mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-23 16:35:59 +00:00
refactor(config): eliminate global mutable state — explicit parameter passing on top of main
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
This commit is contained in:
greatmengqi
@@ -5,15 +5,22 @@ Re-exports the public API of :mod:`~deerflow.runtime.runs` and
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directly from ``deerflow.runtime``.
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"""
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from .runs import ConflictError, DisconnectMode, RunManager, RunRecord, RunStatus, UnsupportedStrategyError, run_agent
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from .checkpointer import checkpointer_context, get_checkpointer, make_checkpointer, reset_checkpointer
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from .runs import ConflictError, DisconnectMode, RunContext, RunManager, RunRecord, RunStatus, UnsupportedStrategyError, run_agent
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from .serialization import serialize, serialize_channel_values, serialize_lc_object, serialize_messages_tuple
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from .store import get_store, make_store, reset_store, store_context
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from .stream_bridge import END_SENTINEL, HEARTBEAT_SENTINEL, MemoryStreamBridge, StreamBridge, StreamEvent, make_stream_bridge
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__all__ = [
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# checkpointer
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"checkpointer_context",
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"get_checkpointer",
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"make_checkpointer",
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"reset_checkpointer",
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# runs
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"ConflictError",
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"DisconnectMode",
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"RunContext",
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"RunManager",
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"RunRecord",
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"RunStatus",
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@@ -0,0 +1,9 @@
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from .async_provider import make_checkpointer
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from .provider import checkpointer_context, get_checkpointer, reset_checkpointer
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__all__ = [
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"get_checkpointer",
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"reset_checkpointer",
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"checkpointer_context",
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"make_checkpointer",
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]
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@@ -0,0 +1,157 @@
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"""Async checkpointer factory.
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Provides an **async context manager** for long-running async servers that need
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proper resource cleanup.
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Supported backends: memory, sqlite, postgres.
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Usage (e.g. FastAPI lifespan)::
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from deerflow.runtime.checkpointer.async_provider import make_checkpointer
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async with make_checkpointer() as checkpointer:
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app.state.checkpointer = checkpointer # InMemorySaver if not configured
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For sync usage see :mod:`deerflow.runtime.checkpointer.provider`.
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"""
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from __future__ import annotations
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import asyncio
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import contextlib
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import logging
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from collections.abc import AsyncIterator
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from langgraph.types import Checkpointer
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from deerflow.config.app_config import AppConfig
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from deerflow.runtime.checkpointer.provider import (
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POSTGRES_CONN_REQUIRED,
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POSTGRES_INSTALL,
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SQLITE_INSTALL,
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)
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from deerflow.runtime.store._sqlite_utils import ensure_sqlite_parent_dir, resolve_sqlite_conn_str
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Async factory
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# ---------------------------------------------------------------------------
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@contextlib.asynccontextmanager
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async def _async_checkpointer(config) -> AsyncIterator[Checkpointer]:
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"""Async context manager that constructs and tears down a checkpointer."""
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if config.type == "memory":
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from langgraph.checkpoint.memory import InMemorySaver
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yield InMemorySaver()
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return
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if config.type == "sqlite":
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try:
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from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
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except ImportError as exc:
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raise ImportError(SQLITE_INSTALL) from exc
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conn_str = resolve_sqlite_conn_str(config.connection_string or "store.db")
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await asyncio.to_thread(ensure_sqlite_parent_dir, conn_str)
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async with AsyncSqliteSaver.from_conn_string(conn_str) as saver:
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await saver.setup()
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yield saver
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return
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if config.type == "postgres":
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try:
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from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
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except ImportError as exc:
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raise ImportError(POSTGRES_INSTALL) from exc
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if not config.connection_string:
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raise ValueError(POSTGRES_CONN_REQUIRED)
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async with AsyncPostgresSaver.from_conn_string(config.connection_string) as saver:
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await saver.setup()
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yield saver
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return
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raise ValueError(f"Unknown checkpointer type: {config.type!r}")
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# ---------------------------------------------------------------------------
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# Public async context manager
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# ---------------------------------------------------------------------------
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@contextlib.asynccontextmanager
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async def _async_checkpointer_from_database(db_config) -> AsyncIterator[Checkpointer]:
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"""Async context manager that constructs a checkpointer from unified DatabaseConfig."""
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if db_config.backend == "memory":
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from langgraph.checkpoint.memory import InMemorySaver
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yield InMemorySaver()
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return
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if db_config.backend == "sqlite":
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try:
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from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
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except ImportError as exc:
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raise ImportError(SQLITE_INSTALL) from exc
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conn_str = db_config.checkpointer_sqlite_path
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ensure_sqlite_parent_dir(conn_str)
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async with AsyncSqliteSaver.from_conn_string(conn_str) as saver:
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await saver.setup()
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yield saver
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return
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if db_config.backend == "postgres":
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try:
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from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
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except ImportError as exc:
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raise ImportError(POSTGRES_INSTALL) from exc
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if not db_config.postgres_url:
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raise ValueError("database.postgres_url is required for the postgres backend")
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async with AsyncPostgresSaver.from_conn_string(db_config.postgres_url) as saver:
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await saver.setup()
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yield saver
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return
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raise ValueError(f"Unknown database backend: {db_config.backend!r}")
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@contextlib.asynccontextmanager
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async def make_checkpointer(app_config: AppConfig) -> AsyncIterator[Checkpointer]:
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"""Async context manager that yields a checkpointer for the caller's lifetime.
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Resources are opened on enter and closed on exit -- no global state::
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async with make_checkpointer(app_config) as checkpointer:
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app.state.checkpointer = checkpointer
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Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
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Priority:
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1. Legacy ``checkpointer:`` config section (backward compatible)
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2. Unified ``database:`` config section
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3. Default InMemorySaver
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"""
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# Legacy: standalone checkpointer config takes precedence
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if app_config.checkpointer is not None:
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async with _async_checkpointer(app_config.checkpointer) as saver:
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yield saver
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return
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# Unified database config
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db_config = getattr(app_config, "database", None)
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if db_config is not None and db_config.backend != "memory":
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async with _async_checkpointer_from_database(db_config) as saver:
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yield saver
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return
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# Default: in-memory
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from langgraph.checkpoint.memory import InMemorySaver
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yield InMemorySaver()
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@@ -0,0 +1,175 @@
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"""Sync checkpointer factory.
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Provides a **sync singleton** and a **sync context manager** for LangGraph
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graph compilation and CLI tools.
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Supported backends: memory, sqlite, postgres.
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Usage::
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from deerflow.runtime.checkpointer.provider import get_checkpointer, checkpointer_context
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# Singleton — reused across calls, closed on process exit
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cp = get_checkpointer()
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# One-shot — fresh connection, closed on block exit
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with checkpointer_context() as cp:
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graph.invoke(input, config={"configurable": {"thread_id": "1"}})
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"""
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from __future__ import annotations
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import contextlib
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import logging
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from collections.abc import Iterator
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from langgraph.types import Checkpointer
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from deerflow.config.app_config import AppConfig
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from deerflow.config.checkpointer_config import CheckpointerConfig
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from deerflow.runtime.store._sqlite_utils import ensure_sqlite_parent_dir, resolve_sqlite_conn_str
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Error message constants — imported by aio.provider too
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# ---------------------------------------------------------------------------
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SQLITE_INSTALL = "langgraph-checkpoint-sqlite is required for the SQLite checkpointer. Install it with: uv add langgraph-checkpoint-sqlite"
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POSTGRES_INSTALL = "langgraph-checkpoint-postgres is required for the PostgreSQL checkpointer. Install it with: uv add langgraph-checkpoint-postgres psycopg[binary] psycopg-pool"
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POSTGRES_CONN_REQUIRED = "checkpointer.connection_string is required for the postgres backend"
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# ---------------------------------------------------------------------------
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# Sync factory
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# ---------------------------------------------------------------------------
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@contextlib.contextmanager
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def _sync_checkpointer_cm(config: CheckpointerConfig) -> Iterator[Checkpointer]:
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"""Context manager that creates and tears down a sync checkpointer.
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Returns a configured ``Checkpointer`` instance. Resource cleanup for any
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underlying connections or pools is handled by higher-level helpers in
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this module (such as the singleton factory or context manager); this
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function does not return a separate cleanup callback.
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"""
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if config.type == "memory":
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from langgraph.checkpoint.memory import InMemorySaver
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logger.info("Checkpointer: using InMemorySaver (in-process, not persistent)")
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yield InMemorySaver()
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return
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if config.type == "sqlite":
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try:
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from langgraph.checkpoint.sqlite import SqliteSaver
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except ImportError as exc:
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raise ImportError(SQLITE_INSTALL) from exc
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conn_str = resolve_sqlite_conn_str(config.connection_string or "store.db")
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ensure_sqlite_parent_dir(conn_str)
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with SqliteSaver.from_conn_string(conn_str) as saver:
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saver.setup()
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logger.info("Checkpointer: using SqliteSaver (%s)", conn_str)
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yield saver
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return
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if config.type == "postgres":
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try:
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from langgraph.checkpoint.postgres import PostgresSaver
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except ImportError as exc:
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raise ImportError(POSTGRES_INSTALL) from exc
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if not config.connection_string:
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raise ValueError(POSTGRES_CONN_REQUIRED)
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with PostgresSaver.from_conn_string(config.connection_string) as saver:
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saver.setup()
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logger.info("Checkpointer: using PostgresSaver")
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yield saver
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return
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raise ValueError(f"Unknown checkpointer type: {config.type!r}")
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# ---------------------------------------------------------------------------
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# Sync singleton
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# ---------------------------------------------------------------------------
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_checkpointer: Checkpointer | None = None
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_checkpointer_ctx = None # open context manager keeping the connection alive
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def get_checkpointer(app_config: AppConfig) -> Checkpointer:
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"""Return the global sync checkpointer singleton, creating it on first call.
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Returns an ``InMemorySaver`` only when ``checkpointer`` is explicitly
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absent from config.yaml. Any other failure (missing config, invalid
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backend, connection error) propagates — silent degradation to in-memory
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would drop persistent-run state on process restart.
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Raises:
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ImportError: If the required package for the configured backend is not installed.
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ValueError: If ``connection_string`` is missing for a backend that requires it.
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"""
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global _checkpointer, _checkpointer_ctx
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if _checkpointer is not None:
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return _checkpointer
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config = app_config.checkpointer
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if config is None:
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from langgraph.checkpoint.memory import InMemorySaver
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logger.info("Checkpointer: using InMemorySaver (in-process, not persistent)")
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_checkpointer = InMemorySaver()
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return _checkpointer
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_checkpointer_ctx = _sync_checkpointer_cm(config)
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_checkpointer = _checkpointer_ctx.__enter__()
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return _checkpointer
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def reset_checkpointer() -> None:
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"""Reset the sync singleton, forcing recreation on the next call.
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Closes any open backend connections and clears the cached instance.
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Useful in tests or after a configuration change.
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"""
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global _checkpointer, _checkpointer_ctx
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if _checkpointer_ctx is not None:
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try:
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_checkpointer_ctx.__exit__(None, None, None)
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except Exception:
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logger.warning("Error during checkpointer cleanup", exc_info=True)
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_checkpointer_ctx = None
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_checkpointer = None
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# ---------------------------------------------------------------------------
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# Sync context manager
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# ---------------------------------------------------------------------------
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@contextlib.contextmanager
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def checkpointer_context(app_config: AppConfig) -> Iterator[Checkpointer]:
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"""Sync context manager that yields a checkpointer and cleans up on exit.
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Unlike :func:`get_checkpointer`, this does **not** cache the instance —
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each ``with`` block creates and destroys its own connection. Use it in
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CLI scripts or tests where you want deterministic cleanup::
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with checkpointer_context(app_config) as cp:
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graph.invoke(input, config={"configurable": {"thread_id": "1"}})
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Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
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"""
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if app_config.checkpointer is None:
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from langgraph.checkpoint.memory import InMemorySaver
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yield InMemorySaver()
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return
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with _sync_checkpointer_cm(app_config.checkpointer) as saver:
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yield saver
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@@ -0,0 +1,134 @@
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"""Pure functions to convert LangChain message objects to OpenAI Chat Completions format.
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Used by RunJournal to build content dicts for event storage.
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"""
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from __future__ import annotations
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import json
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from typing import Any
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_ROLE_MAP = {
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"human": "user",
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"ai": "assistant",
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"system": "system",
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"tool": "tool",
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}
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def langchain_to_openai_message(message: Any) -> dict:
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"""Convert a single LangChain BaseMessage to an OpenAI message dict.
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Handles:
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- HumanMessage → {"role": "user", "content": "..."}
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- AIMessage (text only) → {"role": "assistant", "content": "..."}
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- AIMessage (with tool_calls) → {"role": "assistant", "content": null, "tool_calls": [...]}
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- AIMessage (text + tool_calls) → both content and tool_calls present
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- AIMessage (list content / multimodal) → content preserved as list
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- SystemMessage → {"role": "system", "content": "..."}
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- ToolMessage → {"role": "tool", "tool_call_id": "...", "content": "..."}
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"""
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msg_type = getattr(message, "type", "")
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role = _ROLE_MAP.get(msg_type, msg_type)
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content = getattr(message, "content", "")
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||||
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||||
if role == "tool":
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return {
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||||
"role": "tool",
|
||||
"tool_call_id": getattr(message, "tool_call_id", ""),
|
||||
"content": content,
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||||
}
|
||||
|
||||
if role == "assistant":
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||||
tool_calls = getattr(message, "tool_calls", None) or []
|
||||
result: dict = {"role": "assistant"}
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||||
|
||||
if tool_calls:
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||||
openai_tool_calls = []
|
||||
for tc in tool_calls:
|
||||
args = tc.get("args", {})
|
||||
openai_tool_calls.append(
|
||||
{
|
||||
"id": tc.get("id", ""),
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": tc.get("name", ""),
|
||||
"arguments": json.dumps(args) if not isinstance(args, str) else args,
|
||||
},
|
||||
}
|
||||
)
|
||||
# If no text content, set content to null per OpenAI spec
|
||||
result["content"] = content if (isinstance(content, list) and content) or (isinstance(content, str) and content) else None
|
||||
result["tool_calls"] = openai_tool_calls
|
||||
else:
|
||||
result["content"] = content
|
||||
|
||||
return result
|
||||
|
||||
# user / system / unknown
|
||||
return {"role": role, "content": content}
|
||||
|
||||
|
||||
def _infer_finish_reason(message: Any) -> str:
|
||||
"""Infer OpenAI finish_reason from an AIMessage.
|
||||
|
||||
Returns "tool_calls" if tool_calls present, else looks in
|
||||
response_metadata.finish_reason, else returns "stop".
|
||||
"""
|
||||
tool_calls = getattr(message, "tool_calls", None) or []
|
||||
if tool_calls:
|
||||
return "tool_calls"
|
||||
resp_meta = getattr(message, "response_metadata", None) or {}
|
||||
if isinstance(resp_meta, dict):
|
||||
finish = resp_meta.get("finish_reason")
|
||||
if finish:
|
||||
return finish
|
||||
return "stop"
|
||||
|
||||
|
||||
def langchain_to_openai_completion(message: Any) -> dict:
|
||||
"""Convert an AIMessage and its metadata to an OpenAI completion response dict.
|
||||
|
||||
Returns:
|
||||
{
|
||||
"id": message.id,
|
||||
"model": message.response_metadata.get("model_name"),
|
||||
"choices": [{"index": 0, "message": <openai_message>, "finish_reason": <inferred>}],
|
||||
"usage": {"prompt_tokens": ..., "completion_tokens": ..., "total_tokens": ...} or None,
|
||||
}
|
||||
"""
|
||||
resp_meta = getattr(message, "response_metadata", None) or {}
|
||||
model_name = resp_meta.get("model_name") if isinstance(resp_meta, dict) else None
|
||||
|
||||
openai_msg = langchain_to_openai_message(message)
|
||||
finish_reason = _infer_finish_reason(message)
|
||||
|
||||
usage_metadata = getattr(message, "usage_metadata", None)
|
||||
if usage_metadata is not None:
|
||||
input_tokens = usage_metadata.get("input_tokens", 0) or 0
|
||||
output_tokens = usage_metadata.get("output_tokens", 0) or 0
|
||||
usage: dict | None = {
|
||||
"prompt_tokens": input_tokens,
|
||||
"completion_tokens": output_tokens,
|
||||
"total_tokens": input_tokens + output_tokens,
|
||||
}
|
||||
else:
|
||||
usage = None
|
||||
|
||||
return {
|
||||
"id": getattr(message, "id", None),
|
||||
"model": model_name,
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": openai_msg,
|
||||
"finish_reason": finish_reason,
|
||||
}
|
||||
],
|
||||
"usage": usage,
|
||||
}
|
||||
|
||||
|
||||
def langchain_messages_to_openai(messages: list) -> list[dict]:
|
||||
"""Convert a list of LangChain BaseMessages to OpenAI message dicts."""
|
||||
return [langchain_to_openai_message(m) for m in messages]
|
||||
@@ -0,0 +1,4 @@
|
||||
from deerflow.runtime.events.store.base import RunEventStore
|
||||
from deerflow.runtime.events.store.memory import MemoryRunEventStore
|
||||
|
||||
__all__ = ["MemoryRunEventStore", "RunEventStore"]
|
||||
@@ -0,0 +1,26 @@
|
||||
from deerflow.runtime.events.store.base import RunEventStore
|
||||
from deerflow.runtime.events.store.memory import MemoryRunEventStore
|
||||
|
||||
|
||||
def make_run_event_store(config=None) -> RunEventStore:
|
||||
"""Create a RunEventStore based on run_events.backend configuration."""
|
||||
if config is None or config.backend == "memory":
|
||||
return MemoryRunEventStore()
|
||||
if config.backend == "db":
|
||||
from deerflow.persistence.engine import get_session_factory
|
||||
|
||||
sf = get_session_factory()
|
||||
if sf is None:
|
||||
# database.backend=memory but run_events.backend=db -> fallback
|
||||
return MemoryRunEventStore()
|
||||
from deerflow.runtime.events.store.db import DbRunEventStore
|
||||
|
||||
return DbRunEventStore(sf, max_trace_content=config.max_trace_content)
|
||||
if config.backend == "jsonl":
|
||||
from deerflow.runtime.events.store.jsonl import JsonlRunEventStore
|
||||
|
||||
return JsonlRunEventStore()
|
||||
raise ValueError(f"Unknown run_events backend: {config.backend!r}")
|
||||
|
||||
|
||||
__all__ = ["MemoryRunEventStore", "RunEventStore", "make_run_event_store"]
|
||||
@@ -0,0 +1,109 @@
|
||||
"""Abstract interface for run event storage.
|
||||
|
||||
RunEventStore is the unified storage interface for run event streams.
|
||||
Messages (frontend display) and execution traces (debugging/audit) go
|
||||
through the same interface, distinguished by the ``category`` field.
|
||||
|
||||
Implementations:
|
||||
- MemoryRunEventStore: in-memory dict (development, tests)
|
||||
- Future: DB-backed store (SQLAlchemy ORM), JSONL file store
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import abc
|
||||
|
||||
|
||||
class RunEventStore(abc.ABC):
|
||||
"""Run event stream storage interface.
|
||||
|
||||
All implementations must guarantee:
|
||||
1. put() events are retrievable in subsequent queries
|
||||
2. seq is strictly increasing within the same thread
|
||||
3. list_messages() only returns category="message" events
|
||||
4. list_events() returns all events for the specified run
|
||||
5. Returned dicts match the RunEvent field structure
|
||||
"""
|
||||
|
||||
@abc.abstractmethod
|
||||
async def put(
|
||||
self,
|
||||
*,
|
||||
thread_id: str,
|
||||
run_id: str,
|
||||
event_type: str,
|
||||
category: str,
|
||||
content: str | dict = "",
|
||||
metadata: dict | None = None,
|
||||
created_at: str | None = None,
|
||||
) -> dict:
|
||||
"""Write an event, auto-assign seq, return the complete record."""
|
||||
|
||||
@abc.abstractmethod
|
||||
async def put_batch(self, events: list[dict]) -> list[dict]:
|
||||
"""Batch-write events. Used by RunJournal flush buffer.
|
||||
|
||||
Each dict's keys match put()'s keyword arguments.
|
||||
Returns complete records with seq assigned.
|
||||
"""
|
||||
|
||||
@abc.abstractmethod
|
||||
async def list_messages(
|
||||
self,
|
||||
thread_id: str,
|
||||
*,
|
||||
limit: int = 50,
|
||||
before_seq: int | None = None,
|
||||
after_seq: int | None = None,
|
||||
) -> list[dict]:
|
||||
"""Return displayable messages (category=message) for a thread, ordered by seq ascending.
|
||||
|
||||
Supports bidirectional cursor pagination:
|
||||
- before_seq: return the last ``limit`` records with seq < before_seq (ascending)
|
||||
- after_seq: return the first ``limit`` records with seq > after_seq (ascending)
|
||||
- neither: return the latest ``limit`` records (ascending)
|
||||
"""
|
||||
|
||||
@abc.abstractmethod
|
||||
async def list_events(
|
||||
self,
|
||||
thread_id: str,
|
||||
run_id: str,
|
||||
*,
|
||||
event_types: list[str] | None = None,
|
||||
limit: int = 500,
|
||||
) -> list[dict]:
|
||||
"""Return the full event stream for a run, ordered by seq ascending.
|
||||
|
||||
Optionally filter by event_types.
|
||||
"""
|
||||
|
||||
@abc.abstractmethod
|
||||
async def list_messages_by_run(
|
||||
self,
|
||||
thread_id: str,
|
||||
run_id: str,
|
||||
*,
|
||||
limit: int = 50,
|
||||
before_seq: int | None = None,
|
||||
after_seq: int | None = None,
|
||||
) -> list[dict]:
|
||||
"""Return displayable messages (category=message) for a specific run, ordered by seq ascending.
|
||||
|
||||
Supports bidirectional cursor pagination:
|
||||
- after_seq: return the first ``limit`` records with seq > after_seq (ascending)
|
||||
- before_seq: return the last ``limit`` records with seq < before_seq (ascending)
|
||||
- neither: return the latest ``limit`` records (ascending)
|
||||
"""
|
||||
|
||||
@abc.abstractmethod
|
||||
async def count_messages(self, thread_id: str) -> int:
|
||||
"""Count displayable messages (category=message) in a thread."""
|
||||
|
||||
@abc.abstractmethod
|
||||
async def delete_by_thread(self, thread_id: str) -> int:
|
||||
"""Delete all events for a thread. Return the number of deleted events."""
|
||||
|
||||
@abc.abstractmethod
|
||||
async def delete_by_run(self, thread_id: str, run_id: str) -> int:
|
||||
"""Delete all events for a specific run. Return the number of deleted events."""
|
||||
@@ -0,0 +1,286 @@
|
||||
"""SQLAlchemy-backed RunEventStore implementation.
|
||||
|
||||
Persists events to the ``run_events`` table. Trace content is truncated
|
||||
at ``max_trace_content`` bytes to avoid bloating the database.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
from datetime import UTC, datetime
|
||||
|
||||
from sqlalchemy import delete, func, select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
|
||||
|
||||
from deerflow.persistence.models.run_event import RunEventRow
|
||||
from deerflow.runtime.events.store.base import RunEventStore
|
||||
from deerflow.runtime.user_context import AUTO, _AutoSentinel, get_current_user, resolve_user_id
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DbRunEventStore(RunEventStore):
|
||||
def __init__(self, session_factory: async_sessionmaker[AsyncSession], *, max_trace_content: int = 10240):
|
||||
self._sf = session_factory
|
||||
self._max_trace_content = max_trace_content
|
||||
|
||||
@staticmethod
|
||||
def _row_to_dict(row: RunEventRow) -> dict:
|
||||
d = row.to_dict()
|
||||
d["metadata"] = d.pop("event_metadata", {})
|
||||
val = d.get("created_at")
|
||||
if isinstance(val, datetime):
|
||||
d["created_at"] = val.isoformat()
|
||||
d.pop("id", None)
|
||||
# Restore dict content that was JSON-serialized on write
|
||||
raw = d.get("content", "")
|
||||
if isinstance(raw, str) and d.get("metadata", {}).get("content_is_dict"):
|
||||
try:
|
||||
d["content"] = json.loads(raw)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
# Content looked like JSON (content_is_dict flag) but failed to parse;
|
||||
# keep the raw string as-is.
|
||||
logger.debug("Failed to deserialize content as JSON for event seq=%s", d.get("seq"))
|
||||
return d
|
||||
|
||||
def _truncate_trace(self, category: str, content: str | dict, metadata: dict | None) -> tuple[str | dict, dict]:
|
||||
if category == "trace":
|
||||
text = json.dumps(content, default=str, ensure_ascii=False) if isinstance(content, dict) else content
|
||||
encoded = text.encode("utf-8")
|
||||
if len(encoded) > self._max_trace_content:
|
||||
# Truncate by bytes, then decode back (may cut a multi-byte char, so use errors="ignore")
|
||||
content = encoded[: self._max_trace_content].decode("utf-8", errors="ignore")
|
||||
metadata = {**(metadata or {}), "content_truncated": True, "original_byte_length": len(encoded)}
|
||||
return content, metadata or {}
|
||||
|
||||
@staticmethod
|
||||
def _user_id_from_context() -> str | None:
|
||||
"""Soft read of user_id from contextvar for write paths.
|
||||
|
||||
Returns ``None`` (no filter / no stamp) if contextvar is unset,
|
||||
which is the expected case for background worker writes. HTTP
|
||||
request writes will have the contextvar set by auth middleware
|
||||
and get their user_id stamped automatically.
|
||||
|
||||
Coerces ``user.id`` to ``str`` at the boundary: ``User.id`` is
|
||||
typed as ``UUID`` by the auth layer, but ``run_events.user_id``
|
||||
is ``VARCHAR(64)`` and aiosqlite cannot bind a raw UUID object
|
||||
to a VARCHAR column ("type 'UUID' is not supported") — the
|
||||
INSERT would silently roll back and the worker would hang.
|
||||
"""
|
||||
user = get_current_user()
|
||||
return str(user.id) if user is not None else None
|
||||
|
||||
async def put(self, *, thread_id, run_id, event_type, category, content="", metadata=None, created_at=None): # noqa: D401
|
||||
"""Write a single event — low-frequency path only.
|
||||
|
||||
This opens a dedicated transaction with a FOR UPDATE lock to
|
||||
assign a monotonic *seq*. For high-throughput writes use
|
||||
:meth:`put_batch`, which acquires the lock once for the whole
|
||||
batch. Currently the only caller is ``worker.run_agent`` for
|
||||
the initial ``human_message`` event (once per run).
|
||||
"""
|
||||
content, metadata = self._truncate_trace(category, content, metadata)
|
||||
if isinstance(content, dict):
|
||||
db_content = json.dumps(content, default=str, ensure_ascii=False)
|
||||
metadata = {**(metadata or {}), "content_is_dict": True}
|
||||
else:
|
||||
db_content = content
|
||||
user_id = self._user_id_from_context()
|
||||
async with self._sf() as session:
|
||||
async with session.begin():
|
||||
# Use FOR UPDATE to serialize seq assignment within a thread.
|
||||
# NOTE: with_for_update() on aggregates is a no-op on SQLite;
|
||||
# the UNIQUE(thread_id, seq) constraint catches races there.
|
||||
max_seq = await session.scalar(select(func.max(RunEventRow.seq)).where(RunEventRow.thread_id == thread_id).with_for_update())
|
||||
seq = (max_seq or 0) + 1
|
||||
row = RunEventRow(
|
||||
thread_id=thread_id,
|
||||
run_id=run_id,
|
||||
user_id=user_id,
|
||||
event_type=event_type,
|
||||
category=category,
|
||||
content=db_content,
|
||||
event_metadata=metadata,
|
||||
seq=seq,
|
||||
created_at=datetime.fromisoformat(created_at) if created_at else datetime.now(UTC),
|
||||
)
|
||||
session.add(row)
|
||||
return self._row_to_dict(row)
|
||||
|
||||
async def put_batch(self, events):
|
||||
if not events:
|
||||
return []
|
||||
user_id = self._user_id_from_context()
|
||||
async with self._sf() as session:
|
||||
async with session.begin():
|
||||
# Get max seq for the thread (assume all events in batch belong to same thread).
|
||||
# NOTE: with_for_update() on aggregates is a no-op on SQLite;
|
||||
# the UNIQUE(thread_id, seq) constraint catches races there.
|
||||
thread_id = events[0]["thread_id"]
|
||||
max_seq = await session.scalar(select(func.max(RunEventRow.seq)).where(RunEventRow.thread_id == thread_id).with_for_update())
|
||||
seq = max_seq or 0
|
||||
rows = []
|
||||
for e in events:
|
||||
seq += 1
|
||||
content = e.get("content", "")
|
||||
category = e.get("category", "trace")
|
||||
metadata = e.get("metadata")
|
||||
content, metadata = self._truncate_trace(category, content, metadata)
|
||||
if isinstance(content, dict):
|
||||
db_content = json.dumps(content, default=str, ensure_ascii=False)
|
||||
metadata = {**(metadata or {}), "content_is_dict": True}
|
||||
else:
|
||||
db_content = content
|
||||
row = RunEventRow(
|
||||
thread_id=e["thread_id"],
|
||||
run_id=e["run_id"],
|
||||
user_id=e.get("user_id", user_id),
|
||||
event_type=e["event_type"],
|
||||
category=category,
|
||||
content=db_content,
|
||||
event_metadata=metadata,
|
||||
seq=seq,
|
||||
created_at=datetime.fromisoformat(e["created_at"]) if e.get("created_at") else datetime.now(UTC),
|
||||
)
|
||||
session.add(row)
|
||||
rows.append(row)
|
||||
return [self._row_to_dict(r) for r in rows]
|
||||
|
||||
async def list_messages(
|
||||
self,
|
||||
thread_id,
|
||||
*,
|
||||
limit=50,
|
||||
before_seq=None,
|
||||
after_seq=None,
|
||||
user_id: str | None | _AutoSentinel = AUTO,
|
||||
):
|
||||
resolved_user_id = resolve_user_id(user_id, method_name="DbRunEventStore.list_messages")
|
||||
stmt = select(RunEventRow).where(RunEventRow.thread_id == thread_id, RunEventRow.category == "message")
|
||||
if resolved_user_id is not None:
|
||||
stmt = stmt.where(RunEventRow.user_id == resolved_user_id)
|
||||
if before_seq is not None:
|
||||
stmt = stmt.where(RunEventRow.seq < before_seq)
|
||||
if after_seq is not None:
|
||||
stmt = stmt.where(RunEventRow.seq > after_seq)
|
||||
|
||||
if after_seq is not None:
|
||||
# Forward pagination: first `limit` records after cursor
|
||||
stmt = stmt.order_by(RunEventRow.seq.asc()).limit(limit)
|
||||
async with self._sf() as session:
|
||||
result = await session.execute(stmt)
|
||||
return [self._row_to_dict(r) for r in result.scalars()]
|
||||
else:
|
||||
# before_seq or default (latest): take last `limit` records, return ascending
|
||||
stmt = stmt.order_by(RunEventRow.seq.desc()).limit(limit)
|
||||
async with self._sf() as session:
|
||||
result = await session.execute(stmt)
|
||||
rows = list(result.scalars())
|
||||
return [self._row_to_dict(r) for r in reversed(rows)]
|
||||
|
||||
async def list_events(
|
||||
self,
|
||||
thread_id,
|
||||
run_id,
|
||||
*,
|
||||
event_types=None,
|
||||
limit=500,
|
||||
user_id: str | None | _AutoSentinel = AUTO,
|
||||
):
|
||||
resolved_user_id = resolve_user_id(user_id, method_name="DbRunEventStore.list_events")
|
||||
stmt = select(RunEventRow).where(RunEventRow.thread_id == thread_id, RunEventRow.run_id == run_id)
|
||||
if resolved_user_id is not None:
|
||||
stmt = stmt.where(RunEventRow.user_id == resolved_user_id)
|
||||
if event_types:
|
||||
stmt = stmt.where(RunEventRow.event_type.in_(event_types))
|
||||
stmt = stmt.order_by(RunEventRow.seq.asc()).limit(limit)
|
||||
async with self._sf() as session:
|
||||
result = await session.execute(stmt)
|
||||
return [self._row_to_dict(r) for r in result.scalars()]
|
||||
|
||||
async def list_messages_by_run(
|
||||
self,
|
||||
thread_id,
|
||||
run_id,
|
||||
*,
|
||||
limit=50,
|
||||
before_seq=None,
|
||||
after_seq=None,
|
||||
user_id: str | None | _AutoSentinel = AUTO,
|
||||
):
|
||||
resolved_user_id = resolve_user_id(user_id, method_name="DbRunEventStore.list_messages_by_run")
|
||||
stmt = select(RunEventRow).where(
|
||||
RunEventRow.thread_id == thread_id,
|
||||
RunEventRow.run_id == run_id,
|
||||
RunEventRow.category == "message",
|
||||
)
|
||||
if resolved_user_id is not None:
|
||||
stmt = stmt.where(RunEventRow.user_id == resolved_user_id)
|
||||
if before_seq is not None:
|
||||
stmt = stmt.where(RunEventRow.seq < before_seq)
|
||||
if after_seq is not None:
|
||||
stmt = stmt.where(RunEventRow.seq > after_seq)
|
||||
|
||||
if after_seq is not None:
|
||||
stmt = stmt.order_by(RunEventRow.seq.asc()).limit(limit)
|
||||
async with self._sf() as session:
|
||||
result = await session.execute(stmt)
|
||||
return [self._row_to_dict(r) for r in result.scalars()]
|
||||
else:
|
||||
stmt = stmt.order_by(RunEventRow.seq.desc()).limit(limit)
|
||||
async with self._sf() as session:
|
||||
result = await session.execute(stmt)
|
||||
rows = list(result.scalars())
|
||||
return [self._row_to_dict(r) for r in reversed(rows)]
|
||||
|
||||
async def count_messages(
|
||||
self,
|
||||
thread_id,
|
||||
*,
|
||||
user_id: str | None | _AutoSentinel = AUTO,
|
||||
):
|
||||
resolved_user_id = resolve_user_id(user_id, method_name="DbRunEventStore.count_messages")
|
||||
stmt = select(func.count()).select_from(RunEventRow).where(RunEventRow.thread_id == thread_id, RunEventRow.category == "message")
|
||||
if resolved_user_id is not None:
|
||||
stmt = stmt.where(RunEventRow.user_id == resolved_user_id)
|
||||
async with self._sf() as session:
|
||||
return await session.scalar(stmt) or 0
|
||||
|
||||
async def delete_by_thread(
|
||||
self,
|
||||
thread_id,
|
||||
*,
|
||||
user_id: str | None | _AutoSentinel = AUTO,
|
||||
):
|
||||
resolved_user_id = resolve_user_id(user_id, method_name="DbRunEventStore.delete_by_thread")
|
||||
async with self._sf() as session:
|
||||
count_conditions = [RunEventRow.thread_id == thread_id]
|
||||
if resolved_user_id is not None:
|
||||
count_conditions.append(RunEventRow.user_id == resolved_user_id)
|
||||
count_stmt = select(func.count()).select_from(RunEventRow).where(*count_conditions)
|
||||
count = await session.scalar(count_stmt) or 0
|
||||
if count > 0:
|
||||
await session.execute(delete(RunEventRow).where(*count_conditions))
|
||||
await session.commit()
|
||||
return count
|
||||
|
||||
async def delete_by_run(
|
||||
self,
|
||||
thread_id,
|
||||
run_id,
|
||||
*,
|
||||
user_id: str | None | _AutoSentinel = AUTO,
|
||||
):
|
||||
resolved_user_id = resolve_user_id(user_id, method_name="DbRunEventStore.delete_by_run")
|
||||
async with self._sf() as session:
|
||||
count_conditions = [RunEventRow.thread_id == thread_id, RunEventRow.run_id == run_id]
|
||||
if resolved_user_id is not None:
|
||||
count_conditions.append(RunEventRow.user_id == resolved_user_id)
|
||||
count_stmt = select(func.count()).select_from(RunEventRow).where(*count_conditions)
|
||||
count = await session.scalar(count_stmt) or 0
|
||||
if count > 0:
|
||||
await session.execute(delete(RunEventRow).where(*count_conditions))
|
||||
await session.commit()
|
||||
return count
|
||||
@@ -0,0 +1,187 @@
|
||||
"""JSONL file-backed RunEventStore implementation.
|
||||
|
||||
Each run's events are stored in a single file:
|
||||
``.deer-flow/threads/{thread_id}/runs/{run_id}.jsonl``
|
||||
|
||||
All categories (message, trace, lifecycle) are in the same file.
|
||||
This backend is suitable for lightweight single-node deployments.
|
||||
|
||||
Known trade-off: ``list_messages()`` must scan all run files for a
|
||||
thread since messages from multiple runs need unified seq ordering.
|
||||
``list_events()`` reads only one file -- the fast path.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from datetime import UTC, datetime
|
||||
from pathlib import Path
|
||||
|
||||
from deerflow.runtime.events.store.base import RunEventStore
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_SAFE_ID_PATTERN = re.compile(r"^[A-Za-z0-9_\-]+$")
|
||||
|
||||
|
||||
class JsonlRunEventStore(RunEventStore):
|
||||
def __init__(self, base_dir: str | Path | None = None):
|
||||
self._base_dir = Path(base_dir) if base_dir else Path(".deer-flow")
|
||||
self._seq_counters: dict[str, int] = {} # thread_id -> current max seq
|
||||
|
||||
@staticmethod
|
||||
def _validate_id(value: str, label: str) -> str:
|
||||
"""Validate that an ID is safe for use in filesystem paths."""
|
||||
if not value or not _SAFE_ID_PATTERN.match(value):
|
||||
raise ValueError(f"Invalid {label}: must be alphanumeric/dash/underscore, got {value!r}")
|
||||
return value
|
||||
|
||||
def _thread_dir(self, thread_id: str) -> Path:
|
||||
self._validate_id(thread_id, "thread_id")
|
||||
return self._base_dir / "threads" / thread_id / "runs"
|
||||
|
||||
def _run_file(self, thread_id: str, run_id: str) -> Path:
|
||||
self._validate_id(run_id, "run_id")
|
||||
return self._thread_dir(thread_id) / f"{run_id}.jsonl"
|
||||
|
||||
def _next_seq(self, thread_id: str) -> int:
|
||||
self._seq_counters[thread_id] = self._seq_counters.get(thread_id, 0) + 1
|
||||
return self._seq_counters[thread_id]
|
||||
|
||||
def _ensure_seq_loaded(self, thread_id: str) -> None:
|
||||
"""Load max seq from existing files if not yet cached."""
|
||||
if thread_id in self._seq_counters:
|
||||
return
|
||||
max_seq = 0
|
||||
thread_dir = self._thread_dir(thread_id)
|
||||
if thread_dir.exists():
|
||||
for f in thread_dir.glob("*.jsonl"):
|
||||
for line in f.read_text(encoding="utf-8").strip().splitlines():
|
||||
try:
|
||||
record = json.loads(line)
|
||||
max_seq = max(max_seq, record.get("seq", 0))
|
||||
except json.JSONDecodeError:
|
||||
logger.debug("Skipping malformed JSONL line in %s", f)
|
||||
continue
|
||||
self._seq_counters[thread_id] = max_seq
|
||||
|
||||
def _write_record(self, record: dict) -> None:
|
||||
path = self._run_file(record["thread_id"], record["run_id"])
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(record, default=str, ensure_ascii=False) + "\n")
|
||||
|
||||
def _read_thread_events(self, thread_id: str) -> list[dict]:
|
||||
"""Read all events for a thread, sorted by seq."""
|
||||
events = []
|
||||
thread_dir = self._thread_dir(thread_id)
|
||||
if not thread_dir.exists():
|
||||
return events
|
||||
for f in sorted(thread_dir.glob("*.jsonl")):
|
||||
for line in f.read_text(encoding="utf-8").strip().splitlines():
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
events.append(json.loads(line))
|
||||
except json.JSONDecodeError:
|
||||
logger.debug("Skipping malformed JSONL line in %s", f)
|
||||
continue
|
||||
events.sort(key=lambda e: e.get("seq", 0))
|
||||
return events
|
||||
|
||||
def _read_run_events(self, thread_id: str, run_id: str) -> list[dict]:
|
||||
"""Read events for a specific run file."""
|
||||
path = self._run_file(thread_id, run_id)
|
||||
if not path.exists():
|
||||
return []
|
||||
events = []
|
||||
for line in path.read_text(encoding="utf-8").strip().splitlines():
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
events.append(json.loads(line))
|
||||
except json.JSONDecodeError:
|
||||
logger.debug("Skipping malformed JSONL line in %s", path)
|
||||
continue
|
||||
events.sort(key=lambda e: e.get("seq", 0))
|
||||
return events
|
||||
|
||||
async def put(self, *, thread_id, run_id, event_type, category, content="", metadata=None, created_at=None):
|
||||
self._ensure_seq_loaded(thread_id)
|
||||
seq = self._next_seq(thread_id)
|
||||
record = {
|
||||
"thread_id": thread_id,
|
||||
"run_id": run_id,
|
||||
"event_type": event_type,
|
||||
"category": category,
|
||||
"content": content,
|
||||
"metadata": metadata or {},
|
||||
"seq": seq,
|
||||
"created_at": created_at or datetime.now(UTC).isoformat(),
|
||||
}
|
||||
self._write_record(record)
|
||||
return record
|
||||
|
||||
async def put_batch(self, events):
|
||||
if not events:
|
||||
return []
|
||||
results = []
|
||||
for ev in events:
|
||||
record = await self.put(**ev)
|
||||
results.append(record)
|
||||
return results
|
||||
|
||||
async def list_messages(self, thread_id, *, limit=50, before_seq=None, after_seq=None):
|
||||
all_events = self._read_thread_events(thread_id)
|
||||
messages = [e for e in all_events if e.get("category") == "message"]
|
||||
|
||||
if before_seq is not None:
|
||||
messages = [e for e in messages if e["seq"] < before_seq]
|
||||
return messages[-limit:]
|
||||
elif after_seq is not None:
|
||||
messages = [e for e in messages if e["seq"] > after_seq]
|
||||
return messages[:limit]
|
||||
else:
|
||||
return messages[-limit:]
|
||||
|
||||
async def list_events(self, thread_id, run_id, *, event_types=None, limit=500):
|
||||
events = self._read_run_events(thread_id, run_id)
|
||||
if event_types is not None:
|
||||
events = [e for e in events if e.get("event_type") in event_types]
|
||||
return events[:limit]
|
||||
|
||||
async def list_messages_by_run(self, thread_id, run_id, *, limit=50, before_seq=None, after_seq=None):
|
||||
events = self._read_run_events(thread_id, run_id)
|
||||
filtered = [e for e in events if e.get("category") == "message"]
|
||||
if before_seq is not None:
|
||||
filtered = [e for e in filtered if e.get("seq", 0) < before_seq]
|
||||
if after_seq is not None:
|
||||
filtered = [e for e in filtered if e.get("seq", 0) > after_seq]
|
||||
if after_seq is not None:
|
||||
return filtered[:limit]
|
||||
else:
|
||||
return filtered[-limit:] if len(filtered) > limit else filtered
|
||||
|
||||
async def count_messages(self, thread_id):
|
||||
all_events = self._read_thread_events(thread_id)
|
||||
return sum(1 for e in all_events if e.get("category") == "message")
|
||||
|
||||
async def delete_by_thread(self, thread_id):
|
||||
all_events = self._read_thread_events(thread_id)
|
||||
count = len(all_events)
|
||||
thread_dir = self._thread_dir(thread_id)
|
||||
if thread_dir.exists():
|
||||
for f in thread_dir.glob("*.jsonl"):
|
||||
f.unlink()
|
||||
self._seq_counters.pop(thread_id, None)
|
||||
return count
|
||||
|
||||
async def delete_by_run(self, thread_id, run_id):
|
||||
events = self._read_run_events(thread_id, run_id)
|
||||
count = len(events)
|
||||
path = self._run_file(thread_id, run_id)
|
||||
if path.exists():
|
||||
path.unlink()
|
||||
return count
|
||||
@@ -0,0 +1,128 @@
|
||||
"""In-memory RunEventStore. Used when run_events.backend=memory (default) and in tests.
|
||||
|
||||
Thread-safe for single-process async usage (no threading locks needed
|
||||
since all mutations happen within the same event loop).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import UTC, datetime
|
||||
|
||||
from deerflow.runtime.events.store.base import RunEventStore
|
||||
|
||||
|
||||
class MemoryRunEventStore(RunEventStore):
|
||||
def __init__(self) -> None:
|
||||
self._events: dict[str, list[dict]] = {} # thread_id -> sorted event list
|
||||
self._seq_counters: dict[str, int] = {} # thread_id -> last assigned seq
|
||||
|
||||
def _next_seq(self, thread_id: str) -> int:
|
||||
current = self._seq_counters.get(thread_id, 0)
|
||||
next_val = current + 1
|
||||
self._seq_counters[thread_id] = next_val
|
||||
return next_val
|
||||
|
||||
def _put_one(
|
||||
self,
|
||||
*,
|
||||
thread_id: str,
|
||||
run_id: str,
|
||||
event_type: str,
|
||||
category: str,
|
||||
content: str | dict = "",
|
||||
metadata: dict | None = None,
|
||||
created_at: str | None = None,
|
||||
) -> dict:
|
||||
seq = self._next_seq(thread_id)
|
||||
record = {
|
||||
"thread_id": thread_id,
|
||||
"run_id": run_id,
|
||||
"event_type": event_type,
|
||||
"category": category,
|
||||
"content": content,
|
||||
"metadata": metadata or {},
|
||||
"seq": seq,
|
||||
"created_at": created_at or datetime.now(UTC).isoformat(),
|
||||
}
|
||||
self._events.setdefault(thread_id, []).append(record)
|
||||
return record
|
||||
|
||||
async def put(
|
||||
self,
|
||||
*,
|
||||
thread_id,
|
||||
run_id,
|
||||
event_type,
|
||||
category,
|
||||
content="",
|
||||
metadata=None,
|
||||
created_at=None,
|
||||
):
|
||||
return self._put_one(
|
||||
thread_id=thread_id,
|
||||
run_id=run_id,
|
||||
event_type=event_type,
|
||||
category=category,
|
||||
content=content,
|
||||
metadata=metadata,
|
||||
created_at=created_at,
|
||||
)
|
||||
|
||||
async def put_batch(self, events):
|
||||
results = []
|
||||
for ev in events:
|
||||
record = self._put_one(**ev)
|
||||
results.append(record)
|
||||
return results
|
||||
|
||||
async def list_messages(self, thread_id, *, limit=50, before_seq=None, after_seq=None):
|
||||
all_events = self._events.get(thread_id, [])
|
||||
messages = [e for e in all_events if e["category"] == "message"]
|
||||
|
||||
if before_seq is not None:
|
||||
messages = [e for e in messages if e["seq"] < before_seq]
|
||||
# Take the last `limit` records
|
||||
return messages[-limit:]
|
||||
elif after_seq is not None:
|
||||
messages = [e for e in messages if e["seq"] > after_seq]
|
||||
return messages[:limit]
|
||||
else:
|
||||
# Return the latest `limit` records, ascending
|
||||
return messages[-limit:]
|
||||
|
||||
async def list_events(self, thread_id, run_id, *, event_types=None, limit=500):
|
||||
all_events = self._events.get(thread_id, [])
|
||||
filtered = [e for e in all_events if e["run_id"] == run_id]
|
||||
if event_types is not None:
|
||||
filtered = [e for e in filtered if e["event_type"] in event_types]
|
||||
return filtered[:limit]
|
||||
|
||||
async def list_messages_by_run(self, thread_id, run_id, *, limit=50, before_seq=None, after_seq=None):
|
||||
all_events = self._events.get(thread_id, [])
|
||||
filtered = [e for e in all_events if e["run_id"] == run_id and e["category"] == "message"]
|
||||
if before_seq is not None:
|
||||
filtered = [e for e in filtered if e["seq"] < before_seq]
|
||||
if after_seq is not None:
|
||||
filtered = [e for e in filtered if e["seq"] > after_seq]
|
||||
if after_seq is not None:
|
||||
return filtered[:limit]
|
||||
else:
|
||||
return filtered[-limit:] if len(filtered) > limit else filtered
|
||||
|
||||
async def count_messages(self, thread_id):
|
||||
all_events = self._events.get(thread_id, [])
|
||||
return sum(1 for e in all_events if e["category"] == "message")
|
||||
|
||||
async def delete_by_thread(self, thread_id):
|
||||
events = self._events.pop(thread_id, [])
|
||||
self._seq_counters.pop(thread_id, None)
|
||||
return len(events)
|
||||
|
||||
async def delete_by_run(self, thread_id, run_id):
|
||||
all_events = self._events.get(thread_id, [])
|
||||
if not all_events:
|
||||
return 0
|
||||
remaining = [e for e in all_events if e["run_id"] != run_id]
|
||||
removed = len(all_events) - len(remaining)
|
||||
self._events[thread_id] = remaining
|
||||
return removed
|
||||
@@ -0,0 +1,497 @@
|
||||
"""Run event capture via LangChain callbacks.
|
||||
|
||||
RunJournal sits between LangChain's callback mechanism and the pluggable
|
||||
RunEventStore. It standardizes callback data into RunEvent records and
|
||||
handles token usage accumulation.
|
||||
|
||||
Key design decisions:
|
||||
- on_llm_new_token is NOT implemented -- only complete messages via on_llm_end
|
||||
- on_chat_model_start captures structured prompts as llm_request (OpenAI format)
|
||||
- on_llm_end emits llm_response in OpenAI Chat Completions format
|
||||
- Token usage accumulated in memory, written to RunRow on run completion
|
||||
- Caller identification via tags injection (lead_agent / subagent:{name} / middleware:{name})
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import time
|
||||
from datetime import UTC, datetime
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from uuid import UUID
|
||||
|
||||
from langchain_core.callbacks import BaseCallbackHandler
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from deerflow.runtime.events.store.base import RunEventStore
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RunJournal(BaseCallbackHandler):
|
||||
"""LangChain callback handler that captures events to RunEventStore."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
run_id: str,
|
||||
thread_id: str,
|
||||
event_store: RunEventStore,
|
||||
*,
|
||||
track_token_usage: bool = True,
|
||||
flush_threshold: int = 20,
|
||||
):
|
||||
super().__init__()
|
||||
self.run_id = run_id
|
||||
self.thread_id = thread_id
|
||||
self._store = event_store
|
||||
self._track_tokens = track_token_usage
|
||||
self._flush_threshold = flush_threshold
|
||||
|
||||
# Write buffer
|
||||
self._buffer: list[dict] = []
|
||||
self._pending_flush_tasks: set[asyncio.Task[None]] = set()
|
||||
|
||||
# Token accumulators
|
||||
self._total_input_tokens = 0
|
||||
self._total_output_tokens = 0
|
||||
self._total_tokens = 0
|
||||
self._llm_call_count = 0
|
||||
self._lead_agent_tokens = 0
|
||||
self._subagent_tokens = 0
|
||||
self._middleware_tokens = 0
|
||||
|
||||
# Convenience fields
|
||||
self._last_ai_msg: str | None = None
|
||||
self._first_human_msg: str | None = None
|
||||
self._msg_count = 0
|
||||
|
||||
# Latency tracking
|
||||
self._llm_start_times: dict[str, float] = {} # langchain run_id -> start time
|
||||
|
||||
# LLM request/response tracking
|
||||
self._llm_call_index = 0
|
||||
self._cached_prompts: dict[str, list[dict]] = {} # langchain run_id -> OpenAI messages
|
||||
self._cached_models: dict[str, str] = {} # langchain run_id -> model name
|
||||
|
||||
# Tool call ID cache
|
||||
self._tool_call_ids: dict[str, str] = {} # langchain run_id -> tool_call_id
|
||||
|
||||
# -- Lifecycle callbacks --
|
||||
|
||||
def on_chain_start(self, serialized: dict, inputs: Any, *, run_id: UUID, **kwargs: Any) -> None:
|
||||
if kwargs.get("parent_run_id") is not None:
|
||||
return
|
||||
self._put(
|
||||
event_type="run_start",
|
||||
category="lifecycle",
|
||||
metadata={"input_preview": str(inputs)[:500]},
|
||||
)
|
||||
|
||||
def on_chain_end(self, outputs: Any, *, run_id: UUID, **kwargs: Any) -> None:
|
||||
if kwargs.get("parent_run_id") is not None:
|
||||
return
|
||||
self._put(event_type="run_end", category="lifecycle", metadata={"status": "success"})
|
||||
self._flush_sync()
|
||||
|
||||
def on_chain_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
|
||||
if kwargs.get("parent_run_id") is not None:
|
||||
return
|
||||
self._put(
|
||||
event_type="run_error",
|
||||
category="lifecycle",
|
||||
content=str(error),
|
||||
metadata={"error_type": type(error).__name__},
|
||||
)
|
||||
self._flush_sync()
|
||||
|
||||
# -- LLM callbacks --
|
||||
|
||||
def on_chat_model_start(self, serialized: dict, messages: list[list], *, run_id: UUID, **kwargs: Any) -> None:
|
||||
"""Capture structured prompt messages for llm_request event."""
|
||||
from deerflow.runtime.converters import langchain_messages_to_openai
|
||||
|
||||
rid = str(run_id)
|
||||
self._llm_start_times[rid] = time.monotonic()
|
||||
self._llm_call_index += 1
|
||||
|
||||
model_name = serialized.get("name", "")
|
||||
self._cached_models[rid] = model_name
|
||||
|
||||
# Convert the first message list (LangChain passes list-of-lists)
|
||||
prompt_msgs = messages[0] if messages else []
|
||||
openai_msgs = langchain_messages_to_openai(prompt_msgs)
|
||||
self._cached_prompts[rid] = openai_msgs
|
||||
|
||||
caller = self._identify_caller(kwargs)
|
||||
self._put(
|
||||
event_type="llm_request",
|
||||
category="trace",
|
||||
content={"model": model_name, "messages": openai_msgs},
|
||||
metadata={"caller": caller, "llm_call_index": self._llm_call_index},
|
||||
)
|
||||
|
||||
def on_llm_start(self, serialized: dict, prompts: list[str], *, run_id: UUID, **kwargs: Any) -> None:
|
||||
# Fallback: on_chat_model_start is preferred. This just tracks latency.
|
||||
self._llm_start_times[str(run_id)] = time.monotonic()
|
||||
|
||||
def on_llm_end(self, response: Any, *, run_id: UUID, **kwargs: Any) -> None:
|
||||
from deerflow.runtime.converters import langchain_to_openai_completion
|
||||
|
||||
try:
|
||||
message = response.generations[0][0].message
|
||||
except (IndexError, AttributeError):
|
||||
logger.debug("on_llm_end: could not extract message from response")
|
||||
return
|
||||
|
||||
caller = self._identify_caller(kwargs)
|
||||
|
||||
# Latency
|
||||
rid = str(run_id)
|
||||
start = self._llm_start_times.pop(rid, None)
|
||||
latency_ms = int((time.monotonic() - start) * 1000) if start else None
|
||||
|
||||
# Token usage from message
|
||||
usage = getattr(message, "usage_metadata", None)
|
||||
usage_dict = dict(usage) if usage else {}
|
||||
|
||||
# Resolve call index
|
||||
call_index = self._llm_call_index
|
||||
if rid not in self._cached_prompts:
|
||||
# Fallback: on_chat_model_start was not called
|
||||
self._llm_call_index += 1
|
||||
call_index = self._llm_call_index
|
||||
|
||||
# Clean up caches
|
||||
self._cached_prompts.pop(rid, None)
|
||||
self._cached_models.pop(rid, None)
|
||||
|
||||
# Trace event: llm_response (OpenAI completion format)
|
||||
content = getattr(message, "content", "")
|
||||
self._put(
|
||||
event_type="llm_response",
|
||||
category="trace",
|
||||
content=langchain_to_openai_completion(message),
|
||||
metadata={
|
||||
"caller": caller,
|
||||
"usage": usage_dict,
|
||||
"latency_ms": latency_ms,
|
||||
"llm_call_index": call_index,
|
||||
},
|
||||
)
|
||||
|
||||
# Message events: only lead_agent gets message-category events.
|
||||
# Content uses message.model_dump() to align with checkpoint format.
|
||||
tool_calls = getattr(message, "tool_calls", None) or []
|
||||
if caller == "lead_agent":
|
||||
resp_meta = getattr(message, "response_metadata", None) or {}
|
||||
model_name = resp_meta.get("model_name") if isinstance(resp_meta, dict) else None
|
||||
if tool_calls:
|
||||
# ai_tool_call: agent decided to use tools
|
||||
self._put(
|
||||
event_type="ai_tool_call",
|
||||
category="message",
|
||||
content=message.model_dump(),
|
||||
metadata={"model_name": model_name, "finish_reason": "tool_calls"},
|
||||
)
|
||||
elif isinstance(content, str) and content:
|
||||
# ai_message: final text reply
|
||||
self._put(
|
||||
event_type="ai_message",
|
||||
category="message",
|
||||
content=message.model_dump(),
|
||||
metadata={"model_name": model_name, "finish_reason": "stop"},
|
||||
)
|
||||
self._last_ai_msg = content
|
||||
self._msg_count += 1
|
||||
|
||||
# Token accumulation
|
||||
if self._track_tokens:
|
||||
input_tk = usage_dict.get("input_tokens", 0) or 0
|
||||
output_tk = usage_dict.get("output_tokens", 0) or 0
|
||||
total_tk = usage_dict.get("total_tokens", 0) or 0
|
||||
if total_tk == 0:
|
||||
total_tk = input_tk + output_tk
|
||||
if total_tk > 0:
|
||||
self._total_input_tokens += input_tk
|
||||
self._total_output_tokens += output_tk
|
||||
self._total_tokens += total_tk
|
||||
self._llm_call_count += 1
|
||||
if caller.startswith("subagent:"):
|
||||
self._subagent_tokens += total_tk
|
||||
elif caller.startswith("middleware:"):
|
||||
self._middleware_tokens += total_tk
|
||||
else:
|
||||
self._lead_agent_tokens += total_tk
|
||||
|
||||
def on_llm_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
|
||||
self._llm_start_times.pop(str(run_id), None)
|
||||
self._put(event_type="llm_error", category="trace", content=str(error))
|
||||
|
||||
# -- Tool callbacks --
|
||||
|
||||
def on_tool_start(self, serialized: dict, input_str: str, *, run_id: UUID, **kwargs: Any) -> None:
|
||||
tool_call_id = kwargs.get("tool_call_id")
|
||||
if tool_call_id:
|
||||
self._tool_call_ids[str(run_id)] = tool_call_id
|
||||
self._put(
|
||||
event_type="tool_start",
|
||||
category="trace",
|
||||
metadata={
|
||||
"tool_name": serialized.get("name", ""),
|
||||
"tool_call_id": tool_call_id,
|
||||
"args": str(input_str)[:2000],
|
||||
},
|
||||
)
|
||||
|
||||
def on_tool_end(self, output: Any, *, run_id: UUID, **kwargs: Any) -> None:
|
||||
from langchain_core.messages import ToolMessage
|
||||
from langgraph.types import Command
|
||||
|
||||
# Tools that update graph state return a ``Command`` (e.g.
|
||||
# ``present_files``). LangGraph later unwraps the inner ToolMessage
|
||||
# into checkpoint state, so to stay checkpoint-aligned we must
|
||||
# extract it here rather than storing ``str(Command(...))``.
|
||||
if isinstance(output, Command):
|
||||
update = getattr(output, "update", None) or {}
|
||||
inner_msgs = update.get("messages") if isinstance(update, dict) else None
|
||||
if isinstance(inner_msgs, list):
|
||||
inner_tool_msg = next((m for m in inner_msgs if isinstance(m, ToolMessage)), None)
|
||||
if inner_tool_msg is not None:
|
||||
output = inner_tool_msg
|
||||
|
||||
# Extract fields from ToolMessage object when LangChain provides one.
|
||||
# LangChain's _format_output wraps tool results into a ToolMessage
|
||||
# with tool_call_id, name, status, and artifact — more complete than
|
||||
# what kwargs alone provides.
|
||||
if isinstance(output, ToolMessage):
|
||||
tool_call_id = output.tool_call_id or kwargs.get("tool_call_id") or self._tool_call_ids.pop(str(run_id), None)
|
||||
tool_name = output.name or kwargs.get("name", "")
|
||||
status = getattr(output, "status", "success") or "success"
|
||||
content_str = output.content if isinstance(output.content, str) else str(output.content)
|
||||
# Use model_dump() for checkpoint-aligned message content.
|
||||
# Override tool_call_id if it was resolved from cache.
|
||||
msg_content = output.model_dump()
|
||||
if msg_content.get("tool_call_id") != tool_call_id:
|
||||
msg_content["tool_call_id"] = tool_call_id
|
||||
else:
|
||||
tool_call_id = kwargs.get("tool_call_id") or self._tool_call_ids.pop(str(run_id), None)
|
||||
tool_name = kwargs.get("name", "")
|
||||
status = "success"
|
||||
content_str = str(output)
|
||||
# Construct checkpoint-aligned dict when output is a plain string.
|
||||
msg_content = ToolMessage(
|
||||
content=content_str,
|
||||
tool_call_id=tool_call_id or "",
|
||||
name=tool_name,
|
||||
status=status,
|
||||
).model_dump()
|
||||
|
||||
# Trace event (always)
|
||||
self._put(
|
||||
event_type="tool_end",
|
||||
category="trace",
|
||||
content=content_str,
|
||||
metadata={
|
||||
"tool_name": tool_name,
|
||||
"tool_call_id": tool_call_id,
|
||||
"status": status,
|
||||
},
|
||||
)
|
||||
|
||||
# Message event: tool_result (checkpoint-aligned model_dump format)
|
||||
self._put(
|
||||
event_type="tool_result",
|
||||
category="message",
|
||||
content=msg_content,
|
||||
metadata={"tool_name": tool_name, "status": status},
|
||||
)
|
||||
|
||||
def on_tool_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
|
||||
from langchain_core.messages import ToolMessage
|
||||
|
||||
tool_call_id = kwargs.get("tool_call_id") or self._tool_call_ids.pop(str(run_id), None)
|
||||
tool_name = kwargs.get("name", "")
|
||||
|
||||
# Trace event
|
||||
self._put(
|
||||
event_type="tool_error",
|
||||
category="trace",
|
||||
content=str(error),
|
||||
metadata={
|
||||
"tool_name": tool_name,
|
||||
"tool_call_id": tool_call_id,
|
||||
},
|
||||
)
|
||||
|
||||
# Message event: tool_result with error status (checkpoint-aligned)
|
||||
msg_content = ToolMessage(
|
||||
content=str(error),
|
||||
tool_call_id=tool_call_id or "",
|
||||
name=tool_name,
|
||||
status="error",
|
||||
).model_dump()
|
||||
self._put(
|
||||
event_type="tool_result",
|
||||
category="message",
|
||||
content=msg_content,
|
||||
metadata={"tool_name": tool_name, "status": "error"},
|
||||
)
|
||||
|
||||
# -- Custom event callback --
|
||||
|
||||
def on_custom_event(self, name: str, data: Any, *, run_id: UUID, **kwargs: Any) -> None:
|
||||
from deerflow.runtime.serialization import serialize_lc_object
|
||||
|
||||
if name == "summarization":
|
||||
data_dict = data if isinstance(data, dict) else {}
|
||||
self._put(
|
||||
event_type="summarization",
|
||||
category="trace",
|
||||
content=data_dict.get("summary", ""),
|
||||
metadata={
|
||||
"replaced_message_ids": data_dict.get("replaced_message_ids", []),
|
||||
"replaced_count": data_dict.get("replaced_count", 0),
|
||||
},
|
||||
)
|
||||
self._put(
|
||||
event_type="middleware:summarize",
|
||||
category="middleware",
|
||||
content={"role": "system", "content": data_dict.get("summary", "")},
|
||||
metadata={"replaced_count": data_dict.get("replaced_count", 0)},
|
||||
)
|
||||
else:
|
||||
event_data = serialize_lc_object(data) if not isinstance(data, dict) else data
|
||||
self._put(
|
||||
event_type=name,
|
||||
category="trace",
|
||||
metadata=event_data if isinstance(event_data, dict) else {"data": event_data},
|
||||
)
|
||||
|
||||
# -- Internal methods --
|
||||
|
||||
def _put(self, *, event_type: str, category: str, content: str | dict = "", metadata: dict | None = None) -> None:
|
||||
self._buffer.append(
|
||||
{
|
||||
"thread_id": self.thread_id,
|
||||
"run_id": self.run_id,
|
||||
"event_type": event_type,
|
||||
"category": category,
|
||||
"content": content,
|
||||
"metadata": metadata or {},
|
||||
"created_at": datetime.now(UTC).isoformat(),
|
||||
}
|
||||
)
|
||||
if len(self._buffer) >= self._flush_threshold:
|
||||
self._flush_sync()
|
||||
|
||||
def _flush_sync(self) -> None:
|
||||
"""Best-effort flush of buffer to RunEventStore.
|
||||
|
||||
BaseCallbackHandler methods are synchronous. If an event loop is
|
||||
running we schedule an async ``put_batch``; otherwise the events
|
||||
stay in the buffer and are flushed later by the async ``flush()``
|
||||
call in the worker's ``finally`` block.
|
||||
"""
|
||||
if not self._buffer:
|
||||
return
|
||||
# Skip if a flush is already in flight — avoids concurrent writes
|
||||
# to the same SQLite file from multiple fire-and-forget tasks.
|
||||
if self._pending_flush_tasks:
|
||||
return
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
except RuntimeError:
|
||||
# No event loop — keep events in buffer for later async flush.
|
||||
return
|
||||
batch = self._buffer.copy()
|
||||
self._buffer.clear()
|
||||
task = loop.create_task(self._flush_async(batch))
|
||||
self._pending_flush_tasks.add(task)
|
||||
task.add_done_callback(self._on_flush_done)
|
||||
|
||||
async def _flush_async(self, batch: list[dict]) -> None:
|
||||
try:
|
||||
await self._store.put_batch(batch)
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"Failed to flush %d events for run %s — returning to buffer",
|
||||
len(batch),
|
||||
self.run_id,
|
||||
exc_info=True,
|
||||
)
|
||||
# Return failed events to buffer for retry on next flush
|
||||
self._buffer = batch + self._buffer
|
||||
|
||||
def _on_flush_done(self, task: asyncio.Task) -> None:
|
||||
self._pending_flush_tasks.discard(task)
|
||||
if task.cancelled():
|
||||
return
|
||||
exc = task.exception()
|
||||
if exc:
|
||||
logger.warning("Journal flush task failed: %s", exc)
|
||||
|
||||
def _identify_caller(self, kwargs: dict) -> str:
|
||||
for tag in kwargs.get("tags") or []:
|
||||
if isinstance(tag, str) and (tag.startswith("subagent:") or tag.startswith("middleware:") or tag == "lead_agent"):
|
||||
return tag
|
||||
# Default to lead_agent: the main agent graph does not inject
|
||||
# callback tags, while subagents and middleware explicitly tag
|
||||
# themselves.
|
||||
return "lead_agent"
|
||||
|
||||
# -- Public methods (called by worker) --
|
||||
|
||||
def set_first_human_message(self, content: str) -> None:
|
||||
"""Record the first human message for convenience fields."""
|
||||
self._first_human_msg = content[:2000] if content else None
|
||||
|
||||
def record_middleware(self, tag: str, *, name: str, hook: str, action: str, changes: dict) -> None:
|
||||
"""Record a middleware state-change event.
|
||||
|
||||
Called by middleware implementations when they perform a meaningful
|
||||
state change (e.g., title generation, summarization, HITL approval).
|
||||
Pure-observation middleware should not call this.
|
||||
|
||||
Args:
|
||||
tag: Short identifier for the middleware (e.g., "title", "summarize",
|
||||
"guardrail"). Used to form event_type="middleware:{tag}".
|
||||
name: Full middleware class name.
|
||||
hook: Lifecycle hook that triggered the action (e.g., "after_model").
|
||||
action: Specific action performed (e.g., "generate_title").
|
||||
changes: Dict describing the state changes made.
|
||||
"""
|
||||
self._put(
|
||||
event_type=f"middleware:{tag}",
|
||||
category="middleware",
|
||||
content={"name": name, "hook": hook, "action": action, "changes": changes},
|
||||
)
|
||||
|
||||
async def flush(self) -> None:
|
||||
"""Force flush remaining buffer. Called in worker's finally block."""
|
||||
if self._pending_flush_tasks:
|
||||
await asyncio.gather(*tuple(self._pending_flush_tasks), return_exceptions=True)
|
||||
|
||||
while self._buffer:
|
||||
batch = self._buffer[: self._flush_threshold]
|
||||
del self._buffer[: self._flush_threshold]
|
||||
try:
|
||||
await self._store.put_batch(batch)
|
||||
except Exception:
|
||||
self._buffer = batch + self._buffer
|
||||
raise
|
||||
|
||||
def get_completion_data(self) -> dict:
|
||||
"""Return accumulated token and message data for run completion."""
|
||||
return {
|
||||
"total_input_tokens": self._total_input_tokens,
|
||||
"total_output_tokens": self._total_output_tokens,
|
||||
"total_tokens": self._total_tokens,
|
||||
"llm_call_count": self._llm_call_count,
|
||||
"lead_agent_tokens": self._lead_agent_tokens,
|
||||
"subagent_tokens": self._subagent_tokens,
|
||||
"middleware_tokens": self._middleware_tokens,
|
||||
"message_count": self._msg_count,
|
||||
"last_ai_message": self._last_ai_msg,
|
||||
"first_human_message": self._first_human_msg,
|
||||
}
|
||||
@@ -2,11 +2,12 @@
|
||||
|
||||
from .manager import ConflictError, RunManager, RunRecord, UnsupportedStrategyError
|
||||
from .schemas import DisconnectMode, RunStatus
|
||||
from .worker import run_agent
|
||||
from .worker import RunContext, run_agent
|
||||
|
||||
__all__ = [
|
||||
"ConflictError",
|
||||
"DisconnectMode",
|
||||
"RunContext",
|
||||
"RunManager",
|
||||
"RunRecord",
|
||||
"RunStatus",
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
"""In-memory run registry."""
|
||||
"""In-memory run registry with optional persistent RunStore backing."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
@@ -7,9 +7,13 @@ import logging
|
||||
import uuid
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import UTC, datetime
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from .schemas import DisconnectMode, RunStatus
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from deerflow.runtime.runs.store.base import RunStore
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@@ -38,11 +42,44 @@ class RunRecord:
|
||||
|
||||
|
||||
class RunManager:
|
||||
"""In-memory run registry. All mutations are protected by an asyncio lock."""
|
||||
"""In-memory run registry with optional persistent RunStore backing.
|
||||
|
||||
def __init__(self) -> None:
|
||||
All mutations are protected by an asyncio lock. When a ``store`` is
|
||||
provided, serializable metadata is also persisted to the store so
|
||||
that run history survives process restarts.
|
||||
"""
|
||||
|
||||
def __init__(self, store: RunStore | None = None) -> None:
|
||||
self._runs: dict[str, RunRecord] = {}
|
||||
self._lock = asyncio.Lock()
|
||||
self._store = store
|
||||
|
||||
async def _persist_to_store(self, record: RunRecord, *, follow_up_to_run_id: str | None = None) -> None:
|
||||
"""Best-effort persist run record to backing store."""
|
||||
if self._store is None:
|
||||
return
|
||||
try:
|
||||
await self._store.put(
|
||||
record.run_id,
|
||||
thread_id=record.thread_id,
|
||||
assistant_id=record.assistant_id,
|
||||
status=record.status.value,
|
||||
multitask_strategy=record.multitask_strategy,
|
||||
metadata=record.metadata or {},
|
||||
kwargs=record.kwargs or {},
|
||||
created_at=record.created_at,
|
||||
follow_up_to_run_id=follow_up_to_run_id,
|
||||
)
|
||||
except Exception:
|
||||
logger.warning("Failed to persist run %s to store", record.run_id, exc_info=True)
|
||||
|
||||
async def update_run_completion(self, run_id: str, **kwargs) -> None:
|
||||
"""Persist token usage and completion data to the backing store."""
|
||||
if self._store is not None:
|
||||
try:
|
||||
await self._store.update_run_completion(run_id, **kwargs)
|
||||
except Exception:
|
||||
logger.warning("Failed to persist run completion for %s", run_id, exc_info=True)
|
||||
|
||||
async def create(
|
||||
self,
|
||||
@@ -53,6 +90,7 @@ class RunManager:
|
||||
metadata: dict | None = None,
|
||||
kwargs: dict | None = None,
|
||||
multitask_strategy: str = "reject",
|
||||
follow_up_to_run_id: str | None = None,
|
||||
) -> RunRecord:
|
||||
"""Create a new pending run and register it."""
|
||||
run_id = str(uuid.uuid4())
|
||||
@@ -71,6 +109,7 @@ class RunManager:
|
||||
)
|
||||
async with self._lock:
|
||||
self._runs[run_id] = record
|
||||
await self._persist_to_store(record, follow_up_to_run_id=follow_up_to_run_id)
|
||||
logger.info("Run created: run_id=%s thread_id=%s", run_id, thread_id)
|
||||
return record
|
||||
|
||||
@@ -96,6 +135,11 @@ class RunManager:
|
||||
record.updated_at = _now_iso()
|
||||
if error is not None:
|
||||
record.error = error
|
||||
if self._store is not None:
|
||||
try:
|
||||
await self._store.update_status(run_id, status.value, error=error)
|
||||
except Exception:
|
||||
logger.warning("Failed to persist status update for run %s", run_id, exc_info=True)
|
||||
logger.info("Run %s -> %s", run_id, status.value)
|
||||
|
||||
async def cancel(self, run_id: str, *, action: str = "interrupt") -> bool:
|
||||
@@ -132,6 +176,7 @@ class RunManager:
|
||||
metadata: dict | None = None,
|
||||
kwargs: dict | None = None,
|
||||
multitask_strategy: str = "reject",
|
||||
follow_up_to_run_id: str | None = None,
|
||||
) -> RunRecord:
|
||||
"""Atomically check for inflight runs and create a new one.
|
||||
|
||||
@@ -185,6 +230,7 @@ class RunManager:
|
||||
)
|
||||
self._runs[run_id] = record
|
||||
|
||||
await self._persist_to_store(record, follow_up_to_run_id=follow_up_to_run_id)
|
||||
logger.info("Run created: run_id=%s thread_id=%s", run_id, thread_id)
|
||||
return record
|
||||
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
from deerflow.runtime.runs.store.base import RunStore
|
||||
from deerflow.runtime.runs.store.memory import MemoryRunStore
|
||||
|
||||
__all__ = ["MemoryRunStore", "RunStore"]
|
||||
@@ -0,0 +1,96 @@
|
||||
"""Abstract interface for run metadata storage.
|
||||
|
||||
RunManager depends on this interface. Implementations:
|
||||
- MemoryRunStore: in-memory dict (development, tests)
|
||||
- Future: RunRepository backed by SQLAlchemy ORM
|
||||
|
||||
All methods accept an optional user_id for user isolation.
|
||||
When user_id is None, no user filtering is applied (single-user mode).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import abc
|
||||
from typing import Any
|
||||
|
||||
|
||||
class RunStore(abc.ABC):
|
||||
@abc.abstractmethod
|
||||
async def put(
|
||||
self,
|
||||
run_id: str,
|
||||
*,
|
||||
thread_id: str,
|
||||
assistant_id: str | None = None,
|
||||
user_id: str | None = None,
|
||||
status: str = "pending",
|
||||
multitask_strategy: str = "reject",
|
||||
metadata: dict[str, Any] | None = None,
|
||||
kwargs: dict[str, Any] | None = None,
|
||||
error: str | None = None,
|
||||
created_at: str | None = None,
|
||||
follow_up_to_run_id: str | None = None,
|
||||
) -> None:
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def get(self, run_id: str) -> dict[str, Any] | None:
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def list_by_thread(
|
||||
self,
|
||||
thread_id: str,
|
||||
*,
|
||||
user_id: str | None = None,
|
||||
limit: int = 100,
|
||||
) -> list[dict[str, Any]]:
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def update_status(
|
||||
self,
|
||||
run_id: str,
|
||||
status: str,
|
||||
*,
|
||||
error: str | None = None,
|
||||
) -> None:
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def delete(self, run_id: str) -> None:
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def update_run_completion(
|
||||
self,
|
||||
run_id: str,
|
||||
*,
|
||||
status: 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,
|
||||
last_ai_message: str | None = None,
|
||||
first_human_message: str | None = None,
|
||||
error: str | None = None,
|
||||
) -> None:
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def list_pending(self, *, before: str | None = None) -> list[dict[str, Any]]:
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def aggregate_tokens_by_thread(self, thread_id: str) -> dict[str, Any]:
|
||||
"""Aggregate token usage for completed runs in a thread.
|
||||
|
||||
Returns a dict with keys: total_tokens, total_input_tokens,
|
||||
total_output_tokens, total_runs, by_model (model_name → {tokens, runs}),
|
||||
by_caller ({lead_agent, subagent, middleware}).
|
||||
"""
|
||||
pass
|
||||
@@ -0,0 +1,100 @@
|
||||
"""In-memory RunStore. Used when database.backend=memory (default) and in tests.
|
||||
|
||||
Equivalent to the original RunManager._runs dict behavior.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import UTC, datetime
|
||||
from typing import Any
|
||||
|
||||
from deerflow.runtime.runs.store.base import RunStore
|
||||
|
||||
|
||||
class MemoryRunStore(RunStore):
|
||||
def __init__(self) -> None:
|
||||
self._runs: dict[str, dict[str, Any]] = {}
|
||||
|
||||
async def put(
|
||||
self,
|
||||
run_id,
|
||||
*,
|
||||
thread_id,
|
||||
assistant_id=None,
|
||||
user_id=None,
|
||||
status="pending",
|
||||
multitask_strategy="reject",
|
||||
metadata=None,
|
||||
kwargs=None,
|
||||
error=None,
|
||||
created_at=None,
|
||||
follow_up_to_run_id=None,
|
||||
):
|
||||
now = datetime.now(UTC).isoformat()
|
||||
self._runs[run_id] = {
|
||||
"run_id": run_id,
|
||||
"thread_id": thread_id,
|
||||
"assistant_id": assistant_id,
|
||||
"user_id": user_id,
|
||||
"status": status,
|
||||
"multitask_strategy": multitask_strategy,
|
||||
"metadata": metadata or {},
|
||||
"kwargs": kwargs or {},
|
||||
"error": error,
|
||||
"follow_up_to_run_id": follow_up_to_run_id,
|
||||
"created_at": created_at or now,
|
||||
"updated_at": now,
|
||||
}
|
||||
|
||||
async def get(self, run_id):
|
||||
return self._runs.get(run_id)
|
||||
|
||||
async def list_by_thread(self, thread_id, *, user_id=None, limit=100):
|
||||
results = [r for r in self._runs.values() if r["thread_id"] == thread_id and (user_id is None or r.get("user_id") == user_id)]
|
||||
results.sort(key=lambda r: r["created_at"], reverse=True)
|
||||
return results[:limit]
|
||||
|
||||
async def update_status(self, run_id, status, *, error=None):
|
||||
if run_id in self._runs:
|
||||
self._runs[run_id]["status"] = status
|
||||
if error is not None:
|
||||
self._runs[run_id]["error"] = error
|
||||
self._runs[run_id]["updated_at"] = datetime.now(UTC).isoformat()
|
||||
|
||||
async def delete(self, run_id):
|
||||
self._runs.pop(run_id, None)
|
||||
|
||||
async def update_run_completion(self, run_id, *, status, **kwargs):
|
||||
if run_id in self._runs:
|
||||
self._runs[run_id]["status"] = status
|
||||
for key, value in kwargs.items():
|
||||
if value is not None:
|
||||
self._runs[run_id][key] = value
|
||||
self._runs[run_id]["updated_at"] = datetime.now(UTC).isoformat()
|
||||
|
||||
async def list_pending(self, *, before=None):
|
||||
now = before or datetime.now(UTC).isoformat()
|
||||
results = [r for r in self._runs.values() if r["status"] == "pending" and r["created_at"] <= now]
|
||||
results.sort(key=lambda r: r["created_at"])
|
||||
return results
|
||||
|
||||
async def aggregate_tokens_by_thread(self, thread_id: str) -> dict[str, Any]:
|
||||
completed = [r for r in self._runs.values() if r["thread_id"] == thread_id and r.get("status") in ("success", "error")]
|
||||
by_model: dict[str, dict] = {}
|
||||
for r in completed:
|
||||
model = r.get("model_name") or "unknown"
|
||||
entry = by_model.setdefault(model, {"tokens": 0, "runs": 0})
|
||||
entry["tokens"] += r.get("total_tokens", 0)
|
||||
entry["runs"] += 1
|
||||
return {
|
||||
"total_tokens": sum(r.get("total_tokens", 0) for r in completed),
|
||||
"total_input_tokens": sum(r.get("total_input_tokens", 0) for r in completed),
|
||||
"total_output_tokens": sum(r.get("total_output_tokens", 0) for r in completed),
|
||||
"total_runs": len(completed),
|
||||
"by_model": by_model,
|
||||
"by_caller": {
|
||||
"lead_agent": sum(r.get("lead_agent_tokens", 0) for r in completed),
|
||||
"subagent": sum(r.get("subagent_tokens", 0) for r in completed),
|
||||
"middleware": sum(r.get("middleware_tokens", 0) for r in completed),
|
||||
},
|
||||
}
|
||||
@@ -19,8 +19,14 @@ import asyncio
|
||||
import copy
|
||||
import inspect
|
||||
import logging
|
||||
from typing import Any, Literal
|
||||
from dataclasses import dataclass, field
|
||||
from typing import TYPE_CHECKING, Any, Literal
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
from deerflow.config.app_config import AppConfig
|
||||
from deerflow.config.deer_flow_context import DeerFlowContext
|
||||
from deerflow.runtime.serialization import serialize
|
||||
from deerflow.runtime.stream_bridge import StreamBridge
|
||||
|
||||
@@ -33,13 +39,30 @@ logger = logging.getLogger(__name__)
|
||||
_VALID_LG_MODES = {"values", "updates", "checkpoints", "tasks", "debug", "messages", "custom"}
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class RunContext:
|
||||
"""Infrastructure dependencies for a single agent run.
|
||||
|
||||
Groups checkpointer, store, and persistence-related singletons so that
|
||||
``run_agent`` (and any future callers) receive one object instead of a
|
||||
growing list of keyword arguments.
|
||||
"""
|
||||
|
||||
checkpointer: Any
|
||||
store: Any | None = field(default=None)
|
||||
event_store: Any | None = field(default=None)
|
||||
run_events_config: Any | None = field(default=None)
|
||||
thread_store: Any | None = field(default=None)
|
||||
follow_up_to_run_id: str | None = field(default=None)
|
||||
app_config: AppConfig | None = field(default=None)
|
||||
|
||||
|
||||
async def run_agent(
|
||||
bridge: StreamBridge,
|
||||
run_manager: RunManager,
|
||||
record: RunRecord,
|
||||
*,
|
||||
checkpointer: Any,
|
||||
store: Any | None = None,
|
||||
ctx: RunContext,
|
||||
agent_factory: Any,
|
||||
graph_input: dict,
|
||||
config: dict,
|
||||
@@ -50,6 +73,14 @@ async def run_agent(
|
||||
) -> None:
|
||||
"""Execute an agent in the background, publishing events to *bridge*."""
|
||||
|
||||
# Unpack infrastructure dependencies from RunContext.
|
||||
checkpointer = ctx.checkpointer
|
||||
store = ctx.store
|
||||
event_store = ctx.event_store
|
||||
run_events_config = ctx.run_events_config
|
||||
thread_store = ctx.thread_store
|
||||
follow_up_to_run_id = ctx.follow_up_to_run_id
|
||||
|
||||
run_id = record.run_id
|
||||
thread_id = record.thread_id
|
||||
requested_modes: set[str] = set(stream_modes or ["values"])
|
||||
@@ -57,6 +88,10 @@ async def run_agent(
|
||||
pre_run_snapshot: dict[str, Any] | None = None
|
||||
snapshot_capture_failed = False
|
||||
|
||||
journal = None
|
||||
|
||||
journal = None
|
||||
|
||||
# Track whether "events" was requested but skipped
|
||||
if "events" in requested_modes:
|
||||
logger.info(
|
||||
@@ -65,6 +100,38 @@ async def run_agent(
|
||||
)
|
||||
|
||||
try:
|
||||
# Initialize RunJournal + write human_message event.
|
||||
# These are inside the try block so any exception (e.g. a DB
|
||||
# error writing the event) flows through the except/finally
|
||||
# path that publishes an "end" event to the SSE bridge —
|
||||
# otherwise a failure here would leave the stream hanging
|
||||
# with no terminator.
|
||||
if event_store is not None:
|
||||
from deerflow.runtime.journal import RunJournal
|
||||
|
||||
journal = RunJournal(
|
||||
run_id=run_id,
|
||||
thread_id=thread_id,
|
||||
event_store=event_store,
|
||||
track_token_usage=getattr(run_events_config, "track_token_usage", True),
|
||||
)
|
||||
|
||||
human_msg = _extract_human_message(graph_input)
|
||||
if human_msg is not None:
|
||||
msg_metadata = {}
|
||||
if follow_up_to_run_id:
|
||||
msg_metadata["follow_up_to_run_id"] = follow_up_to_run_id
|
||||
await event_store.put(
|
||||
thread_id=thread_id,
|
||||
run_id=run_id,
|
||||
event_type="human_message",
|
||||
category="message",
|
||||
content=human_msg.model_dump(),
|
||||
metadata=msg_metadata or None,
|
||||
)
|
||||
content = human_msg.content
|
||||
journal.set_first_human_message(content if isinstance(content, str) else str(content))
|
||||
|
||||
# 1. Mark running
|
||||
await run_manager.set_status(run_id, RunStatus.running)
|
||||
|
||||
@@ -98,17 +165,21 @@ async def run_agent(
|
||||
|
||||
# 3. Build the agent
|
||||
from langchain_core.runnables import RunnableConfig
|
||||
from langgraph.runtime import Runtime
|
||||
|
||||
# Inject runtime context so middlewares can access thread_id
|
||||
# (langgraph-cli does this automatically; we must do it manually)
|
||||
runtime = Runtime(context={"thread_id": thread_id}, store=store)
|
||||
# If the caller already set a ``context`` key (LangGraph >= 0.6.0
|
||||
# prefers it over ``configurable`` for thread-level data), make
|
||||
# sure ``thread_id`` is available there too.
|
||||
if "context" in config and isinstance(config["context"], dict):
|
||||
config["context"].setdefault("thread_id", thread_id)
|
||||
config.setdefault("configurable", {})["__pregel_runtime"] = runtime
|
||||
# Construct typed context for the agent run.
|
||||
# LangGraph's astream(context=...) injects this into Runtime.context
|
||||
# so middleware/tools can access it via resolve_context().
|
||||
if ctx.app_config is None:
|
||||
raise RuntimeError("RunContext.app_config is required — Gateway must populate it via get_run_context")
|
||||
deer_flow_context = DeerFlowContext(
|
||||
app_config=ctx.app_config,
|
||||
thread_id=thread_id,
|
||||
)
|
||||
|
||||
# Inject RunJournal as a LangChain callback handler.
|
||||
# on_llm_end captures token usage; on_chain_start/end captures lifecycle.
|
||||
if journal is not None:
|
||||
config.setdefault("callbacks", []).append(journal)
|
||||
|
||||
runnable_config = RunnableConfig(**config)
|
||||
agent = agent_factory(config=runnable_config)
|
||||
@@ -155,7 +226,7 @@ async def run_agent(
|
||||
if len(lg_modes) == 1 and not stream_subgraphs:
|
||||
# Single mode, no subgraphs: astream yields raw chunks
|
||||
single_mode = lg_modes[0]
|
||||
async for chunk in agent.astream(graph_input, config=runnable_config, stream_mode=single_mode):
|
||||
async for chunk in agent.astream(graph_input, config=runnable_config, context=deer_flow_context, stream_mode=single_mode):
|
||||
if record.abort_event.is_set():
|
||||
logger.info("Run %s abort requested — stopping", run_id)
|
||||
break
|
||||
@@ -166,6 +237,7 @@ async def run_agent(
|
||||
async for item in agent.astream(
|
||||
graph_input,
|
||||
config=runnable_config,
|
||||
context=deer_flow_context,
|
||||
stream_mode=lg_modes,
|
||||
subgraphs=stream_subgraphs,
|
||||
):
|
||||
@@ -236,6 +308,41 @@ async def run_agent(
|
||||
)
|
||||
|
||||
finally:
|
||||
# Flush any buffered journal events and persist completion data
|
||||
if journal is not None:
|
||||
try:
|
||||
await journal.flush()
|
||||
except Exception:
|
||||
logger.warning("Failed to flush journal for run %s", run_id, exc_info=True)
|
||||
|
||||
try:
|
||||
# Persist token usage + convenience fields to RunStore
|
||||
completion = journal.get_completion_data()
|
||||
await run_manager.update_run_completion(run_id, status=record.status.value, **completion)
|
||||
except Exception:
|
||||
logger.warning("Failed to persist run completion for %s (non-fatal)", run_id, exc_info=True)
|
||||
|
||||
# Sync title from checkpoint to threads_meta.display_name
|
||||
if checkpointer is not None and thread_store is not None:
|
||||
try:
|
||||
ckpt_config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
|
||||
ckpt_tuple = await checkpointer.aget_tuple(ckpt_config)
|
||||
if ckpt_tuple is not None:
|
||||
ckpt = getattr(ckpt_tuple, "checkpoint", {}) or {}
|
||||
title = ckpt.get("channel_values", {}).get("title")
|
||||
if title:
|
||||
await thread_store.update_display_name(thread_id, title)
|
||||
except Exception:
|
||||
logger.debug("Failed to sync title for thread %s (non-fatal)", thread_id)
|
||||
|
||||
# Update threads_meta status based on run outcome
|
||||
if thread_store is not None:
|
||||
try:
|
||||
final_status = "idle" if record.status == RunStatus.success else record.status.value
|
||||
await thread_store.update_status(thread_id, final_status)
|
||||
except Exception:
|
||||
logger.debug("Failed to update thread_meta status for %s (non-fatal)", thread_id)
|
||||
|
||||
await bridge.publish_end(run_id)
|
||||
asyncio.create_task(bridge.cleanup(run_id, delay=60))
|
||||
|
||||
@@ -355,6 +462,31 @@ def _lg_mode_to_sse_event(mode: str) -> str:
|
||||
return mode
|
||||
|
||||
|
||||
def _extract_human_message(graph_input: dict) -> HumanMessage | None:
|
||||
"""Extract or construct a HumanMessage from graph_input for event recording.
|
||||
|
||||
Returns a LangChain HumanMessage so callers can use .model_dump() to get
|
||||
the checkpoint-aligned serialization format.
|
||||
"""
|
||||
from langchain_core.messages import HumanMessage
|
||||
|
||||
messages = graph_input.get("messages")
|
||||
if not messages:
|
||||
return None
|
||||
last = messages[-1] if isinstance(messages, list) else messages
|
||||
if isinstance(last, HumanMessage):
|
||||
return last
|
||||
if isinstance(last, str):
|
||||
return HumanMessage(content=last) if last else None
|
||||
if hasattr(last, "content"):
|
||||
content = last.content
|
||||
return HumanMessage(content=content)
|
||||
if isinstance(last, dict):
|
||||
content = last.get("content", "")
|
||||
return HumanMessage(content=content) if content else None
|
||||
return None
|
||||
|
||||
|
||||
def _unpack_stream_item(
|
||||
item: Any,
|
||||
lg_modes: list[str],
|
||||
|
||||
@@ -23,7 +23,7 @@ from collections.abc import AsyncIterator
|
||||
|
||||
from langgraph.store.base import BaseStore
|
||||
|
||||
from deerflow.config.app_config import get_app_config
|
||||
from deerflow.config.app_config import AppConfig
|
||||
from deerflow.runtime.store.provider import POSTGRES_CONN_REQUIRED, POSTGRES_STORE_INSTALL, SQLITE_STORE_INSTALL, ensure_sqlite_parent_dir, resolve_sqlite_conn_str
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -86,28 +86,26 @@ async def _async_store(config) -> AsyncIterator[BaseStore]:
|
||||
|
||||
|
||||
@contextlib.asynccontextmanager
|
||||
async def make_store() -> AsyncIterator[BaseStore]:
|
||||
async def make_store(app_config: AppConfig) -> AsyncIterator[BaseStore]:
|
||||
"""Async context manager that yields a Store whose backend matches the
|
||||
configured checkpointer.
|
||||
|
||||
Reads from the same ``checkpointer`` section of *config.yaml* used by
|
||||
:func:`deerflow.agents.checkpointer.async_provider.make_checkpointer` so
|
||||
:func:`deerflow.runtime.checkpointer.async_provider.make_checkpointer` so
|
||||
that both singletons always use the same persistence technology::
|
||||
|
||||
async with make_store() as store:
|
||||
async with make_store(app_config) as store:
|
||||
app.state.store = store
|
||||
|
||||
Yields an :class:`~langgraph.store.memory.InMemoryStore` when no
|
||||
``checkpointer`` section is configured (emits a WARNING in that case).
|
||||
"""
|
||||
config = get_app_config()
|
||||
|
||||
if config.checkpointer is None:
|
||||
if app_config.checkpointer is None:
|
||||
from langgraph.store.memory import InMemoryStore
|
||||
|
||||
logger.warning("No 'checkpointer' section in config.yaml — using InMemoryStore for the store. Thread list will be lost on server restart. Configure a sqlite or postgres backend for persistence.")
|
||||
yield InMemoryStore()
|
||||
return
|
||||
|
||||
async with _async_store(config.checkpointer) as store:
|
||||
async with _async_store(app_config.checkpointer) as store:
|
||||
yield store
|
||||
|
||||
@@ -26,7 +26,7 @@ from collections.abc import Iterator
|
||||
|
||||
from langgraph.store.base import BaseStore
|
||||
|
||||
from deerflow.config.app_config import get_app_config
|
||||
from deerflow.config.app_config import AppConfig
|
||||
from deerflow.runtime.store._sqlite_utils import ensure_sqlite_parent_dir, resolve_sqlite_conn_str
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -100,7 +100,7 @@ _store: BaseStore | None = None
|
||||
_store_ctx = None # open context manager keeping the connection alive
|
||||
|
||||
|
||||
def get_store() -> BaseStore:
|
||||
def get_store(app_config: AppConfig) -> BaseStore:
|
||||
"""Return the global sync Store singleton, creating it on first call.
|
||||
|
||||
Returns an :class:`~langgraph.store.memory.InMemoryStore` when no
|
||||
@@ -115,19 +115,10 @@ def get_store() -> BaseStore:
|
||||
if _store is not None:
|
||||
return _store
|
||||
|
||||
# Lazily load app config, mirroring the checkpointer singleton pattern so
|
||||
# that tests that set the global checkpointer config explicitly remain isolated.
|
||||
from deerflow.config.app_config import _app_config
|
||||
from deerflow.config.checkpointer_config import get_checkpointer_config
|
||||
|
||||
config = get_checkpointer_config()
|
||||
|
||||
if config is None and _app_config is None:
|
||||
try:
|
||||
get_app_config()
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
config = get_checkpointer_config()
|
||||
# See matching comment in checkpointer/provider.py: a missing config.yaml
|
||||
# is a deployment error, not a cue to silently pick InMemoryStore. Only
|
||||
# the explicit "no checkpointer section" path falls through to memory.
|
||||
config = app_config.checkpointer
|
||||
|
||||
if config is None:
|
||||
from langgraph.store.memory import InMemoryStore
|
||||
@@ -163,26 +154,25 @@ def reset_store() -> None:
|
||||
|
||||
|
||||
@contextlib.contextmanager
|
||||
def store_context() -> Iterator[BaseStore]:
|
||||
def store_context(app_config: AppConfig) -> Iterator[BaseStore]:
|
||||
"""Sync context manager that yields a Store and cleans up on exit.
|
||||
|
||||
Unlike :func:`get_store`, this does **not** cache the instance — each
|
||||
``with`` block creates and destroys its own connection. Use it in CLI
|
||||
scripts or tests where you want deterministic cleanup::
|
||||
|
||||
with store_context() as store:
|
||||
with store_context(app_config) as store:
|
||||
store.put(("threads",), thread_id, {...})
|
||||
|
||||
Yields an :class:`~langgraph.store.memory.InMemoryStore` when no
|
||||
checkpointer is configured in *config.yaml*.
|
||||
"""
|
||||
config = get_app_config()
|
||||
if config.checkpointer is None:
|
||||
if app_config.checkpointer is None:
|
||||
from langgraph.store.memory import InMemoryStore
|
||||
|
||||
logger.warning("No 'checkpointer' section in config.yaml — using InMemoryStore for the store. Thread list will be lost on server restart. Configure a sqlite or postgres backend for persistence.")
|
||||
yield InMemoryStore()
|
||||
return
|
||||
|
||||
with _sync_store_cm(config.checkpointer) as store:
|
||||
with _sync_store_cm(app_config.checkpointer) as store:
|
||||
yield store
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
"""Async stream bridge factory.
|
||||
|
||||
Provides an **async context manager** aligned with
|
||||
:func:`deerflow.agents.checkpointer.async_provider.make_checkpointer`.
|
||||
:func:`deerflow.runtime.checkpointer.async_provider.make_checkpointer`.
|
||||
|
||||
Usage (e.g. FastAPI lifespan)::
|
||||
|
||||
@@ -17,7 +17,7 @@ import contextlib
|
||||
import logging
|
||||
from collections.abc import AsyncIterator
|
||||
|
||||
from deerflow.config.stream_bridge_config import get_stream_bridge_config
|
||||
from deerflow.config.app_config import AppConfig
|
||||
|
||||
from .base import StreamBridge
|
||||
|
||||
@@ -25,14 +25,13 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@contextlib.asynccontextmanager
|
||||
async def make_stream_bridge(config=None) -> AsyncIterator[StreamBridge]:
|
||||
async def make_stream_bridge(app_config: AppConfig) -> AsyncIterator[StreamBridge]:
|
||||
"""Async context manager that yields a :class:`StreamBridge`.
|
||||
|
||||
Falls back to :class:`MemoryStreamBridge` when no configuration is
|
||||
provided and nothing is set globally.
|
||||
Falls back to :class:`MemoryStreamBridge` when no ``stream_bridge``
|
||||
section is configured.
|
||||
"""
|
||||
if config is None:
|
||||
config = get_stream_bridge_config()
|
||||
config = app_config.stream_bridge
|
||||
|
||||
if config is None or config.type == "memory":
|
||||
from deerflow.runtime.stream_bridge.memory import MemoryStreamBridge
|
||||
|
||||
@@ -0,0 +1,167 @@
|
||||
"""Request-scoped user context for user-based authorization.
|
||||
|
||||
This module holds a :class:`~contextvars.ContextVar` that the gateway's
|
||||
auth middleware sets after a successful authentication. Repository
|
||||
methods read the contextvar via a sentinel default parameter, letting
|
||||
routers stay free of ``user_id`` boilerplate.
|
||||
|
||||
Three-state semantics for the repository ``user_id`` parameter (the
|
||||
consumer side of this module lives in ``deerflow.persistence.*``):
|
||||
|
||||
- ``_AUTO`` (module-private sentinel, default): read from contextvar;
|
||||
raise :class:`RuntimeError` if unset.
|
||||
- Explicit ``str``: use the provided value, overriding contextvar.
|
||||
- Explicit ``None``: no WHERE clause — used only by migration scripts
|
||||
and admin CLIs that intentionally bypass isolation.
|
||||
|
||||
Dependency direction
|
||||
--------------------
|
||||
``persistence`` (lower layer) reads from this module; ``gateway.auth``
|
||||
(higher layer) writes to it. ``CurrentUser`` is defined here as a
|
||||
:class:`typing.Protocol` so that ``persistence`` never needs to import
|
||||
the concrete ``User`` class from ``gateway.auth.models``. Any object
|
||||
with an ``.id: str`` attribute structurally satisfies the protocol.
|
||||
|
||||
Asyncio semantics
|
||||
-----------------
|
||||
``ContextVar`` is task-local under asyncio, not thread-local. Each
|
||||
FastAPI request runs in its own task, so the context is naturally
|
||||
isolated. ``asyncio.create_task`` and ``asyncio.to_thread`` inherit the
|
||||
parent task's context, which is typically the intended behaviour; if
|
||||
a background task must *not* see the foreground user, wrap it with
|
||||
``contextvars.copy_context()`` to get a clean copy.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from contextvars import ContextVar, Token
|
||||
from typing import Final, Protocol, runtime_checkable
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class CurrentUser(Protocol):
|
||||
"""Structural type for the current authenticated user.
|
||||
|
||||
Any object with an ``.id: str`` attribute satisfies this protocol.
|
||||
Concrete implementations live in ``app.gateway.auth.models.User``.
|
||||
"""
|
||||
|
||||
id: str
|
||||
|
||||
|
||||
_current_user: Final[ContextVar[CurrentUser | None]] = ContextVar("deerflow_current_user", default=None)
|
||||
|
||||
|
||||
def set_current_user(user: CurrentUser) -> Token[CurrentUser | None]:
|
||||
"""Set the current user for this async task.
|
||||
|
||||
Returns a reset token that should be passed to
|
||||
:func:`reset_current_user` in a ``finally`` block to restore the
|
||||
previous context.
|
||||
"""
|
||||
return _current_user.set(user)
|
||||
|
||||
|
||||
def reset_current_user(token: Token[CurrentUser | None]) -> None:
|
||||
"""Restore the context to the state captured by ``token``."""
|
||||
_current_user.reset(token)
|
||||
|
||||
|
||||
def get_current_user() -> CurrentUser | None:
|
||||
"""Return the current user, or ``None`` if unset.
|
||||
|
||||
Safe to call in any context. Used by code paths that can proceed
|
||||
without a user (e.g. migration scripts, public endpoints).
|
||||
"""
|
||||
return _current_user.get()
|
||||
|
||||
|
||||
def require_current_user() -> CurrentUser:
|
||||
"""Return the current user, or raise :class:`RuntimeError`.
|
||||
|
||||
Used by repository code that must not be called outside a
|
||||
request-authenticated context. The error message is phrased so
|
||||
that a caller debugging a stack trace can locate the offending
|
||||
code path.
|
||||
"""
|
||||
user = _current_user.get()
|
||||
if user is None:
|
||||
raise RuntimeError("repository accessed without user context")
|
||||
return user
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Effective user_id helpers (filesystem isolation)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
DEFAULT_USER_ID: Final[str] = "default"
|
||||
|
||||
|
||||
def get_effective_user_id() -> str:
|
||||
"""Return the current user's id as a string, or DEFAULT_USER_ID if unset.
|
||||
|
||||
Unlike :func:`require_current_user` this never raises — it is designed
|
||||
for filesystem-path resolution where a valid user bucket is always needed.
|
||||
"""
|
||||
user = _current_user.get()
|
||||
if user is None:
|
||||
return DEFAULT_USER_ID
|
||||
return str(user.id)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Sentinel-based user_id resolution
|
||||
# ---------------------------------------------------------------------------
|
||||
#
|
||||
# Repository methods accept a ``user_id`` keyword-only argument that
|
||||
# defaults to ``AUTO``. The three possible values drive distinct
|
||||
# behaviours; see the docstring on :func:`resolve_user_id`.
|
||||
|
||||
|
||||
class _AutoSentinel:
|
||||
"""Singleton marker meaning 'resolve user_id from contextvar'."""
|
||||
|
||||
_instance: _AutoSentinel | None = None
|
||||
|
||||
def __new__(cls) -> _AutoSentinel:
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return "<AUTO>"
|
||||
|
||||
|
||||
AUTO: Final[_AutoSentinel] = _AutoSentinel()
|
||||
|
||||
|
||||
def resolve_user_id(
|
||||
value: str | None | _AutoSentinel,
|
||||
*,
|
||||
method_name: str = "repository method",
|
||||
) -> str | None:
|
||||
"""Resolve the user_id parameter passed to a repository method.
|
||||
|
||||
Three-state semantics:
|
||||
|
||||
- :data:`AUTO` (default): read from contextvar; raise
|
||||
:class:`RuntimeError` if no user is in context. This is the
|
||||
common case for request-scoped calls.
|
||||
- Explicit ``str``: use the provided id verbatim, overriding any
|
||||
contextvar value. Useful for tests and admin-override flows.
|
||||
- Explicit ``None``: no filter — the repository should skip the
|
||||
user_id WHERE clause entirely. Reserved for migration scripts
|
||||
and CLI tools that intentionally bypass isolation.
|
||||
"""
|
||||
if isinstance(value, _AutoSentinel):
|
||||
user = _current_user.get()
|
||||
if user is None:
|
||||
raise RuntimeError(f"{method_name} called with user_id=AUTO but no user context is set; pass an explicit user_id, set the contextvar via auth middleware, or opt out with user_id=None for migration/CLI paths.")
|
||||
# Coerce to ``str`` at the boundary: ``User.id`` is typed as
|
||||
# ``UUID`` for the API surface, but the persistence layer
|
||||
# stores ``user_id`` as ``String(64)`` and aiosqlite cannot
|
||||
# bind a raw UUID object to a VARCHAR column ("type 'UUID' is
|
||||
# not supported"). Honour the documented return type here
|
||||
# rather than ripple a type change through every caller.
|
||||
return str(user.id)
|
||||
return value
|
||||
Reference in New Issue
Block a user