mirror of
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* feat(persistence): add SQLAlchemy 2.0 async ORM scaffold
Introduce a unified database configuration (DatabaseConfig) that
controls both the LangGraph checkpointer and the DeerFlow application
persistence layer from a single `database:` config section.
New modules:
- deerflow.config.database_config — Pydantic config with memory/sqlite/postgres backends
- deerflow.persistence — async engine lifecycle, DeclarativeBase with to_dict mixin, Alembic skeleton
- deerflow.runtime.runs.store — RunStore ABC + MemoryRunStore implementation
Gateway integration initializes/tears down the persistence engine in
the existing langgraph_runtime() context manager. Legacy checkpointer
config is preserved for backward compatibility.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(persistence): add RunEventStore ABC + MemoryRunEventStore
Phase 2-A prerequisite for event storage: adds the unified run event
stream interface (RunEventStore) with an in-memory implementation,
RunEventsConfig, gateway integration, and comprehensive tests (27 cases).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(persistence): add ORM models, repositories, DB/JSONL event stores, RunJournal, and API endpoints
Phase 2-B: run persistence + event storage + token tracking.
- ORM models: RunRow (with token fields), ThreadMetaRow, RunEventRow
- RunRepository implements RunStore ABC via SQLAlchemy ORM
- ThreadMetaRepository with owner access control
- DbRunEventStore with trace content truncation and cursor pagination
- JsonlRunEventStore with per-run files and seq recovery from disk
- RunJournal (BaseCallbackHandler) captures LLM/tool/lifecycle events,
accumulates token usage by caller type, buffers and flushes to store
- RunManager now accepts optional RunStore for persistent backing
- Worker creates RunJournal, writes human_message, injects callbacks
- Gateway deps use factory functions (RunRepository when DB available)
- New endpoints: messages, run messages, run events, token-usage
- ThreadCreateRequest gains assistant_id field
- 92 tests pass (33 new), zero regressions
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(persistence): add user feedback + follow-up run association
Phase 2-C: feedback and follow-up tracking.
- FeedbackRow ORM model (rating +1/-1, optional message_id, comment)
- FeedbackRepository with CRUD, list_by_run/thread, aggregate stats
- Feedback API endpoints: create, list, stats, delete
- follow_up_to_run_id in RunCreateRequest (explicit or auto-detected
from latest successful run on the thread)
- Worker writes follow_up_to_run_id into human_message event metadata
- Gateway deps: feedback_repo factory + getter
- 17 new tests (14 FeedbackRepository + 3 follow-up association)
- 109 total tests pass, zero regressions
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* test+config: comprehensive Phase 2 test coverage + deprecate checkpointer config
- config.example.yaml: deprecate standalone checkpointer section, activate
unified database:sqlite as default (drives both checkpointer + app data)
- New: test_thread_meta_repo.py (14 tests) — full ThreadMetaRepository coverage
including check_access owner logic, list_by_owner pagination
- Extended test_run_repository.py (+4 tests) — completion preserves fields,
list ordering desc, limit, owner_none returns all
- Extended test_run_journal.py (+8 tests) — on_chain_error, track_tokens=false,
middleware no ai_message, unknown caller tokens, convenience fields,
tool_error, non-summarization custom event
- Extended test_run_event_store.py (+7 tests) — DB batch seq continuity,
make_run_event_store factory (memory/db/jsonl/fallback/unknown)
- Extended test_phase2b_integration.py (+4 tests) — create_or_reject persists,
follow-up metadata, summarization in history, full DB-backed lifecycle
- Fixed DB integration test to use proper fake objects (not MagicMock)
for JSON-serializable metadata
- 157 total Phase 2 tests pass, zero regressions
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* config: move default sqlite_dir to .deer-flow/data
Keep SQLite databases alongside other DeerFlow-managed data
(threads, memory) under the .deer-flow/ directory instead of a
top-level ./data folder.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* refactor(persistence): remove UTFJSON, use engine-level json_serializer + datetime.now()
- Replace custom UTFJSON type with standard sqlalchemy.JSON in all ORM
models. Add json_serializer=json.dumps(ensure_ascii=False) to all
create_async_engine calls so non-ASCII text (Chinese etc.) is stored
as-is in both SQLite and Postgres.
- Change ORM datetime defaults from datetime.now(UTC) to datetime.now(),
remove UTC imports.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* refactor(gateway): simplify deps.py with getter factory + inline repos
- Replace 6 identical getter functions with _require() factory.
- Inline 3 _make_*_repo() factories into langgraph_runtime(), call
get_session_factory() once instead of 3 times.
- Add thread_meta upsert in start_run (services.py).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(docker): add UV_EXTRAS build arg for optional dependencies
Support installing optional dependency groups (e.g. postgres) at
Docker build time via UV_EXTRAS build arg:
UV_EXTRAS=postgres docker compose build
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* refactor(journal): fix flush, token tracking, and consolidate tests
RunJournal fixes:
- _flush_sync: retain events in buffer when no event loop instead of
dropping them; worker's finally block flushes via async flush().
- on_llm_end: add tool_calls filter and caller=="lead_agent" guard for
ai_message events; mark message IDs for dedup with record_llm_usage.
- worker.py: persist completion data (tokens, message count) to RunStore
in finally block.
Model factory:
- Auto-inject stream_usage=True for BaseChatOpenAI subclasses with
custom api_base, so usage_metadata is populated in streaming responses.
Test consolidation:
- Delete test_phase2b_integration.py (redundant with existing tests).
- Move DB-backed lifecycle test into test_run_journal.py.
- Add tests for stream_usage injection in test_model_factory.py.
- Clean up executor/task_tool dead journal references.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(events): widen content type to str|dict in all store backends
Allow event content to be a dict (for structured OpenAI-format messages)
in addition to plain strings. Dict values are JSON-serialized for the DB
backend and deserialized on read; memory and JSONL backends handle dicts
natively. Trace truncation now serializes dicts to JSON before measuring.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(events): use metadata flag instead of heuristic for dict content detection
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(converters): add LangChain-to-OpenAI message format converters
Pure functions langchain_to_openai_message, langchain_to_openai_completion,
langchain_messages_to_openai, and _infer_finish_reason for converting
LangChain BaseMessage objects to OpenAI Chat Completions format, used by
RunJournal for event storage. 15 unit tests added.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(converters): handle empty list content as null, clean up test
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(events): human_message content uses OpenAI user message format
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat(events): ai_message uses OpenAI format, add ai_tool_call message event
- ai_message content now uses {"role": "assistant", "content": "..."} format
- New ai_tool_call message event emitted when lead_agent LLM responds with tool_calls
- ai_tool_call uses langchain_to_openai_message converter for consistent format
- Both events include finish_reason in metadata ("stop" or "tool_calls")
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(events): add tool_result message event with OpenAI tool message format
Cache tool_call_id from on_tool_start keyed by run_id as fallback for on_tool_end,
then emit a tool_result message event (role=tool, tool_call_id, content) after each
successful tool completion.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat(events): summary content uses OpenAI system message format
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(events): replace llm_start/llm_end with llm_request/llm_response in OpenAI format
Add on_chat_model_start to capture structured prompt messages as llm_request events.
Replace llm_end trace events with llm_response using OpenAI Chat Completions format.
Track llm_call_index to pair request/response events.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(events): add record_middleware method for middleware trace events
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* test(events): add full run sequence integration test for OpenAI content format
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat(events): align message events with checkpoint format and add middleware tag injection
- Message events (ai_message, ai_tool_call, tool_result, human_message) now use
BaseMessage.model_dump() format, matching LangGraph checkpoint values.messages
- on_tool_end extracts tool_call_id/name/status from ToolMessage objects
- on_tool_error now emits tool_result message events with error status
- record_middleware uses middleware:{tag} event_type and middleware category
- Summarization custom events use middleware:summarize category
- TitleMiddleware injects middleware:title tag via get_config() inheritance
- SummarizationMiddleware model bound with middleware:summarize tag
- Worker writes human_message using HumanMessage.model_dump()
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(threads): switch search endpoint to threads_meta table and sync title
- POST /api/threads/search now queries threads_meta table directly,
removing the two-phase Store + Checkpointer scan approach
- Add ThreadMetaRepository.search() with metadata/status filters
- Add ThreadMetaRepository.update_display_name() for title sync
- Worker syncs checkpoint title to threads_meta.display_name on run completion
- Map display_name to values.title in search response for API compatibility
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(threads): history endpoint reads messages from event store
- POST /api/threads/{thread_id}/history now combines two data sources:
checkpointer for checkpoint_id, metadata, title, thread_data;
event store for messages (complete history, not truncated by summarization)
- Strip internal LangGraph metadata keys from response
- Remove full channel_values serialization in favor of selective fields
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: remove duplicate optional-dependencies header in pyproject.toml
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(middleware): pass tagged config to TitleMiddleware ainvoke call
Without the config, the middleware:title tag was not injected,
causing the LLM response to be recorded as a lead_agent ai_message
in run_events.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: resolve merge conflict in .env.example
Keep both DATABASE_URL (from persistence-scaffold) and WECOM
credentials (from main) after the merge.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(persistence): address review feedback on PR #1851
- Fix naive datetime.now() → datetime.now(UTC) in all ORM models
- Fix seq race condition in DbRunEventStore.put() with FOR UPDATE
and UNIQUE(thread_id, seq) constraint
- Encapsulate _store access in RunManager.update_run_completion()
- Deduplicate _store.put() logic in RunManager via _persist_to_store()
- Add update_run_completion to RunStore ABC + MemoryRunStore
- Wire follow_up_to_run_id through the full create path
- Add error recovery to RunJournal._flush_sync() lost-event scenario
- Add migration note for search_threads breaking change
- Fix test_checkpointer_none_fix mock to set database=None
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* chore: update uv.lock
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(persistence): address 22 review comments from CodeQL, Copilot, and Code Quality
Bug fixes:
- Sanitize log params to prevent log injection (CodeQL)
- Reset threads_meta.status to idle/error when run completes
- Attach messages only to latest checkpoint in /history response
- Write threads_meta on POST /threads so new threads appear in search
Lint fixes:
- Remove unused imports (journal.py, migrations/env.py, test_converters.py)
- Convert lambda to named function (engine.py, Ruff E731)
- Remove unused logger definitions in repos (Ruff F841)
- Add logging to JSONL decode errors and empty except blocks
- Separate assert side-effects in tests (CodeQL)
- Remove unused local variables in tests (Ruff F841)
- Fix max_trace_content truncation to use byte length, not char length
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* style: apply ruff format to persistence and runtime files
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Potential fix for pull request finding 'Statement has no effect'
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
* refactor(runtime): introduce RunContext to reduce run_agent parameter bloat
Extract checkpointer, store, event_store, run_events_config, thread_meta_repo,
and follow_up_to_run_id into a frozen RunContext dataclass. Add get_run_context()
in deps.py to build the base context from app.state singletons. start_run() uses
dataclasses.replace() to enrich per-run fields before passing ctx to run_agent.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* refactor(gateway): move sanitize_log_param to app/gateway/utils.py
Extract the log-injection sanitizer from routers/threads.py into a shared
utils module and rename to sanitize_log_param (public API). Eliminates the
reverse service → router import in services.py.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* perf: use SQL aggregation for feedback stats and thread token usage
Replace Python-side counting in FeedbackRepository.aggregate_by_run with
a single SELECT COUNT/SUM query. Add RunStore.aggregate_tokens_by_thread
abstract method with SQL GROUP BY implementation in RunRepository and
Python fallback in MemoryRunStore. Simplify the thread_token_usage
endpoint to delegate to the new method, eliminating the limit=10000
truncation risk.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* docs: annotate DbRunEventStore.put() as low-frequency path
Add docstring clarifying that put() opens a per-call transaction with
FOR UPDATE and should only be used for infrequent writes (currently
just the initial human_message event). High-throughput callers should
use put_batch() instead.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(threads): fall back to Store search when ThreadMetaRepository is unavailable
When database.backend=memory (default) or no SQL session factory is
configured, search_threads now queries the LangGraph Store instead of
returning 503. Returns empty list if neither Store nor repo is available.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* refactor(persistence): introduce ThreadMetaStore ABC for backend-agnostic thread metadata
Add ThreadMetaStore abstract base class with create/get/search/update/delete
interface. ThreadMetaRepository (SQL) now inherits from it. New
MemoryThreadMetaStore wraps LangGraph BaseStore for memory-mode deployments.
deps.py now always provides a non-None thread_meta_repo, eliminating all
`if thread_meta_repo is not None` guards in services.py, worker.py, and
routers/threads.py. search_threads no longer needs a Store fallback branch.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* refactor(history): read messages from checkpointer instead of RunEventStore
The /history endpoint now reads messages directly from the
checkpointer's channel_values (the authoritative source) instead of
querying RunEventStore.list_messages(). The RunEventStore API is
preserved for other consumers.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(persistence): address new Copilot review comments
- feedback.py: validate thread_id/run_id before deleting feedback
- jsonl.py: add path traversal protection with ID validation
- run_repo.py: parse `before` to datetime for PostgreSQL compat
- thread_meta_repo.py: fix pagination when metadata filter is active
- database_config.py: use resolve_path for sqlite_dir consistency
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Implement skill self-evolution and skill_manage flow (#1874)
* chore: ignore .worktrees directory
* Add skill_manage self-evolution flow
* Fix CI regressions for skill_manage
* Address PR review feedback for skill evolution
* fix(skill-evolution): preserve history on delete
* fix(skill-evolution): tighten scanner fallbacks
* docs: add skill_manage e2e evidence screenshot
* fix(skill-manage): avoid blocking fs ops in session runtime
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix(config): resolve sqlite_dir relative to CWD, not Paths.base_dir
resolve_path() resolves relative to Paths.base_dir (.deer-flow),
which double-nested the path to .deer-flow/.deer-flow/data/app.db.
Use Path.resolve() (CWD-relative) instead.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Feature/feishu receive file (#1608)
* feat(feishu): add channel file materialization hook for inbound messages
- Introduce Channel.receive_file(msg, thread_id) as a base method for file materialization; default is no-op.
- Implement FeishuChannel.receive_file to download files/images from Feishu messages, save to sandbox, and inject virtual paths into msg.text.
- Update ChannelManager to call receive_file for any channel if msg.files is present, enabling downstream model access to user-uploaded files.
- No impact on Slack/Telegram or other channels (they inherit the default no-op).
* style(backend): format code with ruff for lint compliance
- Auto-formatted packages/harness/deerflow/agents/factory.py and tests/test_create_deerflow_agent.py using `ruff format`
- Ensured both files conform to project linting standards
- Fixes CI lint check failures caused by code style issues
* fix(feishu): handle file write operation asynchronously to prevent blocking
* fix(feishu): rename GetMessageResourceRequest to _GetMessageResourceRequest and remove redundant code
* test(feishu): add tests for receive_file method and placeholder replacement
* fix(manager): remove unnecessary type casting for channel retrieval
* fix(feishu): update logging messages to reflect resource handling instead of image
* fix(feishu): sanitize filename by replacing invalid characters in file uploads
* fix(feishu): improve filename sanitization and reorder image key handling in message processing
* fix(feishu): add thread lock to prevent filename conflicts during file downloads
* fix(test): correct bad merge in test_feishu_parser.py
* chore: run ruff and apply formatting cleanup
fix(feishu): preserve rich-text attachment order and improve fallback filename handling
* fix(docker): restore gateway env vars and fix langgraph empty arg issue (#1915)
Two production docker-compose.yaml bugs prevent `make up` from working:
1. Gateway missing DEER_FLOW_CONFIG_PATH and DEER_FLOW_EXTENSIONS_CONFIG_PATH
environment overrides. Added in fb2d99f (#1836) but accidentally reverted
by ca2fb95 (#1847). Without them, gateway reads host paths from .env via
env_file, causing FileNotFoundError inside the container.
2. Langgraph command fails when LANGGRAPH_ALLOW_BLOCKING is unset (default).
Empty $${allow_blocking} inserts a bare space between flags, causing
' --no-reload' to be parsed as unexpected extra argument. Fix by building
args string first and conditionally appending --allow-blocking.
Co-authored-by: cooper <cooperfu@tencent.com>
* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities (#1904)
* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities
Fix `<button>` inside `<a>` invalid HTML in artifact components and add
missing `noopener,noreferrer` to `window.open` calls to prevent reverse
tabnabbing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(frontend): address Copilot review on tabnabbing and double-tab-open
Remove redundant parent onClick on web_fetch ChainOfThoughtStep to
prevent opening two tabs on link click, and explicitly null out
window.opener after window.open() for defensive tabnabbing hardening.
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* refactor(persistence): organize entities into per-entity directories
Restructure the persistence layer from horizontal "models/ + repositories/"
split into vertical entity-aligned directories. Each entity (thread_meta,
run, feedback) now owns its ORM model, abstract interface (where applicable),
and concrete implementations under a single directory with an aggregating
__init__.py for one-line imports.
Layout:
persistence/thread_meta/{base,model,sql,memory}.py
persistence/run/{model,sql}.py
persistence/feedback/{model,sql}.py
models/__init__.py is kept as a facade so Alembic autogenerate continues to
discover all ORM tables via Base.metadata. RunEventRow remains under
models/run_event.py because its storage implementation lives in
runtime/events/store/db.py and has no matching repository directory.
The repositories/ directory is removed entirely. All call sites in
gateway/deps.py and tests are updated to import from the new entity
packages, e.g.:
from deerflow.persistence.thread_meta import ThreadMetaRepository
from deerflow.persistence.run import RunRepository
from deerflow.persistence.feedback import FeedbackRepository
Full test suite passes (1690 passed, 14 skipped).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(gateway): sync thread rename and delete through ThreadMetaStore
The POST /threads/{id}/state endpoint previously synced title changes
only to the LangGraph Store via _store_upsert. In sqlite mode the search
endpoint reads from the ThreadMetaRepository SQL table, so renames never
appeared in /threads/search until the next agent run completed (worker.py
syncs title from checkpoint to thread_meta in its finally block).
Likewise the DELETE /threads/{id} endpoint cleaned up the filesystem,
Store, and checkpointer but left the threads_meta row orphaned in sqlite,
so deleted threads kept appearing in /threads/search.
Fix both endpoints by routing through the ThreadMetaStore abstraction
which already has the correct sqlite/memory implementations wired up by
deps.py. The rename path now calls update_display_name() and the delete
path calls delete() — both work uniformly across backends.
Verified end-to-end with curl in gateway mode against sqlite backend.
Existing test suite (1690 passed) and focused router/repo tests pass.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* refactor(gateway): route all thread metadata access through ThreadMetaStore
Following the rename/delete bug fix in PR1, migrate the remaining direct
LangGraph Store reads/writes in the threads router and services to the
ThreadMetaStore abstraction so that the sqlite and memory backends behave
identically and the legacy dual-write paths can be removed.
Migrated endpoints (threads.py):
- create_thread: idempotency check + write now use thread_meta_repo.get/create
instead of dual-writing the LangGraph Store and the SQL row.
- get_thread: reads from thread_meta_repo.get; the checkpoint-only fallback
for legacy threads is preserved.
- patch_thread: replaced _store_get/_store_put with thread_meta_repo.update_metadata.
- delete_thread_data: dropped the legacy store.adelete; thread_meta_repo.delete
already covers it.
Removed dead code (services.py):
- _upsert_thread_in_store — redundant with the immediately following
thread_meta_repo.create() call.
- _sync_thread_title_after_run — worker.py's finally block already syncs
the title via thread_meta_repo.update_display_name() after each run.
Removed dead code (threads.py):
- _store_get / _store_put / _store_upsert helpers (no remaining callers).
- THREADS_NS constant.
- get_store import (router no longer touches the LangGraph Store directly).
New abstract method:
- ThreadMetaStore.update_metadata(thread_id, metadata) merges metadata into
the thread's metadata field. Implemented in both ThreadMetaRepository (SQL,
read-modify-write inside one session) and MemoryThreadMetaStore. Three new
unit tests cover merge / empty / nonexistent behaviour.
Net change: -134 lines. Full test suite: 1693 passed, 14 skipped.
Verified end-to-end with curl in gateway mode against sqlite backend
(create / patch / get / rename / search / delete).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com>
Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: JilongSun <965640067@qq.com>
Co-authored-by: jie <49781832+stan-fu@users.noreply.github.com>
Co-authored-by: cooper <cooperfu@tencent.com>
Co-authored-by: yangzheli <43645580+yangzheli@users.noreply.github.com>
384 lines
16 KiB
Python
384 lines
16 KiB
Python
import logging
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import os
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from contextvars import ContextVar
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from pathlib import Path
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from typing import Any, Self
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import yaml
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from dotenv import load_dotenv
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from pydantic import BaseModel, ConfigDict, Field
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from deerflow.config.acp_config import load_acp_config_from_dict
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from deerflow.config.checkpointer_config import CheckpointerConfig, load_checkpointer_config_from_dict
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from deerflow.config.database_config import DatabaseConfig
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from deerflow.config.extensions_config import ExtensionsConfig
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from deerflow.config.guardrails_config import GuardrailsConfig, load_guardrails_config_from_dict
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from deerflow.config.memory_config import MemoryConfig, load_memory_config_from_dict
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from deerflow.config.model_config import ModelConfig
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from deerflow.config.run_events_config import RunEventsConfig
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from deerflow.config.sandbox_config import SandboxConfig
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from deerflow.config.skill_evolution_config import SkillEvolutionConfig
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from deerflow.config.skills_config import SkillsConfig
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from deerflow.config.stream_bridge_config import StreamBridgeConfig, load_stream_bridge_config_from_dict
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from deerflow.config.subagents_config import SubagentsAppConfig, load_subagents_config_from_dict
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from deerflow.config.summarization_config import SummarizationConfig, load_summarization_config_from_dict
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from deerflow.config.title_config import TitleConfig, load_title_config_from_dict
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from deerflow.config.token_usage_config import TokenUsageConfig
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from deerflow.config.tool_config import ToolConfig, ToolGroupConfig
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from deerflow.config.tool_search_config import ToolSearchConfig, load_tool_search_config_from_dict
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load_dotenv()
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logger = logging.getLogger(__name__)
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def _default_config_candidates() -> tuple[Path, ...]:
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"""Return deterministic config.yaml locations without relying on cwd."""
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backend_dir = Path(__file__).resolve().parents[4]
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repo_root = backend_dir.parent
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return (backend_dir / "config.yaml", repo_root / "config.yaml")
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class AppConfig(BaseModel):
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"""Config for the DeerFlow application"""
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log_level: str = Field(default="info", description="Logging level for deerflow modules (debug/info/warning/error)")
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token_usage: TokenUsageConfig = Field(default_factory=TokenUsageConfig, description="Token usage tracking configuration")
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models: list[ModelConfig] = Field(default_factory=list, description="Available models")
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sandbox: SandboxConfig = Field(description="Sandbox configuration")
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tools: list[ToolConfig] = Field(default_factory=list, description="Available tools")
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tool_groups: list[ToolGroupConfig] = Field(default_factory=list, description="Available tool groups")
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skills: SkillsConfig = Field(default_factory=SkillsConfig, description="Skills configuration")
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skill_evolution: SkillEvolutionConfig = Field(default_factory=SkillEvolutionConfig, description="Agent-managed skill evolution configuration")
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extensions: ExtensionsConfig = Field(default_factory=ExtensionsConfig, description="Extensions configuration (MCP servers and skills state)")
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tool_search: ToolSearchConfig = Field(default_factory=ToolSearchConfig, description="Tool search / deferred loading configuration")
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title: TitleConfig = Field(default_factory=TitleConfig, description="Automatic title generation configuration")
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summarization: SummarizationConfig = Field(default_factory=SummarizationConfig, description="Conversation summarization configuration")
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memory: MemoryConfig = Field(default_factory=MemoryConfig, description="Memory subsystem configuration")
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subagents: SubagentsAppConfig = Field(default_factory=SubagentsAppConfig, description="Subagent runtime configuration")
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guardrails: GuardrailsConfig = Field(default_factory=GuardrailsConfig, description="Guardrail middleware configuration")
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model_config = ConfigDict(extra="allow", frozen=False)
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database: DatabaseConfig = Field(default_factory=DatabaseConfig, description="Unified database backend configuration")
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run_events: RunEventsConfig = Field(default_factory=RunEventsConfig, description="Run event storage configuration")
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checkpointer: CheckpointerConfig | None = Field(default=None, description="Checkpointer configuration")
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stream_bridge: StreamBridgeConfig | None = Field(default=None, description="Stream bridge configuration")
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@classmethod
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def resolve_config_path(cls, config_path: str | None = None) -> Path:
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"""Resolve the config file path.
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Priority:
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1. If provided `config_path` argument, use it.
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2. If provided `DEER_FLOW_CONFIG_PATH` environment variable, use it.
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3. Otherwise, search deterministic backend/repository-root defaults from `_default_config_candidates()`.
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"""
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if config_path:
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path = Path(config_path)
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if not Path.exists(path):
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raise FileNotFoundError(f"Config file specified by param `config_path` not found at {path}")
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return path
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elif os.getenv("DEER_FLOW_CONFIG_PATH"):
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path = Path(os.getenv("DEER_FLOW_CONFIG_PATH"))
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if not Path.exists(path):
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raise FileNotFoundError(f"Config file specified by environment variable `DEER_FLOW_CONFIG_PATH` not found at {path}")
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return path
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else:
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for path in _default_config_candidates():
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if path.exists():
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return path
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raise FileNotFoundError("`config.yaml` file not found at the default backend or repository root locations")
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@classmethod
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def from_file(cls, config_path: str | None = None) -> Self:
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"""Load config from YAML file.
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See `resolve_config_path` for more details.
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Args:
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config_path: Path to the config file.
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Returns:
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AppConfig: The loaded config.
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"""
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resolved_path = cls.resolve_config_path(config_path)
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with open(resolved_path, encoding="utf-8") as f:
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config_data = yaml.safe_load(f) or {}
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# Check config version before processing
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cls._check_config_version(config_data, resolved_path)
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config_data = cls.resolve_env_variables(config_data)
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# Load title config if present
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if "title" in config_data:
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load_title_config_from_dict(config_data["title"])
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# Load summarization config if present
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if "summarization" in config_data:
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load_summarization_config_from_dict(config_data["summarization"])
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# Load memory config if present
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if "memory" in config_data:
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load_memory_config_from_dict(config_data["memory"])
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# Load subagents config if present
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if "subagents" in config_data:
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load_subagents_config_from_dict(config_data["subagents"])
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# Load tool_search config if present
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if "tool_search" in config_data:
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load_tool_search_config_from_dict(config_data["tool_search"])
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# Load guardrails config if present
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if "guardrails" in config_data:
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load_guardrails_config_from_dict(config_data["guardrails"])
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# Load checkpointer config if present
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if "checkpointer" in config_data:
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load_checkpointer_config_from_dict(config_data["checkpointer"])
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# Load stream bridge config if present
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if "stream_bridge" in config_data:
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load_stream_bridge_config_from_dict(config_data["stream_bridge"])
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# Always refresh ACP agent config so removed entries do not linger across reloads.
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load_acp_config_from_dict(config_data.get("acp_agents", {}))
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# Load extensions config separately (it's in a different file)
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extensions_config = ExtensionsConfig.from_file()
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config_data["extensions"] = extensions_config.model_dump()
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result = cls.model_validate(config_data)
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return result
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@classmethod
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def _check_config_version(cls, config_data: dict, config_path: Path) -> None:
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"""Check if the user's config.yaml is outdated compared to config.example.yaml.
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Emits a warning if the user's config_version is lower than the example's.
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Missing config_version is treated as version 0 (pre-versioning).
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"""
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try:
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user_version = int(config_data.get("config_version", 0))
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except (TypeError, ValueError):
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user_version = 0
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# Find config.example.yaml by searching config.yaml's directory and its parents
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example_path = None
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search_dir = config_path.parent
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for _ in range(5): # search up to 5 levels
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candidate = search_dir / "config.example.yaml"
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if candidate.exists():
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example_path = candidate
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break
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parent = search_dir.parent
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if parent == search_dir:
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break
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search_dir = parent
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if example_path is None:
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return
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try:
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with open(example_path, encoding="utf-8") as f:
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example_data = yaml.safe_load(f)
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raw = example_data.get("config_version", 0) if example_data else 0
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try:
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example_version = int(raw)
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except (TypeError, ValueError):
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example_version = 0
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except Exception:
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return
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if user_version < example_version:
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logger.warning(
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"Your config.yaml (version %d) is outdated — the latest version is %d. Run `make config-upgrade` to merge new fields into your config.",
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user_version,
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example_version,
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)
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@classmethod
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def resolve_env_variables(cls, config: Any) -> Any:
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"""Recursively resolve environment variables in the config.
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Environment variables are resolved using the `os.getenv` function. Example: $OPENAI_API_KEY
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Args:
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config: The config to resolve environment variables in.
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Returns:
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The config with environment variables resolved.
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"""
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if isinstance(config, str):
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if config.startswith("$"):
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env_value = os.getenv(config[1:])
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if env_value is None:
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raise ValueError(f"Environment variable {config[1:]} not found for config value {config}")
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return env_value
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return config
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elif isinstance(config, dict):
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return {k: cls.resolve_env_variables(v) for k, v in config.items()}
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elif isinstance(config, list):
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return [cls.resolve_env_variables(item) for item in config]
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return config
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def get_model_config(self, name: str) -> ModelConfig | None:
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"""Get the model config by name.
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Args:
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name: The name of the model to get the config for.
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Returns:
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The model config if found, otherwise None.
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"""
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return next((model for model in self.models if model.name == name), None)
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def get_tool_config(self, name: str) -> ToolConfig | None:
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"""Get the tool config by name.
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Args:
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name: The name of the tool to get the config for.
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Returns:
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The tool config if found, otherwise None.
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"""
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return next((tool for tool in self.tools if tool.name == name), None)
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def get_tool_group_config(self, name: str) -> ToolGroupConfig | None:
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"""Get the tool group config by name.
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Args:
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name: The name of the tool group to get the config for.
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Returns:
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The tool group config if found, otherwise None.
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"""
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return next((group for group in self.tool_groups if group.name == name), None)
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_app_config: AppConfig | None = None
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_app_config_path: Path | None = None
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_app_config_mtime: float | None = None
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_app_config_is_custom = False
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_current_app_config: ContextVar[AppConfig | None] = ContextVar("deerflow_current_app_config", default=None)
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_current_app_config_stack: ContextVar[tuple[AppConfig | None, ...]] = ContextVar("deerflow_current_app_config_stack", default=())
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def _get_config_mtime(config_path: Path) -> float | None:
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"""Get the modification time of a config file if it exists."""
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try:
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return config_path.stat().st_mtime
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except OSError:
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return None
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def _load_and_cache_app_config(config_path: str | None = None) -> AppConfig:
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"""Load config from disk and refresh cache metadata."""
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global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
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resolved_path = AppConfig.resolve_config_path(config_path)
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_app_config = AppConfig.from_file(str(resolved_path))
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_app_config_path = resolved_path
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_app_config_mtime = _get_config_mtime(resolved_path)
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_app_config_is_custom = False
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return _app_config
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def get_app_config() -> AppConfig:
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"""Get the DeerFlow config instance.
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Returns a cached singleton instance and automatically reloads it when the
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underlying config file path or modification time changes. Use
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`reload_app_config()` to force a reload, or `reset_app_config()` to clear
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the cache.
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"""
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global _app_config, _app_config_path, _app_config_mtime
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runtime_override = _current_app_config.get()
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if runtime_override is not None:
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return runtime_override
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if _app_config is not None and _app_config_is_custom:
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return _app_config
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resolved_path = AppConfig.resolve_config_path()
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current_mtime = _get_config_mtime(resolved_path)
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should_reload = _app_config is None or _app_config_path != resolved_path or _app_config_mtime != current_mtime
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if should_reload:
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if _app_config_path == resolved_path and _app_config_mtime is not None and current_mtime is not None and _app_config_mtime != current_mtime:
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logger.info(
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"Config file has been modified (mtime: %s -> %s), reloading AppConfig",
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_app_config_mtime,
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current_mtime,
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)
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_load_and_cache_app_config(str(resolved_path))
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return _app_config
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|
|
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def reload_app_config(config_path: str | None = None) -> AppConfig:
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"""Reload the config from file and update the cached instance.
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|
|
This is useful when the config file has been modified and you want
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to pick up the changes without restarting the application.
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|
Args:
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config_path: Optional path to config file. If not provided,
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uses the default resolution strategy.
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Returns:
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The newly loaded AppConfig instance.
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"""
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return _load_and_cache_app_config(config_path)
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|
|
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def reset_app_config() -> None:
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"""Reset the cached config instance.
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|
|
This clears the singleton cache, causing the next call to
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`get_app_config()` to reload from file. Useful for testing
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or when switching between different configurations.
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"""
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global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
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_app_config = None
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_app_config_path = None
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_app_config_mtime = None
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_app_config_is_custom = False
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|
|
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def set_app_config(config: AppConfig) -> None:
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"""Set a custom config instance.
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|
This allows injecting a custom or mock config for testing purposes.
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|
Args:
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config: The AppConfig instance to use.
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"""
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global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
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_app_config = config
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_app_config_path = None
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_app_config_mtime = None
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_app_config_is_custom = True
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|
|
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def peek_current_app_config() -> AppConfig | None:
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"""Return the runtime-scoped AppConfig override, if one is active."""
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return _current_app_config.get()
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|
|
|
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def push_current_app_config(config: AppConfig) -> None:
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"""Push a runtime-scoped AppConfig override for the current execution context."""
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stack = _current_app_config_stack.get()
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_current_app_config_stack.set(stack + (_current_app_config.get(),))
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_current_app_config.set(config)
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|
|
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def pop_current_app_config() -> None:
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"""Pop the latest runtime-scoped AppConfig override for the current execution context."""
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stack = _current_app_config_stack.get()
|
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if not stack:
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_current_app_config.set(None)
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return
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previous = stack[-1]
|
|
_current_app_config_stack.set(stack[:-1])
|
|
_current_app_config.set(previous)
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