/langgraph/*
#### Docker Commands
@@ -77,24 +77,12 @@ export UV_INDEX_URL=https://pypi.org/simple
export NPM_REGISTRY=https://registry.npmjs.org
```
#### Recommended host resources
Use these as practical starting points for development and review environments:
| Scenario | Starting point | Recommended | Notes |
|---------|-----------|------------|-------|
| `make dev` on one machine | 4 vCPU, 8 GB RAM | 8 vCPU, 16 GB RAM | Best when DeerFlow uses hosted model APIs. |
| `make docker-start` review environment | 4 vCPU, 8 GB RAM | 8 vCPU, 16 GB RAM | Docker image builds and sandbox containers need extra headroom. |
| Shared Linux test server | 8 vCPU, 16 GB RAM | 16 vCPU, 32 GB RAM | Prefer this for heavier multi-agent runs or multiple reviewers. |
`2 vCPU / 4 GB` environments often fail to start reliably or become unresponsive under normal DeerFlow workloads.
#### Linux: Docker daemon permission denied
If `make docker-init`, `make docker-start`, or `make docker-stop` fails on Linux with an error like below, your current user likely does not have permission to access the Docker daemon socket:
```text
unable to get image 'deer-flow-gateway': permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock
unable to get image 'deer-flow-dev-langgraph': permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock
```
Recommended fix: add your current user to the `docker` group so Docker commands work without `sudo`.
@@ -131,8 +119,9 @@ Host Machine
Docker Compose (deer-flow-dev)
├→ nginx (port 2026) ← Reverse proxy
├→ web (port 3000) ← Frontend with hot-reload
├→ gateway (port 8001) ← Gateway API + LangGraph-compatible runtime with hot-reload
└→ provisioner (optional, port 8002) ← Started only in provisioner/K8s sandbox mode
├→ api (port 8001) ← Gateway API with hot-reload
├→ langgraph (port 2024) ← LangGraph server with hot-reload
└→ provisioner (optional, port 8002) ← Started only in provisioner/K8s sandbox mode
```
**Benefits of Docker Development**:
@@ -165,7 +154,7 @@ Required tools:
1. **Configure the application** (same as Docker setup above)
2. **Install dependencies** (this also sets up pre-commit hooks):
2. **Install dependencies**:
```bash
make install
```
@@ -183,13 +172,17 @@ Required tools:
If you need to start services individually:
1. **Start backend service**:
1. **Start backend services**:
```bash
# Terminal 1: Start Gateway API + embedded agent runtime (port 8001)
# Terminal 1: Start LangGraph Server (port 2024)
cd backend
make dev
# Terminal 2: Start Frontend (port 3000)
# Terminal 2: Start Gateway API (port 8001)
cd backend
make gateway
# Terminal 3: Start Frontend (port 3000)
cd frontend
pnpm dev
```
@@ -207,10 +200,10 @@ If you need to start services individually:
The nginx configuration provides:
- Unified entry point on port 2026
- Rewrites `/api/langgraph/*` to Gateway's LangGraph-compatible API (8001)
- Routes `/api/langgraph/*` to LangGraph Server (2024)
- Routes other `/api/*` endpoints to Gateway API (8001)
- Routes non-API requests to Frontend (3000)
- Same-origin API routing; split-origin or port-forwarded browser clients should use the Gateway `GATEWAY_CORS_ORIGINS` allowlist
- Centralized CORS handling
- SSE/streaming support for real-time agent responses
- Optimized timeouts for long-running operations
@@ -230,8 +223,8 @@ deer-flow/
│ └── nginx.local.conf # Nginx config for local dev
├── backend/ # Backend application
│ ├── src/
│ │ ├── gateway/ # Gateway API and LangGraph-compatible runtime (port 8001)
│ │ ├── agents/ # LangGraph agent runtime used by Gateway
│ │ ├── gateway/ # Gateway API (port 8001)
│ │ ├── agents/ # LangGraph agents (port 2024)
│ │ ├── mcp/ # Model Context Protocol integration
│ │ ├── skills/ # Skills system
│ │ └── sandbox/ # Sandbox execution
@@ -251,7 +244,8 @@ Browser
Nginx (port 2026) ← Unified entry point
├→ Frontend (port 3000) ← / (non-API requests)
→ Gateway API (port 8001) ← /api/* and /api/langgraph/* (LangGraph-compatible agent interactions)
→ Gateway API (port 8001) ← /api/models, /api/mcp, /api/skills, /api/threads/*/artifacts
└→ LangGraph Server (port 2024) ← /api/langgraph/* (agent interactions)
```
## Development Workflow
@@ -287,44 +281,24 @@ Nginx (port 2026) ← Unified entry point
git push origin feature/your-feature-name
```
## AI assistance disclosure
DeerFlow is an AI project and we welcome AI-assisted contributions. To help
reviewers calibrate how closely to read a change, **every pull request must
complete the "AI assistance" section of the
[PR template](.github/pull_request_template.md)**:
- which tool(s) you used (or `none`),
- how you used them, and
- a confirmation that a human has read, understands, and takes responsibility
for the change.
Please don't delete the section. PRs that ignore it may be asked to fill it in
before review.
## Testing
```bash
# Backend tests
cd backend
make test
uv run pytest
# Frontend unit tests
# Frontend checks
cd frontend
make test
# Frontend E2E tests (requires Chromium; builds and auto-starts the Next.js production server)
cd frontend
make test-e2e
pnpm check
```
### PR Regression Checks
Every pull request triggers the following CI workflows:
Every pull request runs the backend regression workflow at [.github/workflows/backend-unit-tests.yml](.github/workflows/backend-unit-tests.yml), including:
- **Backend unit tests** — [.github/workflows/backend-unit-tests.yml](.github/workflows/backend-unit-tests.yml)
- **Frontend unit tests** — [.github/workflows/frontend-unit-tests.yml](.github/workflows/frontend-unit-tests.yml)
- **Frontend E2E tests** — [.github/workflows/e2e-tests.yml](.github/workflows/e2e-tests.yml) (triggered only when `frontend/` files change)
- `tests/test_provisioner_kubeconfig.py`
- `tests/test_docker_sandbox_mode_detection.py`
## Code Style
+113 -40
View File
@@ -1,69 +1,54 @@
# DeerFlow - Unified Development Environment
.PHONY: help config config-upgrade check install setup doctor detect-thread-boundaries detect-blocking-io dev dev-daemon start start-daemon stop up down clean docker-init docker-start docker-stop docker-logs docker-logs-frontend docker-logs-gateway
.PHONY: help config config-upgrade check install 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
BASH ?= bash
BACKEND_UV_RUN = cd backend && uv run
# Detect OS for Windows compatibility
ifeq ($(OS),Windows_NT)
SHELL := cmd.exe
PYTHON ?= python
# Run repo shell scripts through Git Bash when Make is launched from cmd.exe / PowerShell.
RUN_WITH_GIT_BASH = call scripts\run-with-git-bash.cmd
else
PYTHON ?= python3
RUN_WITH_GIT_BASH =
endif
help:
@echo "DeerFlow Development Commands:"
@echo " make setup - Interactive setup wizard (recommended for new users)"
@echo " make doctor - Check configuration and system requirements"
@echo " make config - Generate local config files (aborts if config already exists)"
@echo " make config-upgrade - Merge new fields from config.example.yaml into config.yaml"
@echo " make check - Check if all required tools are installed"
@echo " make detect-thread-boundaries - Inventory async/thread boundary points"
@echo " make detect-blocking-io - Inventory blocking IO that may block the backend event loop"
@echo " make install - Install all dependencies (frontend + backend + pre-commit hooks)"
@echo " make install - Install all dependencies (frontend + backend)"
@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"
@echo " make docker-logs-gateway - View Docker gateway logs"
## Setup & Diagnosis
setup:
@$(BACKEND_UV_RUN) python ../scripts/setup_wizard.py
doctor:
@$(BACKEND_UV_RUN) python ../scripts/doctor.py
detect-thread-boundaries:
@$(PYTHON) ./scripts/detect_thread_boundaries.py
detect-blocking-io:
@$(MAKE) -C backend detect-blocking-io
config:
@$(PYTHON) ./scripts/configure.py
config-upgrade:
@$(RUN_WITH_GIT_BASH) ./scripts/config-upgrade.sh
@./scripts/config-upgrade.sh
# Check required tools
check:
@@ -75,8 +60,6 @@ install:
@cd backend && uv sync
@echo "Installing frontend dependencies..."
@cd frontend && pnpm install
@echo "Installing pre-commit hooks..."
@$(BACKEND_UV_RUN) --with pre-commit pre-commit install
@echo "✓ All dependencies installed"
@echo ""
@echo "=========================================="
@@ -89,36 +72,118 @@ install:
# Pre-pull sandbox Docker image (optional but recommended)
setup-sandbox:
@$(RUN_WITH_GIT_BASH) ./scripts/setup-sandbox.sh
@echo "=========================================="
@echo " Pre-pulling Sandbox Container Image"
@echo "=========================================="
@echo ""
@IMAGE=$$(grep -A 20 "# sandbox:" config.yaml 2>/dev/null | grep "image:" | awk '{print $$2}' | head -1); \
if [ -z "$$IMAGE" ]; then \
IMAGE="enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest"; \
echo "Using default image: $$IMAGE"; \
else \
echo "Using configured image: $$IMAGE"; \
fi; \
echo ""; \
if command -v container >/dev/null 2>&1 && [ "$$(uname)" = "Darwin" ]; then \
echo "Detected Apple Container on macOS, pulling image..."; \
container pull "$$IMAGE" || echo "⚠ Apple Container pull failed, will try Docker"; \
fi; \
if command -v docker >/dev/null 2>&1; then \
echo "Pulling image using Docker..."; \
if docker pull "$$IMAGE"; then \
echo ""; \
echo "✓ Sandbox image pulled successfully"; \
else \
echo ""; \
echo "⚠ Failed to pull sandbox image (this is OK for local sandbox mode)"; \
fi; \
else \
echo "✗ Neither Docker nor Apple Container is available"; \
echo " Please install Docker: https://docs.docker.com/get-docker/"; \
exit 1; \
fi
# Start all services in development mode (with hot-reloading)
dev:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev
ifeq ($(OS),Windows_NT)
@call scripts\run-with-git-bash.cmd ./scripts/serve.sh --dev
else
@./scripts/serve.sh --dev
endif
# Start all services in dev + Gateway mode (experimental: agent runtime embedded in Gateway)
dev-pro:
@$(PYTHON) ./scripts/check.py
ifeq ($(OS),Windows_NT)
@call scripts\run-with-git-bash.cmd ./scripts/serve.sh --dev --gateway
else
@./scripts/serve.sh --dev --gateway
endif
# Start all services in production mode (with optimizations)
start:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod
ifeq ($(OS),Windows_NT)
@call scripts\run-with-git-bash.cmd ./scripts/serve.sh --prod
else
@./scripts/serve.sh --prod
endif
# Start all services in prod + Gateway mode (experimental)
start-pro:
@$(PYTHON) ./scripts/check.py
ifeq ($(OS),Windows_NT)
@call scripts\run-with-git-bash.cmd ./scripts/serve.sh --prod --gateway
else
@./scripts/serve.sh --prod --gateway
endif
# Start all services in daemon mode (background)
dev-daemon:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --dev --daemon
ifeq ($(OS),Windows_NT)
@call scripts\run-with-git-bash.cmd ./scripts/serve.sh --dev --daemon
else
@./scripts/serve.sh --dev --daemon
endif
# Start daemon + Gateway mode (experimental)
dev-daemon-pro:
@$(PYTHON) ./scripts/check.py
ifeq ($(OS),Windows_NT)
@call scripts\run-with-git-bash.cmd ./scripts/serve.sh --dev --gateway --daemon
else
@./scripts/serve.sh --dev --gateway --daemon
endif
# Start prod services in daemon mode (background)
start-daemon:
@$(PYTHON) ./scripts/check.py
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --prod --daemon
ifeq ($(OS),Windows_NT)
@call scripts\run-with-git-bash.cmd ./scripts/serve.sh --prod --daemon
else
@./scripts/serve.sh --prod --daemon
endif
# Start prod daemon + Gateway mode (experimental)
start-daemon-pro:
@$(PYTHON) ./scripts/check.py
ifeq ($(OS),Windows_NT)
@call scripts\run-with-git-bash.cmd ./scripts/serve.sh --prod --gateway --daemon
else
@./scripts/serve.sh --prod --gateway --daemon
endif
# Stop all services
stop:
@$(RUN_WITH_GIT_BASH) ./scripts/serve.sh --stop
@./scripts/serve.sh --stop
# Clean up
clean: stop
@echo "Cleaning up..."
@-rm -rf backend/.deer-flow 2>/dev/null || true
@-rm -rf backend/.langgraph_api 2>/dev/null || true
@-rm -rf logs/*.log 2>/dev/null || true
@echo "✓ Cleanup complete"
@@ -128,25 +193,29 @@ clean: stop
# Initialize Docker containers and install dependencies
docker-init:
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh init
@./scripts/docker.sh init
# Start Docker development environment
docker-start:
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh start
@./scripts/docker.sh start
# Start Docker in Gateway mode (experimental)
docker-start-pro:
@./scripts/docker.sh start --gateway
# Stop Docker development environment
docker-stop:
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh stop
@./scripts/docker.sh stop
# View Docker development logs
docker-logs:
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh logs
@./scripts/docker.sh logs
# View Docker development logs
docker-logs-frontend:
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh logs --frontend
@./scripts/docker.sh logs --frontend
docker-logs-gateway:
@$(RUN_WITH_GIT_BASH) ./scripts/docker.sh logs --gateway
@./scripts/docker.sh logs --gateway
# ==========================================
# Production Docker Commands
@@ -154,8 +223,12 @@ docker-logs-gateway:
# Build and start production services
up:
@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh
@./scripts/deploy.sh
# Build and start production services in Gateway mode
up-pro:
@./scripts/deploy.sh --gateway
# Stop and remove production containers
down:
@$(RUN_WITH_GIT_BASH) ./scripts/deploy.sh down
@./scripts/deploy.sh down
+81 -117
View File
@@ -53,7 +53,6 @@ DeerFlow has newly integrated the intelligent search and crawling toolset indepe
- [Quick Start](#quick-start)
- [Configuration](#configuration)
- [Running the Application](#running-the-application)
- [Deployment Sizing](#deployment-sizing)
- [Option 1: Docker (Recommended)](#option-1-docker-recommended)
- [Option 2: Local Development](#option-2-local-development)
- [Advanced](#advanced)
@@ -104,38 +103,35 @@ That prompt is intended for coding agents. It tells the agent to clone the repo
cd deer-flow
```
2. **Run the setup wizard**
2. **Generate local configuration files**
From the project root directory (`deer-flow/`), run:
```bash
make setup
make config
```
This launches an interactive wizard that guides you through choosing an LLM provider, optional web search, and execution/safety preferences such as sandbox mode, bash access, and file-write tools. It generates a minimal `config.yaml` and writes your keys to `.env`. Takes about 2 minutes.
This command creates local configuration files based on the provided example templates.
The wizard also lets you configure an optional web search provider, or skip it for now.
3. **Configure your preferred model(s)**
Run `make doctor` at any time to verify your setup and get actionable fix hints.
> **Advanced / manual configuration**: If you prefer to edit `config.yaml` directly, run `make config` instead to copy the full template. See `config.example.yaml` for the complete reference including CLI-backed providers (Codex CLI, Claude Code OAuth), OpenRouter, Responses API, and more.
<details>
<summary>Manual model configuration examples</summary>
Edit `config.yaml` and define at least one model:
```yaml
models:
- name: gpt-4o
display_name: GPT-4o
use: langchain_openai:ChatOpenAI
model: gpt-4o
api_key: $OPENAI_API_KEY
- name: gpt-4 # Internal identifier
display_name: GPT-4 # Human-readable name
use: langchain_openai:ChatOpenAI # LangChain class path
model: gpt-4 # Model identifier for API
api_key: $OPENAI_API_KEY # API key (recommended: use env var)
max_tokens: 4096 # Maximum tokens per request
temperature: 0.7 # Sampling temperature
- name: openrouter-gemini-2.5-flash
display_name: Gemini 2.5 Flash (OpenRouter)
use: langchain_openai:ChatOpenAI
model: google/gemini-2.5-flash-preview
api_key: $OPENROUTER_API_KEY
api_key: $OPENAI_API_KEY # OpenRouter still uses the OpenAI-compatible field name here
base_url: https://openrouter.ai/api/v1
- name: gpt-5-responses
@@ -185,39 +181,50 @@ That prompt is intended for coding agents. It tells the agent to clone the repo
```
- Codex CLI reads `~/.codex/auth.json`
- Claude Code accepts `CLAUDE_CODE_OAUTH_TOKEN`, `ANTHROPIC_AUTH_TOKEN`, `CLAUDE_CODE_CREDENTIALS_PATH`, or `~/.claude/.credentials.json`
- ACP agent entries are separate from model providers — if you configure `acp_agents.codex`, point it at a Codex ACP adapter such as `npx -y @zed-industries/codex-acp`
- On macOS, export Claude Code auth explicitly if needed:
- The Codex Responses endpoint currently rejects `max_tokens` and `max_output_tokens`, so `CodexChatModel` does not expose a request-level token cap
- Claude Code accepts `CLAUDE_CODE_OAUTH_TOKEN`, `ANTHROPIC_AUTH_TOKEN`, `CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR`, `CLAUDE_CODE_CREDENTIALS_PATH`, or plaintext `~/.claude/.credentials.json`
- ACP agent entries are separate from model providers. If you configure `acp_agents.codex`, point it at a Codex ACP adapter such as `npx -y @zed-industries/codex-acp`; the standard `codex` CLI binary is not ACP-compatible by itself
- On macOS, DeerFlow does not probe Keychain automatically. Export Claude Code auth explicitly if needed:
```bash
eval "$(python3 scripts/export_claude_code_oauth.py --print-export)"
```
4. **Set API keys for your configured model(s)**
Choose one of the following methods:
- Option A: Edit the `.env` file in the project root (Recommended)
API keys can also be set manually in `.env` (recommended) or exported in your shell:
```bash
OPENAI_API_KEY=your-openai-api-key
TAVILY_API_KEY=your-tavily-api-key
OPENAI_API_KEY=your-openai-api-key
# OpenRouter also uses OPENAI_API_KEY when your config uses langchain_openai:ChatOpenAI + base_url.
# Add other provider keys as needed
INFOQUEST_API_KEY=your-infoquest-api-key
```
</details>
- Option B: Export environment variables in your shell
```bash
export OPENAI_API_KEY=your-openai-api-key
```
For CLI-backed providers:
- Codex CLI: `~/.codex/auth.json`
- Claude Code OAuth: explicit env/file handoff or `~/.claude/.credentials.json`
- Option C: Edit `config.yaml` directly (Not recommended for production)
```yaml
models:
- name: gpt-4
api_key: your-actual-api-key-here # Replace placeholder
```
### Running the Application
#### Deployment Sizing
Use the table below as a practical starting point when choosing how to run DeerFlow:
| Deployment target | Starting point | Recommended | Notes |
|---------|-----------|------------|-------|
| Local evaluation / `make dev` | 4 vCPU, 8 GB RAM, 20 GB free SSD | 8 vCPU, 16 GB RAM | Good for one developer or one light session with hosted model APIs. `2 vCPU / 4 GB` is usually not enough. |
| Docker development / `make docker-start` | 4 vCPU, 8 GB RAM, 25 GB free SSD | 8 vCPU, 16 GB RAM | Image builds, bind mounts, and sandbox containers need more headroom than pure local dev. |
| Long-running server / `make up` | 8 vCPU, 16 GB RAM, 40 GB free SSD | 16 vCPU, 32 GB RAM | Preferred for shared use, multi-agent runs, report generation, or heavier sandbox workloads. |
- These numbers cover DeerFlow itself. If you also host a local LLM, size that service separately.
- Linux plus Docker is the recommended deployment target for a persistent server. macOS and Windows are best treated as development or evaluation environments.
- If CPU or memory usage stays pinned, reduce concurrent runs first, then move to the next sizing tier.
#### Option 1: Docker (Recommended)
**Development** (hot-reload, source mounts):
@@ -243,9 +250,10 @@ make up # Build images and start all production services
make down # Stop and remove containers
```
Access: http://localhost:2026
> [!NOTE]
> The LangGraph agent server currently runs via `langgraph dev` (the open-source CLI server).
The unified nginx endpoint is same-origin by default and does not emit browser CORS headers. If you run a split-origin or port-forwarded browser client, set `GATEWAY_CORS_ORIGINS` to comma-separated exact origins such as `http://localhost:3000`; the Gateway then applies the CORS allowlist and matching CSRF origin checks.
Access: http://localhost:2026
See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
@@ -253,7 +261,7 @@ 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. 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.
Prerequisite: complete the "Configuration" steps above first (`make config` and model API keys). `make dev` requires a valid configuration file (defaults to `config.yaml` in the project root; can be overridden via `DEER_FLOW_CONFIG_PATH`).
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**:
@@ -263,7 +271,7 @@ On Windows, run the local development flow from Git Bash. Native `cmd.exe` and P
2. **Install dependencies**:
```bash
make install # Install backend + frontend dependencies + pre-commit hooks
make install # Install backend + frontend dependencies
```
3. **(Optional) Pre-pull sandbox image**:
@@ -288,31 +296,53 @@ On Windows, run the local development flow from Git Bash. Native `cmd.exe` and P
#### Startup Modes
DeerFlow runs the agent runtime inside the Gateway API. Development mode enables hot-reload; production mode uses a pre-built frontend.
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)
| | **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 owns `/api/langgraph/*` and translates those public LangGraph-compatible paths to its native `/api/*` routers behind nginx.
> **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.
#### Docker Production Deployment
`deploy.sh` supports building and starting separately:
`deploy.sh` supports building and starting separately. Images are mode-agnostic — runtime mode is selected at start time:
```bash
# One-step (build + start)
deploy.sh
deploy.sh # standard mode (default)
deploy.sh --gateway # gateway mode
# Two-step (build once, start later)
# Two-step (build once, start with any mode)
deploy.sh build # build all images
deploy.sh start # start pre-built images
deploy.sh start # start in standard mode
deploy.sh start --gateway # start in gateway mode
# Stop
deploy.sh down
@@ -345,16 +375,14 @@ DeerFlow supports receiving tasks from messaging apps. Channels auto-start when
| Telegram | Bot API (long-polling) | Easy |
| Slack | Socket Mode | Moderate |
| 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-compatible Gateway API base URL (default: http://localhost:8001/api)
langgraph_url: http://localhost:8001/api
# LangGraph Server URL (default: http://localhost:2024)
langgraph_url: http://localhost:2024
# Gateway API URL (default: http://localhost:8001)
gateway_url: http://localhost:8001
@@ -391,19 +419,6 @@ channels:
bot_token: $TELEGRAM_BOT_TOKEN
allowed_users: [] # empty = allow all
wechat:
enabled: false
bot_token: $WECHAT_BOT_TOKEN
ilink_bot_id: $WECHAT_ILINK_BOT_ID
qrcode_login_enabled: true # optional: allow first-time QR bootstrap when bot_token is absent
allowed_users: [] # empty = allow all
polling_timeout: 35
state_dir: ./.deer-flow/wechat/state
max_inbound_image_bytes: 20971520
max_outbound_image_bytes: 20971520
max_inbound_file_bytes: 52428800
max_outbound_file_bytes: 52428800
# Optional: per-channel / per-user session settings
session:
assistant_id: mobile-agent # custom agent names are also supported here
@@ -417,19 +432,11 @@ 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:
@@ -445,17 +452,9 @@ SLACK_APP_TOKEN=xapp-...
FEISHU_APP_ID=cli_xxxx
FEISHU_APP_SECRET=your_app_secret
# WeChat iLink
WECHAT_BOT_TOKEN=your_ilink_bot_token
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**
@@ -478,14 +477,6 @@ DINGTALK_CLIENT_SECRET=your_client_secret
3. Under **Events**, subscribe to `im.message.receive_v1` and select **Long Connection** mode.
4. Copy the App ID and App Secret. Set `FEISHU_APP_ID` and `FEISHU_APP_SECRET` in `.env` and enable the channel in `config.yaml`.
**WeChat Setup**
1. Enable the `wechat` channel in `config.yaml`.
2. Either set `WECHAT_BOT_TOKEN` in `.env`, or set `qrcode_login_enabled: true` for first-time QR bootstrap.
3. When `bot_token` is absent and QR bootstrap is enabled, watch backend logs for the QR content returned by iLink and complete the binding flow.
4. After the QR flow succeeds, DeerFlow persists the acquired token under `state_dir` for later restarts.
5. For Docker Compose deployments, keep `state_dir` on a persistent volume so the `get_updates_buf` cursor and saved auth state survive restarts.
**WeCom Setup**
1. Create a bot on the WeCom AI Bot platform and obtain the `bot_id` and `bot_secret`.
@@ -494,15 +485,7 @@ DINGTALK_CLIENT_SECRET=your_client_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.
**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`.
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`.
**Commands**
@@ -546,15 +529,6 @@ LANGFUSE_BASE_URL=https://cloud.langfuse.com
If you are using a self-hosted Langfuse instance, set `LANGFUSE_BASE_URL` to your deployment URL.
**Trace correlation fields.** Every agent run is annotated with Langfuse's reserved trace attributes so the Sessions and Users pages light up automatically:
- `session_id` = LangGraph `thread_id` — groups every trace of the same conversation
- `user_id` = effective user from `get_effective_user_id()` (falls back to `default` in no-auth mode)
- `trace_name` = assistant id (defaults to `lead-agent`)
- `tags` = `[env:<DEER_FLOW_ENV>, model:<model_name>]` (omitted when not set)
These are injected into `RunnableConfig.metadata` at the graph invocation root for both the gateway path (`runtime/runs/worker.py::run_agent`) and the embedded path (`client.py::DeerFlowClient.stream`), so any LangChain-compatible callback can read them. Set `DEER_FLOW_ENV` (or `ENVIRONMENT`) to tag traces by deployment environment.
#### Using Both Providers
If both LangSmith and Langfuse are enabled, DeerFlow attaches both tracing callbacks and reports the same model activity to both systems.
@@ -585,8 +559,6 @@ A standard Agent Skill is a structured capability module — a Markdown file tha
Skills are loaded progressively — only when the task needs them, not all at once. This keeps the context window lean and makes DeerFlow work well even with token-sensitive models.
Users can explicitly activate an enabled skill for a single turn by starting the request with `/skill-name`, for example `/data-analysis analyze uploads/foo.csv`. DeerFlow loads that skill's `SKILL.md` as hidden current-turn context while leaving the base prompt limited to skill metadata. Slash activation respects disabled skills, custom-agent skill whitelists, and existing channel commands such as `/new` and `/help`.
When you install `.skill` archives through the Gateway, DeerFlow accepts standard optional frontmatter metadata such as `version`, `author`, and `compatibility` instead of rejecting otherwise valid external skills.
Tools follow the same philosophy. DeerFlow comes with a core toolset — web search, web fetch, file operations, bash execution — and supports custom tools via MCP servers and Python functions. Swap anything. Add anything.
@@ -639,7 +611,7 @@ See [`skills/public/claude-to-deerflow/SKILL.md`](skills/public/claude-to-deerfl
Complex tasks rarely fit in a single pass. DeerFlow decomposes them.
The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output. When token usage tracking is enabled, completed sub-agent usage is attributed back to the dispatching step.
The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output.
This is how DeerFlow handles tasks that take minutes to hours: a research task might fan out into a dozen sub-agents, each exploring a different angle, then converge into a single report — or a website — or a slide deck with generated visuals. One harness, many hands.
@@ -667,8 +639,6 @@ This is the difference between a chatbot with tool access and an agent with an a
**Summarization**: Within a session, DeerFlow manages context aggressively — summarizing completed sub-tasks, offloading intermediate results to the filesystem, compressing what's no longer immediately relevant. This lets it stay sharp across long, multi-step tasks without blowing the context window.
**Strict Tool-Call Recovery**: When a provider or middleware interrupts a tool-call loop, DeerFlow now strips provider-level raw tool-call metadata on forced-stop assistant messages and injects placeholder tool results for dangling calls before the next model invocation. This keeps OpenAI-compatible reasoning models that strictly validate `tool_call_id` sequences from failing with malformed history errors.
### Long-Term Memory
Most agents forget everything the moment a conversation ends. DeerFlow remembers.
@@ -742,12 +712,6 @@ DeerFlow has key high-privilege capabilities including **system command executio
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for development setup, workflow, and guidelines.
Regression coverage includes Docker sandbox mode detection and provisioner kubeconfig-path handling tests in `backend/tests/`.
Backend blocking-IO diagnostics are available from the repository root with
`make detect-blocking-io`: it statically scans backend business code for
blocking IO that may run on the backend event loop, prints a concise summary,
and writes complete JSON findings to `.deer-flow/blocking-io-findings.json`.
The JSON includes compact review records with `priority`, `location`,
`blocking_call`, `event_loop_exposure`, `reason`, and `code`.
Gateway artifact serving now forces active web content types (`text/html`, `application/xhtml+xml`, `image/svg+xml`) to download as attachments instead of inline rendering, reducing XSS risk for generated artifacts.
## License
+3 -22
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@@ -228,7 +228,7 @@ make down # Stop and remove containers
```
> [!NOTE]
> Le runtime d'agent s'exécute actuellement dans la Gateway. nginx réécrit `/api/langgraph/*` vers l'API compatible LangGraph servie par la Gateway.
> Le serveur d'agents LangGraph fonctionne actuellement via `langgraph dev` (le serveur CLI open source).
Accès : http://localhost:2026
@@ -290,14 +290,13 @@ 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` :**
```yaml
channels:
# LangGraph-compatible Gateway API base URL (default: http://localhost:8001/api)
langgraph_url: http://localhost:8001/api
# LangGraph Server URL (default: http://localhost:2024)
langgraph_url: http://localhost:2024
# Gateway API URL (default: http://localhost:8001)
gateway_url: http://localhost:8001
@@ -342,13 +341,6 @@ 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` :
@@ -364,10 +356,6 @@ 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**
@@ -390,13 +378,6 @@ DINGTALK_CLIENT_SECRET=your_client_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 :
+3 -22
View File
@@ -181,7 +181,7 @@ make down # コンテナを停止して削除
```
> [!NOTE]
> Agentランタイムは現在Gateway内で実行されます。`/api/langgraph/*`はnginxによってGatewayのLangGraph-compatible APIへ書き換えられます。
> LangGraphエージェントサーバーは現在`langgraph dev`(オープンソースCLIサーバー)経由で実行されます。
アクセス: http://localhost:2026
@@ -243,14 +243,13 @@ 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:**
```yaml
channels:
# LangGraph-compatible Gateway API base URL(デフォルト: http://localhost:8001/api
langgraph_url: http://localhost:8001/api
# LangGraphサーバーURL(デフォルト: http://localhost:2024
langgraph_url: http://localhost:2024
# Gateway API URL(デフォルト: http://localhost:8001
gateway_url: http://localhost:8001
@@ -295,13 +294,6 @@ 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:
@@ -317,10 +309,6 @@ 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**
@@ -343,13 +331,6 @@ DINGTALK_CLIENT_SECRET=your_client_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
@@ -256,7 +256,6 @@ 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`:**
@@ -279,13 +278,6 @@ 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**
@@ -293,13 +285,6 @@ 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 |
+4 -38
View File
@@ -40,7 +40,6 @@ https://github.com/user-attachments/assets/a8bcadc4-e040-4cf2-8fda-dd768b999c18
- [快速开始](#快速开始)
- [配置](#配置)
- [运行应用](#运行应用)
- [部署建议与资源规划](#部署建议与资源规划)
- [方式一:Docker(推荐)](#方式一docker推荐)
- [方式二:本地开发](#方式二本地开发)
- [进阶配置](#进阶配置)
@@ -151,20 +150,6 @@ https://github.com/user-attachments/assets/a8bcadc4-e040-4cf2-8fda-dd768b999c18
### 运行应用
#### 部署建议与资源规划
可以先按下面的资源档位来选择 DeerFlow 的运行方式:
| 部署场景 | 起步配置 | 推荐配置 | 说明 |
|---------|-----------|------------|-------|
| 本地体验 / `make dev` | 4 vCPU、8 GB 内存、20 GB SSD 可用空间 | 8 vCPU、16 GB 内存 | 适合单个开发者或单个轻量会话,且模型走外部 API。`2 核 / 4 GB` 通常跑不稳。 |
| Docker 开发 / `make docker-start` | 4 vCPU、8 GB 内存、25 GB SSD 可用空间 | 8 vCPU、16 GB 内存 | 镜像构建、源码挂载和 sandbox 容器都会比纯本地模式更吃资源。 |
| 长期运行服务 / `make up` | 8 vCPU、16 GB 内存、40 GB SSD 可用空间 | 16 vCPU、32 GB 内存 | 更适合共享环境、多 agent 任务、报告生成或更重的 sandbox 负载。 |
- 上面的配置只覆盖 DeerFlow 本身;如果你还要本机部署本地大模型,请单独为模型服务预留资源。
- 持续运行的服务更推荐使用 Linux + Docker。macOS 和 Windows 更适合作为开发机或体验环境。
- 如果 CPU 或内存长期打满,先降低并发会话或重任务数量,再考虑升级到更高一档配置。
#### 方式一:Docker(推荐)
**开发模式**(支持热更新,挂载源码):
@@ -184,7 +169,7 @@ make down # 停止并移除容器
```
> [!NOTE]
> 当前 Agent 运行时嵌入在 Gateway 中运行,`/api/langgraph/*` 会由 nginx 重写到 Gateway 的 LangGraph-compatible API
> 当前 LangGraph agent server 通过开源 CLI 服务 `langgraph dev` 运行
访问地址:http://localhost:2026
@@ -194,7 +179,7 @@ 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`。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。
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。
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**
@@ -248,14 +233,13 @@ 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:**
```yaml
channels:
# LangGraph-compatible Gateway API base URL(默认:http://localhost:8001/api
langgraph_url: http://localhost:8001/api
# LangGraph Server URL(默认:http://localhost:2024
langgraph_url: http://localhost:2024
# Gateway API URL(默认:http://localhost:8001
gateway_url: http://localhost:8001
@@ -305,13 +289,6 @@ 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:
@@ -335,10 +312,6 @@ 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**
@@ -369,13 +342,6 @@ DINGTALK_CLIENT_SECRET=your_client_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:
-5
View File
@@ -24,10 +24,5 @@ config.yaml
# Langgraph
.langgraph_api
# Sandbox runtime working dir — pre-created and excluded from uvicorn reload
# (scripts/serve.sh, docker/dev-entrypoint.sh). Anchored so it does not match
# the source package backend/packages/harness/deerflow/sandbox/.
/sandbox/
# Claude Code settings
.claude/settings.local.json
+72 -160
View File
@@ -7,13 +7,15 @@ 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**:
- **Gateway API** (port 8001): REST API plus embedded LangGraph-compatible agent runtime
- **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
- **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**:
- `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.
**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.
**Project Structure**:
```
@@ -23,7 +25,7 @@ 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 Studio graph configuration
│ ├── langgraph.json # LangGraph server configuration
│ ├── packages/
│ │ └── harness/ # deerflow-harness package (import: deerflow.*)
│ │ ├── pyproject.toml
@@ -81,64 +83,26 @@ 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 (Gateway + Frontend + Nginx), with config.yaml preflight
make start # Start production services locally
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 stop # Stop all services
```
**Backend directory** (for backend development only):
```bash
make install # Install backend dependencies
make dev # Run Gateway API with reload (port 8001)
make gateway # Run Gateway API only (port 8001)
make test # Run all backend tests
make test-blocking-io # Run strict Blockbuster runtime gate on tests/blocking_io/
make lint # Lint with ruff
make format # Format code with ruff
make install # Install backend dependencies
make dev # Run LangGraph server only (port 2024)
make gateway # Run Gateway API only (port 8001)
make test # Run all backend tests
make lint # Lint with ruff
make format # Format code with ruff
```
The `detect-blocking-io` target parses `app/`, `packages/harness/deerflow/`,
and `scripts/` with AST. By default it reports only blocking IO candidates that
are inside async code, reachable from async code in the same file, or reachable
from sync-only `AgentMiddleware` before/after hooks that LangGraph can execute
on the async graph path. It prints a concise summary and writes complete JSON
findings to `.deer-flow/blocking-io-findings.json` at the repository root
(both `make detect-blocking-io` from the repo root and `cd backend && make
detect-blocking-io` resolve to the same repo-root path). JSON findings include
`priority`, `location`, `blocking_call`, `event_loop_exposure`, `reason`, and
`code` for model-assisted or manual review. `priority` is a deterministic
review ordering from operation type, not proof of a bug. Bare-name same-file
calls are resolved by function name, so duplicate helper names in one file can
conservatively over-report async reachability. It is intentionally
informational and is not run from CI in this round.
Regression tests related to Docker/provisioner behavior:
- `tests/test_docker_sandbox_mode_detection.py` (mode detection from `config.yaml`)
- `tests/test_provisioner_kubeconfig.py` (kubeconfig file/directory handling)
Blocking-IO runtime gate (`tests/blocking_io/`):
- Wraps every item under `tests/blocking_io/` with a strict Blockbuster
context scoped to `app.*` and `deerflow.*` (see
`tests/support/detectors/blocking_io_runtime.py`). Any sync blocking IO
call whose stack passes through DeerFlow business code while running on
the asyncio event loop raises `BlockingError` and fails the test.
- Regression anchors live there: `test_skills_load.py` (locks the
`asyncio.to_thread` offload around `LocalSkillStorage.load_skills`, fix
for #1917); `test_sqlite_lifespan.py` (locks the offload around
SQLite path resolution plus `ensure_sqlite_parent_dir`, fix for #1912);
`test_jsonl_run_event_store.py` (locks `JsonlRunEventStore`'s async
API offloading its file IO via `asyncio.to_thread`, fix #3084); and
`test_uploads_middleware.py` (locks `UploadsMiddleware.abefore_agent`
offloading the uploads-directory scan off the event loop).
- `test_gate_smoke.py` is a meta-test asserting the gate actually catches
unoffloaded blocking IO and that the `@pytest.mark.allow_blocking_io`
opt-out works.
- Coverage boundary: the gate only sees code that test execution actually
touches. Static AST coverage is a separate concern (out of scope for
this PR).
- CI: runs on every PR via `.github/workflows/backend-blocking-io-tests.yml`,
hard-fail.
Boundary check (harness → app import firewall):
- `tests/test_harness_boundary.py` — ensures `packages/harness/deerflow/` never imports from `app.*`
@@ -151,7 +115,7 @@ 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, DingTalk).
- **App** (`app/`): Unpublished application code. Import prefix: `app.*`. Contains the FastAPI Gateway API and IM channel integrations (Feishu, Slack, Telegram).
**Dependency rule**: App imports deerflow, but deerflow never imports app. This boundary is enforced by `tests/test_harness_boundary.py` which runs in CI.
@@ -192,27 +156,20 @@ from deerflow.config import get_app_config
### Middleware Chain
Lead-agent middlewares are assembled in strict append order across `packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py` (`build_lead_runtime_middlewares`) and `packages/harness/deerflow/agents/lead_agent/agent.py` (`build_middlewares`):
Middlewares execute in strict order in `packages/harness/deerflow/agents/lead_agent/agent.py`:
1. **ThreadDataMiddleware** - Creates per-thread directories under the user's isolation scope (`backend/.deer-flow/users/{user_id}/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); resolves `user_id` via `get_effective_user_id()` (falls back to `"default"` in no-auth mode); Web UI thread deletion now follows LangGraph thread removal with Gateway cleanup of the local thread directory
1. **ThreadDataMiddleware** - Creates per-thread directories (`backend/.deer-flow/threads/{thread_id}/user-data/{workspace,uploads,outputs}`); Web UI thread deletion now follows LangGraph thread removal with Gateway cleanup of the local `.deer-flow/threads/{thread_id}` directory
2. **UploadsMiddleware** - Tracks and injects newly uploaded files into conversation
3. **SandboxMiddleware** - Acquires sandbox, stores `sandbox_id` in state
4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption), including raw provider tool-call payloads preserved only in `additional_kwargs["tool_calls"]`
5. **LLMErrorHandlingMiddleware** - Normalizes provider/model invocation failures into recoverable assistant-facing errors before later middleware/tool stages run
6. **GuardrailMiddleware** - Pre-tool-call authorization via pluggable `GuardrailProvider` protocol (optional, if `guardrails.enabled` in config). Evaluates each tool call and returns error ToolMessage on deny. Three provider options: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom providers. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md) for setup, usage, and how to implement a provider.
7. **SandboxAuditMiddleware** - Audits sandboxed shell/file operations for security logging before tool execution continues
8. **ToolErrorHandlingMiddleware** - Converts tool exceptions into error `ToolMessage`s so the run can continue instead of aborting
9. **SkillActivationMiddleware** - Detects strict `/skill-name task` syntax on the latest real user message, resolves only enabled and runtime-allowed skills, reads `SKILL.md` from trusted skill storage, injects the skill body as hidden current-turn model context, and records a `middleware:skill_activation` audit event with skill name, category, path, and content hash
10. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
11. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
12. **TokenUsageMiddleware** - Records token usage metrics when token tracking is enabled (optional); subagent usage is cached by `tool_call_id` only while token usage is enabled and merged back into the dispatching AIMessage by message position rather than message id
13. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
14. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
15. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
16. **DeferredToolFilterMiddleware** - Hides deferred (MCP) tool schemas from the bound model using a build-time deferred-name set + catalog hash, reading per-thread promotions from `ThreadState.promoted` (hash-scoped, no ContextVar); a tool becomes bound on subsequent turns after `tool_search` returns its schema (optional, if `tool_search.enabled`)
17. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if `subagent_enabled`)
18. **LoopDetectionMiddleware** - Detects repeated tool-call loops; hard-stop responses clear both structured `tool_calls` and raw provider tool-call metadata before forcing a final text answer
19. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption)
5. **GuardrailMiddleware** - Pre-tool-call authorization via pluggable `GuardrailProvider` protocol (optional, if `guardrails.enabled` in config). Evaluates each tool call and returns error ToolMessage on deny. Three provider options: built-in `AllowlistProvider` (zero deps), OAP policy providers (e.g. `aport-agent-guardrails`), or custom providers. See [docs/GUARDRAILS.md](docs/GUARDRAILS.md) for setup, usage, and how to implement a provider.
6. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
7. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
8. **TitleMiddleware** - Auto-generates thread title after first complete exchange and normalizes structured message content before prompting the title model
9. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
10. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
11. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if subagent_enabled)
12. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
### Configuration System
@@ -224,10 +181,6 @@ Setup: Copy `config.example.yaml` to `config.yaml` in the **project root** direc
**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.
**Config Hot-Reload Boundary**: Gateway dependencies route through `get_app_config()` on every request, so per-run fields like `models[*].max_tokens`, `summarization.*`, `title.*`, `memory.*`, `subagents.*`, `tools[*]`, and the agent system prompt pick up `config.yaml` edits on the next message. `AppConfig` is intentionally **not** cached on `app.state``lifespan()` keeps a local `startup_config` variable for one-shot bootstrap work and passes it to `langgraph_runtime(app, startup_config)`.
Infrastructure fields are **restart-required**. The authoritative list lives in `packages/harness/deerflow/config/reload_boundary.py::STARTUP_ONLY_FIELDS` and is mirrored by the standardised `"startup-only:"` prefix on the corresponding `Field(description=...)` in `AppConfig`, so IDE hover on those fields surfaces the reason inline (no need to context-switch into this table). Currently registered: `database`, `checkpointer`, `run_events`, `stream_bridge`, `sandbox`, `log_level`, `channels`. Adding a new restart-required field requires updating the registry; drift is pinned by `tests/test_reload_boundary.py`.
Configuration priority:
1. Explicit `config_path` argument
2. `DEER_FLOW_CONFIG_PATH` environment variable
@@ -249,9 +202,7 @@ Configuration priority:
### Gateway API (`app/gateway/`)
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).
CORS is same-origin by default when requests enter through nginx on port 2026. Split-origin or port-forwarded browser clients must opt in with `GATEWAY_CORS_ORIGINS` (comma-separated exact origins); Gateway `CORSMiddleware` and `CSRFMiddleware` both read that variable so browser CORS and auth-origin checks stay aligned.
FastAPI application on port 8001 with health check at `GET /health`.
**Routers**:
@@ -264,39 +215,29 @@ CORS is same-origin by default when requests enter through nginx on port 2026. S
| **Uploads** (`/api/threads/{id}/uploads`) | `POST /` - upload files (auto-converts PDF/PPT/Excel/Word); `GET /list` - list; `DELETE /{filename}` - delete |
| **Threads** (`/api/threads/{id}`) | `DELETE /` - remove DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
| **Artifacts** (`/api/threads/{id}/artifacts`) | `GET /{path}` - serve artifacts; active content types (`text/html`, `application/xhtml+xml`, `image/svg+xml`) are always forced as download attachments to reduce XSS risk; `?download=true` still forces download for other file types |
| **Suggestions** (`/api/threads/{id}/suggestions`) | `POST /` - generate follow-up questions; rich list/block model content is normalized and inline reasoning (`<think>...</think>`, including unclosed/truncated blocks from reasoning models like MiniMax-M3) is stripped before JSON parsing |
| **Thread Runs** (`/api/threads/{id}/runs`) | `POST /` - create background run; `POST /stream` - create + SSE stream; `POST /wait` - create + block; `GET /` - list runs; `GET /{rid}` - run details; `POST /{rid}/cancel` - cancel; `GET /{rid}/join` - join SSE; `GET /{rid}/messages` - paginated messages `{data, has_more}`; `GET /{rid}/events` - full event stream; `GET /../messages` - thread messages with feedback; `GET /../token-usage` - aggregate tokens |
| **Feedback** (`/api/threads/{id}/runs/{rid}/feedback`) | `PUT /` - upsert feedback; `DELETE /` - delete user feedback; `POST /` - create feedback; `GET /` - list feedback; `GET /stats` - aggregate stats; `DELETE /{fid}` - delete specific |
| **Runs** (`/api/runs`) | `POST /stream` - stateless run + SSE; `POST /wait` - stateless run + block; `GET /{rid}/messages` - paginated messages by run_id `{data, has_more}` (cursor: `after_seq`/`before_seq`); `GET /{rid}/feedback` - list feedback by run_id |
| **Suggestions** (`/api/threads/{id}/suggestions`) | `POST /` - generate follow-up questions; rich list/block model content is normalized before JSON parsing |
**RunManager / RunStore contract**:
- `RunManager.get()` is async; direct callers must `await` it.
- When a persistent `RunStore` is configured, `get()` and `list_by_thread()` hydrate historical runs from the store. In-memory records win for the same `run_id` so task, abort, and stream-control state stays attached to active local runs.
- `cancel()` and `create_or_reject(..., multitask_strategy="interrupt"|"rollback")` persist interrupted status through `RunStore.update_status()`, matching normal `set_status()` transitions.
- Store-only hydrated runs are readable history. If the current worker has no in-memory task/control state for that run, cancellation APIs can return 409 because this worker cannot stop the task.
- `POST /wait` (both thread-scoped and `/api/runs/wait`) drains the stream bridge via `wait_for_run_completion()` instead of bare `await record.task`, so it honours the run's `on_disconnect` setting and cancels the background run on real client disconnect rather than returning a stale checkpoint (issue #3265).
Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runtime, all other `/api/*` → Gateway REST APIs.
Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` → Gateway.
### Sandbox System (`packages/harness/deerflow/sandbox/`)
**Interface**: Abstract `Sandbox` with `execute_command`, `read_file`, `write_file`, `list_dir`
**Provider Pattern**: `SandboxProvider` with `acquire`, `acquire_async`, `get`, `release` lifecycle. Async agent/tool paths call async sandbox lifecycle hooks so Docker sandbox creation, discovery, cross-process locking, readiness polling, and release stay off the event loop.
**Provider Pattern**: `SandboxProvider` with `acquire`, `get`, `release` lifecycle
**Implementations**:
- `LocalSandboxProvider` - Local filesystem execution. `acquire(thread_id)` returns a per-thread `LocalSandbox` (id `local:{thread_id}`) whose `path_mappings` resolve `/mnt/user-data/{workspace,uploads,outputs}` and `/mnt/acp-workspace` to that thread's host directories, so the public `Sandbox` API honours the `/mnt/user-data` contract uniformly with AIO. `acquire()` / `acquire(None)` keeps the legacy generic singleton (id `local`) for callers without a thread context. Per-thread sandboxes are held in an LRU cache (default 256 entries) guarded by a `threading.Lock`.
- `LocalSandboxProvider` - Singleton local filesystem execution with path mappings
- `AioSandboxProvider` (`packages/harness/deerflow/community/`) - Docker-based isolation
**Virtual Path System**:
- Agent sees: `/mnt/user-data/{workspace,uploads,outputs}`, `/mnt/skills`
- Physical: `backend/.deer-flow/users/{user_id}/threads/{thread_id}/user-data/...`, `deer-flow/skills/`
- Translation: `LocalSandboxProvider` builds per-thread `PathMapping`s for the user-data prefixes at acquire time; `tools.py` keeps `replace_virtual_path()` / `replace_virtual_paths_in_command()` as a defense-in-depth layer (and for path validation). AIO has the directories volume-mounted at the same virtual paths inside its container, so both implementations accept `/mnt/user-data/...` natively.
- Detection: `is_local_sandbox()` accepts both `sandbox_id == "local"` (legacy / no-thread) and `sandbox_id.startswith("local:")` (per-thread)
- Physical: `backend/.deer-flow/threads/{thread_id}/user-data/...`, `deer-flow/skills/`
- Translation: `replace_virtual_path()` / `replace_virtual_paths_in_command()`
- Detection: `is_local_sandbox()` checks `sandbox_id == "local"`
**Sandbox Tools** (in `packages/harness/deerflow/sandbox/tools.py`):
- `bash` - Execute commands with path translation and error handling
- `ls` - Directory listing (tree format, max 2 levels)
- `read_file` - Read file contents with optional line range
- `write_file` - Write/append to files, creates directories; overwrites by default and exposes the `append` argument in the model-facing schema for end-of-file writes
- `write_file` - Write/append to files, creates directories
- `str_replace` - Substring replacement (single or all occurrences); same-path serialization is scoped to `(sandbox.id, path)` so isolated sandboxes do not contend on identical virtual paths inside one process
### Subagent System (`packages/harness/deerflow/subagents/`)
@@ -306,7 +247,6 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
**Concurrency**: `MAX_CONCURRENT_SUBAGENTS = 3` enforced by `SubagentLimitMiddleware` (truncates excess tool calls in `after_model`), 15-minute timeout
**Flow**: `task()` tool → `SubagentExecutor` → background thread → poll 5s → SSE events → result
**Events**: `task_started`, `task_running`, `task_completed`/`task_failed`/`task_timed_out`
**Deferred MCP tools** (if `tool_search.enabled`): `SubagentExecutor._build_initial_state` assembles deferral after policy filtering via the shared `assemble_deferred_tools` (fail-closed), appends the `tool_search` tool, injects the `<available-deferred-tools>` section into the subagent's `SystemMessage`, and threads the setup to `_create_agent`, which attaches `DeferredToolFilterMiddleware` through `build_subagent_runtime_middlewares(deferred_setup=...)`. Subagents thus withhold full MCP schemas until promotion, same as the lead agent; each task run gets a fresh `ThreadState` so promotion is isolated per run
### Tool System (`packages/harness/deerflow/tools/`)
@@ -317,10 +257,8 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
- `present_files` - Make output files visible to user (only `/mnt/user-data/outputs`)
- `ask_clarification` - Request clarification (intercepted by ClarificationMiddleware → interrupts)
- `view_image` - Read image as base64 (added only if model supports vision)
- `setup_agent` - Bootstrap-only: persist a brand-new custom agent's `SOUL.md` and `config.yaml`. Bound only when `is_bootstrap=True`.
- `update_agent` - Custom-agent-only: persist self-updates to the current agent's `SOUL.md` / `config.yaml` from inside a normal chat (partial update + atomic write). Bound when `agent_name` is set and `is_bootstrap=False`.
4. **Subagent tool** (if enabled):
- `task` - Delegate to subagent (description, prompt, subagent_type)
- `task` - Delegate to subagent (description, prompt, subagent_type, max_turns)
**Community tools** (`packages/harness/deerflow/community/`):
- `tavily/` - Web search (5 results default) and web fetch (4KB limit)
@@ -331,7 +269,7 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
- `invoke_acp_agent` - Invokes external ACP-compatible agents from `config.yaml`
- ACP launchers must be real ACP adapters. The standard `codex` CLI is not ACP-compatible by itself; configure a wrapper such as `npx -y @zed-industries/codex-acp` or an installed `codex-acp` binary
- Missing ACP executables now return an actionable error message instead of a raw `[Errno 2]`
- Each ACP agent uses a per-thread workspace at `{base_dir}/users/{user_id}/threads/{thread_id}/acp-workspace/`. The workspace is accessible to the lead agent via the virtual path `/mnt/acp-workspace/` (read-only). In docker sandbox mode, the directory is volume-mounted into the container at `/mnt/acp-workspace` (read-only); in local sandbox mode, path translation is handled by `tools.py`
- Each ACP agent uses a per-thread workspace at `{base_dir}/threads/{thread_id}/acp-workspace/`. The workspace is accessible to the lead agent via the virtual path `/mnt/acp-workspace/` (read-only). In docker sandbox mode, the directory is volume-mounted into the container at `/mnt/acp-workspace` (read-only); in local sandbox mode, path translation is handled by `tools.py`
- `image_search/` - Image search via DuckDuckGo
### MCP System (`packages/harness/deerflow/mcp/`)
@@ -341,7 +279,7 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
- **Cache invalidation**: Detects config file changes via mtime comparison
- **Transports**: stdio (command-based), SSE, HTTP
- **OAuth (HTTP/SSE)**: Supports token endpoint flows (`client_credentials`, `refresh_token`) with automatic token refresh + Authorization header injection
- **Runtime updates**: Gateway API saves to extensions_config.json; the Gateway-embedded runtime detects changes via mtime
- **Runtime updates**: Gateway API saves to extensions_config.json; LangGraph detects via mtime
### Skills System (`packages/harness/deerflow/skills/`)
@@ -349,7 +287,6 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
- **Format**: Directory with `SKILL.md` (YAML frontmatter: name, description, license, allowed-tools)
- **Loading**: `load_skills()` recursively scans `skills/{public,custom}` for `SKILL.md`, parses metadata, and reads enabled state from extensions_config.json
- **Injection**: Enabled skills listed in agent system prompt with container paths
- **Slash activation**: `/skill-name task` loads that enabled skill's `SKILL.md` for the current model call only. The resolver rejects leading whitespace, missing separators, reserved channel commands (`/new`, `/help`, `/bootstrap`, `/status`, `/models`, `/memory`), disabled skills, and skills outside a custom agent's whitelist.
- **Installation**: `POST /api/skills/install` extracts .skill ZIP archive to custom/ directory
### Model Factory (`packages/harness/deerflow/models/factory.py`)
@@ -369,10 +306,9 @@ Proxied through nginx: `/api/langgraph/*` → Gateway LangGraph-compatible runti
### IM Channels System (`app/channels/`)
Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the DeerFlow agent via Gateway's LangGraph-compatible API.
Bridges external messaging platforms (Feishu, Slack, Telegram) to the DeerFlow agent via the LangGraph Server.
**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.
**Architecture**: Channels communicate with the LangGraph Server through `langgraph-sdk` HTTP client (same as the frontend), ensuring threads are created and managed server-side.
**Components**:
- `message_bus.py` - Async pub/sub hub (`InboundMessage` → queue → dispatcher; `OutboundMessage` → callbacks → channels)
@@ -380,52 +316,40 @@ Bridges external messaging platforms (Feishu, Slack, Telegram, DingTalk) to the
- `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` / `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)
- `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)
**Message Flow**:
1. External platform -> Channel impl -> `MessageBus.publish_inbound()`
2. `ChannelManager._dispatch_loop()` consumes from queue
3. For chat: look up/create thread through Gateway's LangGraph-compatible API
3. For chat: look up/create thread on LangGraph Server
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. 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
7. For commands (`/new`, `/status`, `/models`, `/memory`, `/help`): handle locally or query Gateway API
8. Outbound → channel callbacks → platform reply
**Configuration** (`config.yaml` -> `channels`):
- `langgraph_url` - LangGraph-compatible Gateway API base URL (default: `http://localhost:8001/api`)
- `langgraph_url` - LangGraph Server URL (default: `http://localhost:2024`)
- `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://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)
- 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)
### Memory System (`packages/harness/deerflow/agents/memory/`)
**Components**:
- `updater.py` - LLM-based memory updates with fact extraction, whitespace-normalized fact deduplication (trims leading/trailing whitespace before comparing), and atomic file I/O
- `queue.py` - Debounced update queue (per-thread deduplication, configurable wait time); captures `user_id` at enqueue time so it survives the `threading.Timer` boundary
- `queue.py` - Debounced update queue (per-thread deduplication, configurable wait time)
- `prompt.py` - Prompt templates for memory updates
- `storage.py` - File-based storage with per-user isolation; cache keyed by `(user_id, agent_name)` tuple
**Per-User Isolation**:
- Memory is stored per-user at `{base_dir}/users/{user_id}/memory.json`
- Per-agent per-user memory at `{base_dir}/users/{user_id}/agents/{agent_name}/memory.json`
- Custom agent definitions (`SOUL.md` + `config.yaml`) are also per-user at `{base_dir}/users/{user_id}/agents/{agent_name}/`. The legacy shared layout `{base_dir}/agents/{agent_name}/` remains read-only fallback for unmigrated installations
- `user_id` is resolved via `get_effective_user_id()` from `deerflow.runtime.user_context`
- In no-auth mode, `user_id` defaults to `"default"` (constant `DEFAULT_USER_ID`)
- Absolute `storage_path` in config opts out of per-user isolation
- **Migration**: Run `PYTHONPATH=. python scripts/migrate_user_isolation.py` to move legacy `memory.json`, `threads/`, and `agents/` into per-user layout. Supports `--dry-run` (preview changes) and `--user-id USER_ID` (assign unowned legacy data to a user, defaults to `default`).
**Data Structure** (stored in `{base_dir}/users/{user_id}/memory.json`):
**Data Structure** (stored in `backend/.deer-flow/memory.json`):
- **User Context**: `workContext`, `personalContext`, `topOfMind` (1-3 sentence summaries)
- **History**: `recentMonths`, `earlierContext`, `longTermBackground`
- **Facts**: Discrete facts with `id`, `content`, `category` (preference/knowledge/context/behavior/goal), `confidence` (0-1), `createdAt`, `source`
**Workflow**:
1. `MemoryMiddleware` filters messages (user inputs + final AI responses), captures `user_id` via `get_effective_user_id()`, and queues conversation with the captured `user_id`
1. `MemoryMiddleware` filters messages (user inputs + final AI responses) and queues conversation
2. Queue debounces (30s default), batches updates, deduplicates per-thread
3. Background thread invokes LLM to extract context updates and facts, using the stored `user_id` (not the contextvar, which is unavailable on timer threads)
3. Background thread invokes LLM to extract context updates and facts
4. Applies updates atomically (temp file + rename) with cache invalidation, skipping duplicate fact content before append
5. Next interaction injects top 15 facts + context into `<memory>` tags in system prompt
@@ -433,7 +357,7 @@ Focused regression coverage for the updater lives in `backend/tests/test_memory_
**Configuration** (`config.yaml``memory`):
- `enabled` / `injection_enabled` - Master switches
- `storage_path` - Path to memory.json (absolute path opts out of per-user isolation)
- `storage_path` - Path to memory.json
- `debounce_seconds` - Wait time before processing (default: 30)
- `model_name` - LLM for updates (null = default model)
- `max_facts` / `fact_confidence_threshold` - Fact storage limits (100 / 0.7)
@@ -444,24 +368,6 @@ Focused regression coverage for the updater lives in `backend/tests/test_memory_
- `resolve_variable(path)` - Import module and return variable (e.g., `module.path:variable_name`)
- `resolve_class(path, base_class)` - Import and validate class against base class
### Tracing System (`packages/harness/deerflow/tracing/`)
LangSmith and Langfuse are both supported. The wiring lives in two layers:
- `factory.py::build_tracing_callbacks()` — returns the LangChain `CallbackHandler` list for the providers currently enabled via env vars (`LANGSMITH_TRACING`, `LANGFUSE_TRACING`, etc.). The handlers are attached at the **graph invocation root** for in-graph runs (`make_lead_agent` and `DeerFlowClient.stream` both append them to `config["callbacks"]` before invoking the graph) so a single run produces one trace with all node / LLM / tool calls as child spans. Standalone callers — anything that invokes a model outside such a graph (e.g. `MemoryUpdater`) — keep `create_chat_model`'s default `attach_tracing=True`, which falls back to model-level callback attachment.
- `metadata.py::build_langfuse_trace_metadata()` — builds the Langfuse-reserved trace attributes for `RunnableConfig.metadata`. The Langfuse v4 `langchain.CallbackHandler` lifts these onto the root trace (see its `_parse_langfuse_trace_attributes`), but only when it sees `on_chain_start(parent_run_id=None)` — which is why the callbacks have to live at the graph root, not the model.
**Trace-attribute injection points**: both `runtime/runs/worker.py::run_agent` (gateway path) and `client.py::DeerFlowClient.stream` (embedded path) merge the metadata into `config["metadata"]` right before constructing the graph. Caller-supplied keys win via `setdefault`, so an external `session_id` override is preserved. Field mapping:
| Langfuse field | Source |
|-----------------------|----------------------------------------------|
| `langfuse_session_id` | LangGraph `thread_id` |
| `langfuse_user_id` | `get_effective_user_id()` (`default` in no-auth) |
| `langfuse_trace_name` | `RunRecord.assistant_id` / client `agent_name` (defaults to `lead-agent`) |
| `langfuse_tags` | `env:<DEER_FLOW_ENV>` + `model:<model_name>` |
Returns `{}` when Langfuse is not in the enabled providers — LangSmith-only deployments are unaffected. Set `DEER_FLOW_ENV` (or `ENVIRONMENT`) to tag traces by deployment environment. Tests live in `tests/test_tracing_factory.py`, `tests/test_tracing_metadata.py`, `tests/test_worker_langfuse_metadata.py`, and `tests/test_client_langfuse_metadata.py`.
### Config Schema
**`config.yaml`** key sections:
@@ -486,19 +392,17 @@ 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 Gateway API uses. Shares the same config files and data directories. No FastAPI dependency.
**Architecture**: Imports the same `deerflow` modules that LangGraph Server and Gateway API use. Shares the same config files and data directories. No FastAPI dependency.
**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
- `"messages-tuple"` — per-chunk update: for AI text this is a **delta** (concat per `id` to rebuild the full message); tool calls and tool results are emitted once each
- `"custom"` — forwarded from `StreamWriter`
- `"end"` — stream finished (carries cumulative `usage` counted once per message id)
- Agent created lazily via `create_agent()` + `build_middlewares()`, same as `make_lead_agent`
**Agent Conversation** (replaces LangGraph Server):
- `chat(message, thread_id)` — synchronous, returns final text
- `stream(message, thread_id)`yields `StreamEvent` aligned with LangGraph SSE protocol:
- `"values"` — full state snapshot (title, messages, artifacts)
- `"messages-tuple"` — per-message update (AI text, tool calls, tool results)
- `"end"` — stream finished
- Agent created lazily via `create_agent()` + `_build_middlewares()`, same as `make_lead_agent`
- Supports `checkpointer` parameter for state persistence across turns
- `reset_agent()` forces agent recreation (e.g. after memory or skill changes)
- See [docs/STREAMING.md](docs/STREAMING.md) for the full design: why Gateway and DeerFlowClient are parallel paths, LangGraph's `stream_mode` semantics, the per-id dedup invariants, and regression testing strategy
**Gateway Equivalent Methods** (replaces Gateway API):
@@ -551,15 +455,20 @@ 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**:
- `/api/langgraph/*`Gateway embedded runtime (8001), rewritten to `/api/*`
- Standard mode: `/api/langgraph/*`LangGraph Server (2024)
- Gateway mode: `/api/langgraph/*` → Gateway embedded runtime (8001) (via envsubst)
- `/api/*` (other) → Gateway API (8001)
- `/` (non-API) → Frontend (3000)
@@ -568,11 +477,15 @@ This starts all services and makes the application available at `http://localhos
From the **backend** directory:
```bash
# Gateway API
# Terminal 1: LangGraph server
make dev
# Terminal 2: Gateway API
make gateway
```
Direct access (without nginx):
- LangGraph: `http://localhost:2024`
- Gateway: `http://localhost:8001`
### Frontend Configuration
@@ -593,7 +506,6 @@ Multi-file upload with automatic document conversion:
- Rejects directory inputs before copying so uploads stay all-or-nothing
- Reuses one conversion worker per request when called from an active event loop
- Files stored in thread-isolated directories
- Duplicate filenames in a single upload request are auto-renamed with `_N` suffixes so later files do not truncate earlier files
- Agent receives uploaded file list via `UploadsMiddleware`
See [docs/FILE_UPLOAD.md](docs/FILE_UPLOAD.md) for details.
+4 -1
View File
@@ -56,8 +56,11 @@ export OPENAI_API_KEY="your-api-key"
### Run the Development Server
```bash
# Gateway API + embedded agent runtime
# Terminal 1: LangGraph server
make dev
# Terminal 2: Gateway API
make gateway
```
## Project Structure
+4 -14
View File
@@ -50,12 +50,6 @@ 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.
@@ -64,7 +58,7 @@ FROM builder AS dev
# Install Docker CLI (for DooD: allows starting sandbox containers via host Docker socket)
COPY --from=docker:cli /usr/local/bin/docker /usr/local/bin/docker
EXPOSE 8001
EXPOSE 8001 2024
CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]
@@ -72,10 +66,6 @@ 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
@@ -94,8 +84,8 @@ WORKDIR /app
# Copy backend with pre-built virtualenv from builder
COPY --from=builder /app/backend ./backend
# Expose Gateway API port.
EXPOSE 8001
# Expose ports (gateway: 8001, langgraph: 2024)
EXPOSE 8001 2024
# Default command (can be overridden in docker-compose)
CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run --no-sync uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]
CMD ["sh", "-c", "cd backend && PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001"]
+3 -9
View File
@@ -2,16 +2,13 @@ install:
uv sync
dev:
PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001 --reload
uv run langgraph dev --no-browser --no-reload --n-jobs-per-worker 10
gateway:
PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
PYTHONPATH=. uv run uvicorn app.gateway.app:app --host 0.0.0.0 --port 8001
test:
PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run pytest tests/ -v
test-blocking-io:
PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run pytest tests/blocking_io -q --tb=short
PYTHONPATH=. uv run pytest tests/ -v
lint:
uvx ruff check .
@@ -19,6 +16,3 @@ lint:
format:
uvx ruff check . --fix && uvx ruff format .
detect-blocking-io:
@PYTHONPATH=. PYTHONIOENCODING=utf-8 PYTHONUTF8=1 uv run python ../scripts/detect_blocking_io_static.py --output ../.deer-flow/blocking-io-findings.json
+34 -43
View File
@@ -11,26 +11,31 @@ DeerFlow is a LangGraph-based AI super agent with sandbox execution, persistent
│ Nginx (Port 2026) │
│ Unified reverse proxy │
└───────┬──────────────────┬───────────┘
/api/langgraph/* │ /api/* (other)
rewritten to /api/* │
┌────────────────────────────────────────┐
Gateway API (8001)
FastAPI REST + agent runtime
Models, MCP, Skills, Memory, Uploads, │
Artifacts, Threads, Runs, Streaming
┌────────────────────────────────────┐
│ │ Lead Agent │ │
│ │ Middleware Chain, Tools, Subagents │ │
└────────────────────────────────────┘
└────────────────────────────────────────
/api/langgraph/* │ /api/* (other)
▼ ▼
┌────────────────────┐ ┌────────────────────────┐
│ LangGraph Server │ │ Gateway API (8001) │
(Port 2024) │ FastAPI REST
│ │
┌────────────────┐ │ │ Models, MCP, Skills,
│ Lead Agent │ │ │ Memory, Uploads,
│ ┌──────────┐ │ │ │ Artifacts
│ │Middleware│ │ │ └────────────────────────┘
│ │ Chain │ │
│ │ └──────────┘ │ │
│ │ ┌──────────┐ │ │
│ │ Tools │ │
│ │ └──────────┘ │ │
│ │ ┌──────────┐ │ │
│ │ │Subagents │ │ │
│ │ └──────────┘ │ │
│ └────────────────┘ │
└────────────────────┘
```
**Request Routing** (via Nginx):
- `/api/langgraph/*` Gateway LangGraph-compatible API - agent interactions, threads, streaming
- `/api/langgraph/*` → LangGraph Server - agent interactions, threads, streaming
- `/api/*` (other) → Gateway API - models, MCP, skills, memory, artifacts, uploads, thread-local cleanup
- `/` (non-API) → Frontend - Next.js web interface
@@ -69,12 +74,12 @@ Middlewares execute in strict order, each handling a specific concern:
Per-thread isolated execution with virtual path translation:
- **Abstract interface**: `execute_command`, `read_file`, `write_file`, `list_dir`
- **Providers**: `LocalSandboxProvider` (filesystem) and `AioSandboxProvider` (Docker, in community/). Async runtime paths use async sandbox lifecycle hooks so startup, readiness polling, and release do not block the event loop.
- **Providers**: `LocalSandboxProvider` (filesystem) and `AioSandboxProvider` (Docker, in community/)
- **Virtual paths**: `/mnt/user-data/{workspace,uploads,outputs}` → thread-specific physical directories
- **Skills path**: `/mnt/skills``deer-flow/skills/` directory
- **Skills loading**: Recursively discovers nested `SKILL.md` files under `skills/{public,custom}` and preserves nested container paths
- **File-write safety**: `str_replace` serializes read-modify-write per `(sandbox.id, path)` so isolated sandboxes keep concurrency even when virtual paths match
- **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace` (`write_file` overwrites by default and exposes `append` for end-of-file writes; `bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access)
- **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace` (`bash` is disabled by default when using `LocalSandboxProvider`; use `AioSandboxProvider` for isolated shell access)
### Subagent System
@@ -119,7 +124,7 @@ FastAPI application providing REST endpoints for frontend integration:
| `POST /api/memory/reload` | Force memory reload |
| `GET /api/memory/config` | Memory configuration |
| `GET /api/memory/status` | Combined config + data |
| `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths, auto-renames duplicate filenames in one request) |
| `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown, rejects directory paths) |
| `GET /api/threads/{id}/uploads/list` | List uploaded files |
| `DELETE /api/threads/{id}` | Delete DeerFlow-managed local thread data after LangGraph thread deletion; unexpected failures are logged server-side and return a generic 500 detail |
| `GET /api/threads/{id}/artifacts/{path}` | Serve generated artifacts |
@@ -188,7 +193,7 @@ export OPENAI_API_KEY="your-api-key-here"
**Full Application** (from project root):
```bash
make dev # Starts Gateway + Frontend + Nginx
make dev # Starts LangGraph + Gateway + Frontend + Nginx
```
Access at: http://localhost:2026
@@ -196,11 +201,14 @@ Access at: http://localhost:2026
**Backend Only** (from backend directory):
```bash
# Gateway API + embedded agent runtime
# Terminal 1: LangGraph server
make dev
# Terminal 2: Gateway API
make gateway
```
Direct access: Gateway at http://localhost:8001
Direct access: LangGraph at http://localhost:2024, Gateway at http://localhost:8001
---
@@ -236,16 +244,12 @@ backend/
│ └── utils/ # Utilities
├── docs/ # Documentation
├── tests/ # Test suite
├── langgraph.json # LangGraph graph registry for tooling/Studio compatibility
├── langgraph.json # LangGraph server configuration
├── pyproject.toml # Python dependencies
├── Makefile # Development commands
└── Dockerfile # Container build
```
`langgraph.json` is not the default service entrypoint. The scripts and Docker
deployments run the Gateway embedded runtime; the file is kept for LangGraph
tooling, Studio, or direct LangGraph Server compatibility.
---
## Configuration
@@ -358,11 +362,10 @@ If a provider is explicitly enabled but required credentials are missing, or the
```bash
make install # Install dependencies
make dev # Run Gateway API + embedded agent runtime (port 8001)
make gateway # Run Gateway API without reload (port 8001)
make dev # Run LangGraph server (port 2024)
make gateway # Run Gateway API (port 8001)
make lint # Run linter (ruff)
make format # Format code (ruff)
make detect-blocking-io # Inventory blocking IO that may block the backend event loop
```
### Code Style
@@ -379,18 +382,6 @@ make detect-blocking-io # Inventory blocking IO that may block the backend even
uv run pytest
```
`make detect-blocking-io` statically scans backend business code for blocking
IO that may run on the backend event loop and is not test-coverage-bound. It
prints a concise summary for human review and writes complete JSON findings to
`.deer-flow/blocking-io-findings.json` at the repository root (regardless of
whether the target is invoked from the repo root or from `backend/`). JSON
findings include both broad IO category and review-oriented fields such as
`priority`, `location`, `blocking_call`, `event_loop_exposure`, `reason`, and
`code`. `priority` is a deterministic review ordering from the operation type,
not proof of a bug. Bare-name same-file calls are resolved by function name,
so duplicate helper names in one file can conservatively over-report async
reachability.
---
## Technology Stack
+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 Gateway's LangGraph-compatible API.
which uses ``langgraph-sdk`` to communicate with the underlying LangGraph Server.
"""
from app.channels.base import Channel
-4
View File
@@ -31,10 +31,6 @@ class Channel(ABC):
def is_running(self) -> bool:
return self._running
@property
def supports_streaming(self) -> bool:
return False
# -- lifecycle ---------------------------------------------------------
@abstractmethod
-7
View File
@@ -18,10 +18,3 @@ KNOWN_CHANNEL_COMMANDS: frozenset[str] = frozenset(
"/help",
}
)
def is_known_channel_command(text: str) -> bool:
"""Return whether text starts with a registered channel control command."""
if not text.startswith("/"):
return False
return text.split(maxsplit=1)[0].lower() in KNOWN_CHANNEL_COMMANDS
-738
View File
@@ -1,738 +0,0 @@
"""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 is_known_channel_command
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:
return is_known_channel_command(text)
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"
-554
View File
@@ -1,554 +0,0 @@
"""Discord channel integration using discord.py."""
from __future__ import annotations
import asyncio
import json
import logging
import threading
from pathlib import Path
from typing import Any
from app.channels.base import Channel
from app.channels.commands import is_known_channel_command
from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
logger = logging.getLogger(__name__)
_DISCORD_MAX_MESSAGE_LEN = 2000
class DiscordChannel(Channel):
"""Discord bot channel.
Configuration keys (in ``config.yaml`` under ``channels.discord``):
- ``bot_token``: Discord Bot token.
- ``allowed_guilds``: (optional) List of allowed Discord guild IDs. Empty = allow all.
- ``mention_only``: (optional) If true, only respond when the bot is mentioned.
- ``allowed_channels``: (optional) List of channel IDs where messages are always accepted
(even when mention_only is true). Use for channels where you want the bot to respond
without mentions. Empty = mention_only applies everywhere.
- ``thread_mode``: (optional) If true, group a channel conversation into a thread.
Default: same as ``mention_only``.
"""
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
super().__init__(name="discord", bus=bus, config=config)
self._bot_token = str(config.get("bot_token", "")).strip()
self._allowed_guilds: set[int] = set()
for guild_id in config.get("allowed_guilds", []):
try:
self._allowed_guilds.add(int(guild_id))
except (TypeError, ValueError):
continue
self._mention_only: bool = bool(config.get("mention_only", False))
self._thread_mode: bool = config.get("thread_mode", self._mention_only)
self._allowed_channels: set[str] = set()
for channel_id in config.get("allowed_channels", []):
self._allowed_channels.add(str(channel_id))
# Session tracking: channel_id -> Discord thread_id (in-memory, persisted to JSON).
# Uses a dedicated JSON file separate from ChannelStore, which maps IM
# conversations to DeerFlow thread IDs — a different concern.
self._active_threads: dict[str, str] = {}
# Reverse-lookup set for O(1) thread ID checks (avoids O(n) scan of _active_threads.values()).
self._active_thread_ids: set[str] = set()
# Lock protecting _active_threads and the JSON file from concurrent access.
# _run_client (Discord loop thread) and the main thread both read/write.
self._thread_store_lock = threading.Lock()
store = config.get("channel_store")
if store is not None:
self._thread_store_path = store._path.parent / "discord_threads.json"
else:
self._thread_store_path = Path.home() / ".deer-flow" / "channels" / "discord_threads.json"
# Typing indicator management
self._typing_tasks: dict[str, asyncio.Task] = {}
self._client = None
self._thread: threading.Thread | None = None
self._discord_loop: asyncio.AbstractEventLoop | None = None
self._main_loop: asyncio.AbstractEventLoop | None = None
self._discord_module = None
async def start(self) -> None:
if self._running:
return
try:
import discord
except ImportError:
logger.error("discord.py is not installed. Install it with: uv add discord.py")
return
if not self._bot_token:
logger.error("Discord channel requires bot_token")
return
intents = discord.Intents.default()
intents.messages = True
intents.guilds = True
intents.message_content = True
client = discord.Client(
intents=intents,
allowed_mentions=discord.AllowedMentions.none(),
)
self._client = client
self._discord_module = discord
self._main_loop = asyncio.get_event_loop()
@client.event
async def on_message(message) -> None:
await self._on_message(message)
self._running = True
self.bus.subscribe_outbound(self._on_outbound)
self._thread = threading.Thread(target=self._run_client, daemon=True)
self._thread.start()
self._load_active_threads()
logger.info("Discord channel started")
def _load_active_threads(self) -> None:
"""Restore Discord thread mappings from the dedicated JSON file on startup."""
with self._thread_store_lock:
try:
if not self._thread_store_path.exists():
logger.debug("[Discord] no thread mappings file at %s", self._thread_store_path)
return
data = json.loads(self._thread_store_path.read_text())
self._active_threads.clear()
self._active_thread_ids.clear()
for channel_id, thread_id in data.items():
self._active_threads[channel_id] = thread_id
self._active_thread_ids.add(thread_id)
if self._active_threads:
logger.info("[Discord] restored %d thread mappings from %s", len(self._active_threads), self._thread_store_path)
except Exception:
logger.exception("[Discord] failed to load thread mappings")
def _save_thread(self, channel_id: str, thread_id: str) -> None:
"""Persist a Discord thread mapping to the dedicated JSON file."""
with self._thread_store_lock:
try:
data: dict[str, str] = {}
if self._thread_store_path.exists():
data = json.loads(self._thread_store_path.read_text())
old_id = data.get(channel_id)
data[channel_id] = thread_id
# Update reverse-lookup set
if old_id:
self._active_thread_ids.discard(old_id)
self._active_thread_ids.add(thread_id)
self._thread_store_path.parent.mkdir(parents=True, exist_ok=True)
self._thread_store_path.write_text(json.dumps(data, indent=2))
except Exception:
logger.exception("[Discord] failed to save thread mapping for channel %s", channel_id)
async def stop(self) -> None:
self._running = False
self.bus.unsubscribe_outbound(self._on_outbound)
# Cancel all active typing indicator tasks
for target_id, task in list(self._typing_tasks.items()):
if not task.done():
task.cancel()
logger.debug("[Discord] cancelled typing task for target %s", target_id)
self._typing_tasks.clear()
if self._client and self._discord_loop and self._discord_loop.is_running():
close_future = asyncio.run_coroutine_threadsafe(self._client.close(), self._discord_loop)
try:
await asyncio.wait_for(asyncio.wrap_future(close_future), timeout=10)
except TimeoutError:
logger.warning("[Discord] client close timed out after 10s")
except Exception:
logger.exception("[Discord] error while closing client")
if self._thread:
self._thread.join(timeout=10)
self._thread = None
self._client = None
self._discord_loop = None
self._discord_module = None
logger.info("Discord channel stopped")
async def send(self, msg: OutboundMessage) -> None:
# Stop typing indicator once we're sending the response
stop_future = asyncio.run_coroutine_threadsafe(self._stop_typing(msg.chat_id, msg.thread_ts), self._discord_loop)
await asyncio.wrap_future(stop_future)
target = await self._resolve_target(msg)
if target is None:
logger.error("[Discord] target not found for chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
return
text = msg.text or ""
for chunk in self._split_text(text):
send_future = asyncio.run_coroutine_threadsafe(target.send(chunk), self._discord_loop)
await asyncio.wrap_future(send_future)
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
stop_future = asyncio.run_coroutine_threadsafe(self._stop_typing(msg.chat_id, msg.thread_ts), self._discord_loop)
await asyncio.wrap_future(stop_future)
target = await self._resolve_target(msg)
if target is None:
logger.error("[Discord] target not found for file upload chat_id=%s thread_ts=%s", msg.chat_id, msg.thread_ts)
return False
if self._discord_module is None:
return False
try:
fp = open(str(attachment.actual_path), "rb") # noqa: SIM115
file = self._discord_module.File(fp, filename=attachment.filename)
send_future = asyncio.run_coroutine_threadsafe(target.send(file=file), self._discord_loop)
await asyncio.wrap_future(send_future)
logger.info("[Discord] file uploaded: %s", attachment.filename)
return True
except Exception:
logger.exception("[Discord] failed to upload file: %s", attachment.filename)
return False
async def _start_typing(self, channel, chat_id: str, thread_ts: str | None = None) -> None:
"""Starts a loop to send periodic typing indicators."""
target_id = thread_ts or chat_id
if target_id in self._typing_tasks:
return # Already typing for this target
async def _typing_loop():
try:
while True:
try:
await channel.trigger_typing()
except Exception:
pass
await asyncio.sleep(10)
except asyncio.CancelledError:
pass
task = asyncio.create_task(_typing_loop())
self._typing_tasks[target_id] = task
async def _stop_typing(self, chat_id: str, thread_ts: str | None = None) -> None:
"""Stops the typing loop for a specific target."""
target_id = thread_ts or chat_id
task = self._typing_tasks.pop(target_id, None)
if task and not task.done():
task.cancel()
logger.debug("[Discord] stopped typing indicator for target %s", target_id)
async def _add_reaction(self, message) -> None:
"""Add a checkmark reaction to acknowledge the message was received."""
try:
await message.add_reaction("")
except Exception:
logger.debug("[Discord] failed to add reaction to message %s", message.id, exc_info=True)
async def _on_message(self, message) -> None:
if not self._running or not self._client:
return
if message.author.bot:
return
if self._client.user and message.author.id == self._client.user.id:
return
guild = message.guild
if self._allowed_guilds:
if guild is None or guild.id not in self._allowed_guilds:
return
text = (message.content or "").strip()
if not text:
return
if self._discord_module is None:
return
# Determine whether the bot is mentioned in this message
user = self._client.user if self._client else None
if user:
bot_mention = user.mention # <@ID>
alt_mention = f"<@!{user.id}>" # <@!ID> (ping variant)
standard_mention = f"<@{user.id}>"
else:
bot_mention = None
alt_mention = None
standard_mention = ""
has_mention = (bot_mention and bot_mention in message.content) or (alt_mention and alt_mention in message.content) or (standard_mention and standard_mention in message.content)
# Strip mention from text for processing
if has_mention:
text = text.replace(bot_mention or "", "").replace(alt_mention or "", "").replace(standard_mention or "", "").strip()
# Don't return early if text is empty — still process the mention (e.g., create thread)
# --- Determine thread/channel routing and typing target ---
thread_id = None
chat_id = None
typing_target = None # The Discord object to type into
if isinstance(message.channel, self._discord_module.Thread):
# --- Message already inside a thread ---
thread_obj = message.channel
thread_id = str(thread_obj.id)
chat_id = str(thread_obj.parent_id or thread_obj.id)
typing_target = thread_obj
# If this is a known active thread, process normally
if thread_id in self._active_thread_ids:
msg_type = InboundMessageType.COMMAND if is_known_channel_command(text) else InboundMessageType.CHAT
inbound = self._make_inbound(
chat_id=chat_id,
user_id=str(message.author.id),
text=text,
msg_type=msg_type,
thread_ts=thread_id,
metadata={
"guild_id": str(guild.id) if guild else None,
"channel_id": str(message.channel.id),
"message_id": str(message.id),
},
)
inbound.topic_id = thread_id
self._publish(inbound)
# Start typing indicator in the thread
if typing_target:
asyncio.create_task(self._start_typing(typing_target, chat_id, thread_id))
asyncio.create_task(self._add_reaction(message))
return
# Thread not tracked (orphaned) — create new thread and handle below
logger.debug("[Discord] message in orphaned thread %s, will create new thread", thread_id)
thread_id = None
typing_target = None
# At this point we're guaranteed to be in a channel, not a thread
# (the Thread case is handled above). Apply mention_only for all
# non-thread messages — no special case needed.
channel_id = str(message.channel.id)
# Check if there's an active thread for this channel
if channel_id in self._active_threads:
# respect mention_only: if enabled, only process messages that mention the bot
# (unless the channel is in allowed_channels)
# Messages within a thread are always allowed through (continuation).
# At this code point we know the message is in a channel, not a thread
# (Thread case handled above), so always apply the check.
if self._mention_only and not has_mention and channel_id not in self._allowed_channels:
logger.debug("[Discord] skipping no-@ message in channel %s (not in thread)", channel_id)
return
# mention_only + fresh @ → create new thread instead of routing to existing one
if self._mention_only and has_mention:
thread_obj = await self._create_thread(message)
if thread_obj is not None:
target_thread_id = str(thread_obj.id)
self._active_threads[channel_id] = target_thread_id
self._save_thread(channel_id, target_thread_id)
thread_id = target_thread_id
chat_id = channel_id
typing_target = thread_obj
logger.info("[Discord] created new thread %s in channel %s on mention (replacing existing thread)", target_thread_id, channel_id)
else:
logger.info("[Discord] thread creation failed in channel %s, falling back to channel replies", channel_id)
thread_id = channel_id
chat_id = channel_id
typing_target = message.channel
else:
# Existing session → route to the existing thread
target_thread_id = self._active_threads[channel_id]
logger.debug("[Discord] routing message in channel %s to existing thread %s", channel_id, target_thread_id)
thread_id = target_thread_id
chat_id = channel_id
typing_target = await self._get_channel_or_thread(target_thread_id)
elif self._mention_only and not has_mention and channel_id not in self._allowed_channels:
# Not mentioned and not in an allowed channel → skip
logger.debug("[Discord] skipping message without mention in channel %s", channel_id)
return
elif self._mention_only and has_mention:
# First mention in this channel → create thread
thread_obj = await self._create_thread(message)
if thread_obj is not None:
target_thread_id = str(thread_obj.id)
self._active_threads[channel_id] = target_thread_id
self._save_thread(channel_id, target_thread_id)
thread_id = target_thread_id
chat_id = channel_id
typing_target = thread_obj # Type into the new thread
logger.info("[Discord] created thread %s in channel %s for user %s", target_thread_id, channel_id, message.author.display_name)
else:
# Fallback: thread creation failed (disabled/permissions), reply in channel
logger.info("[Discord] thread creation failed in channel %s, falling back to channel replies", channel_id)
thread_id = channel_id
chat_id = channel_id
typing_target = message.channel # Type into the channel
elif self._thread_mode:
# thread_mode but mention_only is False → create thread anyway for conversation grouping
thread_obj = await self._create_thread(message)
if thread_obj is None:
# Thread creation failed (disabled/permissions), fall back to channel replies
logger.info("[Discord] thread creation failed in channel %s, falling back to channel replies", channel_id)
thread_id = channel_id
chat_id = channel_id
typing_target = message.channel # Type into the channel
else:
target_thread_id = str(thread_obj.id)
self._active_threads[channel_id] = target_thread_id
self._save_thread(channel_id, target_thread_id)
thread_id = target_thread_id
chat_id = channel_id
typing_target = thread_obj # Type into the new thread
else:
# No threading — reply directly in channel
thread_id = channel_id
chat_id = channel_id
typing_target = message.channel # Type into the channel
msg_type = InboundMessageType.COMMAND if is_known_channel_command(text) else InboundMessageType.CHAT
inbound = self._make_inbound(
chat_id=chat_id,
user_id=str(message.author.id),
text=text,
msg_type=msg_type,
thread_ts=thread_id,
metadata={
"guild_id": str(guild.id) if guild else None,
"channel_id": str(message.channel.id),
"message_id": str(message.id),
},
)
inbound.topic_id = thread_id
# Start typing indicator in the correct target (thread or channel)
if typing_target:
asyncio.create_task(self._start_typing(typing_target, chat_id, thread_id))
self._publish(inbound)
asyncio.create_task(self._add_reaction(message))
def _publish(self, inbound) -> None:
"""Publish an inbound message to the main event loop."""
if self._main_loop and self._main_loop.is_running():
future = asyncio.run_coroutine_threadsafe(self.bus.publish_inbound(inbound), self._main_loop)
future.add_done_callback(lambda f: logger.exception("[Discord] publish_inbound failed", exc_info=f.exception()) if f.exception() else None)
def _run_client(self) -> None:
self._discord_loop = asyncio.new_event_loop()
asyncio.set_event_loop(self._discord_loop)
try:
self._discord_loop.run_until_complete(self._client.start(self._bot_token))
except Exception:
if self._running:
logger.exception("Discord client error")
finally:
try:
if self._client and not self._client.is_closed():
self._discord_loop.run_until_complete(self._client.close())
except Exception:
logger.exception("Error during Discord shutdown")
async def _create_thread(self, message):
try:
if self._discord_module is None:
return None
# Only TextChannel (type 0) and NewsChannel (type 10) support threads
channel_type = message.channel.type
if channel_type not in (
self._discord_module.ChannelType.text,
self._discord_module.ChannelType.news,
):
logger.info(
"[Discord] channel type %s (%s) does not support threads",
channel_type.value,
channel_type.name,
)
return None
thread_name = f"deerflow-{message.author.display_name}-{message.id}"[:100]
return await message.create_thread(name=thread_name)
except self._discord_module.errors.HTTPException as exc:
if exc.code == 50024:
logger.info(
"[Discord] cannot create thread in channel %s (error code 50024): %s",
message.channel.id,
channel_type.name if (channel_type := message.channel.type) else "unknown",
)
else:
logger.exception(
"[Discord] failed to create thread for message=%s (HTTPException %s)",
message.id,
exc.code,
)
return None
except Exception:
logger.exception("[Discord] failed to create thread for message=%s (threads may be disabled or missing permissions)", message.id)
return None
async def _resolve_target(self, msg: OutboundMessage):
if not self._client or not self._discord_loop:
return None
target_ids: list[str] = []
if msg.thread_ts:
target_ids.append(msg.thread_ts)
if msg.chat_id and msg.chat_id not in target_ids:
target_ids.append(msg.chat_id)
for raw_id in target_ids:
target = await self._get_channel_or_thread(raw_id)
if target is not None:
return target
return None
async def _get_channel_or_thread(self, raw_id: str):
if not self._client or not self._discord_loop:
return None
try:
target_id = int(raw_id)
except (TypeError, ValueError):
return None
get_future = asyncio.run_coroutine_threadsafe(self._fetch_channel(target_id), self._discord_loop)
try:
return await asyncio.wrap_future(get_future)
except Exception:
logger.exception("[Discord] failed to resolve target id=%s", raw_id)
return None
async def _fetch_channel(self, target_id: int):
if not self._client:
return None
channel = self._client.get_channel(target_id)
if channel is not None:
return channel
try:
return await self._client.fetch_channel(target_id)
except Exception:
return None
@staticmethod
def _split_text(text: str) -> list[str]:
if not text:
return [""]
chunks: list[str] = []
remaining = text
while len(remaining) > _DISCORD_MAX_MESSAGE_LEN:
split_at = remaining.rfind("\n", 0, _DISCORD_MAX_MESSAGE_LEN)
if split_at <= 0:
split_at = _DISCORD_MAX_MESSAGE_LEN
chunks.append(remaining[:split_at])
remaining = remaining[split_at:].lstrip("\n")
if remaining:
chunks.append(remaining)
return chunks
+15 -198
View File
@@ -7,30 +7,21 @@ import json
import logging
import re
import threading
import time
from typing import Any, Literal
from app.channels.base import Channel
from app.channels.commands import is_known_channel_command
from app.channels.message_bus import (
PENDING_CLARIFICATION_METADATA_KEY,
RESOLVED_FROM_PENDING_CLARIFICATION_METADATA_KEY,
InboundMessage,
InboundMessageType,
MessageBus,
OutboundMessage,
ResolvedAttachment,
)
from app.channels.commands import KNOWN_CHANNEL_COMMANDS
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
from deerflow.runtime.user_context import get_effective_user_id
from deerflow.sandbox.sandbox_provider import get_sandbox_provider
logger = logging.getLogger(__name__)
PENDING_CLARIFICATION_TTL_SECONDS = 30 * 60
def _is_feishu_command(text: str) -> bool:
return is_known_channel_command(text)
if not text.startswith("/"):
return False
return text.split(maxsplit=1)[0].lower() in KNOWN_CHANNEL_COMMANDS
class FeishuChannel(Channel):
@@ -64,7 +55,6 @@ class FeishuChannel(Channel):
self._background_tasks: set[asyncio.Task] = set()
self._running_card_ids: dict[str, str] = {}
self._running_card_tasks: dict[str, asyncio.Task] = {}
self._pending_clarifications: dict[tuple[str, str], list[dict[str, Any]]] = {}
self._CreateFileRequest = None
self._CreateFileRequestBody = None
self._CreateImageRequest = None
@@ -72,20 +62,6 @@ class FeishuChannel(Channel):
self._GetMessageResourceRequest = None
self._thread_lock = threading.Lock()
@staticmethod
def _non_empty_str(value: Any) -> str | None:
if isinstance(value, str) and value.strip():
return value.strip()
return None
@staticmethod
def _pending_key(chat_id: str, user_id: str) -> tuple[str, str]:
return (chat_id, user_id)
@property
def supports_streaming(self) -> bool:
return True
async def start(self) -> None:
if self._running:
return
@@ -368,9 +344,8 @@ class FeishuChannel(Channel):
return f"Failed to obtain the [{type}]"
paths = get_paths()
user_id = get_effective_user_id()
paths.ensure_thread_dirs(thread_id, user_id=user_id)
uploads_dir = paths.sandbox_uploads_dir(thread_id, user_id=user_id).resolve()
paths.ensure_thread_dirs(thread_id)
uploads_dir = paths.sandbox_uploads_dir(thread_id).resolve()
ext = "png" if type == "image" else "bin"
raw_filename = getattr(response, "file_name", "") or f"feishu_{file_key[-12:]}.{ext}"
@@ -550,25 +525,18 @@ class FeishuChannel(Channel):
"[Feishu] failed to patch running card %s, falling back to final reply",
running_card_id,
)
fallback_card_id = await self._reply_card(source_message_id, msg.text)
self._remember_thread_mapping(msg, source_message_id, fallback_card_id)
self._remember_pending_clarification(msg, fallback_card_id)
await self._reply_card(source_message_id, msg.text)
else:
self._remember_thread_mapping(msg, source_message_id, running_card_id)
self._remember_pending_clarification(msg, running_card_id)
logger.info("[Feishu] running card updated: source=%s card=%s", source_message_id, running_card_id)
elif msg.is_final:
final_card_id = await self._reply_card(source_message_id, msg.text)
self._remember_thread_mapping(msg, source_message_id, final_card_id)
self._remember_pending_clarification(msg, final_card_id)
await self._reply_card(source_message_id, msg.text)
elif awaited_running_card_task:
logger.warning(
"[Feishu] running card task finished without message_id for source=%s, skipping duplicate non-final creation",
source_message_id,
)
else:
created_card_id = await self._ensure_running_card(source_message_id, msg.text)
self._remember_thread_mapping(msg, source_message_id, created_card_id)
await self._ensure_running_card(source_message_id, msg.text)
if msg.is_final:
self._running_card_ids.pop(source_message_id, None)
@@ -579,129 +547,6 @@ class FeishuChannel(Channel):
# -- internal ----------------------------------------------------------
def _remember_thread_mapping(self, msg: OutboundMessage, *topic_ids: str | None) -> None:
store = self.config.get("channel_store")
if store is None or not msg.thread_id:
return
metadata_topic_ids = [
msg.metadata.get("message_id"),
msg.metadata.get("root_id"),
msg.metadata.get("parent_id"),
msg.metadata.get("thread_id"),
msg.metadata.get("topic_id"),
]
user_id = ""
raw_user_id = msg.metadata.get("user_id")
if isinstance(raw_user_id, str):
user_id = raw_user_id
seen: set[str] = set()
for topic_id in [*topic_ids, *metadata_topic_ids]:
topic_id = self._non_empty_str(topic_id)
if not topic_id or topic_id in seen:
continue
seen.add(topic_id)
try:
store.set_thread_id(
self.name,
msg.chat_id,
msg.thread_id,
topic_id=topic_id,
user_id=user_id,
)
except Exception:
logger.exception("[Feishu] failed to remember thread mapping for topic_id=%s", topic_id)
def _remember_pending_clarification(self, msg: OutboundMessage, card_message_id: str | None) -> None:
if not msg.is_final or msg.metadata.get(PENDING_CLARIFICATION_METADATA_KEY) is not True:
return
user_id = self._non_empty_str(msg.metadata.get("user_id"))
topic_id = self._non_empty_str(msg.metadata.get("topic_id"))
source_message_id = self._non_empty_str(msg.thread_ts) or self._non_empty_str(msg.metadata.get("message_id"))
if not (user_id and topic_id and msg.thread_id and source_message_id and card_message_id):
return
key = self._pending_key(msg.chat_id, user_id)
pending = {
"thread_id": msg.thread_id,
"topic_id": topic_id,
"source_message_id": source_message_id,
"card_message_id": card_message_id,
"created_at": time.time(),
}
with self._thread_lock:
# Plain-message clarification continuity is a short-lived in-memory
# hint; explicit Feishu replies are still covered by persisted
# message-id mappings.
self._pending_clarifications.setdefault(key, []).append(pending)
logger.info(
"[Feishu] pending clarification remembered: chat_id=%s user_id=%s topic_id=%s thread_id=%s",
msg.chat_id,
user_id,
topic_id,
msg.thread_id,
)
def _consume_pending_clarification(self, chat_id: str, user_id: str) -> dict[str, Any] | None:
key = self._pending_key(chat_id, user_id)
with self._thread_lock:
pending_items = self._pending_clarifications.get(key)
if not pending_items:
return None
now = time.time()
while pending_items:
pending = pending_items.pop(0)
created_at = pending.get("created_at")
if isinstance(created_at, (int, float)) and now - created_at <= PENDING_CLARIFICATION_TTL_SECONDS:
if pending_items:
self._pending_clarifications[key] = pending_items
else:
self._pending_clarifications.pop(key, None)
return pending
logger.info("[Feishu] pending clarification expired: chat_id=%s user_id=%s", chat_id, user_id)
self._pending_clarifications.pop(key, None)
return None
def _ensure_pending_thread_mapping(self, chat_id: str, user_id: str, pending: dict[str, Any]) -> None:
store = self.config.get("channel_store")
topic_id = self._non_empty_str(pending.get("topic_id"))
thread_id = self._non_empty_str(pending.get("thread_id"))
if store is None or not topic_id or not thread_id:
return
try:
store.set_thread_id(self.name, chat_id, thread_id, topic_id=topic_id, user_id=user_id)
except Exception:
logger.exception("[Feishu] failed to restore pending clarification mapping for topic_id=%s", topic_id)
def _resolve_topic_id(
self,
chat_id: str,
msg_id: str,
*,
root_id: str | None,
parent_id: str | None,
thread_id: str | None,
) -> tuple[str, bool]:
store = self.config.get("channel_store")
candidates = [root_id, parent_id, thread_id]
if store is not None:
for candidate in candidates:
candidate = self._non_empty_str(candidate)
if not candidate:
continue
try:
if store.get_thread_id(self.name, chat_id, topic_id=candidate):
return candidate, True
except Exception:
logger.exception("[Feishu] failed to resolve stored topic mapping for topic_id=%s", candidate)
return root_id or msg_id, False
@staticmethod
def _log_future_error(fut, name: str, msg_id: str) -> None:
"""Callback for run_coroutine_threadsafe futures to surface errors."""
@@ -742,9 +587,7 @@ class FeishuChannel(Channel):
# root_id is set when the message is a reply within a Feishu thread.
# Use it as topic_id so all replies share the same DeerFlow thread.
root_id = self._non_empty_str(getattr(message, "root_id", None))
parent_id = self._non_empty_str(getattr(message, "parent_id", None))
feishu_thread_id = self._non_empty_str(getattr(message, "thread_id", None))
root_id = getattr(message, "root_id", None) or None
# Parse message content
content = json.loads(message.content)
@@ -805,12 +648,10 @@ class FeishuChannel(Channel):
text = text.strip()
logger.info(
"[Feishu] parsed message: chat_id=%s, msg_id=%s, root_id=%s, parent_id=%s, thread_id=%s, sender=%s, text=%r",
"[Feishu] parsed message: chat_id=%s, msg_id=%s, root_id=%s, sender=%s, text=%r",
chat_id,
msg_id,
root_id,
parent_id,
feishu_thread_id,
sender_id,
text[:100] if text else "",
)
@@ -826,24 +667,8 @@ class FeishuChannel(Channel):
else:
msg_type = InboundMessageType.CHAT
# Prefer any platform message id that already maps to a DeerFlow
# thread. This keeps replies to bot clarification cards in the
# original conversation even when Feishu reports the card as root.
topic_id, resolved_from_stored_mapping = self._resolve_topic_id(
chat_id,
msg_id,
root_id=root_id,
parent_id=parent_id,
thread_id=feishu_thread_id,
)
resolved_from_pending = False
if msg_type == InboundMessageType.CHAT and not resolved_from_stored_mapping:
pending = self._consume_pending_clarification(chat_id, sender_id)
pending_topic_id = self._non_empty_str(pending.get("topic_id")) if pending else None
if pending_topic_id:
topic_id = pending_topic_id
self._ensure_pending_thread_mapping(chat_id, sender_id, pending)
resolved_from_pending = True
# topic_id: use root_id for replies (same topic), msg_id for new messages (new topic)
topic_id = root_id or msg_id
inbound = self._make_inbound(
chat_id=chat_id,
@@ -852,15 +677,7 @@ class FeishuChannel(Channel):
msg_type=msg_type,
thread_ts=msg_id,
files=files_list,
metadata={
"message_id": msg_id,
"root_id": root_id,
"parent_id": parent_id,
"thread_id": feishu_thread_id,
"topic_id": topic_id,
"user_id": sender_id,
RESOLVED_FROM_PENDING_CLARIFICATION_METADATA_KEY: resolved_from_pending,
},
metadata={"message_id": msg_id, "root_id": root_id},
)
inbound.topic_id = topic_id
+36 -316
View File
@@ -1,4 +1,4 @@
"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via Gateway."""
"""ChannelManager — consumes inbound messages and dispatches them to the DeerFlow agent via LangGraph Server."""
from __future__ import annotations
@@ -8,36 +8,18 @@ import mimetypes
import re
import time
from collections.abc import Awaitable, Callable, Mapping
from dataclasses import dataclass
from pathlib import Path
from typing import Any
import httpx
from langgraph_sdk.errors import ConflictError
from app.channels.commands import KNOWN_CHANNEL_COMMANDS
from app.channels.message_bus import (
PENDING_CLARIFICATION_METADATA_KEY,
InboundMessage,
InboundMessageType,
MessageBus,
OutboundMessage,
ResolvedAttachment,
)
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.config.agents_config import load_agent_config
from deerflow.config.paths import make_safe_user_id
from deerflow.runtime.user_context import get_effective_user_id
from deerflow.skills.slash import parse_slash_skill_reference
from deerflow.skills.storage import get_or_new_skill_storage
from deerflow.skills.storage.skill_storage import SkillStorage
from deerflow.utils.messages import ORIGINAL_USER_CONTENT_KEY
logger = logging.getLogger(__name__)
DEFAULT_LANGGRAPH_URL = "http://localhost:8001/api"
DEFAULT_LANGGRAPH_URL = "http://localhost:2024"
DEFAULT_GATEWAY_URL = "http://localhost:8001"
DEFAULT_ASSISTANT_ID = "lead_agent"
CUSTOM_AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
@@ -52,24 +34,14 @@ 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},
"telegram": {"supports_streaming": False},
"wechat": {"supports_streaming": False},
"wecom": {"supports_streaming": True},
}
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] = {}
@@ -106,40 +78,13 @@ async def _read_wecom_inbound_file(file_info: dict[str, Any], client: httpx.Asyn
return decrypt_file(data, aeskey)
async def _read_wechat_inbound_file(file_info: dict[str, Any], client: httpx.AsyncClient) -> bytes | None:
raw_path = file_info.get("path")
if isinstance(raw_path, str) and raw_path.strip():
try:
return await asyncio.to_thread(Path(raw_path).read_bytes)
except OSError:
logger.exception("[Manager] failed to read WeChat inbound file from local path: %s", raw_path)
return None
full_url = file_info.get("full_url")
if isinstance(full_url, str) and full_url.strip():
return await _read_http_inbound_file({"url": full_url}, client)
return None
register_inbound_file_reader("wecom", _read_wecom_inbound_file)
register_inbound_file_reader("wechat", _read_wechat_inbound_file)
class InvalidChannelSessionConfigError(ValueError):
"""Raised when IM channel session overrides contain invalid agent config."""
class SlashSkillCommandResolutionError(RuntimeError):
"""Raised when IM slash-skill command resolution cannot complete safely."""
@dataclass(frozen=True, slots=True)
class _SlashSkillCommandResolution:
route_to_chat: bool = False
failure_message: str | None = None
def _is_thread_busy_error(exc: BaseException | None) -> bool:
if exc is None:
return False
@@ -179,6 +124,7 @@ def _extract_response_text(result: dict | list) -> str:
Handles special cases:
- Regular AI text responses
- Clarification interrupts (``ask_clarification`` tool messages)
- AI messages with tool_calls but no text content
"""
if isinstance(result, list):
messages = result
@@ -197,8 +143,6 @@ def _extract_response_text(result: dict | list) -> str:
# Stop at the last human message — anything before it is a previous turn
if msg_type == "human":
if _is_hidden_human_control_message(msg):
continue
break
# Check for tool messages from ask_clarification (interrupt case)
@@ -226,54 +170,6 @@ def _extract_response_text(result: dict | list) -> str:
return ""
def _messages_from_result(result: dict | list) -> list[Any]:
if isinstance(result, list):
return result
if isinstance(result, dict):
messages = result.get("messages", [])
if isinstance(messages, list):
return messages
return []
def _current_turn_messages(result: dict | list) -> list[dict[str, Any]]:
messages = _messages_from_result(result)
current_turn: list[dict[str, Any]] = []
for msg in reversed(messages):
if not isinstance(msg, dict):
continue
if msg.get("type") == "human":
break
current_turn.append(msg)
current_turn.reverse()
return current_turn
def _has_current_turn_clarification(result: dict | list) -> bool:
"""Return True only when the current turn's final result is clarification."""
for msg in reversed(_current_turn_messages(result)):
msg_type = msg.get("type")
if msg_type == "tool":
return msg.get("name") == "ask_clarification"
if msg_type == "ai":
content = msg.get("content")
if isinstance(content, str):
if content:
return False
elif content:
return False
if msg.get("tool_calls"):
return False
return False
def _response_metadata(base_metadata: dict[str, Any], *, pending_clarification: bool = False) -> dict[str, Any]:
metadata = _slim_metadata(base_metadata)
if pending_clarification:
metadata[PENDING_CLARIFICATION_METADATA_KEY] = True
return metadata
def _extract_text_content(content: Any) -> str:
"""Extract text from a streaming payload content field."""
if isinstance(content, str):
@@ -387,8 +283,6 @@ def _extract_artifacts(result: dict | list) -> list[str]:
continue
# Stop at the last human message — anything before it is a previous turn
if msg.get("type") == "human":
if _is_hidden_human_control_message(msg):
continue
break
# Look for AI messages with present_files tool calls
if msg.get("type") == "ai":
@@ -401,18 +295,6 @@ def _extract_artifacts(result: dict | list) -> list[str]:
return artifacts
def _is_hidden_human_control_message(msg: Mapping[str, Any]) -> bool:
"""Return whether a human message is an internal control message hidden from UI."""
if msg.get("type") != "human":
return False
additional_kwargs = msg.get("additional_kwargs")
if not isinstance(additional_kwargs, Mapping):
return False
return additional_kwargs.get("hide_from_ui") is True
def _format_artifact_text(artifacts: list[str]) -> str:
"""Format artifact paths into a human-readable text block listing filenames."""
import posixpath
@@ -426,46 +308,6 @@ def _format_artifact_text(artifacts: list[str]) -> str:
_OUTPUTS_VIRTUAL_PREFIX = "/mnt/user-data/outputs/"
def _unknown_command_reply(command: str | None = None) -> str:
available = " | ".join(sorted(KNOWN_CHANNEL_COMMANDS))
if command:
return f"Unknown command: /{command}. Available commands: {available}"
return f"Unknown command. Available commands: {available}"
def _human_input_message(content: str, *, original_content: str | None = None) -> dict[str, Any]:
message: dict[str, Any] = {"role": "human", "content": content}
if original_content is not None and original_content != content:
message["additional_kwargs"] = {ORIGINAL_USER_CONTENT_KEY: original_content}
return message
def _resolve_slash_skill_command(
text: str,
available_skills: set[str] | None = None,
storage: SkillStorage | Callable[[], SkillStorage] | None = None,
) -> _SlashSkillCommandResolution | None:
reference = parse_slash_skill_reference(text)
if reference is None:
return None
try:
resolved_storage = storage() if callable(storage) else storage or get_or_new_skill_storage()
skills = resolved_storage.load_skills(enabled_only=False)
skill = next((candidate for candidate in skills if candidate.name == reference.name), None)
if skill is None:
return None
if not skill.enabled:
return _SlashSkillCommandResolution(failure_message=f"Skill `/{reference.name}` is installed but disabled. Enable it before using slash activation.")
if available_skills is not None and reference.name not in available_skills:
return _SlashSkillCommandResolution(failure_message=f"Skill `/{reference.name}` is not available for this agent.")
return _SlashSkillCommandResolution(route_to_chat=True)
except Exception as exc:
logger.exception("[Manager] failed to resolve slash skill command")
raise SlashSkillCommandResolutionError("Failed to resolve slash skill command. Please check the skill configuration.") from exc
def _resolve_attachments(thread_id: str, artifacts: list[str]) -> list[ResolvedAttachment]:
"""Resolve virtual artifact paths to host filesystem paths with metadata.
@@ -480,15 +322,14 @@ def _resolve_attachments(thread_id: str, artifacts: list[str]) -> list[ResolvedA
attachments: list[ResolvedAttachment] = []
paths = get_paths()
user_id = get_effective_user_id()
outputs_dir = paths.sandbox_outputs_dir(thread_id, user_id=user_id).resolve()
outputs_dir = paths.sandbox_outputs_dir(thread_id).resolve()
for virtual_path in artifacts:
# Security: only allow files from the agent outputs directory
if not virtual_path.startswith(_OUTPUTS_VIRTUAL_PREFIX):
logger.warning("[Manager] rejected non-outputs artifact path: %s", virtual_path)
continue
try:
actual = paths.resolve_virtual_path(thread_id, virtual_path, user_id=user_id)
actual = paths.resolve_virtual_path(thread_id, virtual_path)
# Verify the resolved path is actually under the outputs directory
# (guards against path-traversal even after prefix check)
try:
@@ -547,13 +388,7 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic
if not msg.files:
return []
from deerflow.uploads.manager import (
UnsafeUploadPathError,
claim_unique_filename,
ensure_uploads_dir,
normalize_filename,
write_upload_file_no_symlink,
)
from deerflow.uploads.manager import claim_unique_filename, ensure_uploads_dir, normalize_filename
uploads_dir = ensure_uploads_dir(thread_id)
seen_names = {entry.name for entry in uploads_dir.iterdir() if entry.is_file()}
@@ -604,10 +439,7 @@ async def _ingest_inbound_files(thread_id: str, msg: InboundMessage) -> list[dic
dest = uploads_dir / safe_name
try:
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
dest.write_bytes(data)
except Exception:
logger.exception("[Manager] failed to write inbound file: %s", dest)
continue
@@ -655,7 +487,7 @@ class ChannelManager:
"""Core dispatcher that bridges IM channels to the DeerFlow agent.
It reads from the MessageBus inbound queue, creates/reuses threads on
Gateway's LangGraph-compatible API, sends messages via ``runs.wait``, and publishes
the LangGraph Server, sends messages via ``runs.wait``, and publishes
outbound responses back through the bus.
"""
@@ -680,21 +512,12 @@ 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._skill_storage: SkillStorage | None = None
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]]:
@@ -717,31 +540,12 @@ class ChannelManager:
user_layer.get("config"),
)
configurable = run_config.get("configurable")
if isinstance(configurable, Mapping):
configurable = dict(configurable)
else:
configurable = {}
run_config["configurable"] = configurable
# Pin channel-triggered runs to the root graph namespace so follow-up
# turns continue from the same conversation checkpoint.
configurable["checkpoint_ns"] = ""
configurable["thread_id"] = thread_id
# ``user_id`` drives user-scoped filesystem buckets that only accept
# ``[A-Za-z0-9_-]``, so normalize the channel id and keep the raw value
# under ``channel_user_id`` for platform-facing lookups.
run_context_identity: dict[str, Any] = {"thread_id": thread_id}
if msg.user_id:
run_context_identity["user_id"] = make_safe_user_id(msg.user_id)
run_context_identity["channel_user_id"] = msg.user_id
run_context = _merge_dicts(
DEFAULT_RUN_CONTEXT,
self._default_session.get("context"),
channel_layer.get("context"),
user_layer.get("context"),
run_context_identity,
{"thread_id": thread_id},
)
# Custom agents are implemented as lead_agent + agent_name context.
@@ -753,21 +557,6 @@ class ChannelManager:
return assistant_id, run_config, run_context
def _resolve_available_skill_names(self, msg: InboundMessage) -> set[str] | None:
thread_id = self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id) or ""
_, _, run_context = self._resolve_run_params(msg, thread_id)
if run_context.get("is_bootstrap"):
return {"bootstrap"}
agent_name = run_context.get("agent_name")
if not isinstance(agent_name, str) or not agent_name.strip():
return None
agent_config = load_agent_config(_normalize_custom_agent_name(agent_name))
if agent_config and agent_config.skills is not None:
return set(agent_config.skills)
return None
# -- LangGraph SDK client (lazy) ----------------------------------------
def _get_client(self):
@@ -775,21 +564,9 @@ class ChannelManager:
if self._client is None:
from langgraph_sdk import get_client
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}",
},
)
self._client = get_client(url=self._langgraph_url)
return self._client
def _get_skill_storage(self) -> SkillStorage:
if self._skill_storage is None:
self._skill_storage = get_or_new_skill_storage()
return self._skill_storage
# -- lifecycle ---------------------------------------------------------
async def start(self) -> None:
@@ -859,14 +636,6 @@ class ChannelManager:
exc,
)
await self._send_error(msg, str(exc))
except SlashSkillCommandResolutionError as exc:
logger.warning(
"Slash skill command resolution failed for %s (chat=%s): %s",
msg.channel_name,
msg.chat_id,
exc,
)
await self._send_error(msg, str(exc))
except Exception:
logger.exception(
"Error handling message from %s (chat=%s)",
@@ -878,7 +647,7 @@ class ChannelManager:
# -- chat handling -----------------------------------------------------
async def _create_thread(self, client, msg: InboundMessage) -> str:
"""Create a new thread through Gateway and store the mapping."""
"""Create a new thread on the LangGraph Server and store the mapping."""
thread = await client.threads.create()
thread_id = thread["thread_id"]
self.store.set_thread_id(
@@ -888,7 +657,7 @@ class ChannelManager:
topic_id=msg.topic_id,
user_id=msg.user_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)
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)
return thread_id
async def _handle_chat(self, msg: InboundMessage, extra_context: dict[str, Any] | None = None) -> None:
@@ -921,11 +690,9 @@ class ChannelManager:
if extra_context:
run_context.update(extra_context)
original_text = msg.text
uploaded = await _ingest_inbound_files(thread_id, msg)
if uploaded:
msg.text = f"{_format_uploaded_files_block(uploaded)}\n\n{msg.text}".strip()
human_message = _human_input_message(msg.text, original_content=original_text)
if self._channel_supports_streaming(msg.channel_name):
await self._handle_streaming_chat(
@@ -935,30 +702,19 @@ class ChannelManager:
assistant_id,
run_config,
run_context,
human_message,
)
return
logger.info("[Manager] invoking runs.wait(thread_id=%s, text=%r)", thread_id, msg.text[:100])
try:
result = await client.runs.wait(
thread_id,
assistant_id,
input={"messages": [human_message]},
config=run_config,
context=run_context,
multitask_strategy="reject",
)
except Exception as exc:
if _is_thread_busy_error(exc):
logger.warning("[Manager] thread busy (concurrent run rejected): thread_id=%s", thread_id)
await self._send_error(msg, THREAD_BUSY_MESSAGE)
return
else:
raise
result = await client.runs.wait(
thread_id,
assistant_id,
input={"messages": [{"role": "human", "content": msg.text}]},
config=run_config,
context=run_context,
)
response_text = _extract_response_text(result)
pending_clarification = _has_current_turn_clarification(result)
artifacts = _extract_artifacts(result)
logger.info(
@@ -984,7 +740,6 @@ class ChannelManager:
artifacts=artifacts,
attachments=attachments,
thread_ts=msg.thread_ts,
metadata=_response_metadata(msg.metadata, pending_clarification=pending_clarification),
)
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)
@@ -997,7 +752,6 @@ class ChannelManager:
assistant_id: str,
run_config: dict[str, Any],
run_context: dict[str, Any],
human_message: dict[str, Any],
) -> None:
logger.info("[Manager] invoking runs.stream(thread_id=%s, text=%r)", thread_id, msg.text[:100])
@@ -1013,7 +767,7 @@ class ChannelManager:
async for chunk in client.runs.stream(
thread_id,
assistant_id,
input={"messages": [human_message]},
input={"messages": [{"role": "human", "content": msg.text}]},
config=run_config,
context=run_context,
stream_mode=["messages-tuple", "values"],
@@ -1047,7 +801,6 @@ class ChannelManager:
text=latest_text,
is_final=False,
thread_ts=msg.thread_ts,
metadata=_response_metadata(msg.metadata),
)
)
last_published_text = latest_text
@@ -1061,7 +814,6 @@ class ChannelManager:
finally:
result = last_values if last_values is not None else {"messages": [{"type": "ai", "content": latest_text}]}
response_text = _extract_response_text(result)
pending_clarification = _has_current_turn_clarification(result)
artifacts = _extract_artifacts(result)
response_text, attachments = _prepare_artifact_delivery(thread_id, response_text, artifacts)
@@ -1093,27 +845,17 @@ class ChannelManager:
attachments=attachments,
is_final=True,
thread_ts=msg.thread_ts,
metadata=_response_metadata(msg.metadata, pending_clarification=pending_clarification),
)
)
# -- command handling --------------------------------------------------
async def _handle_command(self, msg: InboundMessage) -> None:
raw_text = msg.text
text = raw_text.strip()
text = msg.text.strip()
parts = text.split(maxsplit=1)
reply: str | None = None
if not parts:
command = None
reply = _unknown_command_reply()
else:
command = parts[0].lower().removeprefix("/")
command = parts[0].lower().lstrip("/")
if reply is None and not raw_text.startswith("/"):
reply = _unknown_command_reply(command)
if reply is None and command == "bootstrap":
if command == "bootstrap":
from dataclasses import replace as _dc_replace
chat_text = parts[1] if len(parts) > 1 else "Initialize workspace"
@@ -1121,8 +863,8 @@ class ChannelManager:
await self._handle_chat(chat_msg, extra_context={"is_bootstrap": True})
return
if reply is None and command == "new":
# Create a new thread through Gateway
if command == "new":
# Create a new thread on the LangGraph Server
client = self._get_client()
thread = await client.threads.create()
new_thread_id = thread["thread_id"]
@@ -1134,14 +876,14 @@ class ChannelManager:
user_id=msg.user_id,
)
reply = "New conversation started."
elif reply is None and command == "status":
elif command == "status":
thread_id = self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id)
reply = f"Active thread: {thread_id}" if thread_id else "No active conversation."
elif reply is None and command == "models":
elif command == "models":
reply = await self._fetch_gateway("/api/models", "models")
elif reply is None and command == "memory":
elif command == "memory":
reply = await self._fetch_gateway("/api/memory", "memory")
elif reply is None and command == "help":
elif command == "help":
reply = (
"Available commands:\n"
"/bootstrap — Start a bootstrap session (enables agent setup)\n"
@@ -1149,35 +891,18 @@ class ChannelManager:
"/status — Show current thread info\n"
"/models — List available models\n"
"/memory — Show memory status\n"
"/<skill-name> <task> — Activate an enabled skill for one turn\n"
"/help — Show this help"
)
elif reply is None:
slash_resolution = await asyncio.to_thread(
lambda: _resolve_slash_skill_command(
raw_text,
self._resolve_available_skill_names(msg),
self._get_skill_storage,
)
)
if slash_resolution and slash_resolution.failure_message:
reply = slash_resolution.failure_message
elif slash_resolution and slash_resolution.route_to_chat:
from dataclasses import replace as _dc_replace
chat_msg = _dc_replace(msg, msg_type=InboundMessageType.CHAT)
await self._handle_chat(chat_msg)
return
else:
reply = _unknown_command_reply(command)
else:
available = " | ".join(sorted(KNOWN_CHANNEL_COMMANDS))
reply = f"Unknown command: /{command}. Available commands: {available}"
outbound = OutboundMessage(
channel_name=msg.channel_name,
chat_id=msg.chat_id,
thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id) or "",
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)
@@ -1187,11 +912,7 @@ class ChannelManager:
try:
async with httpx.AsyncClient() as http:
resp = await http.get(
f"{self._gateway_url}{path}",
timeout=10,
headers=create_internal_auth_headers(),
)
resp = await http.get(f"{self._gateway_url}{path}", timeout=10)
resp.raise_for_status()
data = resp.json()
except Exception:
@@ -1212,9 +933,8 @@ class ChannelManager:
outbound = OutboundMessage(
channel_name=msg.channel_name,
chat_id=msg.chat_id,
thread_id=self.store.get_thread_id(msg.channel_name, msg.chat_id, topic_id=msg.topic_id) or "",
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)
-3
View File
@@ -13,9 +13,6 @@ from typing import Any
logger = logging.getLogger(__name__)
PENDING_CLARIFICATION_METADATA_KEY = "pending_clarification"
RESOLVED_FROM_PENDING_CLARIFICATION_METADATA_KEY = "resolved_from_pending_clarification"
# ---------------------------------------------------------------------------
# Message types
+9 -43
View File
@@ -4,7 +4,7 @@ from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING, Any
from typing import Any
from app.channels.base import Channel
from app.channels.manager import DEFAULT_GATEWAY_URL, DEFAULT_LANGGRAPH_URL, ChannelManager
@@ -13,31 +13,14 @@ from app.channels.store import ChannelStore
logger = logging.getLogger(__name__)
if TYPE_CHECKING:
from deerflow.config.app_config import AppConfig
# 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",
"telegram": "app.channels.telegram:TelegramChannel",
"wechat": "app.channels.wechat:WechatChannel",
"wecom": "app.channels.wecom:WeComChannel",
}
# 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"],
"telegram": ["bot_token"],
"wecom": ["bot_id", "bot_secret"],
"wechat": ["bot_token"],
}
_CHANNELS_LANGGRAPH_URL_ENV = "DEER_FLOW_CHANNELS_LANGGRAPH_URL"
_CHANNELS_GATEWAY_URL_ENV = "DEER_FLOW_CHANNELS_GATEWAY_URL"
@@ -80,15 +63,14 @@ class ChannelService:
self._running = False
@classmethod
def from_app_config(cls, app_config: AppConfig | None = None) -> ChannelService:
def from_app_config(cls) -> ChannelService:
"""Create a ChannelService from the application config."""
if app_config is None:
from deerflow.config.app_config import get_app_config
from deerflow.config.app_config import get_app_config
app_config = get_app_config()
config = get_app_config()
channels_config = {}
# extra fields are allowed by AppConfig (extra="allow")
extra = app_config.model_extra or {}
extra = config.model_extra or {}
if "channels" in extra:
channels_config = extra["channels"]
return cls(channels_config=channels_config)
@@ -104,16 +86,7 @@ class ChannelService:
if not isinstance(channel_config, dict):
continue
if not channel_config.get("enabled", False):
cred_keys = _CHANNEL_CREDENTIAL_KEYS.get(name, [])
has_creds = any(not isinstance(channel_config.get(k), bool) and channel_config.get(k) is not None and str(channel_config[k]).strip() for k in cred_keys)
if has_creds:
logger.warning(
"Channel '%s' has credentials configured but is disabled. Set enabled: true under channels.%s in config.yaml to activate it.",
name,
name,
)
else:
logger.info("Channel %s is disabled, skipping", name)
logger.info("Channel %s is disabled, skipping", name)
continue
await self._start_channel(name, channel_config)
@@ -167,19 +140,12 @@ class ChannelService:
return False
try:
config = dict(config)
config["channel_store"] = self.store
channel = channel_cls(bus=self.bus, config=config)
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
self._channels[name] = channel
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
@@ -214,12 +180,12 @@ def get_channel_service() -> ChannelService | None:
return _channel_service
async def start_channel_service(app_config: AppConfig | None = None) -> ChannelService:
async def start_channel_service() -> ChannelService:
"""Create and start the global ChannelService from app config."""
global _channel_service
if _channel_service is not None:
return _channel_service
_channel_service = ChannelService.from_app_config(app_config)
_channel_service = ChannelService.from_app_config()
await _channel_service.start()
return _channel_service
+3 -57
View File
@@ -9,7 +9,6 @@ from typing import Any
from markdown_to_mrkdwn import SlackMarkdownConverter
from app.channels.base import Channel
from app.channels.commands import is_known_channel_command
from app.channels.message_bus import InboundMessageType, MessageBus, OutboundMessage, ResolvedAttachment
logger = logging.getLogger(__name__)
@@ -17,45 +16,13 @@ logger = logging.getLogger(__name__)
_slack_md_converter = SlackMarkdownConverter()
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(
"Slack allowed_users should be a list of Slack user IDs or a single Slack user ID string; treating %s as one string value",
type(allowed_users).__name__,
)
values = [allowed_users]
return {str(user_id) for user_id in values if str(user_id)}
def _strip_leading_slack_bot_mention(text: str, bot_user_id: str | None) -> str:
if not bot_user_id:
return text
if not text.startswith("<@"):
return text
end = text.find(">")
if end <= 2:
return text
mentioned_user_id = text[2:end].split("|", 1)[0].lstrip("!")
if mentioned_user_id != bot_user_id:
return text
return text[end + 1 :].lstrip()
class SlackChannel(Channel):
"""Slack IM channel using Socket Mode (WebSocket, no public IP).
Configuration keys (in ``config.yaml`` under ``channels.slack``):
- ``bot_token``: Slack Bot User OAuth Token (xoxb-...).
- ``app_token``: Slack App-Level Token (xapp-...) for Socket Mode.
- ``allowed_users``: (optional) List of allowed Slack user IDs, or a
single Slack user ID string as shorthand. Empty = allow all. Other
scalar values are treated as a single string with a warning.
- ``allowed_users``: (optional) List of allowed Slack user IDs. Empty = allow all.
"""
def __init__(self, bus: MessageBus, config: dict[str, Any]) -> None:
@@ -63,9 +30,7 @@ class SlackChannel(Channel):
self._socket_client = None
self._web_client = None
self._loop: asyncio.AbstractEventLoop | None = None
self._allowed_users = _normalize_allowed_users(config.get("allowed_users", []))
configured_bot_user_id = config.get("bot_user_id")
self._bot_user_id = str(configured_bot_user_id).lstrip("@") if configured_bot_user_id else None
self._allowed_users: set[str] = {str(user_id) for user_id in config.get("allowed_users", [])}
async def start(self) -> None:
if self._running:
@@ -89,17 +54,6 @@ class SlackChannel(Channel):
return
self._web_client = WebClient(token=bot_token)
if self._bot_user_id is None:
try:
auth_info = await asyncio.to_thread(self._web_client.auth_test)
user_id = auth_info.get("user_id") if isinstance(auth_info, dict) else None
if user_id is None:
auth_get = getattr(auth_info, "get", None)
user_id = auth_get("user_id") if callable(auth_get) else None
if isinstance(user_id, str) and user_id:
self._bot_user_id = user_id
except Exception:
logger.warning("[Slack] failed to resolve bot user id; app mention text may include the bot mention", exc_info=True)
self._socket_client = SocketModeClient(
app_token=app_token,
web_client=self._web_client,
@@ -238,12 +192,6 @@ class SlackChannel(Channel):
if event_type != "events_api":
return
if self._bot_user_id is None:
authorization = next((item for item in req.payload.get("authorizations", []) if isinstance(item, dict)), None)
user_id = authorization.get("user_id") if authorization else None
if isinstance(user_id, str) and user_id:
self._bot_user_id = user_id
event = req.payload.get("event", {})
etype = event.get("type", "")
@@ -267,15 +215,13 @@ class SlackChannel(Channel):
return
text = event.get("text", "").strip()
if event.get("type") == "app_mention":
text = _strip_leading_slack_bot_mention(text, self._bot_user_id)
if not text:
return
channel_id = event.get("channel", "")
thread_ts = event.get("thread_ts") or event.get("ts", "")
if is_known_channel_command(text):
if text.startswith("/"):
msg_type = InboundMessageType.COMMAND
else:
msg_type = InboundMessageType.CHAT
+2 -34
View File
@@ -60,17 +60,12 @@ class TelegramChannel(Channel):
# Command handlers
app.add_handler(CommandHandler("start", self._cmd_start))
app.add_handler(CommandHandler("bootstrap", self._cmd_generic))
app.add_handler(CommandHandler("new", self._cmd_generic))
app.add_handler(CommandHandler("status", self._cmd_generic))
app.add_handler(CommandHandler("models", self._cmd_generic))
app.add_handler(CommandHandler("memory", self._cmd_generic))
app.add_handler(CommandHandler("help", self._cmd_generic))
# Slash skill commands are dynamic and cannot all be pre-registered
# with Telegram, so route unknown slash commands through chat handling.
app.add_handler(MessageHandler(filters.TEXT & filters.COMMAND, self._on_text))
# General message handler
app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, self._on_text))
@@ -233,33 +228,6 @@ class TelegramChannel(Channel):
return True
return user_id in self._allowed_users
def _get_bot_username(self, context) -> str | None:
bot = getattr(context, "bot", None)
username = getattr(bot, "username", None)
if not username and self._application is not None:
username = getattr(getattr(self._application, "bot", None), "username", None)
return str(username) if username else None
@staticmethod
def _strip_bot_username_from_leading_command(text: str, bot_username: str | None) -> str:
username = (bot_username or "").lstrip("@").lower()
if not username or not text.startswith("/"):
return text
parts = text.split(maxsplit=1)
command_token = parts[0]
if "@" not in command_token:
return text
command_name, addressed_username = command_token[1:].rsplit("@", 1)
if not command_name or addressed_username.lower() != username:
return text
normalized = f"/{command_name}"
if len(parts) > 1:
normalized = f"{normalized} {parts[1]}"
return normalized
async def _cmd_start(self, update, context) -> None:
"""Handle /start command."""
if not self._check_user(update.effective_user.id):
@@ -275,7 +243,7 @@ class TelegramChannel(Channel):
if not self._check_user(update.effective_user.id):
return
text = self._strip_bot_username_from_leading_command(update.message.text.strip(), self._get_bot_username(context))
text = update.message.text
chat_id = str(update.effective_chat.id)
user_id = str(update.effective_user.id)
msg_id = str(update.message.message_id)
@@ -311,7 +279,7 @@ class TelegramChannel(Channel):
if not self._check_user(update.effective_user.id):
return
text = self._strip_bot_username_from_leading_command(update.message.text.strip(), self._get_bot_username(context))
text = update.message.text.strip()
if not text:
return
File diff suppressed because it is too large Load Diff
+1 -6
View File
@@ -8,7 +8,6 @@ from collections.abc import Awaitable, Callable
from typing import Any, cast
from app.channels.base import Channel
from app.channels.commands import is_known_channel_command
from app.channels.message_bus import (
InboundMessageType,
MessageBus,
@@ -30,10 +29,6 @@ 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
@@ -271,7 +266,7 @@ class WeComChannel(Channel):
user_id = (body.get("from") or {}).get("userid")
inbound_type = InboundMessageType.COMMAND if is_known_channel_command(text) else InboundMessageType.CHAT
inbound_type = InboundMessageType.COMMAND if text.startswith("/") else InboundMessageType.CHAT
inbound = self._make_inbound(
chat_id=user_id, # keep user's conversation in memory
user_id=user_id,
+15 -200
View File
@@ -1,20 +1,15 @@
import asyncio
import logging
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from app.gateway.auth_middleware import AuthMiddleware
from app.gateway.config import get_gateway_config
from app.gateway.csrf_middleware import CSRFMiddleware, get_configured_cors_origins
from app.gateway.deps import langgraph_runtime
from app.gateway.routers import (
agents,
artifacts,
assistants_compat,
auth,
channels,
feedback,
mcp,
@@ -27,13 +22,9 @@ from app.gateway.routers import (
threads,
uploads,
)
from deerflow.config import app_config as deerflow_app_config
from deerflow.config.app_config import apply_logging_level
from deerflow.config.app_config import get_app_config
AppConfig = deerflow_app_config.AppConfig
get_app_config = deerflow_app_config.get_app_config
# Default logging; lifespan overrides from config.yaml log_level.
# Configure logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
@@ -42,135 +33,14 @@ logging.basicConfig(
logger = logging.getLogger(__name__)
# Upper bound (seconds) each lifespan shutdown hook is allowed to run.
# Bounds worker exit time so uvicorn's reload supervisor does not keep
# firing signals into a worker that is stuck waiting for shutdown cleanup.
_SHUTDOWN_HOOK_TIMEOUT_SECONDS = 5.0
async def _ensure_admin_user(app: FastAPI) -> None:
"""Startup hook: handle first boot and migrate orphan threads otherwise.
After admin creation, migrate orphan threads from the LangGraph
store (metadata.user_id unset) to the admin account. This is the
"no-auth → with-auth" upgrade path: users who ran DeerFlow without
authentication have existing LangGraph thread data that needs an
owner assigned.
First boot (no admin exists):
- Does NOT create any user accounts automatically.
- The operator must visit ``/setup`` to create the first admin.
Subsequent boots (admin already exists):
- Runs the one-time "no-auth → with-auth" orphan thread migration for
existing LangGraph thread metadata that has no user_id.
No SQL persistence migration is needed: the four user_id columns
(threads_meta, runs, run_events, feedback) only come into existence
alongside the auth module via create_all, so freshly created tables
never contain NULL-owner rows.
"""
from sqlalchemy import select
from app.gateway.deps import get_local_provider
from deerflow.persistence.engine import get_session_factory
from deerflow.persistence.user.model import UserRow
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:
logger.info("=" * 60)
logger.info(" First boot detected — no admin account exists.")
logger.info(" Visit /setup to complete admin account creation.")
logger.info("=" * 60)
return
# Admin already exists — run orphan thread migration for any
# LangGraph thread metadata that pre-dates the auth module.
async with sf() as session:
stmt = select(UserRow).where(UserRow.system_role == "admin").limit(1)
row = (await session.execute(stmt)).scalar_one_or_none()
if row is None:
return # Should not happen (admin_count > 0 above), but be safe.
admin_id = str(row.id)
# LangGraph store orphan migration — non-fatal.
# This covers the "no-auth → with-auth" upgrade path for users
# whose existing LangGraph thread metadata has no user_id set.
store = getattr(app.state, "store", None)
if store is not None:
try:
migrated = await _migrate_orphaned_threads(store, admin_id)
if migrated:
logger.info("Migrated %d orphan LangGraph thread(s) to admin", migrated)
except Exception:
logger.exception("LangGraph thread migration failed (non-fatal)")
async def _iter_store_items(store, namespace, *, page_size: int = 500):
"""Paginated async iterator over a LangGraph store namespace.
Replaces the old hardcoded ``limit=1000`` call with a cursor-style
loop so that environments with more than one page of orphans do
not silently lose data. Terminates when a page is empty OR when a
short page arrives (indicating the last page).
"""
offset = 0
while True:
batch = await store.asearch(namespace, limit=page_size, offset=offset)
if not batch:
return
for item in batch:
yield item
if len(batch) < page_size:
return
offset += page_size
async def _migrate_orphaned_threads(store, admin_user_id: str) -> int:
"""Migrate LangGraph store threads with no user_id to the given admin.
Uses cursor pagination so all orphans are migrated regardless of
count. Returns the number of rows migrated.
"""
migrated = 0
async for item in _iter_store_items(store, ("threads",)):
metadata = item.value.get("metadata", {})
if not metadata.get("user_id"):
metadata["user_id"] = admin_user_id
item.value["metadata"] = metadata
await store.aput(("threads",), item.key, item.value)
migrated += 1
return migrated
@asynccontextmanager
async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
"""Application lifespan handler."""
# Load config and check necessary environment variables at startup.
# `startup_config` is a local snapshot used only for one-shot bootstrap
# work (logging level, langgraph_runtime engines, channels). Request-time
# config resolution always routes through `get_app_config()` in
# `app/gateway/deps.py::get_config()` so `config.yaml` edits become
# visible without a process restart. We deliberately do NOT cache this
# snapshot on `app.state` to keep that contract enforceable.
# Load config and check necessary environment variables at startup
try:
startup_config = get_app_config()
apply_logging_level(startup_config.log_level)
get_app_config()
logger.info("Configuration loaded successfully")
except Exception as e:
error_msg = f"Failed to load configuration during gateway startup: {e}"
@@ -179,57 +49,26 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
config = get_gateway_config()
logger.info(f"Starting API Gateway on {config.host}:{config.port}")
# Pre-warm tiktoken encoding cache so the first memory-injection request
# never blocks on the BPE data download (which hits an OpenAI/Azure URL
# that may be unreachable in restricted networks — see issue #3402).
try:
from deerflow.agents.memory.prompt import warm_tiktoken_cache
warmed = await asyncio.wait_for(
asyncio.to_thread(warm_tiktoken_cache),
timeout=5,
)
if warmed:
logger.info("tiktoken encoding cache warmed successfully")
else:
logger.warning("tiktoken encoding cache warm-up failed; token counting will use character-based fallback")
except TimeoutError:
logger.warning("tiktoken encoding cache warm-up timed out; token counting will use character-based fallback")
except Exception:
logger.warning("tiktoken warm-up skipped", exc_info=True)
# Initialize LangGraph runtime components (StreamBridge, RunManager, checkpointer, store)
async with langgraph_runtime(app, startup_config):
async with langgraph_runtime(app):
logger.info("LangGraph runtime initialised")
# Check admin bootstrap state and migrate orphan threads after admin exists.
# Must run AFTER langgraph_runtime so app.state.store is available for thread migration
await _ensure_admin_user(app)
# Start IM channel service if any channels are configured
try:
from app.channels.service import start_channel_service
channel_service = await start_channel_service(startup_config)
channel_service = await start_channel_service()
logger.info("Channel service started: %s", channel_service.get_status())
except Exception:
logger.exception("No IM channels configured or channel service failed to start")
yield
# Stop channel service on shutdown (bounded to prevent worker hang)
# Stop channel service on shutdown
try:
from app.channels.service import stop_channel_service
await asyncio.wait_for(
stop_channel_service(),
timeout=_SHUTDOWN_HOOK_TIMEOUT_SECONDS,
)
except TimeoutError:
logger.warning(
"Channel service shutdown exceeded %.1fs; proceeding with worker exit.",
_SHUTDOWN_HOOK_TIMEOUT_SECONDS,
)
await stop_channel_service()
except Exception:
logger.exception("Failed to stop channel service")
@@ -242,10 +81,6 @@ def create_app() -> FastAPI:
Returns:
Configured FastAPI application instance.
"""
config = get_gateway_config()
docs_url = "/docs" if config.enable_docs else None
redoc_url = "/redoc" if config.enable_docs else None
openapi_url = "/openapi.json" if config.enable_docs else None
app = FastAPI(
title="DeerFlow API Gateway",
@@ -265,14 +100,14 @@ API Gateway for DeerFlow - A LangGraph-based AI agent backend with sandbox execu
### Architecture
LangGraph-compatible requests are routed through nginx to this gateway.
This gateway provides runtime endpoints for agent runs plus custom endpoints for models, MCP configuration, skills, and artifacts.
LangGraph requests are handled by nginx reverse proxy.
This gateway provides custom endpoints for models, MCP configuration, skills, and artifacts.
""",
version="0.1.0",
lifespan=lifespan,
docs_url=docs_url,
redoc_url=redoc_url,
openapi_url=openapi_url,
docs_url="/docs",
redoc_url="/redoc",
openapi_url="/openapi.json",
openapi_tags=[
{
"name": "models",
@@ -329,24 +164,7 @@ This gateway provides runtime endpoints for agent runs plus custom endpoints for
],
)
# Auth: reject unauthenticated requests to non-public paths (fail-closed safety net)
app.add_middleware(AuthMiddleware)
# CSRF: Double Submit Cookie pattern for state-changing requests
app.add_middleware(CSRFMiddleware)
# CORS: the unified nginx endpoint is same-origin by default. Split-origin
# browser clients must opt in with this explicit Gateway allowlist so CORS
# and CSRF origin checks share the same source of truth.
cors_origins = sorted(get_configured_cors_origins())
if cors_origins:
app.add_middleware(
CORSMiddleware,
allow_origins=cors_origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# CORS is handled by nginx - no need for FastAPI middleware
# Include routers
# Models API is mounted at /api/models
@@ -382,9 +200,6 @@ This gateway provides runtime endpoints for agent runs plus custom endpoints for
# Assistants compatibility API (LangGraph Platform stub)
app.include_router(assistants_compat.router)
# Auth API is mounted at /api/v1/auth
app.include_router(auth.router)
# Feedback API is mounted at /api/threads/{thread_id}/runs/{run_id}/feedback
app.include_router(feedback.router)
@@ -395,7 +210,7 @@ This gateway provides runtime endpoints for agent runs plus custom endpoints for
app.include_router(runs.router)
@app.get("/health", tags=["health"])
async def health_check() -> dict[str, str]:
async def health_check() -> dict:
"""Health check endpoint.
Returns:
-42
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@@ -1,42 +0,0 @@
"""Authentication module for DeerFlow.
This module provides:
- JWT-based authentication
- Provider Factory pattern for extensible auth methods
- UserRepository interface for storage backends (SQLite)
"""
from app.gateway.auth.config import AuthConfig, get_auth_config, set_auth_config
from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse, TokenError
from app.gateway.auth.jwt import TokenPayload, create_access_token, decode_token
from app.gateway.auth.local_provider import LocalAuthProvider
from app.gateway.auth.models import User, UserResponse
from app.gateway.auth.password import hash_password, verify_password
from app.gateway.auth.providers import AuthProvider
from app.gateway.auth.repositories.base import UserRepository
__all__ = [
# Config
"AuthConfig",
"get_auth_config",
"set_auth_config",
# Errors
"AuthErrorCode",
"AuthErrorResponse",
"TokenError",
# JWT
"TokenPayload",
"create_access_token",
"decode_token",
# Password
"hash_password",
"verify_password",
# Models
"User",
"UserResponse",
# Providers
"AuthProvider",
"LocalAuthProvider",
# Repository
"UserRepository",
]
-85
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@@ -1,85 +0,0 @@
"""Authentication configuration for DeerFlow."""
import logging
import os
import secrets
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
_SECRET_FILE = ".jwt_secret"
class AuthConfig(BaseModel):
"""JWT and auth-related configuration. Parsed once at startup.
Note: the ``users`` table now lives in the shared persistence
database managed by ``deerflow.persistence.engine``. The old
``users_db_path`` config key has been removed — user storage is
configured through ``config.database`` like every other table.
"""
jwt_secret: str = Field(
...,
description="Secret key for JWT signing. MUST be set via AUTH_JWT_SECRET.",
)
token_expiry_days: int = Field(default=7, ge=1, le=30)
oauth_github_client_id: str | None = Field(default=None)
oauth_github_client_secret: str | None = Field(default=None)
_auth_config: AuthConfig | None = None
def _load_or_create_secret() -> str:
"""Load persisted JWT secret from ``{base_dir}/.jwt_secret``, or generate and persist a new one."""
from deerflow.config.paths import get_paths
paths = get_paths()
secret_file = paths.base_dir / _SECRET_FILE
try:
if secret_file.exists():
secret = secret_file.read_text(encoding="utf-8").strip()
if secret:
return secret
except OSError as exc:
raise RuntimeError(f"Failed to read JWT secret from {secret_file}. Set AUTH_JWT_SECRET explicitly or fix DEER_FLOW_HOME/base directory permissions so DeerFlow can read its persisted auth secret.") from exc
secret = secrets.token_urlsafe(32)
try:
secret_file.parent.mkdir(parents=True, exist_ok=True)
fd = os.open(secret_file, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
with os.fdopen(fd, "w", encoding="utf-8") as fh:
fh.write(secret)
except OSError as exc:
raise RuntimeError(f"Failed to persist JWT secret to {secret_file}. Set AUTH_JWT_SECRET explicitly or fix DEER_FLOW_HOME/base directory permissions so DeerFlow can store a stable auth secret.") from exc
return secret
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 = _load_or_create_secret()
os.environ["AUTH_JWT_SECRET"] = jwt_secret
logger.warning(
"⚠ AUTH_JWT_SECRET is not set — using an auto-generated secret "
"persisted to .jwt_secret. Sessions will survive restarts. "
"For production, add AUTH_JWT_SECRET to your .env file: "
'python -c "import secrets; print(secrets.token_urlsafe(32))"'
)
_auth_config = AuthConfig(jwt_secret=jwt_secret)
return _auth_config
def set_auth_config(config: AuthConfig) -> None:
"""Set the global AuthConfig instance (for testing)."""
global _auth_config
_auth_config = config
@@ -1,48 +0,0 @@
"""Write initial admin credentials to a restricted file instead of logs.
Logging secrets to stdout/stderr is a well-known CodeQL finding
(py/clear-text-logging-sensitive-data) — in production those logs
get collected into ELK/Splunk/etc and become a secret sprawl
source. This helper writes the credential to a 0600 file that only
the process user can read, and returns the path so the caller can
log **the path** (not the password) for the operator to pick up.
"""
from __future__ import annotations
import os
from pathlib import Path
from deerflow.config.paths import get_paths
_CREDENTIAL_FILENAME = "admin_initial_credentials.txt"
def write_initial_credentials(email: str, password: str, *, label: str = "initial") -> Path:
"""Write the admin email + password to ``{base_dir}/admin_initial_credentials.txt``.
The file is created **atomically** with mode 0600 via ``os.open``
so the password is never world-readable, even for the single syscall
window between ``write_text`` and ``chmod``.
``label`` distinguishes "initial" (fresh creation) from "reset"
(password reset) in the file header so an operator picking up the
file after a restart can tell which event produced it.
Returns the absolute :class:`Path` to the file.
"""
target = get_paths().base_dir / _CREDENTIAL_FILENAME
target.parent.mkdir(parents=True, exist_ok=True)
content = (
f"# DeerFlow admin {label} credentials\n# This file is generated on first boot or password reset.\n# Change the password after login via Settings -> Account,\n# then delete this file.\n#\nemail: {email}\npassword: {password}\n"
)
# Atomic 0600 create-or-truncate. O_TRUNC (not O_EXCL) so the
# reset-password path can rewrite an existing file without a
# separate unlink-then-create dance.
fd = os.open(target, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
with os.fdopen(fd, "w", encoding="utf-8") as fh:
fh.write(content)
return target.resolve()
-45
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@@ -1,45 +0,0 @@
"""Typed error definitions for auth module.
AuthErrorCode: exhaustive enum of all auth failure conditions.
TokenError: exhaustive enum of JWT decode failures.
AuthErrorResponse: structured error payload for HTTP responses.
"""
from enum import StrEnum
from pydantic import BaseModel
class AuthErrorCode(StrEnum):
"""Exhaustive list of auth error conditions."""
INVALID_CREDENTIALS = "invalid_credentials"
TOKEN_EXPIRED = "token_expired"
TOKEN_INVALID = "token_invalid"
USER_NOT_FOUND = "user_not_found"
EMAIL_ALREADY_EXISTS = "email_already_exists"
PROVIDER_NOT_FOUND = "provider_not_found"
NOT_AUTHENTICATED = "not_authenticated"
SYSTEM_ALREADY_INITIALIZED = "system_already_initialized"
class TokenError(StrEnum):
"""Exhaustive list of JWT decode failure reasons."""
EXPIRED = "expired"
INVALID_SIGNATURE = "invalid_signature"
MALFORMED = "malformed"
class AuthErrorResponse(BaseModel):
"""Structured error response — replaces bare `detail` strings."""
code: AuthErrorCode
message: str
def token_error_to_code(err: TokenError) -> AuthErrorCode:
"""Map TokenError to AuthErrorCode — single source of truth."""
if err == TokenError.EXPIRED:
return AuthErrorCode.TOKEN_EXPIRED
return AuthErrorCode.TOKEN_INVALID
-55
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@@ -1,55 +0,0 @@
"""JWT token creation and verification."""
from datetime import UTC, datetime, timedelta
import jwt
from pydantic import BaseModel
from app.gateway.auth.config import get_auth_config
from app.gateway.auth.errors import TokenError
class TokenPayload(BaseModel):
"""JWT token payload."""
sub: str # user_id
exp: datetime
iat: datetime | None = None
ver: int = 0 # token_version — must match User.token_version
def create_access_token(user_id: str, expires_delta: timedelta | None = None, token_version: int = 0) -> str:
"""Create a JWT access token.
Args:
user_id: The user's UUID as string
expires_delta: Optional custom expiry, defaults to 7 days
token_version: User's current token_version for invalidation
Returns:
Encoded JWT string
"""
config = get_auth_config()
expiry = expires_delta or timedelta(days=config.token_expiry_days)
now = datetime.now(UTC)
payload = {"sub": user_id, "exp": now + expiry, "iat": now, "ver": token_version}
return jwt.encode(payload, config.jwt_secret, algorithm="HS256")
def decode_token(token: str) -> TokenPayload | TokenError:
"""Decode and validate a JWT token.
Returns:
TokenPayload if valid, or a specific TokenError variant.
"""
config = get_auth_config()
try:
payload = jwt.decode(token, config.jwt_secret, algorithms=["HS256"])
return TokenPayload(**payload)
except jwt.ExpiredSignatureError:
return TokenError.EXPIRED
except jwt.InvalidSignatureError:
return TokenError.INVALID_SIGNATURE
except jwt.PyJWTError:
return TokenError.MALFORMED
-104
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@@ -1,104 +0,0 @@
"""Local email/password authentication provider."""
import logging
from app.gateway.auth.models import User
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."""
def __init__(self, repository: UserRepository):
"""Initialize with a UserRepository.
Args:
repository: UserRepository implementation (SQLite)
"""
self._repo = repository
async def authenticate(self, credentials: dict) -> User | None:
"""Authenticate with email and password.
Args:
credentials: dict with 'email' and 'password' keys
Returns:
User if authentication succeeds, None otherwise
"""
email = credentials.get("email")
password = credentials.get("password")
if not email or not password:
return None
user = await self._repo.get_user_by_email(email)
if user is None:
return None
if user.password_hash is None:
# OAuth user without local password
return None
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:
"""Get user by ID."""
return await self._repo.get_user_by_id(user_id)
async def create_user(self, email: str, password: str | None = None, system_role: str = "user", needs_setup: bool = False) -> User:
"""Create a new local user.
Args:
email: User email address
password: Plain text password (will be hashed)
system_role: Role to assign ("admin" or "user")
needs_setup: If True, user must complete setup on first login
Returns:
Created User instance
"""
password_hash = await hash_password_async(password) if password else None
user = User(
email=email,
password_hash=password_hash,
system_role=system_role,
needs_setup=needs_setup,
)
return await self._repo.create_user(user)
async def get_user_by_oauth(self, provider: str, oauth_id: str) -> User | None:
"""Get user by OAuth provider and ID."""
return await self._repo.get_user_by_oauth(provider, oauth_id)
async def count_users(self) -> int:
"""Return total number of registered users."""
return await self._repo.count_users()
async def count_admin_users(self) -> int:
"""Return number of admin users."""
return await self._repo.count_admin_users()
async def update_user(self, user: User) -> User:
"""Update an existing user."""
return await self._repo.update_user(user)
async def get_user_by_email(self, email: str) -> User | None:
"""Get user by email."""
return await self._repo.get_user_by_email(email)
-41
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@@ -1,41 +0,0 @@
"""User Pydantic models for authentication."""
from datetime import UTC, datetime
from typing import Literal
from uuid import UUID, uuid4
from pydantic import BaseModel, ConfigDict, EmailStr, Field
def _utc_now() -> datetime:
"""Return current UTC time (timezone-aware)."""
return datetime.now(UTC)
class User(BaseModel):
"""Internal user representation."""
model_config = ConfigDict(from_attributes=True)
id: UUID = Field(default_factory=uuid4, description="Primary key")
email: EmailStr = Field(..., description="Unique email address")
password_hash: str | None = Field(None, description="bcrypt hash, nullable for OAuth users")
system_role: Literal["admin", "user"] = Field(default="user")
created_at: datetime = Field(default_factory=_utc_now)
# OAuth linkage (optional)
oauth_provider: str | None = Field(None, description="e.g. 'github', 'google'")
oauth_id: str | None = Field(None, description="User ID from OAuth provider")
# Auth lifecycle
needs_setup: bool = Field(default=False, description="True when a reset account must complete setup")
token_version: int = Field(default=0, description="Incremented on password change to invalidate old JWTs")
class UserResponse(BaseModel):
"""Response model for user info endpoint."""
id: str
email: str
system_role: Literal["admin", "user"]
needs_setup: bool = False
-81
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@@ -1,81 +0,0 @@
"""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 (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, 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:
"""Hash a password using bcrypt (non-blocking).
Wraps the blocking bcrypt operation in a thread pool to avoid
blocking the event loop during password hashing.
"""
return await asyncio.to_thread(hash_password, password)
async def verify_password_async(plain_password: str, hashed_password: str) -> bool:
"""Verify a password against its hash (non-blocking).
Wraps the blocking bcrypt operation in a thread pool to avoid
blocking the event loop during password verification.
"""
return await asyncio.to_thread(verify_password, plain_password, hashed_password)
-24
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@@ -1,24 +0,0 @@
"""Auth provider abstraction."""
from abc import ABC, abstractmethod
class AuthProvider(ABC):
"""Abstract base class for authentication providers."""
@abstractmethod
async def authenticate(self, credentials: dict) -> "User | None":
"""Authenticate user with given credentials.
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
from app.gateway.auth.models import User # noqa: E402
@@ -1,102 +0,0 @@
"""User repository interface for abstracting database operations."""
from abc import ABC, abstractmethod
from app.gateway.auth.models import User
class UserNotFoundError(LookupError):
"""Raised when a user repository operation targets a non-existent row.
Subclass of :class:`LookupError` so callers that already catch
``LookupError`` for "missing entity" can keep working unchanged,
while specific call sites can pin to this class to distinguish
"concurrent delete during update" from other lookups.
"""
class UserRepository(ABC):
"""Abstract interface for user data storage.
Implement this interface to support different storage backends
(SQLite)
"""
@abstractmethod
async def create_user(self, user: User) -> User:
"""Create a new user.
Args:
user: User object to create
Returns:
Created User with ID assigned
Raises:
ValueError: If email already exists
"""
raise NotImplementedError
@abstractmethod
async def get_user_by_id(self, user_id: str) -> User | None:
"""Get user by ID.
Args:
user_id: User UUID as string
Returns:
User if found, None otherwise
"""
raise NotImplementedError
@abstractmethod
async def get_user_by_email(self, email: str) -> User | None:
"""Get user by email.
Args:
email: User email address
Returns:
User if found, None otherwise
"""
raise NotImplementedError
@abstractmethod
async def update_user(self, user: User) -> User:
"""Update an existing user.
Args:
user: User object with updated fields
Returns:
Updated User
Raises:
UserNotFoundError: If no row exists for ``user.id``. This is
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:
"""Get user by OAuth provider and ID.
Args:
provider: OAuth provider name (e.g. 'github', 'google')
oauth_id: User ID from the OAuth provider
Returns:
User if found, None otherwise
"""
raise NotImplementedError
@@ -1,127 +0,0 @@
"""SQLAlchemy-backed UserRepository implementation.
Uses the shared async session factory from
``deerflow.persistence.engine`` — the ``users`` table lives in the
same database as ``threads_meta``, ``runs``, ``run_events``, and
``feedback``.
Constructor takes the session factory directly (same pattern as the
other four repositories in ``deerflow.persistence.*``). Callers
construct this after ``init_engine_from_config()`` has run.
"""
from __future__ import annotations
from datetime import UTC
from uuid import UUID
from sqlalchemy import func, select
from sqlalchemy.exc import IntegrityError
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
from app.gateway.auth.models import User
from app.gateway.auth.repositories.base import UserNotFoundError, UserRepository
from deerflow.persistence.user.model import UserRow
class SQLiteUserRepository(UserRepository):
"""Async user repository backed by the shared SQLAlchemy engine."""
def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
self._sf = session_factory
# ── Converters ────────────────────────────────────────────────────
@staticmethod
def _row_to_user(row: UserRow) -> User:
return User(
id=UUID(row.id),
email=row.email,
password_hash=row.password_hash,
system_role=row.system_role, # type: ignore[arg-type]
# SQLite loses tzinfo on read; reattach UTC so downstream
# code can compare timestamps reliably.
created_at=row.created_at if row.created_at.tzinfo else row.created_at.replace(tzinfo=UTC),
oauth_provider=row.oauth_provider,
oauth_id=row.oauth_id,
needs_setup=row.needs_setup,
token_version=row.token_version,
)
@staticmethod
def _user_to_row(user: User) -> UserRow:
return UserRow(
id=str(user.id),
email=user.email,
password_hash=user.password_hash,
system_role=user.system_role,
created_at=user.created_at,
oauth_provider=user.oauth_provider,
oauth_id=user.oauth_id,
needs_setup=user.needs_setup,
token_version=user.token_version,
)
# ── CRUD ──────────────────────────────────────────────────────────
async def create_user(self, user: User) -> User:
"""Insert a new user. Raises ``ValueError`` on duplicate email."""
row = self._user_to_row(user)
async with self._sf() as session:
session.add(row)
try:
await session.commit()
except IntegrityError as exc:
await session.rollback()
raise ValueError(f"Email already registered: {user.email}") from exc
return user
async def get_user_by_id(self, user_id: str) -> User | None:
async with self._sf() as session:
row = await session.get(UserRow, user_id)
return self._row_to_user(row) if row is not None else None
async def get_user_by_email(self, email: str) -> User | None:
stmt = select(UserRow).where(UserRow.email == email)
async with self._sf() as session:
result = await session.execute(stmt)
row = result.scalar_one_or_none()
return self._row_to_user(row) if row is not None else None
async def update_user(self, user: User) -> User:
async with self._sf() as session:
row = await session.get(UserRow, str(user.id))
if row is None:
# Hard fail on concurrent delete: callers (reset_admin,
# password change handlers, _ensure_admin_user) all
# fetched the user just before this call, so a missing
# row here means the row vanished underneath us. Silent
# success would let the caller log "password reset" for
# a row that no longer exists.
raise UserNotFoundError(f"User {user.id} no longer exists")
row.email = user.email
row.password_hash = user.password_hash
row.system_role = user.system_role
row.oauth_provider = user.oauth_provider
row.oauth_id = user.oauth_id
row.needs_setup = user.needs_setup
row.token_version = user.token_version
await session.commit()
return user
async def count_users(self) -> int:
stmt = select(func.count()).select_from(UserRow)
async with self._sf() as session:
return await session.scalar(stmt) or 0
async def count_admin_users(self) -> int:
stmt = select(func.count()).select_from(UserRow).where(UserRow.system_role == "admin")
async with self._sf() as session:
return await session.scalar(stmt) or 0
async def get_user_by_oauth(self, provider: str, oauth_id: str) -> User | None:
stmt = select(UserRow).where(UserRow.oauth_provider == provider, UserRow.oauth_id == oauth_id)
async with self._sf() as session:
result = await session.execute(stmt)
row = result.scalar_one_or_none()
return self._row_to_user(row) if row is not None else None
-91
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@@ -1,91 +0,0 @@
"""CLI tool to reset an admin password.
Usage:
python -m app.gateway.auth.reset_admin
python -m app.gateway.auth.reset_admin --email admin@example.com
Writes the new password to ``.deer-flow/admin_initial_credentials.txt``
(mode 0600) instead of printing it, so CI / log aggregators never see
the cleartext secret.
"""
from __future__ import annotations
import argparse
import asyncio
import secrets
import sys
from sqlalchemy import select
from app.gateway.auth.credential_file import write_initial_credentials
from app.gateway.auth.password import hash_password
from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
from deerflow.persistence.user.model import UserRow
async def _run(email: str | None) -> int:
from deerflow.config import get_app_config
from deerflow.persistence.engine import (
close_engine,
get_session_factory,
init_engine_from_config,
)
config = get_app_config()
await init_engine_from_config(config.database)
try:
sf = get_session_factory()
if sf is None:
print("Error: persistence engine not available (check config.database).", file=sys.stderr)
return 1
repo = SQLiteUserRepository(sf)
if email:
user = await repo.get_user_by_email(email)
else:
# Find first admin via direct SELECT — repository does not
# expose a "first admin" helper and we do not want to add
# one just for this CLI.
async with sf() as session:
stmt = select(UserRow).where(UserRow.system_role == "admin").limit(1)
row = (await session.execute(stmt)).scalar_one_or_none()
if row is None:
user = None
else:
user = await repo.get_user_by_id(row.id)
if user is None:
if email:
print(f"Error: user '{email}' not found.", file=sys.stderr)
else:
print("Error: no admin user found.", file=sys.stderr)
return 1
new_password = secrets.token_urlsafe(16)
user.password_hash = hash_password(new_password)
user.token_version += 1
user.needs_setup = True
await repo.update_user(user)
cred_path = write_initial_credentials(user.email, new_password, label="reset")
print(f"Password reset for: {user.email}")
print(f"Credentials written to: {cred_path} (mode 0600)")
print("Next login will require setup (new email + password).")
return 0
finally:
await close_engine()
def main() -> None:
parser = argparse.ArgumentParser(description="Reset admin password")
parser.add_argument("--email", help="Admin email (default: first admin found)")
args = parser.parse_args()
exit_code = asyncio.run(_run(args.email))
sys.exit(exit_code)
if __name__ == "__main__":
main()
-126
View File
@@ -1,126 +0,0 @@
"""Global authentication middleware — fail-closed safety net.
Rejects unauthenticated requests to non-public paths with 401. When a
request passes the cookie check, resolves the JWT payload to a real
``User`` object and stamps it into both ``request.state.user`` and the
``deerflow.runtime.user_context`` contextvar so that repository-layer
owner filtering works automatically via the sentinel pattern.
Fine-grained permission checks remain in authz.py decorators.
"""
from collections.abc import Callable
from fastapi import HTTPException, Request, Response
from starlette.middleware.base import BaseHTTPMiddleware
from starlette.responses import JSONResponse
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.
_PUBLIC_PATH_PREFIXES: tuple[str, ...] = (
"/health",
"/docs",
"/redoc",
"/openapi.json",
)
# Exact auth paths that are public (login/register/status check).
# /api/v1/auth/me, /api/v1/auth/change-password etc. are NOT public.
_PUBLIC_EXACT_PATHS: frozenset[str] = frozenset(
{
"/api/v1/auth/login/local",
"/api/v1/auth/register",
"/api/v1/auth/logout",
"/api/v1/auth/setup-status",
"/api/v1/auth/initialize",
}
)
def _is_public(path: str) -> bool:
stripped = path.rstrip("/")
if stripped in _PUBLIC_EXACT_PATHS:
return True
return any(path.startswith(prefix) for prefix in _PUBLIC_PATH_PREFIXES)
class AuthMiddleware(BaseHTTPMiddleware):
"""Strict auth gate: reject requests without a valid session.
Two-stage check for non-public paths:
1. Cookie presence — return 401 NOT_AUTHENTICATED if missing
2. JWT validation via ``get_optional_user_from_request`` — return 401
TOKEN_INVALID if the token is absent, malformed, expired, or the
signed user does not exist / is stale
On success, stamps ``request.state.user`` and the
``deerflow.runtime.user_context`` contextvar so that repository-layer
owner filters work downstream without every route needing a
``@require_auth`` decorator. Routes that need per-resource
authorization (e.g. "user A cannot read user B's thread by guessing
the URL") should additionally use ``@require_permission(...,
owner_check=True)`` for explicit enforcement — but authentication
itself is fully handled here.
"""
def __init__(self, app: ASGIApp) -> None:
super().__init__(app)
async def dispatch(self, request: Request, call_next: Callable) -> Response:
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 internal_user is None and not request.cookies.get("access_token"):
return JSONResponse(
status_code=401,
content={
"detail": AuthErrorResponse(
code=AuthErrorCode.NOT_AUTHENTICATED,
message="Authentication required",
).model_dump()
},
)
# Strict JWT validation: reject junk/expired tokens with 401
# right here instead of silently passing through. This closes
# the "junk cookie bypass" gap (AUTH_TEST_PLAN test 7.5.8):
# without this, non-isolation routes like /api/models would
# accept any cookie-shaped string as authentication.
#
# We call the *strict* resolver so that fine-grained error
# codes (token_expired, token_invalid, user_not_found, …)
# propagate from AuthErrorCode, not get flattened into one
# generic code. BaseHTTPMiddleware doesn't let HTTPException
# bubble up, so we catch and render it as JSONResponse here.
from app.gateway.deps import get_current_user_from_request
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
# None" branch short-circuits instead of running the entire
# JWT-decode + DB-lookup pipeline a second time per request).
request.state.user = user
request.state.auth = AuthContext(user=user, permissions=_ALL_PERMISSIONS)
token = set_current_user(user)
try:
return await call_next(request)
finally:
reset_current_user(token)
-301
View File
@@ -1,301 +0,0 @@
"""Authorization decorators and context for DeerFlow.
Inspired by LangGraph Auth system: https://github.com/langchain-ai/langgraph/blob/main/libs/sdk-py/langgraph_sdk/auth/__init__.py
**Usage:**
1. Use ``@require_auth`` on routes that need authentication
2. Use ``@require_permission("resource", "action", filter_key=...)`` for permission checks
3. The decorator chain processes from bottom to top
**Example:**
@router.get("/{thread_id}")
@require_auth
@require_permission("threads", "read", owner_check=True)
async def get_thread(thread_id: str, request: Request):
# User is authenticated and has threads:read permission
...
**Permission Model:**
- threads:read - View thread
- threads:write - Create/update thread
- threads:delete - Delete thread
- runs:create - Run agent
- runs:read - View run
- runs:cancel - Cancel run
"""
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
if TYPE_CHECKING:
from app.gateway.auth.models import User
P = ParamSpec("P")
T = TypeVar("T")
# Permission constants
class Permissions:
"""Permission constants for resource:action format."""
# Threads
THREADS_READ = "threads:read"
THREADS_WRITE = "threads:write"
THREADS_DELETE = "threads:delete"
# Runs
RUNS_CREATE = "runs:create"
RUNS_READ = "runs:read"
RUNS_CANCEL = "runs:cancel"
class AuthContext:
"""Authentication context for the current request.
Stored in request.state.auth after require_auth decoration.
Attributes:
user: The authenticated user, or None if anonymous
permissions: List of permission strings (e.g., "threads:read")
"""
__slots__ = ("user", "permissions")
def __init__(self, user: User | None = None, permissions: list[str] | None = None):
self.user = user
self.permissions = permissions or []
@property
def is_authenticated(self) -> bool:
"""Check if user is authenticated."""
return self.user is not None
def has_permission(self, resource: str, action: str) -> bool:
"""Check if context has permission for resource:action.
Args:
resource: Resource name (e.g., "threads")
action: Action name (e.g., "read")
Returns:
True if user has permission
"""
permission = f"{resource}:{action}"
return permission in self.permissions
def require_user(self) -> User:
"""Get user or raise 401.
Raises:
HTTPException 401 if not authenticated
"""
if not self.user:
raise HTTPException(status_code=401, detail="Authentication required")
return self.user
def get_auth_context(request: Request) -> AuthContext | None:
"""Get AuthContext from request state."""
return getattr(request.state, "auth", None)
_ALL_PERMISSIONS: list[str] = [
Permissions.THREADS_READ,
Permissions.THREADS_WRITE,
Permissions.THREADS_DELETE,
Permissions.RUNS_CREATE,
Permissions.RUNS_READ,
Permissions.RUNS_CANCEL,
]
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.
Delegates to deps.get_optional_user_from_request() for the JWT→User pipeline.
Returns AuthContext with user=None for anonymous requests.
"""
from app.gateway.deps import get_optional_user_from_request
user = await get_optional_user_from_request(request)
if user is None:
return AuthContext(user=None, permissions=[])
# In future, permissions could be stored in user record
return AuthContext(user=user, permissions=_ALL_PERMISSIONS)
def require_auth[**P, T](func: Callable[P, T]) -> Callable[P, T]:
"""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).
Usage:
@router.get("/{thread_id}")
@require_auth # Bottom decorator (executes first after permission check)
@require_permission("threads", "read")
async def get_thread(thread_id: str, request: Request):
auth: AuthContext = request.state.auth
...
Raises:
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:
# 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
def require_permission(
resource: str,
action: str,
owner_check: bool = False,
require_existing: bool = False,
) -> Callable[[Callable[P, T]], Callable[P, T]]:
"""Decorator that checks permission for resource:action.
Must be used AFTER @require_auth.
Args:
resource: Resource name (e.g., "threads", "runs")
action: Action name (e.g., "read", "write", "delete")
owner_check: If True, validates that the current user owns the resource.
Requires 'thread_id' path parameter and performs ownership check.
require_existing: Only meaningful with ``owner_check=True``. If True, a
missing ``threads_meta`` row counts as a denial (404)
instead of "untracked legacy thread, allow". Use on
**destructive / mutating** routes (DELETE, PATCH,
state-update) so a deleted thread can't be re-targeted
by another user via the missing-row code path.
Usage:
# Read-style: legacy untracked threads are allowed
@require_permission("threads", "read", owner_check=True)
async def get_thread(thread_id: str, request: Request):
...
# Destructive: thread row MUST exist and be owned by caller
@require_permission("threads", "delete", owner_check=True, require_existing=True)
async def delete_thread(thread_id: str, request: Request):
...
Raises:
HTTPException 401: If authentication required but user is anonymous
HTTPException 403: If user lacks permission
HTTPException 404: If owner_check=True but user doesn't own the thread
ValueError: If owner_check=True but 'thread_id' parameter is missing
"""
def decorator(func: Callable[P, T]) -> Callable[P, T]:
@functools.wraps(func)
async def wrapper(*args: Any, **kwargs: Any) -> Any:
request = kwargs.get("request")
if request is None:
# 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:
auth = await _authenticate(request)
request.state.auth = auth
if not auth.is_authenticated:
raise HTTPException(status_code=401, detail="Authentication required")
# Check permission
if not auth.has_permission(resource, action):
raise HTTPException(
status_code=403,
detail=f"Permission denied: {resource}:{action}",
)
# Owner check for thread-specific resources.
#
# 2.0-rc moved thread metadata into the SQL persistence layer
# (``threads_meta`` table). We verify ownership via
# ``ThreadMetaStore.check_access``: it returns True for
# missing rows (untracked legacy thread) and for rows whose
# ``user_id`` is NULL (shared / pre-auth data), so this is
# strict-deny rather than strict-allow — only an *existing*
# row with a *different* user_id triggers 404.
if owner_check:
thread_id = kwargs.get("thread_id")
if thread_id is None:
raise ValueError("require_permission with owner_check=True requires 'thread_id' parameter")
from app.gateway.deps import get_thread_store
thread_store = get_thread_store(request)
allowed = await thread_store.check_access(
thread_id,
str(auth.user.id),
require_existing=require_existing,
)
if not allowed:
raise HTTPException(
status_code=404,
detail=f"Thread {thread_id} not found",
)
return await func(*args, **kwargs)
return wrapper
return decorator
+3 -2
View File
@@ -8,7 +8,7 @@ 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")
enable_docs: bool = Field(default=True, description="Enable Swagger/ReDoc/OpenAPI endpoints")
cors_origins: list[str] = Field(default_factory=lambda: ["http://localhost:3000"], description="Allowed CORS origins")
_gateway_config: GatewayConfig | None = None
@@ -18,9 +18,10 @@ def get_gateway_config() -> GatewayConfig:
"""Get gateway config, loading from environment if available."""
global _gateway_config
if _gateway_config is None:
cors_origins_str = os.getenv("CORS_ORIGINS", "http://localhost:3000")
_gateway_config = GatewayConfig(
host=os.getenv("GATEWAY_HOST", "0.0.0.0"),
port=int(os.getenv("GATEWAY_PORT", "8001")),
enable_docs=os.getenv("GATEWAY_ENABLE_DOCS", "true").lower() == "true",
cors_origins=cors_origins_str.split(","),
)
return _gateway_config
-229
View File
@@ -1,229 +0,0 @@
"""CSRF protection middleware for FastAPI.
Per RFC-001:
State-changing operations require CSRF protection.
"""
import os
import secrets
from collections.abc import Awaitable, Callable
from urllib.parse import urlsplit
from fastapi import Request, Response
from starlette.middleware.base import BaseHTTPMiddleware
from starlette.responses import JSONResponse
from starlette.types import ASGIApp
CSRF_COOKIE_NAME = "csrf_token"
CSRF_HEADER_NAME = "X-CSRF-Token"
CSRF_TOKEN_LENGTH = 64 # bytes
def is_secure_request(request: Request) -> bool:
"""Detect whether the original client request was made over HTTPS."""
return _request_scheme(request) == "https"
def generate_csrf_token() -> str:
"""Generate a secure random CSRF token."""
return secrets.token_urlsafe(CSRF_TOKEN_LENGTH)
def should_check_csrf(request: Request) -> bool:
"""Determine if a request needs CSRF validation.
CSRF is checked for state-changing methods (POST, PUT, DELETE, PATCH).
GET, HEAD, OPTIONS, and TRACE are exempt per RFC 7231.
"""
if request.method not in ("POST", "PUT", "DELETE", "PATCH"):
return False
path = request.url.path.rstrip("/")
# Exempt /api/v1/auth/me endpoint
if path == "/api/v1/auth/me":
return False
return True
_AUTH_EXEMPT_PATHS: frozenset[str] = frozenset(
{
"/api/v1/auth/login/local",
"/api/v1/auth/logout",
"/api/v1/auth/register",
"/api/v1/auth/initialize",
}
)
def is_auth_endpoint(request: Request) -> bool:
"""Check if the request is to an auth endpoint.
Auth endpoints don't need CSRF validation on first call (no token).
"""
return request.url.path.rstrip("/") in _AUTH_EXEMPT_PATHS
def _host_with_optional_port(hostname: str, port: int | None, scheme: str) -> str:
"""Return normalized host[:port], omitting default ports."""
host = hostname.lower()
if ":" in host and not host.startswith("["):
host = f"[{host}]"
if port is None or (scheme == "http" and port == 80) or (scheme == "https" and port == 443):
return host
return f"{host}:{port}"
def _normalize_origin(origin: str) -> str | None:
"""Return a normalized scheme://host[:port] origin, or None for invalid input."""
try:
parsed = urlsplit(origin.strip())
port = parsed.port
except ValueError:
return None
scheme = parsed.scheme.lower()
if scheme not in {"http", "https"} or not parsed.hostname:
return None
# Browser Origin is only scheme/host/port. Reject URL-shaped or credentialed values.
if parsed.username or parsed.password or parsed.path or parsed.query or parsed.fragment:
return None
return f"{scheme}://{_host_with_optional_port(parsed.hostname, port, scheme)}"
def _configured_cors_origins() -> set[str]:
"""Return explicit configured browser origins that may call auth routes."""
origins = set()
for raw_origin in os.environ.get("GATEWAY_CORS_ORIGINS", "").split(","):
origin = raw_origin.strip()
if not origin or origin == "*":
continue
normalized = _normalize_origin(origin)
if normalized:
origins.add(normalized)
return origins
def get_configured_cors_origins() -> set[str]:
"""Return normalized explicit browser origins from GATEWAY_CORS_ORIGINS."""
return _configured_cors_origins()
def _first_header_value(value: str | None) -> str | None:
"""Return the first value from a comma-separated proxy header."""
if not value:
return None
first = value.split(",", 1)[0].strip()
return first or None
def _forwarded_param(request: Request, name: str) -> str | None:
"""Extract a parameter from the first RFC 7239 Forwarded header entry."""
forwarded = _first_header_value(request.headers.get("forwarded"))
if not forwarded:
return None
for part in forwarded.split(";"):
key, sep, value = part.strip().partition("=")
if sep and key.lower() == name:
return value.strip().strip('"') or None
return None
def _request_scheme(request: Request) -> str:
"""Resolve the original request scheme from trusted proxy headers."""
scheme = _forwarded_param(request, "proto") or _first_header_value(request.headers.get("x-forwarded-proto")) or request.url.scheme
return scheme.lower()
def _request_origin(request: Request) -> str | None:
"""Build the origin for the URL the browser is targeting."""
scheme = _request_scheme(request)
host = _forwarded_param(request, "host") or _first_header_value(request.headers.get("x-forwarded-host")) or request.headers.get("host") or request.url.netloc
forwarded_port = _first_header_value(request.headers.get("x-forwarded-port"))
if forwarded_port and ":" not in host.rsplit("]", 1)[-1]:
host = f"{host}:{forwarded_port}"
return _normalize_origin(f"{scheme}://{host}")
def is_allowed_auth_origin(request: Request) -> bool:
"""Allow auth POSTs only from the same origin or explicit configured origins.
Login/register/initialize are exempt from the double-submit token because
first-time browser clients do not have a CSRF token yet. They still create
a session cookie, so browser requests with a hostile Origin header must be
rejected to prevent login CSRF / session fixation. Requests without Origin
are allowed for non-browser clients such as curl and mobile integrations.
"""
origin = request.headers.get("origin")
if not origin:
return True
normalized_origin = _normalize_origin(origin)
if normalized_origin is None:
return False
request_origin = _request_origin(request)
return normalized_origin in _configured_cors_origins() or (request_origin is not None and normalized_origin == request_origin)
class CSRFMiddleware(BaseHTTPMiddleware):
"""Middleware that implements CSRF protection using Double Submit Cookie pattern."""
def __init__(self, app: ASGIApp) -> None:
super().__init__(app)
async def dispatch(self, request: Request, call_next: Callable[[Request], Awaitable[Response]]) -> Response:
_is_auth = is_auth_endpoint(request)
if should_check_csrf(request) and _is_auth and not is_allowed_auth_origin(request):
return JSONResponse(
status_code=403,
content={"detail": "Cross-site auth request denied."},
)
if should_check_csrf(request) and not _is_auth:
cookie_token = request.cookies.get(CSRF_COOKIE_NAME)
header_token = request.headers.get(CSRF_HEADER_NAME)
if not cookie_token or not header_token:
return JSONResponse(
status_code=403,
content={"detail": "CSRF token missing. Include X-CSRF-Token header."},
)
if not secrets.compare_digest(cookie_token, header_token):
return JSONResponse(
status_code=403,
content={"detail": "CSRF token mismatch."},
)
response = await call_next(request)
# For auth endpoints that set up session, also set CSRF cookie
if _is_auth and request.method == "POST":
# Generate a new CSRF token for the session
csrf_token = generate_csrf_token()
is_https = is_secure_request(request)
response.set_cookie(
key=CSRF_COOKIE_NAME,
value=csrf_token,
httponly=False, # Must be JS-readable for Double Submit Cookie pattern
secure=is_https,
samesite="strict",
)
return response
def get_csrf_token(request: Request) -> str | None:
"""Get the CSRF token from the current request's cookies.
This is useful for server-side rendering where you need to embed
token in forms or headers.
"""
return request.cookies.get(CSRF_COOKIE_NAME)
+40 -292
View File
@@ -1,265 +1,103 @@
"""Centralized accessors for singleton objects stored on ``app.state``.
**Getters** (used by routers): raise 503 when a required dependency is
missing, except ``get_store`` which returns ``None``.
``AppConfig`` is intentionally *not* cached on ``app.state``. Routers and the
run path resolve it through :func:`deerflow.config.app_config.get_app_config`,
which performs mtime-based hot reload, so edits to ``config.yaml`` take
effect on the next request without a process restart. The engines created in
:func:`langgraph_runtime` (stream bridge, persistence, checkpointer, store,
run-event store) accept a ``startup_config`` snapshot — they are
restart-required by design and stay bound to that snapshot to keep the live
process consistent with itself.
missing, except ``get_store`` and ``get_thread_meta_repo`` which return
``None``.
Initialization is handled directly in ``app.py`` via :class:`AsyncExitStack`.
"""
from __future__ import annotations
import asyncio
import logging
from collections.abc import AsyncGenerator, Callable
from collections.abc import AsyncGenerator
from contextlib import AsyncExitStack, asynccontextmanager
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, get_app_config
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
logger = logging.getLogger(__name__)
# Upper bound (seconds) for draining in-flight runs during shutdown, before the
# AsyncExitStack tears down the checkpointer (and its connection pool). Kept
# local to avoid an app -> deps -> app import cycle. This is a *separate* budget
# from ``app.gateway.app._SHUTDOWN_HOOK_TIMEOUT_SECONDS`` (currently also 5.0s,
# which bounds channel-service stop): the two govern independent teardown steps
# and may diverge, but both count toward the lifespan shutdown window — revisit
# them together if their sum must stay within the server's graceful-shutdown
# timeout.
_RUN_DRAIN_TIMEOUT_SECONDS = 5.0
async def _drain_inflight_runs(run_manager: RunManager) -> None:
"""Drain in-flight runs before the checkpointer is torn down (issue #3373).
Shields the (internally-bounded) drain so that even if the lifespan
coroutine is itself cancelled mid-shutdown — a second SIGINT or the server's
graceful-shutdown timeout, i.e. the same signal storm behind #3373 — the
checkpointer pool is not closed while run tasks are still writing
checkpoints. On such a cancellation we let the already-running drain finish
(it is bounded by ``RunManager.shutdown``'s own timeout) and then propagate
the cancellation.
"""
drain = asyncio.create_task(run_manager.shutdown(timeout=_RUN_DRAIN_TIMEOUT_SECONDS))
try:
await asyncio.shield(drain)
except asyncio.CancelledError:
# Re-shield so this second wait does not abandon the in-flight drain;
# it is bounded, so this cannot hang. Then re-raise to honour shutdown.
try:
await asyncio.shield(drain)
except Exception:
logger.exception("In-flight run drain failed after shutdown cancellation")
raise
except Exception:
logger.exception("Failed to drain in-flight runs during shutdown")
if TYPE_CHECKING:
from app.gateway.auth.local_provider import LocalAuthProvider
from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
from deerflow.persistence.thread_meta.base import ThreadMetaStore
from deerflow.runtime import RunRecord
T = TypeVar("T")
async def _mark_latest_recovered_threads_error(
run_manager: RunManager,
thread_store: ThreadMetaStore,
recovered_runs: list[RunRecord],
) -> None:
"""Mark thread status as error only when its newest run was recovered."""
recovered_by_thread: dict[str, set[str]] = {}
for record in recovered_runs:
recovered_by_thread.setdefault(record.thread_id, set()).add(record.run_id)
for thread_id, recovered_run_ids in recovered_by_thread.items():
try:
latest_runs = await run_manager.list_by_thread(thread_id, user_id=None, limit=1)
except Exception:
logger.warning("Failed to find latest run for thread %s during run reconciliation", thread_id, exc_info=True)
continue
if not latest_runs or latest_runs[0].run_id not in recovered_run_ids:
continue
try:
await thread_store.update_status(thread_id, "error", user_id=None)
except Exception:
logger.warning("Failed to mark thread %s as error during run reconciliation", thread_id, exc_info=True)
def get_config() -> AppConfig:
"""Return the freshest ``AppConfig`` for the current request.
Routes through :func:`deerflow.config.app_config.get_app_config`, which
honours runtime ``ContextVar`` overrides and reloads ``config.yaml`` from
disk when its mtime changes. ``AppConfig`` is not cached on ``app.state``
at all — the only startup-time snapshot lives as a local
``startup_config`` variable inside ``lifespan()`` and is passed
explicitly into :func:`langgraph_runtime` for the engines that are
restart-required by design. Routing every request through
:func:`get_app_config` closes the bytedance/deer-flow issue #3107 BUG-001
split-brain where the worker / lead-agent thread saw a stale startup
snapshot.
Hot-reload boundary: fields backed by startup-time singletons
(engines, sandbox provider, IM channels, logging handler) require a
process restart to change at runtime. The authoritative list lives in
:mod:`deerflow.config.reload_boundary` and is mirrored by the
standardised ``"startup-only:"`` prefix on the matching
``Field(description=...)`` in :class:`AppConfig` — IDE hover on those
fields will surface the boundary inline. See
``backend/CLAUDE.md`` "Config Hot-Reload Boundary" for the operator
summary.
Any failure to materialise the config (missing file, permission denied,
YAML parse error, validation error) is reported as 503 — semantically
"the gateway cannot serve requests without a usable configuration" — and
logged with the original exception so operators have something to debug.
"""
try:
return get_app_config()
except Exception as exc: # noqa: BLE001 - request boundary: log and degrade gracefully
logger.exception("Failed to load AppConfig at request time")
raise HTTPException(status_code=503, detail="Configuration not available") from exc
from deerflow.runtime import RunContext, RunManager
@asynccontextmanager
async def langgraph_runtime(app: FastAPI, startup_config: AppConfig) -> AsyncGenerator[None, None]:
async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
"""Bootstrap and tear down all LangGraph runtime singletons.
``startup_config`` is the ``AppConfig`` snapshot taken once during
``lifespan()`` for one-shot infrastructure bootstrap. The engines and
stores constructed here (stream bridge, persistence engine, checkpointer,
store, run-event store) are restart-required by design — they hold live
connections, file handles, or singleton providers — so they bind to this
snapshot and survive across `config.yaml` edits. Request-time consumers
must still go through :func:`get_config` for any field that should be
hot-reloadable. See ``backend/CLAUDE.md`` "Config Hot-Reload Boundary".
The matching ``run_events_config`` is frozen onto ``app.state`` so
:func:`get_run_context` pairs a freshly-loaded ``AppConfig`` with the
*startup-time* run-events configuration the underlying ``event_store``
was built from — otherwise the runtime could end up combining a live
new ``run_events_config`` with an event store still bound to the
previous backend.
Usage in ``app.py``::
async with langgraph_runtime(app, startup_config):
async with langgraph_runtime(app):
yield
"""
from deerflow.agents.checkpointer.async_provider import make_checkpointer
from deerflow.config import get_app_config
from deerflow.persistence.engine import close_engine, get_session_factory, init_engine_from_config
from deerflow.runtime import make_store, make_stream_bridge
from deerflow.runtime.checkpointer.async_provider import make_checkpointer
from deerflow.runtime.events.store import make_run_event_store
async with AsyncExitStack() as stack:
config = startup_config
app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge(config))
app.state.stream_bridge = await stack.enter_async_context(make_stream_bridge())
# Initialize persistence engine BEFORE checkpointer so that
# auto-create-database logic runs first (postgres backend).
config = get_app_config()
await init_engine_from_config(config.database)
app.state.checkpointer = await stack.enter_async_context(make_checkpointer(config))
app.state.store = await stack.enter_async_context(make_store(config))
app.state.checkpointer = await stack.enter_async_context(make_checkpointer())
app.state.store = await stack.enter_async_context(make_store())
# Initialize repositories — one get_session_factory() call for all.
sf = get_session_factory()
if sf is not None:
from deerflow.persistence.feedback import FeedbackRepository
from deerflow.persistence.run import RunRepository
from deerflow.persistence.thread_meta import ThreadMetaRepository
app.state.run_store = RunRepository(sf)
app.state.feedback_repo = FeedbackRepository(sf)
app.state.thread_meta_repo = ThreadMetaRepository(sf)
else:
from deerflow.persistence.thread_meta import MemoryThreadMetaStore
from deerflow.runtime.runs.store.memory import MemoryRunStore
app.state.run_store = MemoryRunStore()
app.state.feedback_repo = None
app.state.thread_meta_repo = MemoryThreadMetaStore(app.state.store)
from deerflow.persistence.thread_meta import make_thread_store
app.state.thread_store = make_thread_store(sf, app.state.store)
# Run event store. The store and the matching ``run_events_config`` are
# both frozen at startup so ``get_run_context`` does not combine a
# freshly-reloaded ``AppConfig.run_events`` with a store still bound to
# the previous backend.
# Run event store (has its own factory with config-driven backend selection)
run_events_config = getattr(config, "run_events", None)
app.state.run_events_config = run_events_config
app.state.run_event_store = make_run_event_store(run_events_config)
# RunManager with store backing for persistence
app.state.run_manager = RunManager(store=app.state.run_store)
if getattr(config.database, "backend", None) == "sqlite":
from deerflow.utils.time import now_iso
# Startup-only recovery: clean shutdowns return no active rows and
# the thread-status update below becomes a no-op.
recovered_runs = await app.state.run_manager.reconcile_orphaned_inflight_runs(
error="Gateway restarted before this run reached a durable final state.",
before=now_iso(),
)
await _mark_latest_recovered_threads_error(app.state.run_manager, app.state.thread_store, recovered_runs)
try:
yield
finally:
# Drain in-flight run tasks BEFORE the AsyncExitStack tears down the
# checkpointer (and its connection pool). A run still mid-graph would
# otherwise leak into asyncio.run() shutdown, where langgraph's
# _checkpointer_put_after_previous aput races the closed pool and
# raises PoolClosed (issue #3373).
run_manager = getattr(app.state, "run_manager", None)
if run_manager is not None:
await _drain_inflight_runs(run_manager)
await close_engine()
# ---------------------------------------------------------------------------
# Getters called by routers per-request
# Getters -- called by routers per-request
# ---------------------------------------------------------------------------
def _require(attr: str, label: str) -> Callable[[Request], T]:
def _require(attr: str, label: str):
"""Create a FastAPI dependency that returns ``app.state.<attr>`` or 503."""
def dep(request: Request) -> T:
def dep(request: Request):
val = getattr(request.app.state, attr, None)
if val is None:
raise HTTPException(status_code=503, detail=f"{label} not available")
return cast(T, val)
return val
dep.__name__ = dep.__qualname__ = f"get_{attr}"
return dep
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")
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")
def get_store(request: Request):
@@ -267,122 +105,32 @@ def get_store(request: Request):
return getattr(request.app.state, "store", None)
def get_thread_store(request: Request) -> ThreadMetaStore:
"""Return the thread metadata store (SQL or memory-backed)."""
val = getattr(request.app.state, "thread_store", None)
if val is None:
raise HTTPException(status_code=503, detail="Thread metadata store not available")
return val
get_thread_meta_repo = _require("thread_meta_repo", "Thread metadata store")
def get_run_context(request: Request) -> RunContext:
"""Build a :class:`RunContext` from ``app.state`` singletons.
Returns a *base* context with infrastructure dependencies. The
``app_config`` field is resolved live so per-run fields (e.g.
``models[*].max_tokens``) follow ``config.yaml`` edits; the
``event_store`` / ``run_events_config`` pair stays frozen to the snapshot
captured in :func:`langgraph_runtime` so callers never see a store bound
to one backend paired with a config pointing at another.
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`.
"""
from deerflow.config import get_app_config
return RunContext(
checkpointer=get_checkpointer(request),
store=get_store(request),
event_store=get_run_event_store(request),
run_events_config=getattr(request.app.state, "run_events_config", None),
thread_store=get_thread_store(request),
app_config=get_config(),
run_events_config=getattr(get_app_config(), "run_events", None),
thread_meta_repo=get_thread_meta_repo(request),
)
# ---------------------------------------------------------------------------
# Auth helpers (used by authz.py and auth middleware)
# ---------------------------------------------------------------------------
# Cached singletons to avoid repeated instantiation per request
_cached_local_provider: LocalAuthProvider | None = None
_cached_repo: SQLiteUserRepository | None = None
def get_local_provider() -> LocalAuthProvider:
"""Get or create the cached LocalAuthProvider singleton.
Must be called after ``init_engine_from_config()`` — the shared
session factory is required to construct the user repository.
"""
global _cached_local_provider, _cached_repo
if _cached_repo is None:
from app.gateway.auth.repositories.sqlite import SQLiteUserRepository
from deerflow.persistence.engine import get_session_factory
sf = get_session_factory()
if sf is None:
raise RuntimeError("get_local_provider() called before init_engine_from_config(); cannot access users table")
_cached_repo = SQLiteUserRepository(sf)
if _cached_local_provider is None:
from app.gateway.auth.local_provider import LocalAuthProvider
_cached_local_provider = LocalAuthProvider(repository=_cached_repo)
return _cached_local_provider
async def get_current_user_from_request(request: Request):
"""Get the current authenticated user from the request cookie.
Raises HTTPException 401 if not authenticated.
"""
from app.gateway.auth import decode_token
from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse, TokenError, token_error_to_code
access_token = request.cookies.get("access_token")
if not access_token:
raise HTTPException(
status_code=401,
detail=AuthErrorResponse(code=AuthErrorCode.NOT_AUTHENTICATED, message="Not authenticated").model_dump(),
)
payload = decode_token(access_token)
if isinstance(payload, TokenError):
raise HTTPException(
status_code=401,
detail=AuthErrorResponse(code=token_error_to_code(payload), message=f"Token error: {payload.value}").model_dump(),
)
provider = get_local_provider()
user = await provider.get_user(payload.sub)
if user is None:
raise HTTPException(
status_code=401,
detail=AuthErrorResponse(code=AuthErrorCode.USER_NOT_FOUND, message="User not found").model_dump(),
)
# Token version mismatch → password was changed, token is stale
if user.token_version != payload.ver:
raise HTTPException(
status_code=401,
detail=AuthErrorResponse(code=AuthErrorCode.TOKEN_INVALID, message="Token revoked (password changed)").model_dump(),
)
return user
async def get_optional_user_from_request(request: Request):
"""Get optional authenticated user from request.
Returns None if not authenticated.
"""
try:
return await get_current_user_from_request(request)
except HTTPException:
return None
async def get_current_user(request: Request) -> str | None:
"""Extract user_id from request cookie, or None if not authenticated.
"""Extract user identity from request.
Thin adapter that returns the string id for callers that only need
identification (e.g., ``feedback.py``). Full-user callers should use
``get_current_user_from_request`` or ``get_optional_user_from_request``.
Phase 2: always returns None (no authentication).
Phase 3: extract user_id from JWT / session / API key header.
"""
user = await get_optional_user_from_request(request)
return str(user.id) if user else None
return None
-38
View File
@@ -1,38 +0,0 @@
"""Authentication for trusted Gateway internal callers."""
from __future__ import annotations
import os
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_ENV_VAR = "DEER_FLOW_INTERNAL_AUTH_TOKEN"
INTERNAL_SYSTEM_ROLE = "internal"
def _load_internal_auth_token() -> str:
token = os.environ.get(INTERNAL_AUTH_ENV_VAR)
if token:
return token
return secrets.token_urlsafe(32)
_INTERNAL_AUTH_TOKEN = _load_internal_auth_token()
def create_internal_auth_headers() -> dict[str, str]:
"""Return headers that authenticate trusted 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 this Gateway worker's 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_SYSTEM_ROLE)
-110
View File
@@ -1,110 +0,0 @@
"""LangGraph compatibility auth handler — shares JWT logic with Gateway.
The default DeerFlow runtime is embedded in the FastAPI Gateway; scripts and
Docker deployments do not load this module. It is retained for LangGraph
tooling, Studio, or direct LangGraph Server compatibility through
``langgraph.json``'s ``auth.path``.
When that compatibility path is used, this module reuses the same JWT and CSRF
rules as Gateway so both modes validate sessions consistently.
Two layers:
1. @auth.authenticate — validates JWT cookie, extracts user_id,
and enforces CSRF on state-changing methods (POST/PUT/DELETE/PATCH)
2. @auth.on — returns metadata filter so each user only sees own threads
"""
import secrets
from langgraph_sdk import Auth
from app.gateway.auth.errors import TokenError
from app.gateway.auth.jwt import decode_token
from app.gateway.deps import get_local_provider
auth = Auth()
# Methods that require CSRF validation (state-changing per RFC 7231).
_CSRF_METHODS = frozenset({"POST", "PUT", "DELETE", "PATCH"})
def _check_csrf(request) -> None:
"""Enforce Double Submit Cookie CSRF check for state-changing requests.
Mirrors Gateway's CSRFMiddleware logic so that LangGraph routes
proxied directly by nginx have the same CSRF protection.
"""
method = getattr(request, "method", "") or ""
if method.upper() not in _CSRF_METHODS:
return
cookie_token = request.cookies.get("csrf_token")
header_token = request.headers.get("x-csrf-token")
if not cookie_token or not header_token:
raise Auth.exceptions.HTTPException(
status_code=403,
detail="CSRF token missing. Include X-CSRF-Token header.",
)
if not secrets.compare_digest(cookie_token, header_token):
raise Auth.exceptions.HTTPException(
status_code=403,
detail="CSRF token mismatch.",
)
@auth.authenticate
async def authenticate(request):
"""Validate the session cookie, decode JWT, and check token_version.
Same validation chain as Gateway's get_current_user_from_request:
cookie → decode JWT → DB lookup → token_version match
Also enforces CSRF on state-changing methods.
"""
# CSRF check before authentication so forged cross-site requests
# are rejected early, even if the cookie carries a valid JWT.
_check_csrf(request)
token = request.cookies.get("access_token")
if not token:
raise Auth.exceptions.HTTPException(
status_code=401,
detail="Not authenticated",
)
payload = decode_token(token)
if isinstance(payload, TokenError):
raise Auth.exceptions.HTTPException(
status_code=401,
detail="Invalid token",
)
user = await get_local_provider().get_user(payload.sub)
if user is None:
raise Auth.exceptions.HTTPException(
status_code=401,
detail="User not found",
)
if user.token_version != payload.ver:
raise Auth.exceptions.HTTPException(
status_code=401,
detail="Token revoked (password changed)",
)
return payload.sub
@auth.on
async def add_owner_filter(ctx: Auth.types.AuthContext, value: dict):
"""Inject user_id metadata on writes; filter by user_id on reads.
Gateway stores thread ownership as ``metadata.user_id``.
This handler ensures LangGraph Server enforces the same isolation.
"""
# On create/update: stamp user_id into metadata
metadata = value.setdefault("metadata", {})
metadata["user_id"] = ctx.user.identity
# Return filter dict — LangGraph applies it to search/read/delete
return {"user_id": ctx.user.identity}
-15
View File
@@ -1,15 +0,0 @@
"""Shared pagination helpers for gateway routers."""
from __future__ import annotations
def trim_run_message_page(rows: list[dict], *, limit: int, after_seq: int | None) -> tuple[list[dict], bool]:
"""Trim a ``limit + 1`` run-message page while preserving page boundaries."""
has_more = len(rows) > limit
if not has_more:
return rows, False
if after_seq is not None:
return rows[:limit], True
return rows[-limit:], True
+1 -2
View File
@@ -5,7 +5,6 @@ from pathlib import Path
from fastapi import HTTPException
from deerflow.config.paths import get_paths
from deerflow.runtime.user_context import get_effective_user_id
def resolve_thread_virtual_path(thread_id: str, virtual_path: str) -> Path:
@@ -23,7 +22,7 @@ def resolve_thread_virtual_path(thread_id: str, virtual_path: str) -> Path:
HTTPException: If the path is invalid or outside allowed directories.
"""
try:
return get_paths().resolve_virtual_path(thread_id, virtual_path, user_id=get_effective_user_id())
return get_paths().resolve_virtual_path(thread_id, virtual_path)
except ValueError as e:
status = 403 if "traversal" in str(e) else 400
raise HTTPException(status_code=status, detail=str(e))
+57 -145
View File
@@ -1,6 +1,5 @@
"""CRUD API for custom agents."""
import asyncio
import logging
import re
import shutil
@@ -9,10 +8,8 @@ import yaml
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
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.paths import get_paths
from deerflow.runtime.user_context import get_effective_user_id
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api", tags=["agents"])
@@ -27,7 +24,6 @@ class AgentResponse(BaseModel):
description: str = Field(default="", description="Agent description")
model: str | None = Field(default=None, description="Optional model override")
tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
skills: list[str] | None = Field(default=None, description="Optional skill whitelist (None=all, []=none)")
soul: str | None = Field(default=None, description="SOUL.md content")
@@ -44,7 +40,6 @@ class AgentCreateRequest(BaseModel):
description: str = Field(default="", description="Agent description")
model: str | None = Field(default=None, description="Optional model override")
tool_groups: list[str] | None = Field(default=None, description="Optional tool group whitelist")
skills: list[str] | None = Field(default=None, description="Optional skill whitelist (None=all enabled, []=none)")
soul: str = Field(default="", description="SOUL.md content — agent personality and behavioral guardrails")
@@ -54,7 +49,6 @@ class AgentUpdateRequest(BaseModel):
description: str | None = Field(default=None, description="Updated description")
model: str | None = Field(default=None, description="Updated model override")
tool_groups: list[str] | None = Field(default=None, description="Updated tool group whitelist")
skills: list[str] | None = Field(default=None, description="Updated skill whitelist (None=all, []=none)")
soul: str | None = Field(default=None, description="Updated SOUL.md content")
@@ -79,27 +73,17 @@ def _normalize_agent_name(name: str) -> str:
return name.lower()
def _require_agents_api_enabled() -> None:
"""Reject access unless the custom-agent management API is explicitly 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."),
)
def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False, *, user_id: str | None = None) -> AgentResponse:
def _agent_config_to_response(agent_cfg: AgentConfig, include_soul: bool = False) -> AgentResponse:
"""Convert AgentConfig to AgentResponse."""
soul: str | None = None
if include_soul:
soul = load_agent_soul(agent_cfg.name, user_id=user_id) or ""
soul = load_agent_soul(agent_cfg.name) or ""
return AgentResponse(
name=agent_cfg.name,
description=agent_cfg.description,
model=agent_cfg.model,
tool_groups=agent_cfg.tool_groups,
skills=agent_cfg.skills,
soul=soul,
)
@@ -116,12 +100,9 @@ async def list_agents() -> AgentsListResponse:
Returns:
List of all custom agents with their metadata and soul content.
"""
_require_agents_api_enabled()
user_id = get_effective_user_id()
try:
agents = list_custom_agents(user_id=user_id)
return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True, user_id=user_id) for a in agents])
agents = list_custom_agents()
return AgentsListResponse(agents=[_agent_config_to_response(a, include_soul=True) for a in agents])
except Exception as e:
logger.error(f"Failed to list agents: {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to list agents: {str(e)}")
@@ -144,15 +125,9 @@ async def check_agent_name(name: str) -> dict:
Raises:
HTTPException: 422 if the name is invalid.
"""
_require_agents_api_enabled()
_validate_agent_name(name)
normalized = _normalize_agent_name(name)
user_id = get_effective_user_id()
paths = get_paths()
# Treat the name as taken if either the per-user path or the legacy shared
# path holds an agent — picking a name that collides with an unmigrated
# legacy agent would shadow the legacy entry once migration runs.
available = not paths.user_agent_dir(user_id, normalized).exists() and not paths.agent_dir(normalized).exists()
available = not get_paths().agent_dir(normalized).exists()
return {"available": available, "name": normalized}
@@ -174,14 +149,12 @@ async def get_agent(name: str) -> AgentResponse:
Raises:
HTTPException: 404 if agent not found.
"""
_require_agents_api_enabled()
_validate_agent_name(name)
name = _normalize_agent_name(name)
user_id = get_effective_user_id()
try:
agent_cfg = load_agent_config(name, user_id=user_id)
return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)
agent_cfg = load_agent_config(name)
return _agent_config_to_response(agent_cfg, include_soul=True)
except FileNotFoundError:
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
except Exception as e:
@@ -208,66 +181,47 @@ async def create_agent_endpoint(request: AgentCreateRequest) -> AgentResponse:
Raises:
HTTPException: 409 if agent already exists, 422 if name is invalid.
"""
_require_agents_api_enabled()
_validate_agent_name(request.name)
normalized_name = _normalize_agent_name(request.name)
user_id = get_effective_user_id()
paths = get_paths()
def _create_agent() -> AgentResponse | None:
# Worker thread: base-dir resolution, existence checks, directory/file
# creation, read-back, and failure cleanup are all blocking filesystem
# IO that must stay off the event loop.
agent_dir = paths.user_agent_dir(user_id, normalized_name)
legacy_dir = paths.agent_dir(normalized_name)
agent_dir = get_paths().agent_dir(normalized_name)
if legacy_dir.exists():
return None # signals 409 to the caller
try:
try:
agent_dir.mkdir(parents=True, exist_ok=False)
except FileExistsError:
return None # signals 409 to the caller
# Write config.yaml
config_data: dict = {"name": normalized_name}
if request.description:
config_data["description"] = request.description
if request.model is not None:
config_data["model"] = request.model
if request.tool_groups is not None:
config_data["tool_groups"] = request.tool_groups
if request.skills is not None:
config_data["skills"] = request.skills
config_file = agent_dir / "config.yaml"
with open(config_file, "w", encoding="utf-8") as f:
yaml.dump(config_data, f, default_flow_style=False, allow_unicode=True)
# Write SOUL.md
soul_file = agent_dir / "SOUL.md"
soul_file.write_text(request.soul, encoding="utf-8")
logger.info(f"Created agent '{normalized_name}' at {agent_dir}")
agent_cfg = load_agent_config(normalized_name, user_id=user_id)
return _agent_config_to_response(agent_cfg, include_soul=True, user_id=user_id)
except Exception:
# Clean up partial state on failure before surfacing the error.
if agent_dir.exists():
shutil.rmtree(agent_dir)
raise
try:
response = await asyncio.to_thread(_create_agent)
except Exception as e:
logger.error(f"Failed to create agent '{request.name}': {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to create agent: {str(e)}")
if response is None:
if agent_dir.exists():
raise HTTPException(status_code=409, detail=f"Agent '{normalized_name}' already exists")
return response
try:
agent_dir.mkdir(parents=True, exist_ok=True)
# Write config.yaml
config_data: dict = {"name": normalized_name}
if request.description:
config_data["description"] = request.description
if request.model is not None:
config_data["model"] = request.model
if request.tool_groups is not None:
config_data["tool_groups"] = request.tool_groups
config_file = agent_dir / "config.yaml"
with open(config_file, "w", encoding="utf-8") as f:
yaml.dump(config_data, f, default_flow_style=False, allow_unicode=True)
# Write SOUL.md
soul_file = agent_dir / "SOUL.md"
soul_file.write_text(request.soul, encoding="utf-8")
logger.info(f"Created agent '{normalized_name}' at {agent_dir}")
agent_cfg = load_agent_config(normalized_name)
return _agent_config_to_response(agent_cfg, include_soul=True)
except HTTPException:
raise
except Exception as e:
# Clean up on failure
if agent_dir.exists():
shutil.rmtree(agent_dir)
logger.error(f"Failed to create agent '{request.name}': {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to create agent: {str(e)}")
@router.put(
@@ -289,52 +243,33 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
Raises:
HTTPException: 404 if agent not found.
"""
_require_agents_api_enabled()
_validate_agent_name(name)
name = _normalize_agent_name(name)
user_id = get_effective_user_id()
try:
agent_cfg = load_agent_config(name, user_id=user_id)
agent_cfg = load_agent_config(name)
except FileNotFoundError:
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
paths = get_paths()
agent_dir = paths.user_agent_dir(user_id, name)
if not agent_dir.exists() and paths.agent_dir(name).exists():
raise HTTPException(
status_code=409,
detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before updating."),
)
agent_dir = get_paths().agent_dir(name)
try:
# Update config if any config fields changed
# Use model_fields_set to distinguish "field omitted" from "explicitly set to null".
# This is critical for skills where None means "inherit all" (not "don't change").
fields_set = request.model_fields_set
config_changed = bool(fields_set & {"description", "model", "tool_groups", "skills"})
config_changed = any(v is not None for v in [request.description, request.model, request.tool_groups])
if config_changed:
updated: dict = {
"name": agent_cfg.name,
"description": request.description if "description" in fields_set else agent_cfg.description,
"description": request.description if request.description is not None else agent_cfg.description,
}
new_model = request.model if "model" in fields_set else agent_cfg.model
new_model = request.model if request.model is not None else agent_cfg.model
if new_model is not None:
updated["model"] = new_model
new_tool_groups = request.tool_groups if "tool_groups" in fields_set else agent_cfg.tool_groups
new_tool_groups = request.tool_groups if request.tool_groups is not None else agent_cfg.tool_groups
if new_tool_groups is not None:
updated["tool_groups"] = new_tool_groups
# skills: None = inherit all, [] = no skills, ["a","b"] = whitelist
if "skills" in fields_set:
new_skills = request.skills
else:
new_skills = agent_cfg.skills
if new_skills is not None:
updated["skills"] = new_skills
config_file = agent_dir / "config.yaml"
with open(config_file, "w", encoding="utf-8") as f:
yaml.dump(updated, f, default_flow_style=False, allow_unicode=True)
@@ -346,8 +281,8 @@ async def update_agent(name: str, request: AgentUpdateRequest) -> AgentResponse:
logger.info(f"Updated agent '{name}'")
refreshed_cfg = load_agent_config(name, user_id=user_id)
return _agent_config_to_response(refreshed_cfg, include_soul=True, user_id=user_id)
refreshed_cfg = load_agent_config(name)
return _agent_config_to_response(refreshed_cfg, include_soul=True)
except HTTPException:
raise
@@ -380,8 +315,6 @@ async def get_user_profile() -> UserProfileResponse:
Returns:
UserProfileResponse with content=None if USER.md does not exist yet.
"""
_require_agents_api_enabled()
try:
user_md_path = get_paths().user_md_file
if not user_md_path.exists():
@@ -408,8 +341,6 @@ async def update_user_profile(request: UserProfileUpdateRequest) -> UserProfileR
Returns:
UserProfileResponse with the saved content.
"""
_require_agents_api_enabled()
try:
paths = get_paths()
paths.base_dir.mkdir(parents=True, exist_ok=True)
@@ -434,38 +365,19 @@ async def delete_agent(name: str) -> None:
name: The agent name.
Raises:
HTTPException: 404 if no per-user copy exists; 409 if only a legacy
shared copy exists (suggesting the migration script).
HTTPException: 404 if agent not found.
"""
_require_agents_api_enabled()
_validate_agent_name(name)
name = _normalize_agent_name(name)
user_id = get_effective_user_id()
paths = get_paths()
def _remove_agent_dir() -> tuple[str, str]:
# Runs in a worker thread: resolving the base dir, probing the directory
# (`exists`), and removing it (`rmtree`) are all blocking filesystem IO
# that must stay off the event loop.
agent_dir = paths.user_agent_dir(user_id, name)
if not agent_dir.exists():
outcome = "legacy" if paths.agent_dir(name).exists() else "missing"
return outcome, str(agent_dir)
shutil.rmtree(agent_dir)
return "deleted", str(agent_dir)
agent_dir = get_paths().agent_dir(name)
if not agent_dir.exists():
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
try:
outcome, agent_dir = await asyncio.to_thread(_remove_agent_dir)
shutil.rmtree(agent_dir)
logger.info(f"Deleted agent '{name}' from {agent_dir}")
except Exception as e:
logger.error(f"Failed to delete agent '{name}': {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to delete agent: {str(e)}")
if outcome == "legacy":
raise HTTPException(
status_code=409,
detail=(f"Agent '{name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before deleting."),
)
if outcome == "missing":
raise HTTPException(status_code=404, detail=f"Agent '{name}' not found")
logger.info(f"Deleted agent '{name}' from {agent_dir}")
+5 -26
View File
@@ -7,7 +7,6 @@ from urllib.parse import quote
from fastapi import APIRouter, HTTPException, Request
from fastapi.responses import FileResponse, PlainTextResponse, Response
from app.gateway.authz import require_permission
from app.gateway.path_utils import resolve_thread_virtual_path
logger = logging.getLogger(__name__)
@@ -20,9 +19,6 @@ ACTIVE_CONTENT_MIME_TYPES = {
"image/svg+xml",
}
MAX_SKILL_ARCHIVE_MEMBER_BYTES = 16 * 1024 * 1024
_SKILL_ARCHIVE_READ_CHUNK_SIZE = 64 * 1024
def _build_content_disposition(disposition_type: str, filename: str) -> str:
"""Build an RFC 5987 encoded Content-Disposition header value."""
@@ -47,22 +43,6 @@ def is_text_file_by_content(path: Path, sample_size: int = 8192) -> bool:
return False
def _read_skill_archive_member(zip_ref: zipfile.ZipFile, info: zipfile.ZipInfo) -> bytes:
"""Read a .skill archive member while enforcing an uncompressed size cap."""
if info.file_size > MAX_SKILL_ARCHIVE_MEMBER_BYTES:
raise HTTPException(status_code=413, detail="Skill archive member is too large to preview")
chunks: list[bytes] = []
total_read = 0
with zip_ref.open(info, "r") as src:
while chunk := src.read(_SKILL_ARCHIVE_READ_CHUNK_SIZE):
total_read += len(chunk)
if total_read > MAX_SKILL_ARCHIVE_MEMBER_BYTES:
raise HTTPException(status_code=413, detail="Skill archive member is too large to preview")
chunks.append(chunk)
return b"".join(chunks)
def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> bytes | None:
"""Extract a file from a .skill ZIP archive.
@@ -79,16 +59,16 @@ def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> byte
try:
with zipfile.ZipFile(zip_path, "r") as zip_ref:
# List all files in the archive
infos_by_name = {info.filename: info for info in zip_ref.infolist()}
namelist = zip_ref.namelist()
# Try direct path first
if internal_path in infos_by_name:
return _read_skill_archive_member(zip_ref, infos_by_name[internal_path])
if internal_path in namelist:
return zip_ref.read(internal_path)
# Try with any top-level directory prefix (e.g., "skill-name/SKILL.md")
for name, info in infos_by_name.items():
for name in namelist:
if name.endswith("/" + internal_path) or name == internal_path:
return _read_skill_archive_member(zip_ref, info)
return zip_ref.read(name)
# Not found
return None
@@ -101,7 +81,6 @@ def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> byte
summary="Get Artifact File",
description="Retrieve an artifact file generated by the AI agent. Text and binary files can be viewed inline, while active web content is always downloaded.",
)
@require_permission("threads", "read", owner_check=True)
async def get_artifact(thread_id: str, path: str, request: Request, download: bool = False) -> Response:
"""Get an artifact file by its path.
-527
View File
@@ -1,527 +0,0 @@
"""Authentication endpoints."""
import asyncio
import logging
import os
import time
from ipaddress import ip_address, ip_network
from fastapi import APIRouter, Depends, HTTPException, Request, Response, status
from fastapi.security import OAuth2PasswordRequestForm
from pydantic import BaseModel, EmailStr, Field, field_validator
from app.gateway.auth import (
UserResponse,
create_access_token,
)
from app.gateway.auth.config import get_auth_config
from app.gateway.auth.errors import AuthErrorCode, AuthErrorResponse
from app.gateway.csrf_middleware import is_secure_request
from app.gateway.deps import get_current_user_from_request, get_local_provider
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/v1/auth", tags=["auth"])
# ── Request/Response Models ──────────────────────────────────────────────
class LoginResponse(BaseModel):
"""Response model for login — token only lives in HttpOnly cookie."""
expires_in: int # seconds
needs_setup: bool = False
# Top common-password blocklist. Drawn from the public SecLists "10k worst
# passwords" set, lowercased + length>=8 only (shorter ones already fail
# the min_length check). Kept tight on purpose: this is the **lower bound**
# defense, not a full HIBP / passlib check, and runs in-process per request.
_COMMON_PASSWORDS: frozenset[str] = frozenset(
{
"password",
"password1",
"password12",
"password123",
"password1234",
"12345678",
"123456789",
"1234567890",
"qwerty12",
"qwertyui",
"qwerty123",
"abc12345",
"abcd1234",
"iloveyou",
"letmein1",
"welcome1",
"welcome123",
"admin123",
"administrator",
"passw0rd",
"p@ssw0rd",
"monkey12",
"trustno1",
"sunshine",
"princess",
"football",
"baseball",
"superman",
"batman123",
"starwars",
"dragon123",
"master123",
"shadow12",
"michael1",
"jennifer",
"computer",
}
)
def _password_is_common(password: str) -> bool:
"""Case-insensitive blocklist check.
Lowercases the input so trivial mutations like ``Password`` /
``PASSWORD`` are also rejected. Does not normalize digit substitutions
(``p@ssw0rd`` is included as a literal entry instead) keeping the
rule cheap and predictable.
"""
return password.lower() in _COMMON_PASSWORDS
def _validate_strong_password(value: str) -> str:
"""Pydantic field-validator body shared by Register + ChangePassword.
Constraint = function, not type-level mixin. The two request models
have no "is-a" relationship; they only share the password-strength
rule. Lifting it into a free function lets each model bind it via
``@field_validator(field_name)`` without inheritance gymnastics.
"""
if _password_is_common(value):
raise ValueError("Password is too common; choose a stronger password.")
return value
class RegisterRequest(BaseModel):
"""Request model for user registration."""
email: EmailStr
password: str = Field(..., min_length=8)
_strong_password = field_validator("password")(classmethod(lambda cls, v: _validate_strong_password(v)))
class ChangePasswordRequest(BaseModel):
"""Request model for password change (also handles setup flow)."""
current_password: str
new_password: str = Field(..., min_length=8)
new_email: EmailStr | None = None
_strong_password = field_validator("new_password")(classmethod(lambda cls, v: _validate_strong_password(v)))
class MessageResponse(BaseModel):
"""Generic message response."""
message: str
# ── Helpers ───────────────────────────────────────────────────────────────
def _set_session_cookie(response: Response, token: str, request: Request) -> None:
"""Set the access_token HttpOnly cookie on the response."""
config = get_auth_config()
is_https = is_secure_request(request)
response.set_cookie(
key="access_token",
value=token,
httponly=True,
secure=is_https,
samesite="lax",
max_age=config.token_expiry_days * 24 * 3600 if is_https else None,
)
# ── Rate Limiting ────────────────────────────────────────────────────────
# 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
# ip → (fail_count, lock_until_timestamp)
_login_attempts: dict[str, tuple[int, float]] = {}
def _trusted_proxies() -> list:
"""Parse ``AUTH_TRUSTED_PROXIES`` env var into a list of ip_network objects.
Comma-separated CIDR or single-IP entries. Empty / unset = no proxy is
trusted (direct mode). Invalid entries are skipped with a logger warning.
Read live so env-var overrides take effect immediately and tests can
``monkeypatch.setenv`` without poking a module-level cache.
"""
raw = os.getenv("AUTH_TRUSTED_PROXIES", "").strip()
if not raw:
return []
nets = []
for entry in raw.split(","):
entry = entry.strip()
if not entry:
continue
try:
nets.append(ip_network(entry, strict=False))
except ValueError:
logger.warning("AUTH_TRUSTED_PROXIES: ignoring invalid entry %r", entry)
return nets
def _get_client_ip(request: Request) -> str:
"""Extract the real client IP for rate limiting.
Trust model:
- The TCP peer (``request.client.host``) is always the baseline. It is
whatever the kernel reports as the connecting socket unforgeable
by the client itself.
- ``X-Real-IP`` is **only** honored if the TCP peer is in the
``AUTH_TRUSTED_PROXIES`` allowlist (set via env var, comma-separated
CIDR or single IPs). When set, the gateway is assumed to be behind a
reverse proxy (nginx, Cloudflare, ALB, ) that overwrites
``X-Real-IP`` with the original client address.
- With no ``AUTH_TRUSTED_PROXIES`` set, ``X-Real-IP`` is silently
ignored closing the bypass where any client could rotate the
header to dodge per-IP rate limits in dev / direct-gateway mode.
``X-Forwarded-For`` is intentionally NOT used because it is naturally
client-controlled at the *first* hop and the trust chain is harder to
audit per-request.
"""
peer_host = request.client.host if request.client else None
trusted = _trusted_proxies()
if trusted and peer_host:
try:
peer_ip = ip_address(peer_host)
if any(peer_ip in net for net in trusted):
real_ip = request.headers.get("x-real-ip", "").strip()
if real_ip:
return real_ip
except ValueError:
# peer_host wasn't a parseable IP (e.g. "unknown") — fall through
pass
return peer_host or "unknown"
def _check_rate_limit(ip: str) -> None:
"""Raise 429 if the IP is currently locked out."""
record = _login_attempts.get(ip)
if record is None:
return
fail_count, lock_until = record
if fail_count >= _MAX_LOGIN_ATTEMPTS:
if time.time() < lock_until:
raise HTTPException(
status_code=429,
detail="Too many login attempts. Try again later.",
)
del _login_attempts[ip]
_MAX_TRACKED_IPS = 10000
def _record_login_failure(ip: str) -> None:
"""Record a failed login attempt for the given IP."""
# Evict expired lockouts when dict grows too large
if len(_login_attempts) >= _MAX_TRACKED_IPS:
now = time.time()
expired = [k for k, (c, t) in _login_attempts.items() if c >= _MAX_LOGIN_ATTEMPTS and now >= t]
for k in expired:
del _login_attempts[k]
# If still too large, evict cheapest-to-lose half: below-threshold
# IPs (lock_until=0.0) sort first, then earliest-expiring lockouts.
if len(_login_attempts) >= _MAX_TRACKED_IPS:
by_time = sorted(_login_attempts.items(), key=lambda kv: kv[1][1])
for k, _ in by_time[: len(by_time) // 2]:
del _login_attempts[k]
record = _login_attempts.get(ip)
if record is None:
_login_attempts[ip] = (1, 0.0)
else:
new_count = record[0] + 1
lock_until = time.time() + _LOCKOUT_SECONDS if new_count >= _MAX_LOGIN_ATTEMPTS else 0.0
_login_attempts[ip] = (new_count, lock_until)
def _record_login_success(ip: str) -> None:
"""Clear failure counter for the given IP on successful login."""
_login_attempts.pop(ip, None)
# ── Endpoints ─────────────────────────────────────────────────────────────
@router.post("/login/local", response_model=LoginResponse)
async def login_local(
request: Request,
response: Response,
form_data: OAuth2PasswordRequestForm = Depends(),
):
"""Local email/password login."""
client_ip = _get_client_ip(request)
_check_rate_limit(client_ip)
user = await get_local_provider().authenticate({"email": form_data.username, "password": form_data.password})
if user is None:
_record_login_failure(client_ip)
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail=AuthErrorResponse(code=AuthErrorCode.INVALID_CREDENTIALS, message="Incorrect email or password").model_dump(),
)
_record_login_success(client_ip)
token = create_access_token(str(user.id), token_version=user.token_version)
_set_session_cookie(response, token, request)
return LoginResponse(
expires_in=get_auth_config().token_expiry_days * 24 * 3600,
needs_setup=user.needs_setup,
)
@router.post("/register", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
async def register(request: Request, response: Response, body: RegisterRequest):
"""Register a new user account (always 'user' role).
The first admin is created explicitly through /initialize. This endpoint creates regular users.
Auto-login by setting the session cookie.
"""
try:
user = await get_local_provider().create_user(email=body.email, password=body.password, system_role="user")
except ValueError:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=AuthErrorResponse(code=AuthErrorCode.EMAIL_ALREADY_EXISTS, message="Email already registered").model_dump(),
)
token = create_access_token(str(user.id), token_version=user.token_version)
_set_session_cookie(response, token, request)
return UserResponse(id=str(user.id), email=user.email, system_role=user.system_role)
@router.post("/logout", response_model=MessageResponse)
async def logout(request: Request, response: Response):
"""Logout current user by clearing the cookie."""
response.delete_cookie(key="access_token", secure=is_secure_request(request), samesite="lax")
return MessageResponse(message="Successfully logged out")
@router.post("/change-password", response_model=MessageResponse)
async def change_password(request: Request, response: Response, body: ChangePasswordRequest):
"""Change password for the currently authenticated user.
Also handles the first-boot setup flow:
- If new_email is provided, updates email (checks uniqueness)
- If user.needs_setup is True and new_email is given, clears needs_setup
- Always increments token_version to invalidate old sessions
- Re-issues session cookie with new token_version
"""
from app.gateway.auth.password import hash_password_async, verify_password_async
user = await get_current_user_from_request(request)
if user.password_hash is None:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=AuthErrorResponse(code=AuthErrorCode.INVALID_CREDENTIALS, message="OAuth users cannot change password").model_dump())
if not await verify_password_async(body.current_password, user.password_hash):
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=AuthErrorResponse(code=AuthErrorCode.INVALID_CREDENTIALS, message="Current password is incorrect").model_dump())
provider = get_local_provider()
# Update email if provided
if body.new_email is not None:
existing = await provider.get_user_by_email(body.new_email)
if existing and str(existing.id) != str(user.id):
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=AuthErrorResponse(code=AuthErrorCode.EMAIL_ALREADY_EXISTS, message="Email already in use").model_dump())
user.email = body.new_email
# Update password + bump version
user.password_hash = await hash_password_async(body.new_password)
user.token_version += 1
# Clear setup flag if this is the setup flow
if user.needs_setup and body.new_email is not None:
user.needs_setup = False
await provider.update_user(user)
# Re-issue cookie with new token_version
token = create_access_token(str(user.id), token_version=user.token_version)
_set_session_cookie(response, token, request)
return MessageResponse(message="Password changed successfully")
@router.get("/me", response_model=UserResponse)
async def get_me(request: Request):
"""Get current authenticated user info."""
user = await get_current_user_from_request(request)
return UserResponse(id=str(user.id), email=user.email, system_role=user.system_role, needs_setup=user.needs_setup)
# Per-IP cache: ip → (timestamp, result_dict).
# Returns the cached result within the TTL instead of 429, because
# the answer (whether an admin exists) rarely changes and returning
# 429 breaks multi-tab / post-restart reconnection storms.
_SETUP_STATUS_CACHE: dict[str, tuple[float, dict]] = {}
_SETUP_STATUS_CACHE_TTL_SECONDS = 60
_MAX_TRACKED_SETUP_STATUS_IPS = 10000
_SETUP_STATUS_INFLIGHT: dict[str, asyncio.Task[dict]] = {}
_SETUP_STATUS_INFLIGHT_GUARD = asyncio.Lock()
@router.get("/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()
# Return cached result when within TTL — avoids 429 on multi-tab reconnection.
cached = _SETUP_STATUS_CACHE.get(client_ip)
if cached is not None:
cached_time, cached_result = cached
if now - cached_time < _SETUP_STATUS_CACHE_TTL_SECONDS:
return cached_result
async with _SETUP_STATUS_INFLIGHT_GUARD:
# Recheck cache after waiting for the inflight guard.
now = time.time()
cached = _SETUP_STATUS_CACHE.get(client_ip)
if cached is not None:
cached_time, cached_result = cached
if now - cached_time < _SETUP_STATUS_CACHE_TTL_SECONDS:
return cached_result
task = _SETUP_STATUS_INFLIGHT.get(client_ip)
if task is None:
# Evict stale entries when dict grows too large to bound memory usage.
if len(_SETUP_STATUS_CACHE) >= _MAX_TRACKED_SETUP_STATUS_IPS:
cutoff = now - _SETUP_STATUS_CACHE_TTL_SECONDS
stale = [k for k, (t, _) in _SETUP_STATUS_CACHE.items() if t < cutoff]
for k in stale:
del _SETUP_STATUS_CACHE[k]
if len(_SETUP_STATUS_CACHE) >= _MAX_TRACKED_SETUP_STATUS_IPS:
by_time = sorted(_SETUP_STATUS_CACHE.items(), key=lambda entry: entry[1][0])
for k, _ in by_time[: len(by_time) // 2]:
del _SETUP_STATUS_CACHE[k]
async def _compute_setup_status() -> dict:
admin_count = await get_local_provider().count_admin_users()
return {"needs_setup": admin_count == 0}
task = asyncio.create_task(_compute_setup_status())
_SETUP_STATUS_INFLIGHT[client_ip] = task
try:
result = await task
finally:
async with _SETUP_STATUS_INFLIGHT_GUARD:
if _SETUP_STATUS_INFLIGHT.get(client_ip) is task:
del _SETUP_STATUS_INFLIGHT[client_ip]
# Cache only the stable "initialized" result to avoid stale setup redirects.
if result["needs_setup"] is False:
_SETUP_STATUS_CACHE[client_ip] = (time.time(), result)
else:
_SETUP_STATUS_CACHE.pop(client_ip, None)
return result
class InitializeAdminRequest(BaseModel):
"""Request model for first-boot admin account creation."""
email: EmailStr
password: str = Field(..., min_length=8)
_strong_password = field_validator("password")(classmethod(lambda cls, v: _validate_strong_password(v)))
@router.post("/initialize", response_model=UserResponse, status_code=status.HTTP_201_CREATED)
async def initialize_admin(request: Request, response: Response, body: InitializeAdminRequest):
"""Create the first admin account on initial system setup.
Only callable when no admin exists. Returns 409 Conflict if an admin
already exists.
On success, the admin account is created with ``needs_setup=False`` and
the session cookie is set.
"""
admin_count = await get_local_provider().count_admin_users()
if admin_count > 0:
raise HTTPException(
status_code=status.HTTP_409_CONFLICT,
detail=AuthErrorResponse(code=AuthErrorCode.SYSTEM_ALREADY_INITIALIZED, message="System already initialized").model_dump(),
)
try:
user = await get_local_provider().create_user(email=body.email, password=body.password, system_role="admin", needs_setup=False)
except ValueError:
# DB unique-constraint race: another concurrent request beat us.
raise HTTPException(
status_code=status.HTTP_409_CONFLICT,
detail=AuthErrorResponse(code=AuthErrorCode.SYSTEM_ALREADY_INITIALIZED, message="System already initialized").model_dump(),
)
token = create_access_token(str(user.id), token_version=user.token_version)
_set_session_cookie(response, token, request)
return UserResponse(id=str(user.id), email=user.email, system_role=user.system_role)
# ── OAuth Endpoints (Future/Placeholder) ─────────────────────────────────
@router.get("/oauth/{provider}")
async def oauth_login(provider: str):
"""Initiate OAuth login flow.
Redirects to the OAuth provider's authorization URL.
Currently a placeholder - requires OAuth provider implementation.
"""
if provider not in ["github", "google"]:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"Unsupported OAuth provider: {provider}",
)
raise HTTPException(
status_code=status.HTTP_501_NOT_IMPLEMENTED,
detail="OAuth login not yet implemented",
)
@router.get("/callback/{provider}")
async def oauth_callback(provider: str, code: str, state: str):
"""OAuth callback endpoint.
Handles the OAuth provider's callback after user authorization.
Currently a placeholder.
"""
raise HTTPException(
status_code=status.HTTP_501_NOT_IMPLEMENTED,
detail="OAuth callback not yet implemented",
)
+2 -63
View File
@@ -12,7 +12,6 @@ from typing import Any
from fastapi import APIRouter, HTTPException, Request
from pydantic import BaseModel, Field
from app.gateway.authz import require_permission
from app.gateway.deps import get_current_user, get_feedback_repo, get_run_store
logger = logging.getLogger(__name__)
@@ -30,16 +29,11 @@ class FeedbackCreateRequest(BaseModel):
message_id: str | None = Field(default=None, description="Optional: scope feedback to a specific message")
class FeedbackUpsertRequest(BaseModel):
rating: int = Field(..., description="Feedback rating: +1 (positive) or -1 (negative)")
comment: str | None = Field(default=None, description="Optional text feedback")
class FeedbackResponse(BaseModel):
feedback_id: str
run_id: str
thread_id: str
user_id: str | None = None
owner_id: str | None = None
message_id: str | None = None
rating: int
comment: str | None = None
@@ -58,59 +52,7 @@ class FeedbackStatsResponse(BaseModel):
# ---------------------------------------------------------------------------
@router.put("/{thread_id}/runs/{run_id}/feedback", response_model=FeedbackResponse)
@require_permission("threads", "write", owner_check=True, require_existing=True)
async def upsert_feedback(
thread_id: str,
run_id: str,
body: FeedbackUpsertRequest,
request: Request,
) -> dict[str, Any]:
"""Create or update feedback for a run (idempotent)."""
if body.rating not in (1, -1):
raise HTTPException(status_code=400, detail="rating must be +1 or -1")
user_id = await get_current_user(request)
run_store = get_run_store(request)
run = await run_store.get(run_id)
if run is None:
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
if run.get("thread_id") != thread_id:
raise HTTPException(status_code=404, detail=f"Run {run_id} not found in thread {thread_id}")
feedback_repo = get_feedback_repo(request)
return await feedback_repo.upsert(
run_id=run_id,
thread_id=thread_id,
rating=body.rating,
user_id=user_id,
comment=body.comment,
)
@router.delete("/{thread_id}/runs/{run_id}/feedback")
@require_permission("threads", "delete", owner_check=True, require_existing=True)
async def delete_run_feedback(
thread_id: str,
run_id: str,
request: Request,
) -> dict[str, bool]:
"""Delete the current user's feedback for a run."""
user_id = await get_current_user(request)
feedback_repo = get_feedback_repo(request)
deleted = await feedback_repo.delete_by_run(
thread_id=thread_id,
run_id=run_id,
user_id=user_id,
)
if not deleted:
raise HTTPException(status_code=404, detail="No feedback found for this run")
return {"success": True}
@router.post("/{thread_id}/runs/{run_id}/feedback", response_model=FeedbackResponse)
@require_permission("threads", "write", owner_check=True, require_existing=True)
async def create_feedback(
thread_id: str,
run_id: str,
@@ -136,14 +78,13 @@ async def create_feedback(
run_id=run_id,
thread_id=thread_id,
rating=body.rating,
user_id=user_id,
owner_id=user_id,
message_id=body.message_id,
comment=body.comment,
)
@router.get("/{thread_id}/runs/{run_id}/feedback", response_model=list[FeedbackResponse])
@require_permission("threads", "read", owner_check=True)
async def list_feedback(
thread_id: str,
run_id: str,
@@ -155,7 +96,6 @@ async def list_feedback(
@router.get("/{thread_id}/runs/{run_id}/feedback/stats", response_model=FeedbackStatsResponse)
@require_permission("threads", "read", owner_check=True)
async def feedback_stats(
thread_id: str,
run_id: str,
@@ -167,7 +107,6 @@ async def feedback_stats(
@router.delete("/{thread_id}/runs/{run_id}/feedback/{feedback_id}")
@require_permission("threads", "delete", owner_check=True, require_existing=True)
async def delete_feedback(
thread_id: str,
run_id: str,
+17 -221
View File
@@ -1,10 +1,9 @@
import json
import logging
import os
from pathlib import Path
from typing import Literal
from fastapi import APIRouter, HTTPException, Request, status
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
from deerflow.config.extensions_config import ExtensionsConfig, get_extensions_config, reload_extensions_config
@@ -13,11 +12,6 @@ logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api", tags=["mcp"])
_MCP_STDIO_COMMAND_ALLOWLIST_ENV = "DEER_FLOW_MCP_STDIO_COMMAND_ALLOWLIST"
_DEFAULT_MCP_STDIO_COMMAND_ALLOWLIST = frozenset({"npx", "uvx"})
_SHELL_METACHARS = frozenset(";|&`$<>\n\r")
class McpOAuthConfigResponse(BaseModel):
"""OAuth configuration for an MCP server."""
@@ -69,178 +63,13 @@ class McpConfigUpdateRequest(BaseModel):
)
_MASKED_VALUE = "***"
async def _require_admin_user(request: Request) -> None:
"""Require the authenticated caller to be an admin user.
``AuthMiddleware`` normally stamps ``request.state.user`` before the
request reaches this router. Falling back to the strict dependency keeps
this route safe even in tests or alternative ASGI compositions that mount
the router without the global middleware.
"""
user = getattr(request.state, "user", None)
if user is None:
from app.gateway.deps import get_current_user_from_request
user = await get_current_user_from_request(request)
if getattr(user, "system_role", None) != "admin":
raise HTTPException(
status_code=status.HTTP_403_FORBIDDEN,
detail="Admin privileges required to manage MCP configuration.",
)
def _allowed_stdio_commands() -> set[str]:
"""Return executable names allowed for API-managed stdio MCP servers."""
raw = os.environ.get(_MCP_STDIO_COMMAND_ALLOWLIST_ENV)
base = set(_DEFAULT_MCP_STDIO_COMMAND_ALLOWLIST)
if raw is None:
return base
extra = {item.strip() for item in raw.split(",") if item.strip()}
return base | extra
def _stdio_command_name(command: str | None, *, server_name: str) -> str:
"""Normalize and validate a stdio command field from the API boundary."""
if command is None or not command.strip():
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=f"MCP server '{server_name}' with stdio transport requires a command.",
)
stripped = command.strip()
has_path_separator = "/" in stripped or "\\" in stripped
if stripped != command or has_path_separator or any(ch.isspace() for ch in stripped) or any(ch in stripped for ch in _SHELL_METACHARS):
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=(f"MCP server '{server_name}' command must be a single executable name; put parameters in args instead."),
)
return stripped
def _validate_mcp_update_request(request: McpConfigUpdateRequest) -> None:
"""Validate API-submitted MCP config before it is persisted.
Local config files can still express arbitrary advanced setups, but the
HTTP API is an untrusted boundary. Restricting stdio commands here reduces
the blast radius of a compromised authenticated browser session.
"""
allowed_commands = _allowed_stdio_commands()
for name, server in request.mcp_servers.items():
transport_type = (server.type or "stdio").lower()
if transport_type != "stdio":
continue
command_name = _stdio_command_name(server.command, server_name=name)
if command_name not in allowed_commands:
allowed = ", ".join(sorted(allowed_commands)) or "<none>"
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=(f"MCP server '{name}' uses disallowed stdio command '{command_name}'. Allowed commands: {allowed}. Configure {_MCP_STDIO_COMMAND_ALLOWLIST_ENV} to extend this list."),
)
def _mask_server_config(server: McpServerConfigResponse) -> McpServerConfigResponse:
"""Return a copy of server config with sensitive fields masked.
Masks env values, header values, and removes OAuth secrets so they
are not exposed through the GET API endpoint.
"""
masked_env = {k: _MASKED_VALUE for k in server.env}
masked_headers = {k: _MASKED_VALUE for k in server.headers}
masked_oauth = None
if server.oauth is not None:
masked_oauth = server.oauth.model_copy(
update={
"client_secret": None,
"refresh_token": None,
}
)
return server.model_copy(
update={
"env": masked_env,
"headers": masked_headers,
"oauth": masked_oauth,
}
)
def _merge_preserving_secrets(
incoming: McpServerConfigResponse,
existing: McpServerConfigResponse,
) -> McpServerConfigResponse:
"""Merge incoming config with existing, preserving secrets masked by GET.
When the frontend toggles ``enabled`` it round-trips the full config:
GET (masked) modify enabled PUT (masked values sent back).
This function ensures masked values (``***``) are replaced with the
real secrets from the current on-disk config.
``***`` is only accepted for keys that already exist in *existing*.
New keys must provide a real value.
For OAuth secrets, ``None`` means "preserve the existing stored value"
so masked GET responses can be safely round-tripped. To explicitly clear
a stored secret, clients may send an empty string, which is converted
to ``None`` before persisting.
"""
merged_env = {}
for k, v in incoming.env.items():
if v == _MASKED_VALUE:
if k in existing.env:
merged_env[k] = existing.env[k]
else:
raise HTTPException(
status_code=400,
detail=f"Cannot set env key '{k}' to masked value '***'; provide a real value.",
)
else:
merged_env[k] = v
merged_headers = {}
for k, v in incoming.headers.items():
if v == _MASKED_VALUE:
if k in existing.headers:
merged_headers[k] = existing.headers[k]
else:
raise HTTPException(
status_code=400,
detail=f"Cannot set header '{k}' to masked value '***'; provide a real value.",
)
else:
merged_headers[k] = v
merged_oauth = incoming.oauth
if incoming.oauth is not None and existing.oauth is not None:
# None = preserve (masked round-trip), "" = explicitly clear, else = new value
merged_client_secret = existing.oauth.client_secret if incoming.oauth.client_secret is None else (None if incoming.oauth.client_secret == "" else incoming.oauth.client_secret)
merged_refresh_token = existing.oauth.refresh_token if incoming.oauth.refresh_token is None else (None if incoming.oauth.refresh_token == "" else incoming.oauth.refresh_token)
merged_oauth = incoming.oauth.model_copy(
update={
"client_secret": merged_client_secret,
"refresh_token": merged_refresh_token,
}
)
return incoming.model_copy(
update={
"env": merged_env,
"headers": merged_headers,
"oauth": merged_oauth,
}
)
@router.get(
"/mcp/config",
response_model=McpConfigResponse,
summary="Get MCP Configuration",
description="Retrieve the current Model Context Protocol (MCP) server configurations.",
)
async def get_mcp_configuration(request: Request) -> McpConfigResponse:
async def get_mcp_configuration() -> McpConfigResponse:
"""Get the current MCP configuration.
Returns:
@@ -254,19 +83,16 @@ async def get_mcp_configuration(request: Request) -> McpConfigResponse:
"enabled": true,
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {"GITHUB_TOKEN": "***"},
"env": {"GITHUB_TOKEN": "ghp_xxx"},
"description": "GitHub MCP server for repository operations"
}
}
}
```
"""
await _require_admin_user(request)
config = get_extensions_config()
servers = {name: _mask_server_config(McpServerConfigResponse(**server.model_dump())) for name, server in config.mcp_servers.items()}
return McpConfigResponse(mcp_servers=servers)
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in config.mcp_servers.items()})
@router.put(
@@ -275,7 +101,7 @@ async def get_mcp_configuration(request: Request) -> McpConfigResponse:
summary="Update MCP Configuration",
description="Update Model Context Protocol (MCP) server configurations and save to file.",
)
async def update_mcp_configuration(request: Request, body: McpConfigUpdateRequest) -> McpConfigResponse:
async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfigResponse:
"""Update the MCP configuration.
This will:
@@ -308,9 +134,6 @@ async def update_mcp_configuration(request: Request, body: McpConfigUpdateReques
```
"""
try:
await _require_admin_user(request)
_validate_mcp_update_request(body)
# Get the current config path (or determine where to save it)
config_path = ExtensionsConfig.resolve_config_path()
@@ -319,39 +142,14 @@ async def update_mcp_configuration(request: Request, body: McpConfigUpdateReques
config_path = Path.cwd().parent / "extensions_config.json"
logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
# Load current config to preserve skills
# Load current config to preserve skills configuration
current_config = get_extensions_config()
# Load raw (un-resolved) JSON from disk to use as the merge source.
# This preserves $VAR placeholders in env values and top-level keys
# like mcpInterceptors that would otherwise be lost.
raw_servers: dict[str, dict] = {}
raw_other_keys: dict = {}
if config_path is not None and config_path.exists():
with open(config_path, encoding="utf-8") as f:
raw_data = json.load(f)
raw_servers = raw_data.get("mcpServers", {})
# Preserve any top-level keys beyond mcpServers/skills
for key, value in raw_data.items():
if key not in ("mcpServers", "skills"):
raw_other_keys[key] = value
# Merge incoming server configs with raw on-disk secrets
merged_servers: dict[str, McpServerConfigResponse] = {}
for name, incoming in body.mcp_servers.items():
raw_server = raw_servers.get(name)
if raw_server is not None:
merged_servers[name] = _merge_preserving_secrets(
incoming,
McpServerConfigResponse(**raw_server),
)
else:
merged_servers[name] = incoming
# Build config data preserving all top-level keys from the original file
config_data = dict(raw_other_keys)
config_data["mcpServers"] = {name: server.model_dump() for name, server in merged_servers.items()}
config_data["skills"] = {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()}
# 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_config.skills.items()},
}
# Write the configuration to file
with open(config_path, "w", encoding="utf-8") as f:
@@ -359,15 +157,13 @@ async def update_mcp_configuration(request: Request, body: McpConfigUpdateReques
logger.info(f"MCP configuration updated and saved to: {config_path}")
# Reload the Gateway configuration and update the global cache. The
# agent runtime lives in Gateway, so this keeps API reads and tool
# execution aligned after extensions_config.json changes.
reloaded_config = reload_extensions_config()
servers = {name: _mask_server_config(McpServerConfigResponse(**server.model_dump())) for name, server in reloaded_config.mcp_servers.items()}
return McpConfigResponse(mcp_servers=servers)
# 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 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 HTTPException:
raise
except Exception as e:
logger.error(f"Failed to update MCP configuration: {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to update MCP configuration: {str(e)}")
+7 -10
View File
@@ -13,7 +13,6 @@ from deerflow.agents.memory.updater import (
update_memory_fact,
)
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"])
@@ -148,7 +147,7 @@ async def get_memory() -> MemoryResponse:
}
```
"""
memory_data = get_memory_data(user_id=get_effective_user_id())
memory_data = get_memory_data()
return MemoryResponse(**memory_data)
@@ -168,7 +167,7 @@ async def reload_memory() -> MemoryResponse:
Returns:
The reloaded memory data.
"""
memory_data = reload_memory_data(user_id=get_effective_user_id())
memory_data = reload_memory_data()
return MemoryResponse(**memory_data)
@@ -182,7 +181,7 @@ async def reload_memory() -> MemoryResponse:
async def clear_memory() -> MemoryResponse:
"""Clear all persisted memory data."""
try:
memory_data = clear_memory_data(user_id=get_effective_user_id())
memory_data = clear_memory_data()
except OSError as exc:
raise HTTPException(status_code=500, detail="Failed to clear memory data.") from exc
@@ -203,7 +202,6 @@ async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryRespo
content=request.content,
category=request.category,
confidence=request.confidence,
user_id=get_effective_user_id(),
)
except ValueError as exc:
raise _map_memory_fact_value_error(exc) from exc
@@ -223,7 +221,7 @@ async def create_memory_fact_endpoint(request: FactCreateRequest) -> MemoryRespo
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(fact_id, user_id=get_effective_user_id())
memory_data = delete_memory_fact(fact_id)
except KeyError as exc:
raise HTTPException(status_code=404, detail=f"Memory fact '{fact_id}' not found.") from exc
except OSError as exc:
@@ -247,7 +245,6 @@ async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -
content=request.content,
category=request.category,
confidence=request.confidence,
user_id=get_effective_user_id(),
)
except ValueError as exc:
raise _map_memory_fact_value_error(exc) from exc
@@ -268,7 +265,7 @@ async def update_memory_fact_endpoint(fact_id: str, request: FactPatchRequest) -
)
async def export_memory() -> MemoryResponse:
"""Export the current memory data."""
memory_data = get_memory_data(user_id=get_effective_user_id())
memory_data = get_memory_data()
return MemoryResponse(**memory_data)
@@ -282,7 +279,7 @@ async def export_memory() -> MemoryResponse:
async def import_memory(request: MemoryResponse) -> MemoryResponse:
"""Import and persist memory data."""
try:
memory_data = import_memory_data(request.model_dump(), user_id=get_effective_user_id())
memory_data = import_memory_data(request.model_dump())
except OSError as exc:
raise HTTPException(status_code=500, detail="Failed to import memory data.") from exc
@@ -340,7 +337,7 @@ async def get_memory_status() -> MemoryStatusResponse:
Combined memory configuration and current data.
"""
config = get_memory_config()
memory_data = get_memory_data(user_id=get_effective_user_id())
memory_data = get_memory_data()
return MemoryStatusResponse(
config=MemoryConfigResponse(
+11 -27
View File
@@ -1,8 +1,7 @@
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.app_config import AppConfig
from deerflow.config import get_app_config
router = APIRouter(prefix="/api", tags=["models"])
@@ -18,17 +17,10 @@ class ModelResponse(BaseModel):
supports_reasoning_effort: bool = Field(default=False, description="Whether model supports reasoning effort")
class TokenUsageResponse(BaseModel):
"""Token usage display configuration."""
enabled: bool = Field(default=False, description="Whether token usage display is enabled")
class ModelsListResponse(BaseModel):
"""Response model for listing all models."""
models: list[ModelResponse]
token_usage: TokenUsageResponse
@router.get(
@@ -37,14 +29,14 @@ class ModelsListResponse(BaseModel):
summary="List All Models",
description="Retrieve a list of all available AI models configured in the system.",
)
async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResponse:
async def list_models() -> ModelsListResponse:
"""List all available models from configuration.
Returns model information suitable for frontend display,
excluding sensitive fields like API keys and internal configuration.
Returns:
A list of all configured models with their metadata and token usage display settings.
A list of all configured models with their metadata.
Example Response:
```json
@@ -52,27 +44,21 @@ async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResp
"models": [
{
"name": "gpt-4",
"model": "gpt-4",
"display_name": "GPT-4",
"description": "OpenAI GPT-4 model",
"supports_thinking": false,
"supports_reasoning_effort": false
"supports_thinking": false
},
{
"name": "claude-3-opus",
"model": "claude-3-opus",
"display_name": "Claude 3 Opus",
"description": "Anthropic Claude 3 Opus model",
"supports_thinking": true,
"supports_reasoning_effort": false
"supports_thinking": true
}
],
"token_usage": {
"enabled": true
}
]
}
```
"""
config = get_app_config()
models = [
ModelResponse(
name=model.name,
@@ -84,10 +70,7 @@ async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResp
)
for model in config.models
]
return ModelsListResponse(
models=models,
token_usage=TokenUsageResponse(enabled=config.token_usage.enabled),
)
return ModelsListResponse(models=models)
@router.get(
@@ -96,7 +79,7 @@ async def list_models(config: AppConfig = Depends(get_config)) -> ModelsListResp
summary="Get Model Details",
description="Retrieve detailed information about a specific AI model by its name.",
)
async def get_model(model_name: str, config: AppConfig = Depends(get_config)) -> ModelResponse:
async def get_model(model_name: str) -> ModelResponse:
"""Get a specific model by name.
Args:
@@ -118,6 +101,7 @@ async def get_model(model_name: str, config: AppConfig = Depends(get_config)) ->
}
```
"""
config = get_app_config()
model = config.get_model_config(model_name)
if model is None:
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
+18 -74
View File
@@ -7,17 +7,16 @@ is reused so that conversation history is preserved across calls.
from __future__ import annotations
import asyncio
import logging
import uuid
from fastapi import APIRouter, HTTPException, Query, Request
from fastapi import APIRouter, Request
from fastapi.responses import StreamingResponse
from app.gateway.authz import require_permission
from app.gateway.deps import get_checkpointer, get_feedback_repo, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
from app.gateway.pagination import trim_run_message_page
from app.gateway.deps import get_checkpointer, get_run_manager, get_stream_bridge
from app.gateway.routers.thread_runs import RunCreateRequest
from app.gateway.services import sse_consumer, start_run, wait_for_run_completion
from app.gateway.services import sse_consumer, start_run
from deerflow.runtime import serialize_channel_values
logger = logging.getLogger(__name__)
@@ -66,78 +65,23 @@ async def stateless_wait(body: RunCreateRequest, request: Request) -> dict:
Otherwise a new temporary thread is created.
"""
thread_id = _resolve_thread_id(body)
bridge = get_stream_bridge(request)
run_mgr = get_run_manager(request)
record = await start_run(body, thread_id, request)
completed = True
if record.task is not None:
completed = await wait_for_run_completion(bridge, record, request, run_mgr)
if completed:
checkpointer = get_checkpointer(request)
config = {"configurable": {"thread_id": thread_id}}
try:
checkpoint_tuple = await checkpointer.aget_tuple(config)
if checkpoint_tuple is not None:
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
channel_values = checkpoint.get("channel_values", {})
return serialize_channel_values(channel_values)
except Exception:
logger.exception("Failed to fetch final state for run %s", record.run_id)
await record.task
except asyncio.CancelledError:
pass
checkpointer = get_checkpointer(request)
config = {"configurable": {"thread_id": thread_id}}
try:
checkpoint_tuple = await checkpointer.aget_tuple(config)
if checkpoint_tuple is not None:
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
channel_values = checkpoint.get("channel_values", {})
return serialize_channel_values(channel_values)
except Exception:
logger.exception("Failed to fetch final state for run %s", record.run_id)
return {"status": record.status.value, "error": record.error}
# ---------------------------------------------------------------------------
# Run-scoped read endpoints
# ---------------------------------------------------------------------------
async def _resolve_run(run_id: str, request: Request) -> dict:
"""Fetch run by run_id with user ownership check. Raises 404 if not found."""
run_store = get_run_store(request)
record = await run_store.get(run_id) # user_id=AUTO filters by contextvar
if record is None:
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
return record
@router.get("/{run_id}/messages")
@require_permission("runs", "read")
async def run_messages(
run_id: str,
request: Request,
limit: int = Query(default=50, le=200, ge=1),
before_seq: int | None = Query(default=None),
after_seq: int | None = Query(default=None),
) -> dict:
"""Return paginated messages for a run (cursor-based).
Pagination:
- after_seq: messages with seq > after_seq (forward)
- before_seq: messages with seq < before_seq (backward)
- neither: latest messages
Response: { data: [...], has_more: bool }
"""
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,
limit=limit + 1,
before_seq=before_seq,
after_seq=after_seq,
)
data, has_more = trim_run_message_page(rows, limit=limit, after_seq=after_seq)
return {"data": data, "has_more": has_more}
@router.get("/{run_id}/feedback")
@require_permission("runs", "read")
async def run_feedback(run_id: str, request: Request) -> list[dict]:
"""Return all feedback for a run."""
run = await _resolve_run(run_id, request)
feedback_repo = get_feedback_repo(request)
return await feedback_repo.list_by_run(run["thread_id"], run_id)
+69 -67
View File
@@ -1,20 +1,29 @@
import json
import logging
import shutil
from pathlib import Path
from fastapi import APIRouter, Depends, HTTPException
from fastapi import APIRouter, 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.agents.lead_agent.prompt import clear_skills_system_prompt_cache
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 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.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__)
@@ -27,7 +36,7 @@ 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: SkillCategory = Field(..., description="Category of the skill (public or custom)")
category: str = Field(..., description="Category of the skill (public or custom)")
enabled: bool = Field(default=True, description="Whether this skill is enabled")
@@ -91,9 +100,9 @@ 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(config: AppConfig = Depends(get_config)) -> SkillsListResponse:
async def list_skills() -> SkillsListResponse:
try:
skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
skills = 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)
@@ -106,11 +115,10 @@ async def list_skills(config: AppConfig = Depends(get_config)) -> SkillsListResp
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, config: AppConfig = Depends(get_config)) -> SkillInstallResponse:
async def install_skill(request: SkillInstallRequest) -> SkillInstallResponse:
try:
skill_file_path = resolve_thread_virtual_path(request.thread_id, request.path)
result = await get_or_new_skill_storage(app_config=config).ainstall_skill_from_archive(skill_file_path)
await refresh_skills_system_prompt_cache_async()
result = install_skill_from_archive(skill_file_path)
return SkillInstallResponse(**result)
except FileNotFoundError as e:
raise HTTPException(status_code=404, detail=str(e))
@@ -126,9 +134,9 @@ async def install_skill(request: SkillInstallRequest, config: AppConfig = Depend
@router.get("/skills/custom", response_model=SkillsListResponse, summary="List Custom Skills")
async def list_custom_skills(config: AppConfig = Depends(get_config)) -> SkillsListResponse:
async def list_custom_skills() -> SkillsListResponse:
try:
skills = [skill for skill in get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False) if skill.category == SkillCategory.CUSTOM]
skills = [skill for skill in load_skills(enabled_only=False) if skill.category == "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)
@@ -136,14 +144,13 @@ async def list_custom_skills(config: AppConfig = Depends(get_config)) -> SkillsL
@router.get("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Get Custom Skill Content")
async def get_custom_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
async def get_custom_skill(skill_name: str) -> CustomSkillContentResponse:
try:
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)
skills = load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == skill_name and s.category == "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=get_or_new_skill_storage(app_config=config).read_custom_skill(skill_name))
return CustomSkillContentResponse(**_skill_to_response(skill).model_dump(), content=read_custom_skill_content(skill_name))
except HTTPException:
raise
except Exception as e:
@@ -152,31 +159,30 @@ async def get_custom_skill(skill_name: str, config: AppConfig = Depends(get_conf
@router.put("/skills/custom/{skill_name}", response_model=CustomSkillContentResponse, summary="Edit Custom Skill")
async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest) -> CustomSkillContentResponse:
try:
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)
ensure_custom_skill_is_editable(skill_name)
validate_skill_markdown_content(skill_name, request.content)
scan = await scan_skill_content(request.content, executable=False, location=f"{skill_name}/SKILL.md")
if scan.decision == "block":
raise HTTPException(status_code=400, detail=f"Security scan blocked the edit: {scan.reason}")
prev_content = storage.read_custom_skill(skill_name)
storage.write_custom_skill(skill_name, SKILL_MD_FILE, request.content)
storage.append_history(
skill_file = get_custom_skill_dir(skill_name) / "SKILL.md"
prev_content = skill_file.read_text(encoding="utf-8")
atomic_write(skill_file, request.content)
append_history(
skill_name,
{
"action": "human_edit",
"author": "human",
"thread_id": None,
"file_path": SKILL_MD_FILE,
"file_path": "SKILL.md",
"prev_content": prev_content,
"new_content": request.content,
"scanner": {"decision": scan.decision, "reason": scan.reason},
},
)
await refresh_skills_system_prompt_cache_async()
return await get_custom_skill(skill_name, config)
clear_skills_system_prompt_cache()
return await get_custom_skill(skill_name)
except HTTPException:
raise
except FileNotFoundError as e:
@@ -189,23 +195,25 @@ async def update_custom_skill(skill_name: str, request: CustomSkillUpdateRequest
@router.delete("/skills/custom/{skill_name}", summary="Delete Custom Skill")
async def delete_custom_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> dict[str, bool]:
async def delete_custom_skill(skill_name: str) -> dict[str, bool]:
try:
skill_name = skill_name.replace("\r\n", "").replace("\n", "")
storage = get_or_new_skill_storage(app_config=config)
storage.delete_custom_skill(
ensure_custom_skill_is_editable(skill_name)
skill_dir = get_custom_skill_dir(skill_name)
prev_content = read_custom_skill_content(skill_name)
append_history(
skill_name,
history_meta={
{
"action": "human_delete",
"author": "human",
"thread_id": None,
"file_path": SKILL_MD_FILE,
"prev_content": None,
"file_path": "SKILL.md",
"prev_content": prev_content,
"new_content": None,
"scanner": {"decision": "allow", "reason": "Deletion requested."},
},
)
await refresh_skills_system_prompt_cache_async()
shutil.rmtree(skill_dir)
clear_skills_system_prompt_cache()
return {"success": True}
except FileNotFoundError as e:
raise HTTPException(status_code=404, detail=str(e))
@@ -217,13 +225,11 @@ async def delete_custom_skill(skill_name: str, config: AppConfig = Depends(get_c
@router.get("/skills/custom/{skill_name}/history", response_model=CustomSkillHistoryResponse, summary="Get Custom Skill History")
async def get_custom_skill_history(skill_name: str, config: AppConfig = Depends(get_config)) -> CustomSkillHistoryResponse:
async def get_custom_skill_history(skill_name: str) -> CustomSkillHistoryResponse:
try:
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():
if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
return CustomSkillHistoryResponse(history=storage.read_history(skill_name))
return CustomSkillHistoryResponse(history=read_history(skill_name))
except HTTPException:
raise
except Exception as e:
@@ -232,39 +238,38 @@ async def get_custom_skill_history(skill_name: str, config: AppConfig = Depends(
@router.post("/skills/custom/{skill_name}/rollback", response_model=CustomSkillContentResponse, summary="Rollback Custom Skill")
async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest, config: AppConfig = Depends(get_config)) -> CustomSkillContentResponse:
async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest) -> CustomSkillContentResponse:
try:
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():
if not custom_skill_exists(skill_name) and not get_skill_history_file(skill_name).exists():
raise HTTPException(status_code=404, detail=f"Custom skill '{skill_name}' not found")
history = storage.read_history(skill_name)
history = 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")
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)
validate_skill_markdown_content(skill_name, target_content)
scan = await scan_skill_content(target_content, executable=False, location=f"{skill_name}/SKILL.md")
skill_file = 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,
"file_path": "SKILL.md",
"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":
storage.append_history(skill_name, history_entry)
append_history(skill_name, history_entry)
raise HTTPException(status_code=400, detail=f"Rollback blocked by security scanner: {scan.reason}")
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)
atomic_write(skill_file, target_content)
append_history(skill_name, history_entry)
clear_skills_system_prompt_cache()
return await get_custom_skill(skill_name)
except HTTPException:
raise
except IndexError:
@@ -284,10 +289,9 @@ async def rollback_custom_skill(skill_name: str, request: SkillRollbackRequest,
summary="Get Skill Details",
description="Retrieve detailed information about a specific skill by its name.",
)
async def get_skill(skill_name: str, config: AppConfig = Depends(get_config)) -> SkillResponse:
async def get_skill(skill_name: str) -> SkillResponse:
try:
skill_name = skill_name.replace("\r\n", "").replace("\n", "")
skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
skills = load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == skill_name), None)
if skill is None:
@@ -307,10 +311,9 @@ async def get_skill(skill_name: str, 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, config: AppConfig = Depends(get_config)) -> SkillResponse:
async def update_skill(skill_name: str, request: SkillUpdateRequest) -> SkillResponse:
try:
skill_name = skill_name.replace("\r\n", "").replace("\n", "")
skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
skills = load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == skill_name), None)
if skill is None:
@@ -334,9 +337,8 @@ async def update_skill(skill_name: str, request: SkillUpdateRequest, config: App
logger.info(f"Skills configuration updated and saved to: {config_path}")
reload_extensions_config()
await refresh_skills_system_prompt_cache_async()
skills = get_or_new_skill_storage(app_config=config).load_skills(enabled_only=False)
skills = load_skills(enabled_only=False)
updated_skill = next((s for s in skills if s.name == skill_name), None)
if updated_skill is None:
+8 -44
View File
@@ -1,14 +1,10 @@
import json
import logging
import re
from fastapi import APIRouter, Depends, Request
from fastapi import APIRouter
from langchain_core.messages import HumanMessage, SystemMessage
from pydantic import BaseModel, Field
from app.gateway.authz import require_permission
from app.gateway.deps import get_config
from deerflow.config.app_config import AppConfig
from deerflow.models import create_chat_model
logger = logging.getLogger(__name__)
@@ -31,31 +27,6 @@ class SuggestionsResponse(BaseModel):
suggestions: list[str] = Field(default_factory=list, description="Suggested follow-up questions")
# Matches a complete <think>...</think> block (case-insensitive, spans newlines).
_THINK_BLOCK_RE = re.compile(r"<think\b[^>]*>.*?</think\s*>", re.IGNORECASE | re.DOTALL)
# Matches a dangling, unclosed <think> (model truncated at max_tokens mid-thought).
_OPEN_THINK_RE = re.compile(r"<think\b[^>]*>", re.IGNORECASE)
def _strip_think_blocks(text: str) -> str:
"""Remove reasoning-model ``<think>...</think>`` blocks from the response.
Reasoning models such as MiniMax-M3 inline their chain-of-thought into the
message ``content`` wrapped in ``<think>...</think>`` (``reasoning_split``
defaults to false), rather than exposing a separate ``reasoning_content``
field. The thinking text frequently contains ``[`` / ``]`` characters, which
corrupted the downstream ``find('[')`` / ``rfind(']')`` JSON extraction and
produced empty suggestions. We strip the reasoning before parsing so only
the actual answer remains.
"""
text = _THINK_BLOCK_RE.sub("", text)
# Drop any unclosed <think> (and everything after it) left by truncation.
open_match = _OPEN_THINK_RE.search(text)
if open_match:
text = text[: open_match.start()]
return text.strip()
def _strip_markdown_code_fence(text: str) -> str:
stripped = text.strip()
if not stripped.startswith("```"):
@@ -67,8 +38,7 @@ def _strip_markdown_code_fence(text: str) -> str:
def _parse_json_string_list(text: str) -> list[str] | None:
candidate = _strip_think_blocks(text)
candidate = _strip_markdown_code_fence(candidate)
candidate = _strip_markdown_code_fence(text)
start = candidate.find("[")
end = candidate.rfind("]")
if start == -1 or end == -1 or end <= start:
@@ -128,18 +98,12 @@ def _format_conversation(messages: list[SuggestionMessage]) -> str:
summary="Generate Follow-up Questions",
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,
config: AppConfig = Depends(get_config),
) -> SuggestionsResponse:
if not body.messages:
async def generate_suggestions(thread_id: str, request: SuggestionsRequest) -> SuggestionsResponse:
if not request.messages:
return SuggestionsResponse(suggestions=[])
n = body.n
conversation = _format_conversation(body.messages)
n = request.n
conversation = _format_conversation(request.messages)
if not conversation:
return SuggestionsResponse(suggestions=[])
@@ -156,8 +120,8 @@ async def generate_suggestions(
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=config)
response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)], config={"run_name": "suggest_agent"})
model = create_chat_model(name=request.model_name, thinking_enabled=False)
response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)])
raw = _extract_response_text(response.content)
suggestions = _parse_json_string_list(raw) or []
cleaned = [s.replace("\n", " ").strip() for s in suggestions if s.strip()]
+39 -162
View File
@@ -19,11 +19,9 @@ from fastapi import APIRouter, HTTPException, Query, Request
from fastapi.responses import Response, StreamingResponse
from pydantic import BaseModel, Field
from app.gateway.authz import require_permission
from app.gateway.deps import get_checkpointer, get_current_user, get_feedback_repo, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
from app.gateway.pagination import trim_run_message_page
from app.gateway.services import sse_consumer, start_run, wait_for_run_completion
from deerflow.runtime import RunRecord, RunStatus, serialize_channel_values
from app.gateway.deps import get_checkpointer, get_run_event_store, get_run_manager, get_run_store, get_stream_bridge
from app.gateway.services import sse_consumer, start_run
from deerflow.runtime import RunRecord, serialize_channel_values
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/threads", tags=["runs"])
@@ -55,6 +53,7 @@ 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):
@@ -67,35 +66,6 @@ class RunResponse(BaseModel):
multitask_strategy: str = "reject"
created_at: str = ""
updated_at: str = ""
total_input_tokens: int = 0
total_output_tokens: int = 0
total_tokens: int = 0
llm_call_count: int = 0
lead_agent_tokens: int = 0
subagent_tokens: int = 0
middleware_tokens: int = 0
message_count: int = 0
class ThreadTokenUsageModelBreakdown(BaseModel):
tokens: int = 0
runs: int = 0
class ThreadTokenUsageCallerBreakdown(BaseModel):
lead_agent: int = 0
subagent: int = 0
middleware: int = 0
class ThreadTokenUsageResponse(BaseModel):
thread_id: str
total_tokens: int = 0
total_input_tokens: int = 0
total_output_tokens: int = 0
total_runs: int = 0
by_model: dict[str, ThreadTokenUsageModelBreakdown] = Field(default_factory=dict)
by_caller: ThreadTokenUsageCallerBreakdown = Field(default_factory=ThreadTokenUsageCallerBreakdown)
# ---------------------------------------------------------------------------
@@ -103,12 +73,6 @@ class ThreadTokenUsageResponse(BaseModel):
# ---------------------------------------------------------------------------
def _cancel_conflict_detail(run_id: str, record: RunRecord) -> str:
if record.status in (RunStatus.pending, RunStatus.running):
return f"Run {run_id} is not active on this worker and cannot be cancelled"
return f"Run {run_id} is not cancellable (status: {record.status.value})"
def _record_to_response(record: RunRecord) -> RunResponse:
return RunResponse(
run_id=record.run_id,
@@ -120,14 +84,6 @@ def _record_to_response(record: RunRecord) -> RunResponse:
multitask_strategy=record.multitask_strategy,
created_at=record.created_at,
updated_at=record.updated_at,
total_input_tokens=record.total_input_tokens,
total_output_tokens=record.total_output_tokens,
total_tokens=record.total_tokens,
llm_call_count=record.llm_call_count,
lead_agent_tokens=record.lead_agent_tokens,
subagent_tokens=record.subagent_tokens,
middleware_tokens=record.middleware_tokens,
message_count=record.message_count,
)
@@ -137,7 +93,6 @@ def _record_to_response(record: RunRecord) -> RunResponse:
@router.post("/{thread_id}/runs", response_model=RunResponse)
@require_permission("runs", "create", owner_check=True, require_existing=True)
async def create_run(thread_id: str, body: RunCreateRequest, request: Request) -> RunResponse:
"""Create a background run (returns immediately)."""
record = await start_run(body, thread_id, request)
@@ -145,7 +100,6 @@ async def create_run(thread_id: str, body: RunCreateRequest, request: Request) -
@router.post("/{thread_id}/runs/stream")
@require_permission("runs", "create", owner_check=True, require_existing=True)
async def stream_run(thread_id: str, body: RunCreateRequest, request: Request) -> StreamingResponse:
"""Create a run and stream events via SSE.
@@ -173,56 +127,49 @@ async def stream_run(thread_id: str, body: RunCreateRequest, request: Request) -
@router.post("/{thread_id}/runs/wait", response_model=dict)
@require_permission("runs", "create", owner_check=True, require_existing=True)
async def wait_run(thread_id: str, body: RunCreateRequest, request: Request) -> dict:
"""Create a run and block until it completes, returning the final state."""
bridge = get_stream_bridge(request)
run_mgr = get_run_manager(request)
record = await start_run(body, thread_id, request)
completed = True
if record.task is not None:
completed = await wait_for_run_completion(bridge, record, request, run_mgr)
if completed:
checkpointer = get_checkpointer(request)
config = {"configurable": {"thread_id": thread_id}}
try:
checkpoint_tuple = await checkpointer.aget_tuple(config)
if checkpoint_tuple is not None:
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
channel_values = checkpoint.get("channel_values", {})
return serialize_channel_values(channel_values)
except Exception:
logger.exception("Failed to fetch final state for run %s", record.run_id)
await record.task
except asyncio.CancelledError:
pass
checkpointer = get_checkpointer(request)
config = {"configurable": {"thread_id": thread_id}}
try:
checkpoint_tuple = await checkpointer.aget_tuple(config)
if checkpoint_tuple is not None:
checkpoint = getattr(checkpoint_tuple, "checkpoint", {}) or {}
channel_values = checkpoint.get("channel_values", {})
return serialize_channel_values(channel_values)
except Exception:
logger.exception("Failed to fetch final state for run %s", record.run_id)
return {"status": record.status.value, "error": record.error}
@router.get("/{thread_id}/runs", response_model=list[RunResponse])
@require_permission("runs", "read", owner_check=True)
async def list_runs(thread_id: str, request: Request) -> list[RunResponse]:
"""List all runs for a thread."""
run_mgr = get_run_manager(request)
user_id = await get_current_user(request)
records = await run_mgr.list_by_thread(thread_id, user_id=user_id)
records = await run_mgr.list_by_thread(thread_id)
return [_record_to_response(r) for r in records]
@router.get("/{thread_id}/runs/{run_id}", response_model=RunResponse)
@require_permission("runs", "read", owner_check=True)
async def get_run(thread_id: str, run_id: str, request: Request) -> RunResponse:
"""Get details of a specific run."""
run_mgr = get_run_manager(request)
user_id = await get_current_user(request)
record = await run_mgr.get(run_id, user_id=user_id)
record = run_mgr.get(run_id)
if record is None or record.thread_id != thread_id:
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
return _record_to_response(record)
@router.post("/{thread_id}/runs/{run_id}/cancel")
@require_permission("runs", "cancel", owner_check=True, require_existing=True)
async def cancel_run(
thread_id: str,
run_id: str,
@@ -238,13 +185,16 @@ async def cancel_run(
- wait=false: Return immediately with 202
"""
run_mgr = get_run_manager(request)
record = await run_mgr.get(run_id)
record = run_mgr.get(run_id)
if record is None or record.thread_id != thread_id:
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
cancelled = await run_mgr.cancel(run_id, action=action)
if not cancelled:
raise HTTPException(status_code=409, detail=_cancel_conflict_detail(run_id, record))
raise HTTPException(
status_code=409,
detail=f"Run {run_id} is not cancellable (status: {record.status.value})",
)
if wait and record.task is not None:
try:
@@ -257,17 +207,14 @@ async def cancel_run(
@router.get("/{thread_id}/runs/{run_id}/join")
@require_permission("runs", "read", owner_check=True)
async def join_run(thread_id: str, run_id: str, request: Request) -> StreamingResponse:
"""Join an existing run's SSE stream."""
bridge = get_stream_bridge(request)
run_mgr = get_run_manager(request)
record = await run_mgr.get(run_id)
record = run_mgr.get(run_id)
if record is None or record.thread_id != thread_id:
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
if record.store_only:
raise HTTPException(status_code=409, detail=f"Run {run_id} is not active on this worker and cannot be streamed")
bridge = get_stream_bridge(request)
return StreamingResponse(
sse_consumer(bridge, record, request, run_mgr),
media_type="text/event-stream",
@@ -279,13 +226,7 @@ async def join_run(thread_id: str, run_id: str, request: Request) -> StreamingRe
)
# Register GET and POST as separate routes so each method gets a unique OpenAPI
# operationId. ``api_route(methods=["GET", "POST"])`` shares one route registration
# across both methods, which makes FastAPI emit the same ``operationId`` twice and
# warn about a duplicate operation id during OpenAPI generation.
@router.get("/{thread_id}/runs/{run_id}/stream", response_model=None)
@router.post("/{thread_id}/runs/{run_id}/stream", response_model=None)
@require_permission("runs", "read", owner_check=True)
@router.api_route("/{thread_id}/runs/{run_id}/stream", methods=["GET", "POST"], response_model=None)
async def stream_existing_run(
thread_id: str,
run_id: str,
@@ -301,18 +242,14 @@ async def stream_existing_run(
remaining buffered events so the client observes a clean shutdown.
"""
run_mgr = get_run_manager(request)
record = await run_mgr.get(run_id)
record = run_mgr.get(run_id)
if record is None or record.thread_id != thread_id:
raise HTTPException(status_code=404, detail=f"Run {run_id} not found")
if record.store_only and action is None:
raise HTTPException(status_code=409, detail=f"Run {run_id} is not active on this worker and cannot be streamed")
# Cancel if an action was requested (stop-button / interrupt flow)
if action is not None:
cancelled = await run_mgr.cancel(run_id, action=action)
if not cancelled:
raise HTTPException(status_code=409, detail=_cancel_conflict_detail(run_id, record))
if wait and record.task is not None:
if cancelled and wait and record.task is not None:
try:
await record.task
except (asyncio.CancelledError, Exception):
@@ -337,7 +274,6 @@ async def stream_existing_run(
@router.get("/{thread_id}/messages")
@require_permission("runs", "read", owner_check=True)
async def list_thread_messages(
thread_id: str,
request: Request,
@@ -345,70 +281,19 @@ async def list_thread_messages(
before_seq: int | None = Query(default=None),
after_seq: int | None = Query(default=None),
) -> list[dict]:
"""Return displayable messages for a thread (across all runs), with feedback attached."""
"""Return displayable messages for a thread (across all runs)."""
event_store = get_run_event_store(request)
messages = await event_store.list_messages(thread_id, limit=limit, before_seq=before_seq, after_seq=after_seq)
# Attach feedback to the last AI message of each run
feedback_repo = get_feedback_repo(request)
user_id = await get_current_user(request)
feedback_map = await feedback_repo.list_by_thread_grouped(thread_id, user_id=user_id)
# Find the last ai_message per run_id
last_ai_per_run: dict[str, int] = {} # run_id -> index in messages list
for i, msg in enumerate(messages):
if msg.get("event_type") == "ai_message":
last_ai_per_run[msg["run_id"]] = i
# Attach feedback field
last_ai_indices = set(last_ai_per_run.values())
for i, msg in enumerate(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
)
else:
msg["feedback"] = None
return messages
return await event_store.list_messages(thread_id, limit=limit, before_seq=before_seq, after_seq=after_seq)
@router.get("/{thread_id}/runs/{run_id}/messages")
@require_permission("runs", "read", owner_check=True)
async def list_run_messages(
thread_id: str,
run_id: str,
request: Request,
limit: int = Query(default=50, le=200, ge=1),
before_seq: int | None = Query(default=None),
after_seq: int | None = Query(default=None),
) -> dict:
"""Return paginated messages for a specific run.
Response: { data: [...], has_more: bool }
"""
async def list_run_messages(thread_id: str, run_id: str, request: Request) -> list[dict]:
"""Return displayable messages for a specific run."""
event_store = get_run_event_store(request)
rows = await event_store.list_messages_by_run(
thread_id,
run_id,
limit=limit + 1,
before_seq=before_seq,
after_seq=after_seq,
)
data, has_more = trim_run_message_page(rows, limit=limit, after_seq=after_seq)
return {"data": data, "has_more": has_more}
return await event_store.list_messages_by_run(thread_id, run_id)
@router.get("/{thread_id}/runs/{run_id}/events")
@require_permission("runs", "read", owner_check=True)
async def list_run_events(
thread_id: str,
run_id: str,
@@ -422,17 +307,9 @@ async def list_run_events(
return await event_store.list_events(thread_id, run_id, event_types=types, limit=limit)
@router.get("/{thread_id}/token-usage", response_model=ThreadTokenUsageResponse)
@require_permission("threads", "read", owner_check=True)
async def thread_token_usage(
thread_id: str,
request: Request,
include_active: bool = Query(default=False, description="Include running run progress snapshots"),
) -> ThreadTokenUsageResponse:
@router.get("/{thread_id}/token-usage")
async def thread_token_usage(thread_id: str, request: Request) -> dict:
"""Thread-level token usage aggregation."""
run_store = get_run_store(request)
if include_active:
agg = await run_store.aggregate_tokens_by_thread(thread_id, include_active=True)
else:
agg = await run_store.aggregate_tokens_by_thread(thread_id)
return ThreadTokenUsageResponse(thread_id=thread_id, **agg)
agg = await run_store.aggregate_tokens_by_thread(thread_id)
return {"thread_id": thread_id, **agg}
+56 -130
View File
@@ -13,41 +13,22 @@ matching the LangGraph Platform wire format expected by the
from __future__ import annotations
import logging
import time
import uuid
from typing import Any
from fastapi import APIRouter, HTTPException, Request
from langgraph.checkpoint.base import empty_checkpoint, uuid6
from pydantic import BaseModel, Field, field_validator
from pydantic import BaseModel, Field
from app.gateway.authz import require_permission
from app.gateway.deps import get_checkpointer
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"])
# Metadata keys that the server controls; clients are not allowed to set
# them. Pydantic ``@field_validator("metadata")`` strips them on every
# inbound model below so a malicious client cannot reflect a forged
# owner identity through the API surface. Defense-in-depth — the
# row-level invariant is still ``threads_meta.user_id`` populated from
# the auth contextvar; this list closes the metadata-blob echo gap.
_SERVER_RESERVED_METADATA_KEYS: frozenset[str] = frozenset({"owner_id", "user_id"})
def _strip_reserved_metadata(metadata: dict[str, Any] | None) -> dict[str, Any]:
"""Return ``metadata`` with server-controlled keys removed."""
if not metadata:
return metadata or {}
return {k: v for k, v in metadata.items() if k not in _SERVER_RESERVED_METADATA_KEYS}
# ---------------------------------------------------------------------------
# Response / request models
# ---------------------------------------------------------------------------
@@ -79,8 +60,6 @@ class ThreadCreateRequest(BaseModel):
assistant_id: str | None = Field(default=None, description="Associate thread with an assistant")
metadata: dict[str, Any] = Field(default_factory=dict, description="Initial metadata")
_strip_reserved = field_validator("metadata")(classmethod(lambda cls, v: _strip_reserved_metadata(v)))
class ThreadSearchRequest(BaseModel):
"""Request body for searching threads."""
@@ -90,28 +69,6 @@ class ThreadSearchRequest(BaseModel):
offset: int = Field(default=0, ge=0, description="Pagination offset")
status: str | None = Field(default=None, description="Filter by thread status")
@field_validator("metadata")
@classmethod
def _validate_metadata_filters(cls, v: dict[str, Any]) -> dict[str, Any]:
"""Reject filter entries the SQL backend cannot compile.
Enforces consistent behaviour across SQL and memory backends.
See ``deerflow.persistence.json_compat`` for the shared validators.
"""
if not v:
return v
from deerflow.persistence.json_compat import validate_metadata_filter_key, validate_metadata_filter_value
bad_entries: list[str] = []
for key, value in v.items():
if not validate_metadata_filter_key(key):
bad_entries.append(f"{key!r} (unsafe key)")
elif not validate_metadata_filter_value(value):
bad_entries.append(f"{key!r} (unsupported value type {type(value).__name__})")
if bad_entries:
raise ValueError(f"Invalid metadata filter entries: {', '.join(bad_entries)}")
return v
class ThreadStateResponse(BaseModel):
"""Response model for thread state."""
@@ -131,8 +88,6 @@ class ThreadPatchRequest(BaseModel):
metadata: dict[str, Any] = Field(default_factory=dict, description="Metadata to merge")
_strip_reserved = field_validator("metadata")(classmethod(lambda cls, v: _strip_reserved_metadata(v)))
class ThreadStateUpdateRequest(BaseModel):
"""Request body for updating thread state (human-in-the-loop resume)."""
@@ -166,11 +121,11 @@ class ThreadHistoryRequest(BaseModel):
# ---------------------------------------------------------------------------
def _delete_thread_data(thread_id: str, paths: Paths | None = None, *, user_id: str | None = None) -> ThreadDeleteResponse:
def _delete_thread_data(thread_id: str, paths: Paths | None = None) -> ThreadDeleteResponse:
"""Delete local persisted filesystem data for a thread."""
path_manager = paths or get_paths()
try:
path_manager.delete_thread_dir(thread_id, user_id=user_id)
path_manager.delete_thread_dir(thread_id)
except ValueError as exc:
raise HTTPException(status_code=422, detail=str(exc)) from exc
except FileNotFoundError:
@@ -210,7 +165,6 @@ def _derive_thread_status(checkpoint_tuple) -> str:
@router.delete("/{thread_id}", response_model=ThreadDeleteResponse)
@require_permission("threads", "delete", owner_check=True, require_existing=True)
async def delete_thread_data(thread_id: str, request: Request) -> ThreadDeleteResponse:
"""Delete local persisted filesystem data for a thread.
@@ -218,10 +172,10 @@ async def delete_thread_data(thread_id: str, request: Request) -> ThreadDeleteRe
and removes the thread_meta row from the configured ThreadMetaStore
(sqlite or memory).
"""
from app.gateway.deps import get_thread_store
from app.gateway.deps import get_thread_meta_repo
# Clean local filesystem
response = _delete_thread_data(thread_id, user_id=get_effective_user_id())
response = _delete_thread_data(thread_id)
# Remove checkpoints (best-effort)
checkpointer = getattr(request.app.state, "checkpointer", None)
@@ -235,8 +189,8 @@ async def delete_thread_data(thread_id: str, request: Request) -> ThreadDeleteRe
# Remove thread_meta row (best-effort) — required for sqlite backend
# so the deleted thread no longer appears in /threads/search.
try:
thread_store = get_thread_store(request)
await thread_store.delete(thread_id)
thread_meta_repo = get_thread_meta_repo(request)
await thread_meta_repo.delete(thread_id)
except Exception:
logger.debug("Could not delete thread_meta for %s (not critical)", sanitize_log_param(thread_id))
@@ -251,29 +205,27 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
and an empty checkpoint (so state endpoints work immediately).
Idempotent: returns the existing record when ``thread_id`` already exists.
"""
from app.gateway.deps import get_thread_store
from app.gateway.deps import get_thread_meta_repo
checkpointer = get_checkpointer(request)
thread_store = get_thread_store(request)
thread_meta_repo = get_thread_meta_repo(request)
thread_id = body.thread_id or str(uuid.uuid4())
now = now_iso()
# ``body.metadata`` is already stripped of server-reserved keys by
# ``ThreadCreateRequest._strip_reserved`` — see the model definition.
now = time.time()
# Idempotency: return existing record when already present
existing_record = await thread_store.get(thread_id)
existing_record = await thread_meta_repo.get(thread_id)
if existing_record is not None:
return ThreadResponse(
thread_id=thread_id,
status=existing_record.get("status", "idle"),
created_at=coerce_iso(existing_record.get("created_at", "")),
updated_at=coerce_iso(existing_record.get("updated_at", "")),
created_at=str(existing_record.get("created_at", "")),
updated_at=str(existing_record.get("updated_at", "")),
metadata=existing_record.get("metadata", {}),
)
# Write thread_meta so the thread appears in /threads/search immediately
try:
await thread_store.create(
await thread_meta_repo.create(
thread_id,
assistant_id=getattr(body, "assistant_id", None),
metadata=body.metadata,
@@ -285,6 +237,8 @@ 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",
@@ -302,8 +256,8 @@ async def create_thread(body: ThreadCreateRequest, request: Request) -> ThreadRe
return ThreadResponse(
thread_id=thread_id,
status="idle",
created_at=now,
updated_at=now,
created_at=str(now),
updated_at=str(now),
metadata=body.metadata,
)
@@ -315,28 +269,21 @@ async def search_threads(body: ThreadSearchRequest, request: Request) -> list[Th
Delegates to the configured ThreadMetaStore implementation
(SQL-backed for sqlite/postgres, Store-backed for memory mode).
"""
from app.gateway.deps import get_thread_store
from deerflow.persistence.thread_meta import InvalidMetadataFilterError
from app.gateway.deps import get_thread_meta_repo
repo = get_thread_store(request)
try:
rows = await repo.search(
metadata=body.metadata or None,
status=body.status,
limit=body.limit,
offset=body.offset,
)
except InvalidMetadataFilterError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
repo = get_thread_meta_repo(request)
rows = await repo.search(
metadata=body.metadata or None,
status=body.status,
limit=body.limit,
offset=body.offset,
)
return [
ThreadResponse(
thread_id=r["thread_id"],
status=r.get("status", "idle"),
# ``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", "")),
created_at=r.get("created_at", ""),
updated_at=r.get("updated_at", ""),
metadata=r.get("metadata", {}),
values={"title": r["display_name"]} if r.get("display_name") else {},
interrupts={},
@@ -346,36 +293,33 @@ async def search_threads(body: ThreadSearchRequest, request: Request) -> list[Th
@router.patch("/{thread_id}", response_model=ThreadResponse)
@require_permission("threads", "write", owner_check=True, require_existing=True)
async def patch_thread(thread_id: str, body: ThreadPatchRequest, request: Request) -> ThreadResponse:
"""Merge metadata into a thread record."""
from app.gateway.deps import get_thread_store
from app.gateway.deps import get_thread_meta_repo
thread_store = get_thread_store(request)
record = await thread_store.get(thread_id)
thread_meta_repo = get_thread_meta_repo(request)
record = await thread_meta_repo.get(thread_id)
if record is None:
raise HTTPException(status_code=404, detail=f"Thread {thread_id} not found")
# ``body.metadata`` already stripped by ``ThreadPatchRequest._strip_reserved``.
try:
await thread_store.update_metadata(thread_id, body.metadata)
await thread_meta_repo.update_metadata(thread_id, body.metadata)
except Exception:
logger.exception("Failed to patch thread %s", sanitize_log_param(thread_id))
raise HTTPException(status_code=500, detail="Failed to update thread")
# Re-read to get the merged metadata + refreshed updated_at
record = await thread_store.get(thread_id) or record
record = await thread_meta_repo.get(thread_id) or record
return ThreadResponse(
thread_id=thread_id,
status=record.get("status", "idle"),
created_at=coerce_iso(record.get("created_at", "")),
updated_at=coerce_iso(record.get("updated_at", "")),
created_at=str(record.get("created_at", "")),
updated_at=str(record.get("updated_at", "")),
metadata=record.get("metadata", {}),
)
@router.get("/{thread_id}", response_model=ThreadResponse)
@require_permission("threads", "read", owner_check=True)
async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
"""Get thread info.
@@ -383,12 +327,12 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
execution status from the checkpointer. Falls back to the checkpointer
alone for threads that pre-date ThreadMetaStore adoption (backward compat).
"""
from app.gateway.deps import get_thread_store
from app.gateway.deps import get_thread_meta_repo
thread_store = get_thread_store(request)
thread_meta_repo = get_thread_meta_repo(request)
checkpointer = get_checkpointer(request)
record: dict | None = await thread_store.get(thread_id)
record: dict | None = await thread_meta_repo.get(thread_id)
# Derive accurate status from the checkpointer
config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
@@ -409,8 +353,8 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
record = {
"thread_id": thread_id,
"status": "idle",
"created_at": coerce_iso(ckpt_meta.get("created_at", "")),
"updated_at": coerce_iso(ckpt_meta.get("updated_at", ckpt_meta.get("created_at", ""))),
"created_at": ckpt_meta.get("created_at", ""),
"updated_at": 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")},
}
@@ -424,16 +368,14 @@ async def get_thread(thread_id: str, request: Request) -> ThreadResponse:
return ThreadResponse(
thread_id=thread_id,
status=status,
created_at=coerce_iso(record.get("created_at", "")),
updated_at=coerce_iso(record.get("updated_at", "")),
created_at=str(record.get("created_at", "")),
updated_at=str(record.get("updated_at", "")),
metadata=record.get("metadata", {}),
values=serialize_channel_values(channel_values),
)
# ---------------------------------------------------------------------------
@router.get("/{thread_id}/state", response_model=ThreadStateResponse)
@require_permission("threads", "read", owner_check=True)
async def get_thread_state(thread_id: str, request: Request) -> ThreadStateResponse:
"""Get the latest state snapshot for a thread.
@@ -470,22 +412,19 @@ async def get_thread_state(thread_id: str, request: Request) -> ThreadStateRespo
next_tasks = [t.name for t in tasks_raw if hasattr(t, "name")]
tasks = [{"id": getattr(t, "id", ""), "name": getattr(t, "name", "")} for t in tasks_raw]
values = serialize_channel_values(channel_values)
return ThreadStateResponse(
values=values,
values=serialize_channel_values(channel_values),
next=next_tasks,
metadata=metadata,
checkpoint={"id": checkpoint_id, "ts": coerce_iso(metadata.get("created_at", ""))},
checkpoint={"id": checkpoint_id, "ts": str(metadata.get("created_at", ""))},
checkpoint_id=checkpoint_id,
parent_checkpoint_id=parent_checkpoint_id,
created_at=coerce_iso(metadata.get("created_at", "")),
created_at=str(metadata.get("created_at", "")),
tasks=tasks,
)
@router.post("/{thread_id}/state", response_model=ThreadStateResponse)
@require_permission("threads", "write", owner_check=True, require_existing=True)
async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, request: Request) -> ThreadStateResponse:
"""Update thread state (e.g. for human-in-the-loop resume or title rename).
@@ -494,10 +433,10 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
ThreadMetaStore abstraction so that ``/threads/search`` reflects the
change immediately in both sqlite and memory backends.
"""
from app.gateway.deps import get_thread_store
from app.gateway.deps import get_thread_meta_repo
checkpointer = get_checkpointer(request)
thread_store = get_thread_store(request)
thread_meta_repo = get_thread_meta_repo(request)
# checkpoint_ns must be present in the config for aput — default to ""
# (the root graph namespace). checkpoint_id is optional; omitting it
@@ -529,28 +468,16 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
channel_values.update(body.values)
checkpoint["channel_values"] = channel_values
metadata["updated_at"] = now_iso()
metadata["updated_at"] = time.time()
if body.as_node:
metadata["source"] = "update"
metadata["step"] = metadata.get("step", 0) + 1
metadata["writes"] = {body.as_node: body.values}
# Assign a new checkpoint ID so aput performs an INSERT rather than an
# in-place REPLACE of the existing row. Use uuid6 (time-ordered) rather
# than uuid4 (random) so the new ID is always lexicographically greater
# than the previous one — LangGraph's checkpointers determine the "latest"
# checkpoint by max(checkpoint_ids) string order, matching the uuid6 epoch.
checkpoint["id"] = str(uuid6())
# aput requires checkpoint_ns in the config — use the same config used for the
# read (which always includes checkpoint_ns=""). The fresh checkpoint ID is
# assigned above via checkpoint["id"]; keep checkpoint_id out of the config so
# the write is keyed by the new checkpoint payload rather than the prior read.
# All supported savers (InMemorySaver, AsyncSqliteSaver, AsyncPostgresSaver)
# persist and echo back checkpoint["id"] verbatim — none mint their own — so
# the new_config below carries the uuid6 we assigned here. (Regression-locked
# by test_update_thread_state_inserts_new_checkpoint_each_call.)
# read (which always includes checkpoint_ns=""). Do NOT include checkpoint_id
# so that aput generates a fresh checkpoint ID for the new snapshot.
write_config: dict[str, Any] = {
"configurable": {
"thread_id": thread_id,
@@ -569,11 +496,11 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
# Sync title changes through the ThreadMetaStore abstraction so /threads/search
# reflects them immediately in both sqlite and memory backends.
if thread_store and body.values and "title" in body.values:
if body.values and "title" in body.values:
new_title = body.values["title"]
if new_title: # Skip empty strings and None
try:
await thread_store.update_display_name(thread_id, new_title)
await thread_meta_repo.update_display_name(thread_id, new_title)
except Exception:
logger.debug("Failed to sync title to thread_meta for %s (non-fatal)", sanitize_log_param(thread_id))
@@ -582,12 +509,11 @@ async def update_thread_state(thread_id: str, body: ThreadStateUpdateRequest, re
next=[],
metadata=metadata,
checkpoint_id=new_checkpoint_id,
created_at=coerce_iso(metadata.get("created_at", "")),
created_at=str(metadata.get("created_at", "")),
)
@router.post("/{thread_id}/history", response_model=list[HistoryEntry])
@require_permission("threads", "read", owner_check=True)
async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request: Request) -> list[HistoryEntry]:
"""Get checkpoint history for a thread.
@@ -626,7 +552,7 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
if thread_data := channel_values.get("thread_data"):
values["thread_data"] = thread_data
# Attach messages only to the latest checkpoint entry.
# Attach messages from checkpointer only for the latest checkpoint
if is_latest_checkpoint:
messages = channel_values.get("messages")
if messages:
@@ -649,7 +575,7 @@ async def get_thread_history(thread_id: str, body: ThreadHistoryRequest, request
parent_checkpoint_id=parent_id,
metadata=user_meta,
values=values,
created_at=coerce_iso(metadata.get("created_at", "")),
created_at=str(metadata.get("created_at", "")),
next=next_tasks,
)
)
+28 -255
View File
@@ -4,26 +4,19 @@ import logging
import os
import stat
from fastapi import APIRouter, Depends, File, HTTPException, Request, UploadFile
from pydantic import BaseModel, Field
from fastapi import APIRouter, File, HTTPException, UploadFile
from pydantic import BaseModel
from app.gateway.authz import require_permission
from app.gateway.deps import get_config
from deerflow.config.app_config import AppConfig
from deerflow.config.paths import get_paths
from deerflow.runtime.user_context import get_effective_user_id
from deerflow.sandbox.sandbox_provider import SandboxProvider, get_sandbox_provider
from deerflow.sandbox.sandbox_provider import get_sandbox_provider
from deerflow.uploads.manager import (
PathTraversalError,
UnsafeUploadPathError,
claim_unique_filename,
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,
)
@@ -33,51 +26,13 @@ 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 UploadedFileInfo(BaseModel):
"""Uploaded file metadata exposed by upload and list APIs."""
filename: str
size: int
path: str
virtual_path: str
artifact_url: str
extension: str | None = None
modified: float | None = None
original_filename: str | None = None
markdown_file: str | None = None
markdown_path: str | None = None
markdown_virtual_path: str | None = None
markdown_artifact_url: str | None = None
class UploadResponse(BaseModel):
"""Response model for file upload."""
success: bool
files: list[UploadedFileInfo]
files: list[dict[str, str]]
message: str
skipped_files: list[str] = Field(default_factory=list)
class UploadListResponse(BaseModel):
"""Response model for uploaded file listing."""
files: list[UploadedFileInfo]
count: int
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:
@@ -93,212 +48,71 @@ def _make_file_sandbox_writable(file_path: os.PathLike[str] | str) -> None:
logger.warning("Skipping sandbox chmod for symlinked upload path: %s", file_path)
return
writable_mode = stat.S_IMODE(file_stat.st_mode) | stat.S_IWUSR | stat.S_IWGRP | stat.S_IWOTH | stat.S_IRGRP | stat.S_IROTH
writable_mode = stat.S_IMODE(file_stat.st_mode) | stat.S_IWUSR | stat.S_IWGRP | stat.S_IWOTH
chmod_kwargs = {"follow_symlinks": False} if os.chmod in os.supports_follow_symlinks else {}
os.chmod(file_path, writable_mode, **chmod_kwargs)
def _make_file_sandbox_readable(file_path: os.PathLike[str] | str) -> None:
"""Ensure uploaded files are readable by the sandbox process.
For Docker sandboxes (AIO), the gateway writes files as root with 0o600
permissions, then bind-mounts the host directory into the container. The
sandbox process inside the container runs as a non-root user and cannot
read those files without group/other read bits. This function adds
``S_IRGRP | S_IROTH`` so the sandbox can read the uploaded content.
"""
file_stat = os.lstat(file_path)
if stat.S_ISLNK(file_stat.st_mode):
logger.warning("Skipping sandbox chmod for symlinked upload path: %s", file_path)
return
readable_mode = stat.S_IMODE(file_stat.st_mode) | stat.S_IRGRP | stat.S_IROTH
chmod_kwargs = {"follow_symlinks": False} if os.chmod in os.supports_follow_symlinks else {}
os.chmod(file_path, readable_mode, **chmod_kwargs)
def _uses_thread_data_mounts(sandbox_provider: SandboxProvider) -> bool:
return bool(getattr(sandbox_provider, "uses_thread_data_mounts", False))
def _get_uploads_config_value(app_config: AppConfig, key: str, default: object) -> object:
"""Read a value from the uploads config, supporting dict and attribute access."""
uploads_cfg = getattr(app_config, "uploads", None)
if isinstance(uploads_cfg, dict):
return uploads_cfg.get(key, default)
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.
The secure default is disabled unless an operator explicitly opts in via
uploads.auto_convert_documents in config.yaml.
"""
try:
raw = _get_uploads_config_value(app_config, "auto_convert_documents", False)
if isinstance(raw, str):
return raw.strip().lower() in {"1", "true", "yes", "on"}
return bool(raw)
except Exception:
return False
@router.post("", response_model=UploadResponse)
@require_permission("threads", "write", owner_check=True, require_existing=False)
async def upload_files(
thread_id: str,
request: Request,
files: list[UploadFile] = File(...),
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())
sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id)
uploaded_files = []
written_paths = []
sandbox_sync_targets = []
skipped_files = []
total_size = 0
# Track filenames within this request so duplicate form parts do not
# silently truncate each other. Existing uploads keep the historical
# overwrite behavior for a single replacement upload.
seen_filenames: set[str] = set()
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)
if sandbox is None:
raise HTTPException(status_code=500, detail="Failed to acquire sandbox")
auto_convert_documents = _auto_convert_documents_enabled(config)
sandbox_id = sandbox_provider.acquire(thread_id)
sandbox = sandbox_provider.get(sandbox_id)
for file in files:
if not file.filename:
continue
try:
original_filename = normalize_filename(file.filename)
safe_filename = claim_unique_filename(original_filename, seen_filenames)
safe_filename = normalize_filename(file.filename)
except ValueError:
logger.warning(f"Skipping file with unsafe filename: {file.filename!r}")
continue
try:
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)
content = await file.read()
file_path = uploads_dir / safe_filename
file_path.write_bytes(content)
virtual_path = upload_virtual_path(safe_filename)
if sync_to_sandbox:
sandbox_sync_targets.append((file_path, virtual_path))
if sandbox_id != "local":
_make_file_sandbox_writable(file_path)
sandbox.update_file(virtual_path, content)
file_info = {
"filename": safe_filename,
"size": file_size,
"size": str(len(content)),
"path": str(sandbox_uploads / safe_filename),
"virtual_path": virtual_path,
"artifact_url": upload_artifact_url(thread_id, safe_filename),
}
if safe_filename != original_filename:
file_info["original_filename"] = original_filename
logger.info(f"Saved file: {safe_filename} ({file_size} bytes) to {file_info['path']}")
logger.info(f"Saved file: {safe_filename} ({len(content)} bytes) to {file_info['path']}")
file_ext = file_path.suffix.lower()
if auto_convert_documents and file_ext in CONVERTIBLE_EXTENSIONS:
if 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:
sandbox_sync_targets.append((md_path, md_virtual_path))
if sandbox_id != "local":
_make_file_sandbox_writable(md_path)
sandbox.update_file(md_virtual_path, md_path.read_bytes())
file_info["markdown_file"] = md_path.name
file_info["markdown_path"] = str(sandbox_uploads / md_path.name)
@@ -307,59 +121,19 @@ 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)}")
# Uploaded files are created with 0o600 permissions (owner read/write only).
# In Docker sandbox deployments the gateway writes as root but the sandbox
# process runs as a non-root user (typically UID 1000). Without group/other
# read bits the sandbox cannot access the files — whether the uploads
# directory is bind-mounted into the container or synced via
# sandbox.update_file. Always add group/other read bits so every sandbox
# configuration can read the uploaded content.
for file_path in written_paths:
_make_file_sandbox_readable(file_path)
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=not skipped_files,
success=True,
files=uploaded_files,
message=message,
skipped_files=skipped_files,
message=f"Successfully uploaded {len(uploaded_files)} file(s)",
)
@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=UploadListResponse)
@require_permission("threads", "read", owner_check=True)
async def list_uploaded_files(thread_id: str, request: Request) -> UploadListResponse:
@router.get("/list", response_model=dict)
async def list_uploaded_files(thread_id: str) -> dict:
"""List all files in a thread's uploads directory."""
try:
uploads_dir = get_uploads_dir(thread_id)
@@ -369,16 +143,15 @@ async def list_uploaded_files(thread_id: str, request: Request) -> UploadListRes
enrich_file_listing(result, thread_id)
# Gateway additionally includes the sandbox-relative path.
sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id, user_id=get_effective_user_id())
sandbox_uploads = get_paths().sandbox_uploads_dir(thread_id)
for f in result["files"]:
f["path"] = str(sandbox_uploads / f["filename"])
return UploadListResponse(**result)
return result
@router.delete("/{filename}")
@require_permission("threads", "delete", owner_check=True, require_existing=True)
async def delete_uploaded_file(thread_id: str, filename: str, request: Request) -> dict:
async def delete_uploaded_file(thread_id: str, filename: str) -> dict:
"""Delete a file from a thread's uploads directory."""
try:
uploads_dir = get_uploads_dir(thread_id)
+63 -203
View File
@@ -8,20 +8,17 @@ frames, and consuming stream bridge events. Router modules
from __future__ import annotations
import asyncio
import dataclasses
import json
import logging
import re
from collections.abc import Mapping
from typing import Any
from fastapi import HTTPException, Request
from langchain_core.messages import BaseMessage
from langchain_core.messages.utils import convert_to_messages
from langchain_core.messages import HumanMessage
from app.gateway.deps import get_run_context, get_run_manager, get_stream_bridge
from app.gateway.internal_auth import INTERNAL_SYSTEM_ROLE
from app.gateway.deps import get_run_context, get_run_manager, get_run_store, get_stream_bridge
from app.gateway.utils import sanitize_log_param
from deerflow.config.app_config import get_app_config
from deerflow.runtime import (
END_SENTINEL,
HEARTBEAT_SENTINEL,
@@ -34,7 +31,6 @@ from deerflow.runtime import (
UnsupportedStrategyError,
run_agent,
)
from deerflow.runtime.runs.naming import resolve_root_run_name
logger = logging.getLogger(__name__)
@@ -78,35 +74,21 @@ def normalize_stream_modes(raw: list[str] | str | None) -> list[str]:
def normalize_input(raw_input: dict[str, Any] | None) -> dict[str, Any]:
"""Convert LangGraph Platform input format to LangChain state dict.
Delegates dictmessage coercion to ``langchain_core.messages.utils.convert_to_messages``
so that ``additional_kwargs`` (e.g. uploaded-file metadata gh #3132), ``id``,
``name``, and non-human roles (ai/system/tool) survive unchanged. An earlier
hand-rolled version only forwarded ``content`` and collapsed every role to
``HumanMessage``, which silently stripped frontend-supplied attachments.
Malformed message dicts (missing ``role``/``type``/``content``, unsupported
role, etc.) raise ``HTTPException(400)`` with the offending index, instead
of bubbling up as a 500. The gateway is a system boundary, so per-entry
validation errors are the right shape for clients to retry against.
"""
"""Convert LangGraph Platform input format to LangChain state dict."""
if raw_input is None:
return {}
messages = raw_input.get("messages")
if messages and isinstance(messages, list):
converted: list[Any] = []
for index, msg in enumerate(messages):
if isinstance(msg, BaseMessage):
converted.append(msg)
elif isinstance(msg, dict):
try:
converted.extend(convert_to_messages([msg]))
except (ValueError, TypeError, NotImplementedError) as exc:
raise HTTPException(
status_code=400,
detail=f"Invalid message at input.messages[{index}]: {exc}",
) from exc
converted = []
for msg in messages:
if isinstance(msg, dict):
role = msg.get("role", msg.get("type", "user"))
content = msg.get("content", "")
if role in ("user", "human"):
converted.append(HumanMessage(content=content))
else:
# TODO: handle other message types (system, ai, tool)
converted.append(HumanMessage(content=content))
else:
converted.append(msg)
return {**raw_input, "messages": converted}
@@ -116,82 +98,13 @@ 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).
``user_id`` is intentionally propagated into ``config['context']`` in addition to
the whitelisted keys, so non-web callers (e.g. IM channels) that supply identity in
``body.context`` keep it on ``ToolRuntime.context``. It is merged with
``setdefault`` so a server-authenticated id stamped by
:func:`inject_authenticated_user_context` always wins over the client-supplied one.
"""
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])
if "user_id" in context and isinstance(runtime_context, dict):
runtime_context.setdefault("user_id", context["user_id"])
def inject_authenticated_user_context(config: dict[str, Any], request: Request) -> None:
"""Stamp the authenticated user into the run context for background tools.
Tool execution may happen after the request handler has returned, so tools
that persist user-scoped files should not rely only on ambient ContextVars.
The value comes from server-side auth state, never from client context.
"""
user = getattr(request.state, "user", None)
user_id = getattr(user, "id", None)
if user_id is None:
return
if getattr(user, "system_role", None) == INTERNAL_SYSTEM_ROLE:
return
runtime_context = config.setdefault("context", {})
if isinstance(runtime_context, dict):
runtime_context["user_id"] = str(user_id)
def resolve_agent_factory(assistant_id: str | None):
"""Resolve the agent factory callable from config.
Custom agents are implemented as ``lead_agent`` + an ``agent_name``
injected into ``configurable`` or ``context`` see
:func:`build_run_config`. All ``assistant_id`` values therefore map to the
same factory; the routing happens inside ``make_lead_agent`` when it reads
``cfg["agent_name"]``.
injected into ``configurable`` see :func:`build_run_config`. All
``assistant_id`` values therefore map to the same factory; the routing
happens inside ``make_lead_agent`` when it reads ``cfg["agent_name"]``.
"""
from deerflow.agents.lead_agent.agent import make_lead_agent
@@ -208,12 +121,10 @@ def build_run_config(
"""Build a RunnableConfig dict for the agent.
When *assistant_id* refers to a custom agent (anything other than
``"lead_agent"`` / ``None``), the name is forwarded as ``agent_name`` in
whichever runtime options container is active: ``context`` for
LangGraph >= 0.6.0 requests, otherwise ``configurable``.
``make_lead_agent`` reads this key to load the matching
``agents/<name>/SOUL.md`` and per-agent config without it the agent
silently runs as the default lead agent.
``"lead_agent"`` / ``None``), the name is forwarded as
``configurable["agent_name"]``. ``make_lead_agent`` reads this key to
load the matching ``agents/<name>/SOUL.md`` and per-agent config
without it the agent silently runs as the default lead agent.
This mirrors the channel manager's ``_resolve_run_params`` logic so that
the LangGraph Platform-compatible HTTP API and the IM channel path behave
@@ -232,14 +143,7 @@ def build_run_config(
thread_id,
list(request_config.get("configurable", {}).keys()),
)
context_value = request_config["context"]
if context_value is None:
context = {}
elif isinstance(context_value, Mapping):
context = dict(context_value)
else:
raise ValueError("request config 'context' must be a mapping or null.")
config["context"] = context
config["context"] = request_config["context"]
else:
configurable = {"thread_id": thread_id}
configurable.update(request_config.get("configurable", {}))
@@ -251,20 +155,13 @@ def build_run_config(
config["configurable"] = {"thread_id": thread_id}
# Inject custom agent name when the caller specified a non-default assistant.
# Honour an explicit agent_name in the active runtime options container.
if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID:
normalized = assistant_id.strip().lower().replace("_", "-")
if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
if "configurable" in config:
target = config["configurable"]
elif "context" in config:
target = config["context"]
else:
target = config.setdefault("configurable", {})
if target is not None and "agent_name" not in target:
target["agent_name"] = normalized
config.setdefault("run_name", resolve_root_run_name(config, normalized))
# Honour an explicit configurable["agent_name"] in the request if already set.
if assistant_id and assistant_id != _DEFAULT_ASSISTANT_ID and "configurable" in config:
if "agent_name" not in config["configurable"]:
normalized = assistant_id.strip().lower().replace("_", "-")
if not normalized or not re.fullmatch(r"[a-z0-9-]+", normalized):
raise ValueError(f"Invalid assistant_id {assistant_id!r}: must contain only letters, digits, and hyphens after normalization.")
config["configurable"]["agent_name"] = normalized
if metadata:
config.setdefault("metadata", {}).update(metadata)
return config
@@ -298,22 +195,20 @@ async def start_run(
disconnect = DisconnectMode.cancel if body.on_disconnect == "cancel" else DisconnectMode.continue_
body_context = getattr(body, "context", None) or {}
model_name = body_context.get("model_name")
# 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
# Coerce non-string model_name values to str before truncation.
if model_name is not None and not isinstance(model_name, str):
model_name = str(model_name)
# Validate model against the allowlist when a model_name is provided.
if model_name:
app_config = get_app_config()
resolved = app_config.get_model_config(model_name)
if resolved is None:
raise HTTPException(
status_code=400,
detail=f"Model {model_name!r} is not in the configured model allowlist",
)
# 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(
@@ -323,7 +218,7 @@ async def start_run(
metadata=body.metadata or {},
kwargs={"input": body.input, "config": body.config},
multitask_strategy=body.multitask_strategy,
model_name=model_name,
follow_up_to_run_id=follow_up_to_run_id,
)
except ConflictError as exc:
raise HTTPException(status_code=409, detail=str(exc)) from exc
@@ -334,15 +229,15 @@ async def start_run(
# even for threads that were never explicitly created via POST /threads
# (e.g. stateless runs).
try:
existing = await run_ctx.thread_store.get(thread_id)
existing = await run_ctx.thread_meta_repo.get(thread_id)
if existing is None:
await run_ctx.thread_store.create(
await run_ctx.thread_meta_repo.create(
thread_id,
assistant_id=body.assistant_id,
metadata=body.metadata,
)
else:
await run_ctx.thread_store.update_status(thread_id, "running")
await run_ctx.thread_meta_repo.update_status(thread_id, "running")
except Exception:
logger.warning("Failed to upsert thread_meta for %s (non-fatal)", sanitize_log_param(thread_id))
@@ -350,12 +245,25 @@ 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 both ``configurable`` and ``context``.
# Merge DeerFlow-specific context overrides into configurable.
# 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.
merge_run_context_overrides(config, getattr(body, "context", None))
inject_authenticated_user_context(config, request)
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",
}
configurable = config.setdefault("configurable", {})
for key in _CONTEXT_CONFIGURABLE_KEYS:
if key in context:
configurable.setdefault(key, context[key])
stream_modes = normalize_stream_modes(body.stream_mode)
@@ -377,7 +285,7 @@ async def start_run(
record.task = task
# Title sync is handled by worker.py's finally block which reads the
# title from the checkpoint and calls thread_store.update_display_name
# title from the checkpoint and calls thread_meta_repo.update_display_name
# after the run completes.
return record
@@ -415,51 +323,3 @@ async def sse_consumer(
if record.status in (RunStatus.pending, RunStatus.running):
if record.on_disconnect == DisconnectMode.cancel:
await run_mgr.cancel(record.run_id)
async def wait_for_run_completion(
bridge: StreamBridge,
record: RunRecord,
request: Request,
run_mgr: RunManager,
) -> bool:
"""Block until the run publishes ``END_SENTINEL``, honouring on_disconnect.
The non-streaming ``/wait`` endpoints used to ``await record.task``
directly with no disconnect handling. When the client (or an
intermediate HTTP proxy) timed out during a long tool call such as
``pip install``, the handler would swallow ``CancelledError`` and
serialize whatever checkpoint happened to exist masking a half-finished
run as a normal completion (issue #3265).
This helper consumes the same bridge that ``sse_consumer`` does so the
wait path shares its disconnect semantics: each wake-up polls
``request.is_disconnected()``; on a real disconnect it cancels the
background run when ``record.on_disconnect`` is ``cancel``. The bridge's
heartbeat sentinels guarantee at least one wake-up per
``heartbeat_interval`` even when the agent emits no events for a while.
Returns:
``True`` when ``END_SENTINEL`` was observed (run reached a terminal
state), ``False`` when the loop exited because the client
disconnected. Callers must skip checkpoint serialization on
``False`` so a partial checkpoint is not returned as a normal
response.
"""
completed = False
try:
async for entry in bridge.subscribe(record.run_id):
# END_SENTINEL means the run reached a terminal state; honour it
# even if the client just disconnected so the caller still serializes
# the real final checkpoint.
if entry is END_SENTINEL:
completed = True
return True
if await request.is_disconnected():
break
# Heartbeats and regular events: keep waiting for END_SENTINEL.
return completed
finally:
if not completed and record.status in (RunStatus.pending, RunStatus.running):
if record.on_disconnect == DisconnectMode.cancel:
await run_mgr.cancel(record.run_id)
+13 -90
View File
@@ -19,72 +19,24 @@ import asyncio
import logging
from dotenv import load_dotenv
from langchain_core.messages import HumanMessage
try:
from prompt_toolkit import PromptSession
from prompt_toolkit.history import InMemoryHistory
_HAS_PROMPT_TOOLKIT = True
except ImportError:
_HAS_PROMPT_TOOLKIT = False
from deerflow.agents import make_lead_agent
load_dotenv()
_LOG_FMT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
_LOG_DATEFMT = "%Y-%m-%d %H:%M:%S"
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``.
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.
"""
root = logging.root
for h in list(root.handlers):
root.removeHandler(h)
h.close()
root.setLevel(log_level)
file_handler = logging.FileHandler("debug.log", mode="a", encoding="utf-8")
file_handler.setLevel(log_level)
file_handler.setFormatter(logging.Formatter(_LOG_FMT, datefmt=_LOG_DATEFMT))
root.addHandler(file_handler)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
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()
from deerflow.config import get_app_config
from deerflow.config.app_config import apply_logging_level
app_config = get_app_config()
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
# background skill-loader thread on import) emit logs to debug.log instead
# of leaking onto the interactive terminal via Python's lastResort handler.
from langchain_core.messages import HumanMessage
from langgraph.runtime import Runtime
from deerflow.agents import make_lead_agent
from deerflow.config.paths import get_paths
from deerflow.mcp import initialize_mcp_tools
from deerflow.runtime.user_context import get_effective_user_id
# Initialize MCP tools at startup
try:
from deerflow.mcp import initialize_mcp_tools
await initialize_mcp_tools()
except Exception as e:
print(f"Warning: Failed to initialize MCP tools: {e}")
@@ -100,29 +52,16 @@ async def main():
}
}
runtime = Runtime(context={"thread_id": config["configurable"]["thread_id"]})
config["configurable"]["__pregel_runtime"] = runtime
agent = make_lead_agent(config)
session = PromptSession(history=InMemoryHistory()) if _HAS_PROMPT_TOOLKIT else None
print("=" * 50)
print("Lead Agent Debug Mode")
print("Type 'quit' or 'exit' to stop")
print(f"Logs: debug.log (log_level={app_config.log_level})")
if not _HAS_PROMPT_TOOLKIT:
print("Tip: `uv sync --group dev` to enable arrow-key & history support")
print("=" * 50)
seen_artifacts: set[str] = set()
while True:
try:
if session:
user_input = (await session.prompt_async("\nYou: ")).strip()
else:
user_input = input("\nYou: ").strip()
user_input = input("\nYou: ").strip()
if not user_input:
continue
if user_input.lower() in ("quit", "exit"):
@@ -131,31 +70,15 @@ async def main():
# Invoke the agent
state = {"messages": [HumanMessage(content=user_input)]}
result = await agent.ainvoke(state, config=config)
result = await agent.ainvoke(state, config=config, context={"thread_id": "debug-thread-001"})
# Print the response
if result.get("messages"):
last_message = result["messages"][-1]
print(f"\nAgent: {last_message.content}")
# Show files presented to the user this turn (new artifacts only)
artifacts = result.get("artifacts") or []
new_artifacts = [p for p in artifacts if p not in seen_artifacts]
if new_artifacts:
thread_id = config["configurable"]["thread_id"]
user_id = get_effective_user_id()
paths = get_paths()
print("\n[Presented files]")
for virtual in new_artifacts:
try:
physical = paths.resolve_virtual_path(thread_id, virtual, user_id=user_id)
print(f" - {virtual}\n{physical}")
except ValueError as exc:
print(f" - {virtual} (failed to resolve physical path: {exc})")
seen_artifacts.update(new_artifacts)
except (KeyboardInterrupt, EOFError):
print("\nGoodbye!")
except KeyboardInterrupt:
print("\nInterrupted. Goodbye!")
break
except Exception as e:
print(f"\nError: {e}")
+32 -84
View File
@@ -6,16 +6,16 @@ This document provides a complete reference for the DeerFlow backend APIs.
DeerFlow backend exposes two sets of APIs:
1. **LangGraph-compatible API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
1. **LangGraph API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
2. **Gateway API** - Models, MCP, skills, uploads, and artifacts (`/api/*`)
All APIs are accessed through the Nginx reverse proxy at port 2026.
## LangGraph-compatible API
## LangGraph API
Base URL: `/api/langgraph`
The public LangGraph-compatible API follows LangGraph SDK conventions. In the unified nginx deployment, Gateway owns `/api/langgraph/*` and translates those paths to its native `/api/*` run, thread, and streaming routers.
The LangGraph API is provided by the LangGraph server and follows the LangGraph SDK conventions.
### Threads
@@ -86,7 +86,6 @@ Content-Type: application/json
]
},
"config": {
"recursion_limit": 100,
"configurable": {
"model_name": "gpt-4",
"thinking_enabled": false,
@@ -101,15 +100,6 @@ Content-Type: application/json
- Use: `values`, `messages-tuple`, `custom`, `updates`, `events`, `debug`, `tasks`, `checkpoints`
- Do not use: `tools` (deprecated/invalid in current `langgraph-api` and will trigger schema validation errors)
**Recursion Limit:**
`config.recursion_limit` caps the number of graph steps LangGraph will execute
in a single run. The unified Gateway path defaults to `100` in
`build_run_config` (see `backend/app/gateway/services.py`), which is a safer
starting point for plan-mode or subagent-heavy runs. Clients can still set
`recursion_limit` explicitly in the request body; increase it if you run deeply
nested subagent graphs.
**Configurable Options:**
- `model_name` (string): Override the default model
- `thinking_enabled` (boolean): Enable extended thinking for supported models
@@ -228,13 +218,10 @@ Get current MCP server configurations.
GET /api/mcp/config
```
Requires an authenticated admin session. Sensitive env/header/OAuth secret
values are masked in the response.
**Response:**
```json
{
"mcp_servers": {
"mcpServers": {
"github": {
"enabled": true,
"type": "stdio",
@@ -244,6 +231,13 @@ values are masked in the response.
"GITHUB_TOKEN": "***"
},
"description": "GitHub operations"
},
"filesystem": {
"enabled": false,
"type": "stdio",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem"],
"description": "File system access"
}
}
}
@@ -258,15 +252,10 @@ PUT /api/mcp/config
Content-Type: application/json
```
Requires an authenticated admin session. API-managed `stdio` MCP servers may
only use allowed executable names for `command` (default: `npx`, `uvx`). Set
`DEER_FLOW_MCP_STDIO_COMMAND_ALLOWLIST` to a comma-separated list when a
deployment needs additional trusted launchers.
**Request Body:**
```json
{
"mcp_servers": {
"mcpServers": {
"github": {
"enabled": true,
"type": "stdio",
@@ -284,18 +273,8 @@ deployment needs additional trusted launchers.
**Response:**
```json
{
"mcp_servers": {
"github": {
"enabled": true,
"type": "stdio",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {
"GITHUB_TOKEN": "***"
},
"description": "GitHub operations"
}
}
"success": true,
"message": "MCP configuration updated"
}
```
@@ -546,28 +525,14 @@ All APIs return errors in a consistent format:
## Authentication
DeerFlow enforces authentication for all non-public HTTP routes. Public routes are limited to health/docs metadata and these public auth endpoints:
Currently, DeerFlow does not implement authentication. All APIs are accessible without credentials.
- `POST /api/v1/auth/initialize` creates the first admin account when no admin exists.
- `POST /api/v1/auth/login/local` logs in with email/password and sets an HttpOnly `access_token` cookie.
- `POST /api/v1/auth/register` creates a regular `user` account and sets the session cookie.
- `POST /api/v1/auth/logout` clears the session cookie.
- `GET /api/v1/auth/setup-status` reports whether the first admin still needs to be created.
Note: This is about DeerFlow API authentication. MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers.
The authenticated auth endpoints are:
- `GET /api/v1/auth/me` returns the current user.
- `POST /api/v1/auth/change-password` changes password, optionally changes email during setup, increments `token_version`, and reissues the cookie.
Protected state-changing requests also require the CSRF double-submit token: send the `csrf_token` cookie value as the `X-CSRF-Token` header. Login/register/initialize/logout are bootstrap auth endpoints: they are exempt from the double-submit token but still reject hostile browser `Origin` headers.
User isolation is enforced from the authenticated user context:
- Thread metadata is scoped by `threads_meta.user_id`; search/read/write/delete APIs only expose the current user's threads.
- Thread files live under `{base_dir}/users/{user_id}/threads/{thread_id}/user-data/` and are exposed inside the sandbox as `/mnt/user-data/`.
- Memory and custom agents are stored under `{base_dir}/users/{user_id}/...`.
Note: MCP outbound connections can still use OAuth for configured HTTP/SSE MCP servers; that is separate from DeerFlow API authentication.
For production deployments, it is recommended to:
1. Use Nginx for basic auth or OAuth integration
2. Deploy behind a VPN or private network
3. Implement custom authentication middleware
---
@@ -586,13 +551,12 @@ location /api/ {
---
## Streaming Support
## WebSocket Support
Gateway's LangGraph-compatible API streams run events with Server-Sent Events (SSE):
The LangGraph server supports WebSocket connections for real-time streaming. Connect to:
```http
POST /api/langgraph/threads/{thread_id}/runs/stream
Accept: text/event-stream
```
ws://localhost:2026/api/langgraph/threads/{thread_id}/runs/stream
```
---
@@ -628,21 +592,13 @@ const response = await fetch('/api/models');
const data = await response.json();
console.log(data.models);
// Create a run and stream SSE events
const streamResponse = await fetch(`/api/langgraph/threads/${threadId}/runs/stream`, {
method: "POST",
headers: {
"Content-Type": "application/json",
Accept: "text/event-stream",
},
body: JSON.stringify({
input: { messages: [{ role: "user", content: "Hello" }] },
stream_mode: ["values", "messages-tuple", "custom"],
}),
});
const reader = streamResponse.body?.getReader();
// Decode and parse SSE frames from reader in your client code.
// Using EventSource for streaming
const eventSource = new EventSource(
`/api/langgraph/threads/${threadId}/runs/stream`
);
eventSource.onmessage = (event) => {
console.log(JSON.parse(event.data));
};
```
### cURL Examples
@@ -670,14 +626,6 @@ curl -X POST http://localhost:2026/api/langgraph/threads/abc123/runs \
-H "Content-Type: application/json" \
-d '{
"input": {"messages": [{"role": "user", "content": "Hello"}]},
"config": {
"recursion_limit": 100,
"configurable": {"model_name": "gpt-4"}
}
"config": {"configurable": {"model_name": "gpt-4"}}
}'
```
> The unified Gateway path defaults `config.recursion_limit` to 100 for
> plan-mode and subagent-heavy runs. Clients may still set
> `config.recursion_limit` explicitly — see the [Create Run](#create-run)
> section for details.
+30 -30
View File
@@ -14,28 +14,30 @@ This document provides a comprehensive overview of the DeerFlow backend architec
│ Nginx (Port 2026) │
│ Unified Reverse Proxy Entry Point │
│ ┌────────────────────────────────────────────────────────────────────┐ │
│ │ /api/langgraph/* → Gateway LangGraph-compatible runtime (8001) │ │
│ │ /api/* → Gateway REST APIs (8001) │ │
│ │ /api/langgraph/* → LangGraph Server (2024) │ │
│ │ /api/* → Gateway API (8001) │ │
│ │ /* → Frontend (3000) │ │
│ └────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────┬────────────────────────────────────────┘
┌──────────────────────────────────────────────┐
┌─────────────────────────────────────────────┐ ┌─────────────────────┐
Gateway API │ │ Frontend │
│ (Port 8001) │ │ (Port 3000) │
│ │ │
│ - LangGraph-compatible runs/threads API │ │ - Next.js App │
│ - Embedded Agent Runtime │ │ - React UI │
│ - SSE Streaming │ │ - Chat Interface │
│ - Checkpointing │ │ │
- Models, MCP, Skills, Uploads, Artifacts │ │ │
- Thread Cleanup │ │ │
└─────────────────────────────────────────────┘ └─────────────────────┘
┌──────────────────────────────────────────────┐
┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
LangGraph Server │ │ Gateway API │ │ Frontend │
(Port 2024) │ │ (Port 8001) │ │ (Port 3000) │
│ │ │ │ │
│ - Agent Runtime │ │ - Models API │ │ - Next.js App │
│ - Thread Mgmt │ │ - MCP Config │ │ - React UI │
│ - SSE Streaming │ │ - Skills Mgmt │ │ - Chat Interface │
│ - Checkpointing │ │ - File Uploads │ │ │
│ │ - Thread Cleanup │ │ │
│ │ - Artifacts │ │ │
└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
│ ┌─────────────────┘
│ │
▼ ▼
┌──────────────────────────────────────────────────────────────────────────┐
│ Shared Configuration │
│ ┌─────────────────────────┐ ┌────────────────────────────────────────┐ │
@@ -50,9 +52,9 @@ This document provides a comprehensive overview of the DeerFlow backend architec
## Component Details
### Gateway Embedded Agent Runtime
### LangGraph Server
The agent runtime is embedded in the FastAPI Gateway and built on LangGraph for robust multi-agent workflow orchestration. Nginx rewrites `/api/langgraph/*` to Gateway's native `/api/*` routes, so the public API remains compatible with LangGraph SDK clients without running a separate LangGraph server.
The LangGraph server is the core agent runtime, built on LangGraph for robust multi-agent workflow orchestration.
**Entry Point**: `packages/harness/deerflow/agents/lead_agent/agent.py:make_lead_agent`
@@ -63,8 +65,7 @@ The agent runtime is embedded in the FastAPI Gateway and built on LangGraph for
- Tool execution orchestration
- SSE streaming for real-time responses
**Graph registry**: `langgraph.json` remains available for tooling, Studio, or direct LangGraph Server compatibility.
It is not the default service entrypoint; scripts and Docker deployments run the Gateway embedded runtime.
**Configuration**: `langgraph.json`
```json
{
@@ -77,13 +78,12 @@ It is not the default service entrypoint; scripts and Docker deployments run the
### Gateway API
FastAPI application providing REST endpoints plus the public LangGraph-compatible `/api/langgraph/*` runtime routes.
FastAPI application providing REST endpoints for non-agent operations.
**Entry Point**: `app/gateway/app.py`
**Routers**:
- `models.py` - `/api/models` - Model listing and details
- `thread_runs.py` / `runs.py` - `/api/threads/{id}/runs`, `/api/runs/*` - LangGraph-compatible runs and streaming
- `mcp.py` - `/api/mcp` - MCP server configuration
- `skills.py` - `/api/skills` - Skills management
- `uploads.py` - `/api/threads/{id}/uploads` - File upload
@@ -91,7 +91,7 @@ FastAPI application providing REST endpoints plus the public LangGraph-compatibl
- `artifacts.py` - `/api/threads/{id}/artifacts` - Artifact serving
- `suggestions.py` - `/api/threads/{id}/suggestions` - Follow-up suggestion generation
The web conversation delete flow first deletes Gateway-managed thread state through the LangGraph-compatible route, then the Gateway `threads.py` router removes DeerFlow-managed filesystem data via `Paths.delete_thread_dir()`.
The web conversation delete flow is now split across both backend surfaces: LangGraph handles `DELETE /api/langgraph/threads/{thread_id}` for thread state, then the Gateway `threads.py` router removes DeerFlow-managed filesystem data via `Paths.delete_thread_dir()`.
### Agent Architecture
@@ -199,7 +199,7 @@ class ThreadState(AgentState):
│ Built-in Tools │ │ Configured Tools │ │ MCP Tools │
│ (packages/harness/deerflow/tools/) │ │ (config.yaml) │ │ (extensions.json) │
├─────────────────────┤ ├─────────────────────┤ ├─────────────────────┤
│ - present_files │ │ - web_search │ │ - github │
│ - present_file │ │ - web_search │ │ - github │
│ - ask_clarification │ │ - web_fetch │ │ - filesystem │
│ - view_image │ │ - bash │ │ - postgres │
│ │ │ - read_file │ │ - brave-search │
@@ -353,10 +353,10 @@ SKILL.md Format:
POST /api/langgraph/threads/{thread_id}/runs
{"input": {"messages": [{"role": "user", "content": "Hello"}]}}
2. Nginx → Gateway API (8001)
`/api/langgraph/*` is rewritten to Gateway's LangGraph-compatible `/api/*` routes
2. Nginx → LangGraph Server (2024)
Proxied to LangGraph server
3. Gateway embedded runtime
3. LangGraph Server
a. Load/create thread state
b. Execute middleware chain:
- ThreadDataMiddleware: Set up paths
@@ -412,7 +412,7 @@ SKILL.md Format:
### Thread Cleanup Flow
```
1. Client deletes conversation via the LangGraph-compatible Gateway route
1. Client deletes conversation via LangGraph
DELETE /api/langgraph/threads/{thread_id}
2. Web UI follows up with Gateway cleanup
-331
View File
@@ -1,331 +0,0 @@
# 用户认证与隔离设计
本文档描述 DeerFlow 当前内置认证模块的设计,而不是历史 RFC。它覆盖浏览器登录、API 认证、CSRF、用户隔离、首次初始化、密码重置、内部调用和升级迁移。
## 设计目标
认证模块的核心目标是把 DeerFlow 从“本地单用户工具”提升为“可多用户部署的 agent runtime”,并让用户身份贯穿 HTTP API、LangGraph-compatible runtime、文件系统、memory、自定义 agent 和反馈数据。
设计约束:
- 默认强制认证:除健康检查、文档和 auth bootstrap 端点外,HTTP 路由都必须有有效 session。
- 服务端持有所有权:客户端 metadata 不能声明 `user_id``owner_id`
- 隔离默认开启:repository(仓储)、文件路径、memory、agent 配置默认按当前用户解析。
- 旧数据可升级:无认证版本留下的 thread 可以在 admin 存在后迁移到 admin。
- 密码不进日志:首次初始化由操作者设置密码;`reset_admin` 只写 0600 凭据文件。
非目标:
- 当前 OAuth 端点只是占位,尚未实现第三方登录。
- 当前用户角色只有 `admin``user`,尚未实现细粒度 RBAC。
- 当前登录限速是进程内字典,多 worker 下不是全局精确限速。
## 核心模型
```mermaid
graph TB
classDef actor fill:#D8CFC4,stroke:#6E6259,color:#2F2A26;
classDef api fill:#C9D7D2,stroke:#5D706A,color:#21302C;
classDef state fill:#D7D3E8,stroke:#6B6680,color:#29263A;
classDef data fill:#E5D2C4,stroke:#806A5B,color:#30251E;
Browser["Browser — access_token cookie and csrf_token cookie"]:::actor
AuthMiddleware["AuthMiddleware — strict session gate"]:::api
CSRFMiddleware["CSRFMiddleware — double-submit token and Origin check"]:::api
AuthRoutes["Auth routes — initialize login register logout me change-password"]:::api
UserContext["Current user ContextVar — request-scoped identity"]:::state
Repositories["Repositories — AUTO resolves user_id from context"]:::state
Files["Filesystem — users/{user_id}/threads/{thread_id}/user-data"]:::data
Memory["Memory and agents — users/{user_id}/memory.json and agents"]:::data
Browser --> AuthMiddleware
Browser --> CSRFMiddleware
AuthMiddleware --> AuthRoutes
AuthMiddleware --> UserContext
UserContext --> Repositories
UserContext --> Files
UserContext --> Memory
```
### 用户表
用户记录定义在 `app.gateway.auth.models.User`,持久化到 `users` 表。关键字段:
| 字段 | 语义 |
|---|---|
| `id` | 用户主键,JWT `sub` 使用该值 |
| `email` | 唯一登录名 |
| `password_hash` | bcrypt hashOAuth 用户可为空 |
| `system_role` | `admin``user` |
| `needs_setup` | reset 后要求用户完成邮箱 / 密码设置 |
| `token_version` | 改密码或 reset 时递增,用于废弃旧 JWT |
### 运行时身份
认证成功后,`AuthMiddleware` 把用户同时写入:
- `request.state.user`
- `request.state.auth`
- `deerflow.runtime.user_context``ContextVar`
`ContextVar` 是这里的核心边界。上层 Gateway 负责写入身份,下层 persistence / file path 只读取结构化的当前用户,不反向依赖 `app.gateway.auth` 具体类型。
可以把 repository 调用的用户参数理解成一个三态 ADT:
```scala
enum UserScope:
case AutoFromContext
case Explicit(userId: String)
case BypassForMigration
```
对应 Python 实现是 `AUTO | str | None`
- `AUTO`:从 `ContextVar` 解析当前用户;没有上下文则抛错。
- `str`:显式指定用户,主要用于测试或管理脚本。
- `None`:跳过用户过滤,只允许迁移脚本或 admin CLI 使用。
## 登录与初始化流程
### 首次初始化
首次启动时,如果没有 admin,服务不会自动创建账号,只记录日志提示访问 `/setup`
流程:
1. 用户访问 `/setup`
2. 前端调用 `GET /api/v1/auth/setup-status`
3. 如果返回 `{"needs_setup": true}`,前端展示创建 admin 表单。
4. 表单提交 `POST /api/v1/auth/initialize`
5. 服务端确认当前没有 admin,创建 `system_role="admin"``needs_setup=false` 的用户。
6. 服务端设置 `access_token` HttpOnly cookie,用户进入 workspace。
`/api/v1/auth/initialize` 只在没有 admin 时可用。并发初始化由数据库唯一约束兜底,失败方返回 409。
### 普通登录
`POST /api/v1/auth/login/local` 使用 `OAuth2PasswordRequestForm`
- `username` 是邮箱。
- `password` 是密码。
- 成功后签发 JWT,放入 `access_token` HttpOnly cookie。
- 响应体只返回 `expires_in``needs_setup`,不返回 token。
登录失败会按客户端 IP 计数。IP 解析只在 TCP peer 属于 `AUTH_TRUSTED_PROXIES` 时信任 `X-Real-IP`,不使用 `X-Forwarded-For`
### 注册
`POST /api/v1/auth/register` 创建普通 `user`,并自动登录。
当前实现允许在没有 admin 时注册普通用户,但 `setup-status` 仍会返回 `needs_setup=true`,因为 admin 仍不存在。这是当前产品策略边界:如果后续要求“必须先初始化 admin 才能注册普通用户”,需要在 `/register` 增加 admin-exists gate。
### 改密码与 reset setup
`POST /api/v1/auth/change-password` 需要当前密码和新密码:
- 校验当前密码。
- 更新 bcrypt hash。
- `token_version += 1`,使旧 JWT 立即失效。
- 重新签发 cookie。
- 如果 `needs_setup=true` 且传了 `new_email`,则更新邮箱并清除 `needs_setup`
`python -m app.gateway.auth.reset_admin` 会:
- 找到 admin 或指定邮箱用户。
- 生成随机密码。
- 更新密码 hash。
- `token_version += 1`
- 设置 `needs_setup=true`
- 写入 `.deer-flow/admin_initial_credentials.txt`,权限 `0600`
命令行只输出凭据文件路径,不输出明文密码。
## HTTP 认证边界
`AuthMiddleware` 是 fail-closed(默认拒绝)的全局认证门。
公开路径:
- `/health`
- `/docs`
- `/redoc`
- `/openapi.json`
- `/api/v1/auth/login/local`
- `/api/v1/auth/register`
- `/api/v1/auth/logout`
- `/api/v1/auth/setup-status`
- `/api/v1/auth/initialize`
其余路径都要求有效 `access_token` cookie。存在 cookie 但 JWT 无效、过期、用户不存在或 `token_version` 不匹配时,直接返回 401,而不是让请求穿透到业务路由。
路由级别的 owner check 由 `require_permission(..., owner_check=True)` 完成:
- 读类请求允许旧的未追踪 legacy thread 兼容读取。
- 写 / 删除类请求使用 `require_existing=True`,要求 thread row 存在且属于当前用户,避免删除后缺 row 导致其他用户误通过。
## CSRF 设计
DeerFlow 使用 Double Submit Cookie
- 服务端设置 `csrf_token` cookie。
- 前端 state-changing 请求发送同值 `X-CSRF-Token` header。
- 服务端用 `secrets.compare_digest` 比较 cookie/header。
需要 CSRF 的方法:
- `POST`
- `PUT`
- `DELETE`
- `PATCH`
auth bootstrap 端点(login/register/initialize/logout)不要求 double-submit token,因为首次调用时浏览器还没有 token;但这些端点会校验 browser `Origin`,拒绝 hostile Origin,避免 login CSRF / session fixation。
## 用户隔离
### Thread metadata
Thread metadata 存在 `threads_meta`,关键隔离字段是 `user_id`
创建 thread 时:
- 客户端传入的 `metadata.user_id``metadata.owner_id` 会被剥离。
- `ThreadMetaRepository.create(..., user_id=AUTO)``ContextVar` 解析真实用户。
- `/api/threads/search` 默认只返回当前用户的 thread。
读取 / 修改 / 删除时:
- `get()` 默认按当前用户过滤。
- `check_access()` 用于路由 owner check。
- 对其他用户的 thread 返回 404,避免泄露资源存在性。
### 文件系统
当前线程文件布局:
```text
{base_dir}/users/{user_id}/threads/{thread_id}/user-data/
├── workspace/
├── uploads/
└── outputs/
```
agent 在 sandbox 内看到统一虚拟路径:
```text
/mnt/user-data/workspace
/mnt/user-data/uploads
/mnt/user-data/outputs
```
`ThreadDataMiddleware` 使用 `get_effective_user_id()` 解析当前用户并生成线程路径。没有认证上下文时会落到 `default` 用户桶,主要用于内部调用、嵌入式 client 或无 HTTP 的本地执行路径。
### Memory
默认 memory 存储:
```text
{base_dir}/users/{user_id}/memory.json
{base_dir}/users/{user_id}/agents/{agent_name}/memory.json
```
有用户上下文时,空或相对 `memory.storage_path` 都使用上述 per-user 默认路径;只有绝对 `memory.storage_path` 会视为显式 opt-out(退出) per-user isolation,所有用户共享该路径。无用户上下文的 legacy 路径仍会把相对 `storage_path` 解析到 `Paths.base_dir` 下。
### 自定义 agent
用户自定义 agent 写入:
```text
{base_dir}/users/{user_id}/agents/{agent_name}/
├── config.yaml
├── SOUL.md
└── memory.json
```
旧布局 `{base_dir}/agents/{agent_name}/` 只作为只读兼容回退。更新或删除旧共享 agent 会要求先运行迁移脚本。
## 内部调用与 IM 渠道
IM channel worker 不是浏览器用户,不持有浏览器 cookie。它们通过 Gateway 内部认证:
- 请求带 `X-DeerFlow-Internal-Token`
- 同时带匹配的 CSRF cookie/header。
- 服务端识别为内部用户,`id="default"``system_role="internal"`
这意味着 channel 产生的数据默认进入 `default` 用户桶。这个选择适合“平台级 bot 身份”,但不是“每个 IM 用户单独隔离”。如果后续要做到外部 IM 用户隔离,需要把外部 platform user 映射到 DeerFlow user,并让 channel manager 设置对应的 scoped identity。
## LangGraph-compatible 认证
Gateway 内嵌 runtime 路径由 `AuthMiddleware``CSRFMiddleware` 保护。
仓库仍保留 `app.gateway.langgraph_auth`,用于 LangGraph Server 直连模式:
- `@auth.authenticate` 校验 JWT cookie、CSRF、用户存在性和 `token_version`
- `@auth.on` 在写入 metadata 时注入 `user_id`,并在读路径返回 `{"user_id": current_user}` 过滤条件。
这保证 Gateway 路由和 LangGraph-compatible 直连模式使用同一 JWT 语义。
## 升级与迁移
从无认证版本升级时,可能存在没有 `user_id` 的历史 thread。
当前策略:
1. 首次启动如果没有 admin,只提示访问 `/setup`,不迁移。
2. 操作者创建 admin。
3. 后续启动时,`_ensure_admin_user()` 找到 admin,并把 LangGraph store 中缺少 `metadata.user_id` 的 thread 迁移到 admin。
文件系统旧布局迁移由脚本处理:
```bash
cd backend
PYTHONPATH=. python scripts/migrate_user_isolation.py --dry-run
PYTHONPATH=. python scripts/migrate_user_isolation.py --user-id <target-user-id>
```
迁移脚本覆盖 legacy `memory.json``threads/``agents/` 到 per-user layout。
## 安全不变量
必须长期保持的不变量:
- JWT 只在 HttpOnly cookie 中传输,不出现在响应 JSON。
- 任何非 public HTTP 路由都不能只靠“cookie 存在”放行,必须严格验证 JWT。
- `token_version` 不匹配必须拒绝,保证改密码 / reset 后旧 session 失效。
- 客户端 metadata 中的 `user_id` / `owner_id` 必须剥离。
- repository 默认 `AUTO` 必须从当前用户上下文解析,不能静默退化成全局查询。
- 只有迁移脚本和 admin CLI 可以显式传 `user_id=None` 绕过隔离。
- 本地文件路径必须通过 `Paths` 和 sandbox path validation 解析,不能拼接未校验的用户输入。
- 捕获认证、迁移、后台任务异常必须记录日志;不能空 catch。
## 已知边界
| 边界 | 当前行为 | 后续方向 |
|---|---|---|
| 无 admin 时注册普通用户 | 允许注册普通 `user` | 如产品要求先初始化 admin,给 `/register` 加 gate |
| 登录限速 | 进程内 dict,单 worker 精确,多 worker 近似 | Redis / DB-backed rate limiter |
| OAuth | 端点占位,未实现 | 接入 provider 并统一 `token_version` / role 语义 |
| IM 用户隔离 | channel 使用 `default` 内部用户 | 建立外部用户到 DeerFlow user 的映射 |
| 绝对 memory path | 显式共享 memory | UI / docs 明确提示 opt-out 风险 |
## 相关文件
| 文件 | 职责 |
|---|---|
| `app/gateway/auth_middleware.py` | 全局认证门、JWT 严格验证、写入 user context |
| `app/gateway/csrf_middleware.py` | CSRF double-submit 和 auth Origin 校验 |
| `app/gateway/routers/auth.py` | initialize/login/register/logout/me/change-password |
| `app/gateway/auth/jwt.py` | JWT 创建与解析 |
| `app/gateway/auth/reset_admin.py` | 密码 reset CLI |
| `app/gateway/auth/credential_file.py` | 0600 凭据文件写入 |
| `app/gateway/authz.py` | 路由权限与 owner check |
| `deerflow/runtime/user_context.py` | 当前用户 ContextVar 与 `AUTO` sentinel |
| `deerflow/persistence/thread_meta/` | thread metadata owner filter |
| `deerflow/config/paths.py` | per-user filesystem layout |
| `deerflow/agents/middlewares/thread_data_middleware.py` | run 时解析用户线程目录 |
| `deerflow/agents/memory/storage.py` | per-user memory storage |
| `deerflow/config/agents_config.py` | per-user custom agents |
| `app/channels/manager.py` | IM channel 内部认证调用 |
| `scripts/migrate_user_isolation.py` | legacy 数据迁移到 per-user layout |
| `.deer-flow/data/deerflow.db` | 统一 SQLite 数据库,包含 users / threads_meta / runs / feedback 等表 |
| `.deer-flow/users/{user_id}/agents/{agent_name}/` | 用户自定义 agent 配置、SOUL 和 agent memory |
| `.deer-flow/admin_initial_credentials.txt` | `reset_admin` 生成的新凭据文件(0600,读完应删除) |
-77
View File
@@ -1,77 +0,0 @@
# Docker Test Gap (Section 七 7.4)
This file documents the only **un-executed** test cases from
`backend/docs/AUTH_TEST_PLAN.md` after the full release validation pass.
## Why this gap exists
The release validation environment (sg_dev: `10.251.229.92`) **does not have
a Docker daemon installed**. The TC-DOCKER cases are container-runtime
behavior tests that need an actual Docker engine to spin up
`docker/docker-compose.yaml` services.
```bash
$ ssh sg_dev "which docker; docker --version"
# (empty)
# bash: docker: command not found
```
All other test plan sections were executed against either:
- The local dev box (Mac, all services running locally), or
- The deployed sg_dev instance (gateway + frontend + nginx via SSH tunnel)
## Cases not executed
| Case | Title | What it covers | Why not run |
|---|---|---|---|
| TC-DOCKER-01 | `deerflow.db` volume persistence | Verify the `DEER_FLOW_HOME` bind mount survives container restart | needs `docker compose up` |
| TC-DOCKER-02 | Session persistence across container restart | `AUTH_JWT_SECRET` env var keeps cookies valid after `docker compose down && up` | needs `docker compose down/up` |
| TC-DOCKER-03 | Per-worker rate limiter divergence | Confirms in-process `_login_attempts` dict doesn't share state across `gunicorn` workers (4 by default in the compose file); known limitation, documented | needs multi-worker container |
| TC-DOCKER-04 | IM channels use internal Gateway auth | Verify Feishu/Slack/Telegram dispatchers attach the process-local internal auth header plus CSRF cookie/header when calling Gateway-compatible LangGraph APIs | needs `docker logs` |
| TC-DOCKER-05 | Reset credentials surfacing | `reset_admin` writes a 0600 credential file in `DEER_FLOW_HOME` instead of logging plaintext. The file-based behavior is validated by non-Docker reset tests, so the only Docker-specific gap is verifying the volume mount carries the file out to the host | needs container + host volume |
| TC-DOCKER-06 | Docker deploy uses Gateway embedded runtime | `./scripts/deploy.sh` produces a Gateway + frontend + nginx topology (no `langgraph` container); same auth flow as local `make dev` | needs `docker compose up` |
## Coverage already provided by non-Docker tests
The **auth-relevant** behavior in each Docker case is already exercised by
the test cases that ran on sg_dev or local:
| Docker case | Auth behavior covered by |
|---|---|
| TC-DOCKER-01 (volume persistence) | TC-REENT-01 on sg_dev (admin row survives gateway restart) — same SQLite file, just no container layer between |
| TC-DOCKER-02 (session persistence) | TC-API-02/03/06 (cookie roundtrip), plus TC-REENT-04 (multi-cookie) — JWT verification is process-state-free, container restart is equivalent to `pkill uvicorn && uv run uvicorn` |
| TC-DOCKER-03 (per-worker rate limit) | TC-GW-04 + TC-REENT-09 (single-worker rate limit + 5min expiry). The cross-worker divergence is an architectural property of the in-memory dict; no auth code path differs |
| TC-DOCKER-04 (IM channels use internal auth) | Code-level: `app/channels/manager.py` creates the `langgraph_sdk` client with `create_internal_auth_headers()` plus CSRF cookie/header, so channel workers do not rely on browser cookies |
| TC-DOCKER-05 (credential surfacing) | `reset_admin` writes `.deer-flow/admin_initial_credentials.txt` with mode 0600 and logs only the path — the only Docker-unique step is whether the bind mount projects this path onto the host, which is a `docker compose` config check, not a runtime behavior change |
| TC-DOCKER-06 (Gateway embedded runtime container) | Section 七 7.2 covered by TC-GW-01..05 + Section 二 (Gateway auth flow on sg_dev) — same Gateway code, container is just a packaging change |
## Reproduction steps when Docker becomes available
Anyone with `docker` + `docker compose` installed can reproduce the gap by
running the test plan section verbatim. Pre-flight:
```bash
# Required on the host
docker --version # >=24.x
docker compose version # plugin >=2.x
# Required env var (otherwise sessions reset on every container restart)
echo "AUTH_JWT_SECRET=$(python3 -c 'import secrets; print(secrets.token_urlsafe(32))')" \
>> .env
# Optional: pin DEER_FLOW_HOME to a stable host path
echo "DEER_FLOW_HOME=$HOME/deer-flow-data" >> .env
```
Then run TC-DOCKER-01..06 from the test plan as written.
## Decision log
- **Not blocking the release.** The auth-relevant behavior in every Docker
case has an already-validated equivalent on bare metal. The gap is purely
about *container packaging* details (bind mounts, multi-worker, log
collection), not about whether the auth code paths work.
- **TC-DOCKER-05 was updated in place** in `AUTH_TEST_PLAN.md` to reflect
the current reset flow (`reset_admin` → 0600 credentials file, no log leak).
The old "grep 'Password:' in docker logs" expectation would have failed
silently and given a false sense of coverage.
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# Authentication Upgrade Guide
DeerFlow 内置了认证模块。本文档面向从无认证版本升级的用户。
完整设计见 [AUTH_DESIGN.md](AUTH_DESIGN.md)。
## 核心概念
认证模块采用**始终强制**策略:
- 首次启动时不会自动创建账号;首次访问 `/setup` 时由操作者创建第一个 admin 账号
- 认证从一开始就是强制的,无竞争窗口
- 已有 admin 后,服务启动时会把历史对话(升级前创建且缺少 `user_id` 的 thread)迁移到 admin 名下
- 新数据按用户隔离:thread、workspace/uploads/outputs、memory、自定义 agent 都归属当前用户
## 升级步骤
### 1. 更新代码
```bash
git pull origin main
cd backend && make install
```
### 2. 首次启动
```bash
make dev
```
如果没有 admin 账号,控制台只会提示:
```
============================================================
First boot detected — no admin account exists.
Visit /setup to complete admin account creation.
============================================================
```
首次启动不会在日志里打印随机密码,也不会写入默认 admin。这样避免启动日志泄露凭据,也避免在操作者创建账号前出现可被猜测的默认身份。
### 3. 创建 admin
访问 `http://localhost:2026/setup`,填写邮箱和密码创建第一个 admin 账号。创建成功后会自动登录并进入 workspace。
如果这是从无认证版本升级,创建 admin 后重启一次服务,让启动迁移把缺少 `user_id` 的历史 thread 归属到 admin。
### 4. 登录
后续访问 `http://localhost:2026/login`,使用已创建的邮箱和密码登录。
### 5. 添加用户(可选)
其他用户通过 `/login` 页面注册,自动获得 **user** 角色。每个用户只能看到自己的对话、上传文件、输出文件、memory 和自定义 agent。
## 安全机制
| 机制 | 说明 |
|------|------|
| JWT HttpOnly Cookie | Token 不暴露给 JavaScript,防止 XSS 窃取 |
| CSRF Double Submit Cookie | 受保护的 POST/PUT/PATCH/DELETE 请求需携带 `X-CSRF-Token`;登录/注册/初始化/登出走 auth 端点 Origin 校验 |
| bcrypt 密码哈希 | 密码不以明文存储 |
| Thread owner filter | `threads_meta.user_id` 由服务端认证上下文写入,搜索、读取、更新、删除默认按当前用户过滤 |
| 文件系统隔离 | 线程数据写入 `{base_dir}/users/{user_id}/threads/{thread_id}/user-data/`sandbox 内统一映射为 `/mnt/user-data/` |
| Memory / agent 隔离 | 用户 memory 和自定义 agent 写入 `{base_dir}/users/{user_id}/...`;旧共享 agent 只作为只读兼容回退 |
| HTTPS 自适应 | 检测 `x-forwarded-proto`,自动设置 `Secure` cookie 标志 |
## 常见操作
### 忘记密码
```bash
cd backend
# 重置 admin 密码
python -m app.gateway.auth.reset_admin
# 重置指定用户密码
python -m app.gateway.auth.reset_admin --email user@example.com
```
会把新的随机密码写入 `.deer-flow/admin_initial_credentials.txt`,文件权限为 `0600`。命令行只输出文件路径,不输出明文密码。
### 完全重置
删除统一 SQLite 数据库,重启后重新访问 `/setup` 创建新 admin
```bash
rm -f backend/.deer-flow/data/deerflow.db
# 重启服务后访问 http://localhost:2026/setup
```
## 数据存储
| 文件 | 内容 |
|------|------|
| `.deer-flow/data/deerflow.db` | 统一 SQLite 数据库(users、threads_meta、runs、feedback 等应用数据) |
| `.deer-flow/users/{user_id}/threads/{thread_id}/user-data/` | 用户线程的 workspace、uploads、outputs |
| `.deer-flow/users/{user_id}/memory.json` | 用户级 memory |
| `.deer-flow/users/{user_id}/agents/{agent_name}/` | 用户自定义 agent 配置、SOUL 和 agent memory |
| `.deer-flow/admin_initial_credentials.txt` | `reset_admin` 生成的新凭据文件(0600,读完应删除) |
| `.env` 中的 `AUTH_JWT_SECRET` | JWT 签名密钥(未设置时自动生成并持久化到 `.deer-flow/.jwt_secret`,重启后 session 保持) |
### 生产环境建议
```bash
# 生成持久化 JWT 密钥,避免重启后所有用户需重新登录
python -c "import secrets; print(secrets.token_urlsafe(32))"
# 将输出添加到 .env
# AUTH_JWT_SECRET=<生成的密钥>
```
## API 端点
| 端点 | 方法 | 说明 |
|------|------|------|
| `/api/v1/auth/login/local` | POST | 邮箱密码登录(OAuth2 form |
| `/api/v1/auth/register` | POST | 注册新用户(user 角色) |
| `/api/v1/auth/logout` | POST | 登出(清除 cookie |
| `/api/v1/auth/me` | GET | 获取当前用户信息 |
| `/api/v1/auth/change-password` | POST | 修改密码 |
| `/api/v1/auth/setup-status` | GET | 检查 admin 是否存在 |
| `/api/v1/auth/initialize` | POST | 首次初始化第一个 admin(仅无 admin 时可调用) |
## 兼容性
- **本地开发**`make dev`):Gateway embedded runtime 完全兼容;无 admin 时访问 `/setup` 初始化
- **Gateway embedded runtime**:标准脚本、Docker dev 和生产部署均通过 Gateway 提供认证与 LangGraph-compatible API
- **Docker 部署**:完全兼容,`.deer-flow/data/deerflow.db` 需持久化卷挂载
- **IM 渠道**Feishu/Slack/Telegram):通过 Gateway 内部认证通信,使用 `default` 用户桶
- **DeerFlowClient**(嵌入式):不经过 HTTP,不受认证影响
## 故障排查
| 症状 | 原因 | 解决 |
|------|------|------|
| 启动后没看到密码 | 当前实现不在启动日志输出密码 | 首次安装访问 `/setup`;忘记密码用 `reset_admin` |
| `/login` 自动跳到 `/setup` | 系统还没有 admin | 在 `/setup` 创建第一个 admin |
| 登录后 POST 返回 403 | CSRF token 缺失 | 确认前端已更新 |
| 重启后需要重新登录 | `.jwt_secret` 文件被删除且 `.env` 未设置 `AUTH_JWT_SECRET` | 在 `.env` 中设置固定密钥 |
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# Blocking IO detection usage and maintenance
This document describes how to use and maintain DeerFlow backend blocking-IO
detection for async event-loop safety.
The goal is narrow: find and prevent synchronous IO from blocking backend
async event-loop paths. Static and runtime detection are complementary, but
they have different jobs.
## Static detector
The static detector is the discovery tool. It scans backend source code and
reports candidate blocking-IO call sites that may need human review.
Run it from the repository root:
```bash
make detect-blocking-io
```
Or from `backend/`:
```bash
make detect-blocking-io
```
The report is written to:
```text
.deer-flow/blocking-io-findings.json
```
Use this output for review and triage. A static finding is a candidate, not
proof that production blocks the event loop at runtime. The current static
rules are intentionally broad; prefer triaging existing output before adding
new static rules.
Add a static rule only when review finds a recurring high-risk blocking
pattern that is invisible to the current detector.
## Runtime detector
The runtime detector is the CI regression guard. It uses Blockbuster to fail a
focused test when code under `app.*` or `deerflow.*` performs blocking IO on
the asyncio event-loop thread.
Run it from `backend/`:
```bash
make test-blocking-io
```
The runtime gate starts from confirmed production bugs and protects those
paths from regressing. It does not prove that the entire backend is free of
blocking IO; it only covers the production paths exercised by
`backend/tests/blocking_io/`.
## Maintenance workflow
Use the static detector to find candidates, then use review to decide which
async production paths are worth protecting in CI.
The normal workflow is:
1. Run the static detector to find backend blocking-IO candidates.
2. Use human review to pick high-risk production async paths.
3. Add or update a focused runtime anchor in `backend/tests/blocking_io/`.
4. Let CI prevent that path from regressing.
Runtime detection has two maintenance paths.
### Add a runtime rule
Add a runtime rule when Blockbuster's default rules do not cover a generic
blocking primitive used by production code.
Rules belong in:
```text
backend/tests/support/detectors/blocking_io_runtime.py
```
Add them to `_PROJECT_BLOCKING_RULES`, not directly inside individual tests.
Keeping rules centralized makes it clear which extra primitives DeerFlow
expects Blockbuster to catch.
Example shape:
```python
import subprocess
from blockbuster import BlockBusterFunction
_PROJECT_BLOCKING_RULES = (
(
"subprocess.Popen.__init__",
BlockBusterFunction(
subprocess.Popen,
"__init__",
scanned_modules=["app", "deerflow"],
),
),
)
```
Do not add a runtime rule just because a business path is not tested. A rule
only expands what Blockbuster can intercept after code runs.
### Add a runtime anchor
Add a runtime anchor when a high-risk async production path should be protected
by CI but no existing `backend/tests/blocking_io/` test executes it.
Anchors belong in:
```text
backend/tests/blocking_io/
```
A good anchor should:
- Call the real production async entry point.
- Avoid bypassing the blocking surface with test-only `asyncio.to_thread`
wrappers.
- Use real local filesystem inputs when the bug shape is filesystem IO.
- Mock only the external dependency boundary, such as a network service or
third-party saver class.
- Fail if a future change moves the blocking operation back onto the event
loop.
Avoid testing only the low-level helper unless that helper is the production
async entry point. The runtime gate is most useful when it protects the caller
that production actually executes.
## Current runtime coverage
The runtime anchors protect confirmed blocking-IO bug shapes:
- SQLite checkpointer setup, including path resolution and parent-directory
creation.
- Subagent skill metadata loading through `SubagentExecutor._load_skills()`.
- `JsonlRunEventStore` async API (`put` / `list_*` / `delete_*`): the JSONL
run-event backend offloads its synchronous file IO via `asyncio.to_thread`
(fix #3084); this anchor drives the real async API under the gate so any
blocking IO reintroduced on the loop fails, not only removal of one
`to_thread` call.
- `UploadsMiddleware.before_agent` uploads-directory scan: a sync-only middleware
hook runs on the event loop under async graph execution, so the scan is
offloaded via `abefore_agent` + `run_in_executor`.
- Gate health checks: Blockbuster catches unoffloaded calls, opt-out works, and
patches are restored after exceptions.
As static detection and review identify more high-risk async paths, add new
runtime anchors incrementally.

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