56d5fa3337cb6ecf54620c2fe09379aedffaa5e7
17 Commits
| Author | SHA1 | Message | Date | |
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 rayhpeng
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56d5fa3337 |
feat(persistence):Unified persistence layer with event store, feedback, and rebase cleanup (#2134)
* feat(persistence): add unified persistence layer with event store, token tracking, and feedback (#1930) * feat(persistence): add SQLAlchemy 2.0 async ORM scaffold Introduce a unified database configuration (DatabaseConfig) that controls both the LangGraph checkpointer and the DeerFlow application persistence layer from a single `database:` config section. New modules: - deerflow.config.database_config — Pydantic config with memory/sqlite/postgres backends - deerflow.persistence — async engine lifecycle, DeclarativeBase with to_dict mixin, Alembic skeleton - deerflow.runtime.runs.store — RunStore ABC + MemoryRunStore implementation Gateway integration initializes/tears down the persistence engine in the existing langgraph_runtime() context manager. Legacy checkpointer config is preserved for backward compatibility. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(persistence): add RunEventStore ABC + MemoryRunEventStore Phase 2-A prerequisite for event storage: adds the unified run event stream interface (RunEventStore) with an in-memory implementation, RunEventsConfig, gateway integration, and comprehensive tests (27 cases). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(persistence): add ORM models, repositories, DB/JSONL event stores, RunJournal, and API endpoints Phase 2-B: run persistence + event storage + token tracking. - ORM models: RunRow (with token fields), ThreadMetaRow, RunEventRow - RunRepository implements RunStore ABC via SQLAlchemy ORM - ThreadMetaRepository with owner access control - DbRunEventStore with trace content truncation and cursor pagination - JsonlRunEventStore with per-run files and seq recovery from disk - RunJournal (BaseCallbackHandler) captures LLM/tool/lifecycle events, accumulates token usage by caller type, buffers and flushes to store - RunManager now accepts optional RunStore for persistent backing - Worker creates RunJournal, writes human_message, injects callbacks - Gateway deps use factory functions (RunRepository when DB available) - New endpoints: messages, run messages, run events, token-usage - ThreadCreateRequest gains assistant_id field - 92 tests pass (33 new), zero regressions Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(persistence): add user feedback + follow-up run association Phase 2-C: feedback and follow-up tracking. - FeedbackRow ORM model (rating +1/-1, optional message_id, comment) - FeedbackRepository with CRUD, list_by_run/thread, aggregate stats - Feedback API endpoints: create, list, stats, delete - follow_up_to_run_id in RunCreateRequest (explicit or auto-detected from latest successful run on the thread) - Worker writes follow_up_to_run_id into human_message event metadata - Gateway deps: feedback_repo factory + getter - 17 new tests (14 FeedbackRepository + 3 follow-up association) - 109 total tests pass, zero regressions Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * test+config: comprehensive Phase 2 test coverage + deprecate checkpointer config - config.example.yaml: deprecate standalone checkpointer section, activate unified database:sqlite as default (drives both checkpointer + app data) - New: test_thread_meta_repo.py (14 tests) — full ThreadMetaRepository coverage including check_access owner logic, list_by_owner pagination - Extended test_run_repository.py (+4 tests) — completion preserves fields, list ordering desc, limit, owner_none returns all - Extended test_run_journal.py (+8 tests) — on_chain_error, track_tokens=false, middleware no ai_message, unknown caller tokens, convenience fields, tool_error, non-summarization custom event - Extended test_run_event_store.py (+7 tests) — DB batch seq continuity, make_run_event_store factory (memory/db/jsonl/fallback/unknown) - Extended test_phase2b_integration.py (+4 tests) — create_or_reject persists, follow-up metadata, summarization in history, full DB-backed lifecycle - Fixed DB integration test to use proper fake objects (not MagicMock) for JSON-serializable metadata - 157 total Phase 2 tests pass, zero regressions Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * config: move default sqlite_dir to .deer-flow/data Keep SQLite databases alongside other DeerFlow-managed data (threads, memory) under the .deer-flow/ directory instead of a top-level ./data folder. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(persistence): remove UTFJSON, use engine-level json_serializer + datetime.now() - Replace custom UTFJSON type with standard sqlalchemy.JSON in all ORM models. Add json_serializer=json.dumps(ensure_ascii=False) to all create_async_engine calls so non-ASCII text (Chinese etc.) is stored as-is in both SQLite and Postgres. - Change ORM datetime defaults from datetime.now(UTC) to datetime.now(), remove UTC imports. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(gateway): simplify deps.py with getter factory + inline repos - Replace 6 identical getter functions with _require() factory. - Inline 3 _make_*_repo() factories into langgraph_runtime(), call get_session_factory() once instead of 3 times. - Add thread_meta upsert in start_run (services.py). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(docker): add UV_EXTRAS build arg for optional dependencies Support installing optional dependency groups (e.g. postgres) at Docker build time via UV_EXTRAS build arg: UV_EXTRAS=postgres docker compose build Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(journal): fix flush, token tracking, and consolidate tests RunJournal fixes: - _flush_sync: retain events in buffer when no event loop instead of dropping them; worker's finally block flushes via async flush(). - on_llm_end: add tool_calls filter and caller=="lead_agent" guard for ai_message events; mark message IDs for dedup with record_llm_usage. - worker.py: persist completion data (tokens, message count) to RunStore in finally block. Model factory: - Auto-inject stream_usage=True for BaseChatOpenAI subclasses with custom api_base, so usage_metadata is populated in streaming responses. Test consolidation: - Delete test_phase2b_integration.py (redundant with existing tests). - Move DB-backed lifecycle test into test_run_journal.py. - Add tests for stream_usage injection in test_model_factory.py. - Clean up executor/task_tool dead journal references. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(events): widen content type to str|dict in all store backends Allow event content to be a dict (for structured OpenAI-format messages) in addition to plain strings. Dict values are JSON-serialized for the DB backend and deserialized on read; memory and JSONL backends handle dicts natively. Trace truncation now serializes dicts to JSON before measuring. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(events): use metadata flag instead of heuristic for dict content detection Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(converters): add LangChain-to-OpenAI message format converters Pure functions langchain_to_openai_message, langchain_to_openai_completion, langchain_messages_to_openai, and _infer_finish_reason for converting LangChain BaseMessage objects to OpenAI Chat Completions format, used by RunJournal for event storage. 15 unit tests added. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(converters): handle empty list content as null, clean up test Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(events): human_message content uses OpenAI user message format Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(events): ai_message uses OpenAI format, add ai_tool_call message event - ai_message content now uses {"role": "assistant", "content": "..."} format - New ai_tool_call message event emitted when lead_agent LLM responds with tool_calls - ai_tool_call uses langchain_to_openai_message converter for consistent format - Both events include finish_reason in metadata ("stop" or "tool_calls") Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(events): add tool_result message event with OpenAI tool message format Cache tool_call_id from on_tool_start keyed by run_id as fallback for on_tool_end, then emit a tool_result message event (role=tool, tool_call_id, content) after each successful tool completion. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(events): summary content uses OpenAI system message format Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(events): replace llm_start/llm_end with llm_request/llm_response in OpenAI format Add on_chat_model_start to capture structured prompt messages as llm_request events. Replace llm_end trace events with llm_response using OpenAI Chat Completions format. Track llm_call_index to pair request/response events. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(events): add record_middleware method for middleware trace events Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * test(events): add full run sequence integration test for OpenAI content format Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(events): align message events with checkpoint format and add middleware tag injection - Message events (ai_message, ai_tool_call, tool_result, human_message) now use BaseMessage.model_dump() format, matching LangGraph checkpoint values.messages - on_tool_end extracts tool_call_id/name/status from ToolMessage objects - on_tool_error now emits tool_result message events with error status - record_middleware uses middleware:{tag} event_type and middleware category - Summarization custom events use middleware:summarize category - TitleMiddleware injects middleware:title tag via get_config() inheritance - SummarizationMiddleware model bound with middleware:summarize tag - Worker writes human_message using HumanMessage.model_dump() Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(threads): switch search endpoint to threads_meta table and sync title - POST /api/threads/search now queries threads_meta table directly, removing the two-phase Store + Checkpointer scan approach - Add ThreadMetaRepository.search() with metadata/status filters - Add ThreadMetaRepository.update_display_name() for title sync - Worker syncs checkpoint title to threads_meta.display_name on run completion - Map display_name to values.title in search response for API compatibility Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * feat(threads): history endpoint reads messages from event store - POST /api/threads/{thread_id}/history now combines two data sources: checkpointer for checkpoint_id, metadata, title, thread_data; event store for messages (complete history, not truncated by summarization) - Strip internal LangGraph metadata keys from response - Remove full channel_values serialization in favor of selective fields Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: remove duplicate optional-dependencies header in pyproject.toml Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(middleware): pass tagged config to TitleMiddleware ainvoke call Without the config, the middleware:title tag was not injected, causing the LLM response to be recorded as a lead_agent ai_message in run_events. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: resolve merge conflict in .env.example Keep both DATABASE_URL (from persistence-scaffold) and WECOM credentials (from main) after the merge. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(persistence): address review feedback on PR #1851 - Fix naive datetime.now() → datetime.now(UTC) in all ORM models - Fix seq race condition in DbRunEventStore.put() with FOR UPDATE and UNIQUE(thread_id, seq) constraint - Encapsulate _store access in RunManager.update_run_completion() - Deduplicate _store.put() logic in RunManager via _persist_to_store() - Add update_run_completion to RunStore ABC + MemoryRunStore - Wire follow_up_to_run_id through the full create path - Add error recovery to RunJournal._flush_sync() lost-event scenario - Add migration note for search_threads breaking change - Fix test_checkpointer_none_fix mock to set database=None Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * chore: update uv.lock Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(persistence): address 22 review comments from CodeQL, Copilot, and Code Quality Bug fixes: - Sanitize log params to prevent log injection (CodeQL) - Reset threads_meta.status to idle/error when run completes - Attach messages only to latest checkpoint in /history response - Write threads_meta on POST /threads so new threads appear in search Lint fixes: - Remove unused imports (journal.py, migrations/env.py, test_converters.py) - Convert lambda to named function (engine.py, Ruff E731) - Remove unused logger definitions in repos (Ruff F841) - Add logging to JSONL decode errors and empty except blocks - Separate assert side-effects in tests (CodeQL) - Remove unused local variables in tests (Ruff F841) - Fix max_trace_content truncation to use byte length, not char length Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * style: apply ruff format to persistence and runtime files Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Potential fix for pull request finding 'Statement has no effect' Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> * refactor(runtime): introduce RunContext to reduce run_agent parameter bloat Extract checkpointer, store, event_store, run_events_config, thread_meta_repo, and follow_up_to_run_id into a frozen RunContext dataclass. Add get_run_context() in deps.py to build the base context from app.state singletons. start_run() uses dataclasses.replace() to enrich per-run fields before passing ctx to run_agent. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(gateway): move sanitize_log_param to app/gateway/utils.py Extract the log-injection sanitizer from routers/threads.py into a shared utils module and rename to sanitize_log_param (public API). Eliminates the reverse service → router import in services.py. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * perf: use SQL aggregation for feedback stats and thread token usage Replace Python-side counting in FeedbackRepository.aggregate_by_run with a single SELECT COUNT/SUM query. Add RunStore.aggregate_tokens_by_thread abstract method with SQL GROUP BY implementation in RunRepository and Python fallback in MemoryRunStore. Simplify the thread_token_usage endpoint to delegate to the new method, eliminating the limit=10000 truncation risk. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * docs: annotate DbRunEventStore.put() as low-frequency path Add docstring clarifying that put() opens a per-call transaction with FOR UPDATE and should only be used for infrequent writes (currently just the initial human_message event). High-throughput callers should use put_batch() instead. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(threads): fall back to Store search when ThreadMetaRepository is unavailable When database.backend=memory (default) or no SQL session factory is configured, search_threads now queries the LangGraph Store instead of returning 503. Returns empty list if neither Store nor repo is available. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(persistence): introduce ThreadMetaStore ABC for backend-agnostic thread metadata Add ThreadMetaStore abstract base class with create/get/search/update/delete interface. ThreadMetaRepository (SQL) now inherits from it. New MemoryThreadMetaStore wraps LangGraph BaseStore for memory-mode deployments. deps.py now always provides a non-None thread_meta_repo, eliminating all `if thread_meta_repo is not None` guards in services.py, worker.py, and routers/threads.py. search_threads no longer needs a Store fallback branch. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * refactor(history): read messages from checkpointer instead of RunEventStore The /history endpoint now reads messages directly from the checkpointer's channel_values (the authoritative source) instead of querying RunEventStore.list_messages(). The RunEventStore API is preserved for other consumers. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix(persistence): address new Copilot review comments - feedback.py: validate thread_id/run_id before deleting feedback - jsonl.py: add path traversal protection with ID validation - run_repo.py: parse `before` to datetime for PostgreSQL compat - thread_meta_repo.py: fix pagination when metadata filter is active - database_config.py: use resolve_path for sqlite_dir consistency Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Implement skill self-evolution and skill_manage flow (#1874) * chore: ignore .worktrees directory * Add skill_manage self-evolution flow * Fix CI regressions for skill_manage * Address PR review feedback for skill evolution * fix(skill-evolution): preserve history on delete * fix(skill-evolution): tighten scanner fallbacks * docs: add skill_manage e2e evidence screenshot * fix(skill-manage): avoid blocking fs ops in session runtime --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> * fix(config): resolve sqlite_dir relative to CWD, not Paths.base_dir resolve_path() resolves relative to Paths.base_dir (.deer-flow), which double-nested the path to .deer-flow/.deer-flow/data/app.db. Use Path.resolve() (CWD-relative) instead. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Feature/feishu receive file (#1608) * feat(feishu): add channel file materialization hook for inbound messages - Introduce Channel.receive_file(msg, thread_id) as a base method for file materialization; default is no-op. - Implement FeishuChannel.receive_file to download files/images from Feishu messages, save to sandbox, and inject virtual paths into msg.text. - Update ChannelManager to call receive_file for any channel if msg.files is present, enabling downstream model access to user-uploaded files. - No impact on Slack/Telegram or other channels (they inherit the default no-op). * style(backend): format code with ruff for lint compliance - Auto-formatted packages/harness/deerflow/agents/factory.py and tests/test_create_deerflow_agent.py using `ruff format` - Ensured both files conform to project linting standards - Fixes CI lint check failures caused by code style issues * fix(feishu): handle file write operation asynchronously to prevent blocking * fix(feishu): rename GetMessageResourceRequest to _GetMessageResourceRequest and remove redundant code * test(feishu): add tests for receive_file method and placeholder replacement * fix(manager): remove unnecessary type casting for channel retrieval * fix(feishu): update logging messages to reflect resource handling instead of image * fix(feishu): sanitize filename by replacing invalid characters in file uploads * fix(feishu): improve filename sanitization and reorder image key handling in message processing * fix(feishu): add thread lock to prevent filename conflicts during file downloads * fix(test): correct bad merge in test_feishu_parser.py * chore: run ruff and apply formatting cleanup fix(feishu): preserve rich-text attachment order and improve fallback filename handling * fix(docker): restore gateway env vars and fix langgraph empty arg issue (#1915) Two production docker-compose.yaml bugs prevent `make up` from working: 1. Gateway missing DEER_FLOW_CONFIG_PATH and DEER_FLOW_EXTENSIONS_CONFIG_PATH environment overrides. Added in |
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YuJitang
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105db00987 |
feat: show token usage per assistant response (#2270)
* feat: show token usage per assistant response * fix: align client models response with token usage * fix: address token usage review feedback * docs: clarify token usage config example --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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greatmengqi
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b1aabe88b8 |
fix(backend): stream DeerFlowClient AI text as token deltas (#1969) (#1974)
* fix(backend): stream DeerFlowClient AI text as token deltas (#1969) DeerFlowClient.stream() subscribed to LangGraph stream_mode=["values", "custom"] which only delivers full-state snapshots at graph-node boundaries, so AI replies were dumped as a single messages-tuple event per node instead of streaming token-by-token. `client.stream("hello")` looked identical to `client.chat("hello")` — the bug reported in #1969. Subscribe to "messages" mode as well, forward AIMessageChunk deltas as messages-tuple events with delta semantics (consumers accumulate by id), and dedup the values-snapshot path so it does not re-synthesize AI text that was already streamed. Introduce a per-id usage_metadata counter so the final AIMessage in the values snapshot and the final "messages" chunk — which carry the same cumulative usage — are not double-counted. chat() now accumulates per-id deltas and returns the last message's full accumulated text. Non-streaming mock sources (single event per id) are a degenerate case of the same logic, keeping existing callers and tests backward compatible. Verified end-to-end against a real LLM: a 15-number count emits 35 messages-tuple events with BPE subword boundaries clearly visible ("eleven" -> "ele" / "ven", "twelve" -> "tw" / "elve"), 476ms across the window, end-event usage matches the values-snapshot usage exactly (not doubled). tests/test_client_live.py::TestLiveStreaming passes. New unit tests: - test_messages_mode_emits_token_deltas: 3 AIMessageChunks produce 3 delta events with correct content/id/usage, values-snapshot does not duplicate, usage counted once. - test_chat_accumulates_streamed_deltas: chat() rebuilds full text from deltas. - test_messages_mode_tool_message: ToolMessage delivered via messages mode is not duplicated by the values-snapshot synthesis path. The stream() docstring now documents why this client does not reuse Gateway's run_agent() / StreamBridge pipeline (sync vs async, raw LangChain objects vs serialized dicts, single caller vs HTTP fan-out). Fixes #1969 * refactor(backend): simplify DeerFlowClient streaming helpers (#1969) Post-review cleanup for the token-level streaming fix. No behavior change for correct inputs; one efficiency regression fixed. Fix: chat() O(n²) accumulator ----------------------------- `chat()` accumulated per-id text via `buffers[id] = buffers.get(id,"") + delta`, which is O(n) per concat → O(n²) total over a streamed response. At ~2 KB cumulative text this becomes user-visible; at 50 KB / 5000 chunks it costs roughly 100-300 ms of pure copying. Switched to `dict[str, list[str]]` + `"".join()` once at return. Cleanup ------- - Extract `_serialize_tool_calls`, `_ai_text_event`, `_ai_tool_calls_event`, and `_tool_message_event` static helpers. The messages-mode and values-mode branches previously repeated four inline dict literals each; they now call the same builders. - `StreamEvent.type` is now typed as `Literal["values", "messages-tuple", "custom", "end"]` via a `StreamEventType` alias. Makes the closed set explicit and catches typos at type-check time. - Direct attribute access on `AIMessage`/`AIMessageChunk`: `.usage_metadata`, `.tool_calls`, `.id` all have default values on the base class, so the `getattr(..., None)` fallbacks were dead code. Removed from the hot path. - `_account_usage` parameter type loosened to `Any` so that LangChain's `UsageMetadata` TypedDict is accepted under strict type checking. - Trimmed narrating comments on `seen_ids` / `streamed_ids` / the values-synthesis skip block; kept the non-obvious ones that document the cross-mode dedup invariant. Net diff: -15 lines. All 132 unit tests + harness boundary test still pass; ruff check and ruff format pass. * docs(backend): add STREAMING.md design note (#1969) Dedicated design document for the token-level streaming architecture, prompted by the bug investigation in #1969. Contents: - Why two parallel streaming paths exist (Gateway HTTP/async vs DeerFlowClient sync/in-process) and why they cannot be merged. - LangGraph's three-layer mode naming (Graph "messages" vs Platform SDK "messages-tuple" vs HTTP SSE) and why a shared string constant would be harmful. - Gateway path: run_agent + StreamBridge + sse_consumer with a sequence diagram. - DeerFlowClient path: sync generator + direct yield, delta semantics, chat() accumulator. - Why the three id sets (seen_ids / streamed_ids / counted_usage_ids) each carry an independent invariant and cannot be collapsed. - End-to-end sequence for a real conversation turn. - Lessons from #1969: why mock-based tests missed the bug, why BPE subword boundaries in live output are the strongest correctness signal, and the regression test that locks it in. - Source code location index. Also: - Link from backend/CLAUDE.md Embedded Client section. - Link from backend/docs/README.md under Feature Documentation. * test(backend): add refactor regression guards for stream() (#1969) Three new tests in TestStream that lock the contract introduced by PR #1974 so any future refactor (sync->async migration, sharing a core with Gateway's run_agent, dedup strategy change) cannot silently change behavior. - test_dedup_requires_messages_before_values_invariant: canary that documents the order-dependence of cross-mode dedup. streamed_ids is populated only by the messages branch, so values-before-messages for the same id produces duplicate AI text events. Real LangGraph never inverts this order, but a refactor that does (or that makes dedup idempotent) must update this test deliberately. - test_messages_mode_golden_event_sequence: locks the *exact* event sequence (4 events: 2 messages-tuple deltas, 1 values snapshot, 1 end) for a canonical streaming turn. List equality gives a clear diff on any drift in order, type, or payload shape. - test_chat_accumulates_in_linear_time: perf canary for the O(n^2) fix in commit 1f11ba10. 10,000 single-char chunks must accumulate in under 1s; the threshold is wide enough to pass on slow CI but tight enough to fail if buffer = buffer + delta is restored. All three tests pass alongside the existing 12 TestStream tests (15/15). ruff check + ruff format clean. * docs(backend): clarify stream() docstring on JSON serialization (#1969) Replace the misleading "raw LangChain objects (AIMessage, usage_metadata as dataclasses), not dicts" claim in the "Why not reuse Gateway's run_agent?" section. The implementation already yields plain Python dicts (StreamEvent.data is dict, and usage_metadata is a TypedDict), so the original wording suggested a richer return type than the API actually delivers. The corrected wording focuses on what is actually true and relevant: this client skips the JSON/SSE serialization layer that Gateway adds for HTTP wire transmission, and yields stream event payloads directly as Python data structures. Addresses Copilot review feedback on PR #1974. * test(backend): document none-id messages dedup limitation (#1969) Add test_none_id_chunks_produce_duplicates_known_limitation to TestStream that explicitly documents and asserts the current behavior when an LLM provider emits AIMessageChunk with id=None (vLLM, certain custom backends). The cross-mode dedup machinery cannot record a None id in streamed_ids (guarded by ``if msg_id:``), so the values snapshot's reassembled AIMessage with a real id falls through and synthesizes a duplicate AI text event. The test asserts len == 2 and locks this as a known limitation rather than silently letting future contributors hit it without context. Why this is documented rather than fixed: * Falling back to ``metadata.get("id")`` does not help — LangGraph's messages-mode metadata never carries the message id. * Synthesizing ``f"_synth_{id(msg_chunk)}"`` only helps if the values snapshot uses the same fallback, which it does not. * A real fix requires provider cooperation (always emit chunk ids) or content-based dedup (false-positive risk), neither of which belongs in this PR. If a real fix lands, replace this test with a positive assertion that dedup works for None-id chunks. Addresses Copilot review feedback on PR #1974 (client.py:515). * fix(frontend): UI polish - fix CSS typo, dark mode border, and hardcoded colors (#1942) - Fix `font-norma` typo to `font-normal` in message-list subtask count - Fix dark mode `--border` using reddish hue (22.216) instead of neutral - Replace hardcoded `rgb(184,184,192)` in hero with `text-muted-foreground` - Replace hardcoded `bg-[#a3a1a1]` in streaming indicator with `bg-muted-foreground` - Add missing `font-sans` to welcome description `<pre>` for consistency - Make case-study-section padding responsive (`px-4 md:px-20`) Closes #1940 * docs: clarify deployment sizing guidance (#1963) * fix(frontend): prevent stale 'new' thread ID from triggering 422 history requests (#1960) After history.replaceState updates the URL from /chats/new to /chats/{UUID}, Next.js useParams does not update because replaceState bypasses the router. The useEffect in useThreadChat would then set threadIdFromPath ('new') as the threadId, causing the LangGraph SDK to call POST /threads/new/history which returns HTTP 422 (Invalid thread ID: must be a UUID). This fix adds a guard to skip the threadId update when threadIdFromPath is the literal string 'new', preserving the already-correct UUID that was set when the thread was created. * fix(frontend): avoid using route new as thread id (#1967) Co-authored-by: luoxiao6645 <luoxiao6645@gmail.com> * Fix(subagent): Event loop conflict in SubagentExecutor.execute() (#1965) * Fix event loop conflict in SubagentExecutor.execute() When SubagentExecutor.execute() is called from within an already-running event loop (e.g., when the parent agent uses async/await), calling asyncio.run() creates a new event loop that conflicts with asyncio primitives (like httpx.AsyncClient) that were created in and bound to the parent loop. This fix detects if we're already in a running event loop, and if so, runs the subagent in a separate thread with its own isolated event loop to avoid conflicts. Fixes: sub-task cards not appearing in Ultra mode when using async parent agents Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(subagent): harden isolated event loop execution --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * refactor(backend): remove dead getattr in _tool_message_event --------- Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com> Co-authored-by: Xinmin Zeng <135568692+fancyboi999@users.noreply.github.com> Co-authored-by: 13ernkastel <LennonCMJ@live.com> Co-authored-by: siwuai <458372151@qq.com> Co-authored-by: 肖 <168966994+luoxiao6645@users.noreply.github.com> Co-authored-by: luoxiao6645 <luoxiao6645@gmail.com> Co-authored-by: Saber <11769524+hawkli-1994@users.noreply.github.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
||
|
knukn
|
31a3c9a3de |
feat(client): add thread query methods list_threads and get_thread (#1609)
* feat(client): add thread query methods `list_threads` and `get_thread` Implemented two public API methods in `DeerFlowClient` to query threads using the underlying `checkpointer`. * Update backend/packages/harness/deerflow/client.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update backend/packages/harness/deerflow/client.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update backend/tests/test_client.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update backend/packages/harness/deerflow/client.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * fix(deerflow): Fix possible KeyError issue when sorting threads * fix unit test --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> |
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|
suyua9
|
29575c32f9 |
fix: expose custom events from DeerFlowClient.stream() (#1827)
* fix: expose custom client stream events Signed-off-by: suyua9 <1521777066@qq.com> * fix(client): normalize streamed custom mode values * test(client): satisfy backend ruff import ordering --------- Signed-off-by: suyua9 <1521777066@qq.com> Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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|
knukn
|
76fad8b08d |
feat(client): add available_skills parameter to DeerFlowClient (#1779)
* feat(client): add `available_skills` parameter to DeerFlowClient for dynamic runtime skill filtering * Update backend/packages/harness/deerflow/client.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * fix(client): include `agent_name` and `available_skills` in agent config cache key --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> |
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|
Admire
|
9a557751d6 |
feat: support memory import and export (#1521)
* feat: support memory import and export * fix(memory): address review feedback * style: format memory settings page --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
||
|
Admire
|
fc7de7fffe |
feat: support manual add and edit for memory facts (#1538)
* feat: support manual add and edit for memory facts * fix: restore memory updater save helper * fix: address memory fact review feedback * fix: remove duplicate memory fact edit action * docs: simplify memory fact review setup * docs: relax memory review startup instructions * fix: clear rebase marker in memory settings page * fix: address memory fact review and format issues * fix: address memory fact review feedback * refactor: make memory fact updates explicit patch semantics --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
||
|
Admire
|
7eb3a150b5 |
feat: add memory management actions and local filters in memory settings (#1467)
* Add MVP memory management actions * Fix memory settings locale coverage * Polish memory management interactions * Add memory search and type filters * Refine memory settings review feedback * docs: simplify memory settings review setup * fix: restore memory updater compatibility helpers * fix: address memory settings review feedback * docs: soften memory sample review wording --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> Co-authored-by: JeffJiang <for-eleven@hotmail.com> |
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|
knukn
|
481494b9c0 |
feat(client): support custom middleware injection (#1520)
* feat(client): support custom middleware injection Add support for custom middleware, allowing custom middleware list to be passed when initializing DeerFlowClient. These middleware will be injected after the default middleware when creating the agent, extending the agent's functionality. * feat: inject custom middlewares before ClarificationMiddleware to preserve ordering - Add `custom_middlewares` param to `_build_middlewares` - Inject custom middlewares right before `ClarificationMiddleware` to keep it as the last in the chain - Remove unsafe `.extend()` in `client.py` - Update tests in `test_client.py` and `test_lead_agent_model_resolution.py` to assert correct injection ordering |
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|
DanielWalnut
|
d119214fee |
feat(harness): integration ACP agent tool (#1344)
* refactor: extract shared utils to break harness→app cross-layer imports Move _validate_skill_frontmatter to src/skills/validation.py and CONVERTIBLE_EXTENSIONS + convert_file_to_markdown to src/utils/file_conversion.py. This eliminates the two reverse dependencies from client.py (harness layer) into gateway/routers/ (app layer), preparing for the harness/app package split. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor: split backend/src into harness (deerflow.*) and app (app.*) Physically split the monolithic backend/src/ package into two layers: - **Harness** (`packages/harness/deerflow/`): publishable agent framework package with import prefix `deerflow.*`. Contains agents, sandbox, tools, models, MCP, skills, config, and all core infrastructure. - **App** (`app/`): unpublished application code with import prefix `app.*`. Contains gateway (FastAPI REST API) and channels (IM integrations). Key changes: - Move 13 harness modules to packages/harness/deerflow/ via git mv - Move gateway + channels to app/ via git mv - Rename all imports: src.* → deerflow.* (harness) / app.* (app layer) - Set up uv workspace with deerflow-harness as workspace member - Update langgraph.json, config.example.yaml, all scripts, Docker files - Add build-system (hatchling) to harness pyproject.toml - Add PYTHONPATH=. to gateway startup commands for app.* resolution - Update ruff.toml with known-first-party for import sorting - Update all documentation to reflect new directory structure Boundary rule enforced: harness code never imports from app. All 429 tests pass. Lint clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: add harness→app boundary check test and update docs Add test_harness_boundary.py that scans all Python files in packages/harness/deerflow/ and fails if any `from app.*` or `import app.*` statement is found. This enforces the architectural rule that the harness layer never depends on the app layer. Update CLAUDE.md to document the harness/app split architecture, import conventions, and the boundary enforcement test. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add config versioning with auto-upgrade on startup When config.example.yaml schema changes, developers' local config.yaml files can silently become outdated. This adds a config_version field and auto-upgrade mechanism so breaking changes (like src.* → deerflow.* renames) are applied automatically before services start. - Add config_version: 1 to config.example.yaml - Add startup version check warning in AppConfig.from_file() - Add scripts/config-upgrade.sh with migration registry for value replacements - Add `make config-upgrade` target - Auto-run config-upgrade in serve.sh and start-daemon.sh before starting services - Add config error hints in service failure messages Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix comments * fix: update src.* import in test_sandbox_tools_security to deerflow.* Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: handle empty config and search parent dirs for config.example.yaml Address Copilot review comments on PR #1131: - Guard against yaml.safe_load() returning None for empty config files - Search parent directories for config.example.yaml instead of only looking next to config.yaml, fixing detection in common setups Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: correct skills root path depth and config_version type coercion - loader.py: fix get_skills_root_path() to use 5 parent levels (was 3) after harness split, file lives at packages/harness/deerflow/skills/ so parent×3 resolved to backend/packages/harness/ instead of backend/ - app_config.py: coerce config_version to int() before comparison in _check_config_version() to prevent TypeError when YAML stores value as string (e.g. config_version: "1") - tests: add regression tests for both fixes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: update test imports from src.* to deerflow.*/app.* after harness refactor Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(harness): add tool-first ACP agent invocation (#37) * feat(harness): add tool-first ACP agent invocation * build(harness): make ACP dependency required * fix(harness): address ACP review feedback * feat(harness): decouple ACP agent workspace from thread data ACP agents (codex, claude-code) previously used per-thread workspace directories, causing path resolution complexity and coupling task execution to DeerFlow's internal thread data layout. This change: - Replace _resolve_cwd() with a fixed _get_work_dir() that always uses {base_dir}/acp-workspace/, eliminating virtual path translation and thread_id lookups - Introduce /mnt/acp-workspace virtual path for lead agent read-only access to ACP agent output files (same pattern as /mnt/skills) - Add security guards: read-only validation, path traversal prevention, command path allowlisting, and output masking for acp-workspace - Update system prompt and tool description to guide LLM: send self-contained tasks to ACP agents, copy results via /mnt/acp-workspace - Add 11 new security tests for ACP workspace path handling Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor(prompt): inject ACP section only when ACP agents are configured The ACP agent guidance in the system prompt is now conditionally built by _build_acp_section(), which checks get_acp_agents() and returns an empty string when no ACP agents are configured. This avoids polluting the prompt with irrelevant instructions for users who don't use ACP. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix lint * fix(harness): address Copilot review comments on sandbox path handling and ACP tool - local_sandbox: fix path-segment boundary bug in _resolve_path (== or startswith +"/") and add lookahead in _resolve_paths_in_command regex to prevent /mnt/skills matching inside /mnt/skills-extra - local_sandbox_provider: replace print() with logger.warning(..., exc_info=True) - invoke_acp_agent_tool: guard getattr(option, "optionId") with None default + continue; move full prompt from INFO to DEBUG level (truncated to 200 chars) - sandbox/tools: fix _get_acp_workspace_host_path docstring to match implementation; remove misleading "read-only" language from validate_local_bash_command_paths Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(acp): thread-isolated workspaces, permission guardrail, and ContextVar registry P1.1 – ACP workspace thread isolation - Add `Paths.acp_workspace_dir(thread_id)` for per-thread paths - `_get_work_dir(thread_id)` in invoke_acp_agent_tool now uses `{base_dir}/threads/{thread_id}/acp-workspace/`; falls back to global workspace when thread_id is absent or invalid - `_invoke` extracts thread_id from `RunnableConfig` via `Annotated[RunnableConfig, InjectedToolArg]` - `sandbox/tools.py`: `_get_acp_workspace_host_path(thread_id)`, `_resolve_acp_workspace_path(path, thread_id)`, and all callers (`replace_virtual_paths_in_command`, `mask_local_paths_in_output`, `ls_tool`, `read_file_tool`) now resolve ACP paths per-thread P1.2 – ACP permission guardrail - New `auto_approve_permissions: bool = False` field in `ACPAgentConfig` - `_build_permission_response(options, *, auto_approve: bool)` now defaults to deny; only approves when `auto_approve=True` - Document field in `config.example.yaml` P2 – Deferred tool registry race condition - Replace module-level `_registry` global with `contextvars.ContextVar` - Each asyncio request context gets its own registry; worker threads inherit the context automatically via `loop.run_in_executor` - Expose `get_deferred_registry` / `set_deferred_registry` / `reset_deferred_registry` helpers Tests: 831 pass (57 for affected modules, 3 new tests) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(sandbox): mount /mnt/acp-workspace in docker sandbox container The AioSandboxProvider was not mounting the ACP workspace into the sandbox container, so /mnt/acp-workspace was inaccessible when the lead agent tried to read ACP results in docker mode. Changes: - `ensure_thread_dirs`: also create `acp-workspace/` (chmod 0o777) so the directory exists before the sandbox container starts — required for Docker volume mounts - `_get_thread_mounts`: add read-only `/mnt/acp-workspace` mount using the per-thread host path (`host_paths.acp_workspace_dir(thread_id)`) - Update stale CLAUDE.md description (was "fixed global workspace") Tests: `test_aio_sandbox_provider.py` (4 new tests) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(lint): remove unused imports in test_aio_sandbox_provider Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix config --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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|
greatmengqi
|
b8bc80d89b |
refactor: extract shared skill installer and upload manager to harness (#1202)
* refactor: extract shared skill installer and upload manager to harness Move duplicated business logic from Gateway routers and Client into shared harness modules, eliminating code duplication. New shared modules: - deerflow.skills.installer: 6 functions (zip security, extraction, install) - deerflow.uploads.manager: 7 functions (normalize, deduplicate, validate, list, delete, get_uploads_dir, ensure_uploads_dir) Key improvements: - SkillAlreadyExistsError replaces stringly-typed 409 status routing - normalize_filename rejects backslash-containing filenames - Read paths (list/delete) no longer mkdir via get_uploads_dir - Write paths use ensure_uploads_dir for explicit directory creation - list_files_in_dir does stat inside scandir context (no re-stat) - install_skill_from_archive uses single is_file() check (one syscall) - Fix agent config key not reset on update_mcp_config/update_skill Tests: 42 new (22 installer + 20 upload manager) + client hardening * refactor: centralize upload URL construction and clean up installer - Extract upload_virtual_path(), upload_artifact_url(), enrich_file_listing() into shared manager.py, eliminating 6 duplicated URL constructions across Gateway router and Client - Derive all upload URLs from VIRTUAL_PATH_PREFIX constant instead of hardcoded "mnt/user-data/uploads" strings - Eliminate TOCTOU pre-checks and double file read in installer — single ZipFile() open with exception handling replaces is_file() + is_zipfile() + ZipFile() sequence - Add missing re-exports: ensure_uploads_dir in uploads/__init__.py, SkillAlreadyExistsError in skills/__init__.py - Remove redundant .lower() on already-lowercase CONVERTIBLE_EXTENSIONS - Hoist sandbox_uploads_dir(thread_id) before loop in uploads router * fix: add input validation for thread_id and filename length - Reject thread_id containing unsafe filesystem characters (only allow alphanumeric, hyphens, underscores, dots) — prevents 500 on inputs like <script> or shell metacharacters - Reject filenames longer than 255 bytes (OS limit) in normalize_filename - Gateway upload router maps ValueError to 400 for invalid thread_id * fix: address PR review — symlink safety, input validation coverage, error ordering - list_files_in_dir: use follow_symlinks=False to prevent symlink metadata leakage; check is_dir() instead of exists() for non-directory paths - install_skill_from_archive: restore is_file() pre-check before extension validation so error messages match the documented exception contract - validate_thread_id: move from ensure_uploads_dir to get_uploads_dir so all entry points (upload/list/delete) are protected - delete_uploaded_file: catch ValueError from thread_id validation (was 500) - requires_llm marker: also skip when OPENAI_API_KEY is unset - e2e fixture: update TitleMiddleware exclusion comment (kept filtering — middleware triggers extra LLM calls that add non-determinism to tests) * chore: revert uv.lock to main — no dependency changes in this PR * fix: use monkeypatch for global config in e2e fixture to prevent test pollution The e2e_env fixture was calling set_title_config() and set_summarization_config() directly, which mutated global singletons without automatic cleanup. When pytest ran test_client_e2e.py before test_title_middleware_core_logic.py, the leaked enabled=False caused 5 title tests to fail in CI. Switched to monkeypatch.setattr on the module-level private variables so pytest restores the originals after each test. * fix: address code review — URL encoding, API consistency, test isolation - upload_artifact_url: percent-encode filename to handle spaces/#/? - deduplicate_filename: mutate seen set in place (caller no longer needs manual .add() — less error-prone API) - list_files_in_dir: document that size is int, enrich stringifies - e2e fixture: monkeypatch _app_config instead of set_app_config() to prevent global singleton pollution (same pattern as title/summarization fix) - _make_e2e_config: read LLM connection details from env vars so external contributors can override defaults - Update tests to match new deduplicate_filename contract * docs: rewrite RFC in English and add alternatives/breaking changes sections * fix: address code review feedback on PR #1202 - Rename deduplicate_filename to claim_unique_filename to make the in-place set mutation explicit in the function name - Replace PermissionError with PathTraversalError(ValueError) for path traversal detection — malformed input is 400, not 403 * fix: set _app_config_is_custom in e2e test fixture to prevent config.yaml lookup in CI --------- Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com> Co-authored-by: Willem Jiang <willem.jiang@gmail.com> Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com> |
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|
knukn
|
fe75cb35ca |
feat(client): support agent_name injection to enable isolated memory and custom prompts (#1253)
* feat(client): 添加agent_name参数支持自定义代理名称 允许在初始化DeerFlowClient时指定代理名称，该名称将用于中间件构建和系统提示模板 * test: add coverage for agent_name parameter in DeerFlowClient * fix(client): address PR review comments for agent_name injection --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
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|
haoliangxu
|
3af709097e |
fix: normalize structured LLM content in serialization and memory updater (#1215)
* fix: normalize ToolMessage structured content in serialization
When models return ToolMessage content as a list of content blocks
(e.g. [{"type": "text", "text": "..."}]), the UI previously displayed
the raw Python repr string instead of the extracted text.
Replace str(msg.content) with the existing _extract_text() helper in
both _serialize_message() and stream() to properly normalize
list-of-blocks content to plain text.
Fixes #1149
Also fixes the same root cause as #1188 (characters displayed one per
line when tool response content is returned as structured blocks).
Added 11 regression tests covering string, list-of-blocks, mixed,
empty, and fallback content types.
* fix(memory): extract text from structured LLM responses in memory updater
When LLMs return response content as list of content blocks
(e.g. [{"type": "text", "text": "..."}]) instead of plain strings,
str() produces Python repr which breaks JSON parsing in the memory
updater. This caused memory updates to silently fail.
Changes:
- Add _extract_text() helper in updater.py for safe content normalization
- Use _extract_text() instead of str(response.content) in update_memory()
- Fix format_conversation_for_update() to handle plain strings in list content
- Fix subagent executor fallback path to extract text from list content
- Replace print() with structured logging (logger.info/warning/error)
- Add 13 regression tests covering _extract_text, format_conversation,
and update_memory with structured LLM responses
* fix: address Copilot review - defensive text extraction + logger.exception
- client.py _extract_text: use block.get('text') + isinstance check (prevent KeyError/TypeError)
- prompt.py format_conversation_for_update: same defensive check for dict text blocks
- executor.py: type-safe text extraction in both code paths, fallback to placeholder instead of str(raw_content)
- updater.py: use logger.exception() instead of logger.error() for traceback preservation
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* fix: preserve chunked structured content without spurious newlines
* fix: restore backend unit test compatibility
---------
Co-authored-by: Exploreunive <Exploreunive@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
|
||
|
haoliangxu
|
06cba217c3 |
feat: track token usage per conversation turn (#1218)
* feat: track token usage per conversation turn
Add token usage tracking to the streaming API so consumers can monitor
cost per turn without additional API calls.
Changes:
1. _serialize_message now includes usage_metadata for AI messages in
values events, exposing input_tokens/output_tokens/total_tokens
from LangChain's native metadata.
2. stream() accumulates token usage across all AI messages in a turn
and emits the cumulative totals in the end event:
{usage: {input_tokens: N, output_tokens: N, total_tokens: N}}
3. Each messages-tuple AI event with text content now includes a
per-message usage_metadata field for granular tracking.
This enables the frontend to display token consumption per turn,
support cost-aware UX, and let users monitor API spending.
10 tests added covering serialization passthrough and cumulative
aggregation logic.
Co-Authored-By: OpenClaw <noreply@openclaw.ai>
* fix: address Copilot review - use Mapping access for usage_metadata
- Replace getattr(usage, 'input_tokens', 0) with usage.get('input_tokens', 0)
since LangChain usage_metadata is a dict, not an object
- Remove unused 'import pytest' (fixes Ruff F401)
- Add proper stream() integration tests for cumulative usage in end event
and per-message usage_metadata in messages-tuple events
---------
Co-authored-by: Exploreunive <Exploreunive@users.noreply.github.com>
Co-authored-by: OpenClaw <noreply@openclaw.ai>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
|
||
|
Simon Su
|
ceab7fac14 |
fix: improve MiniMax code plan integration (#1169)
This PR improves MiniMax Code Plan integration in DeerFlow by fixing three issues in the current flow: stream errors were not clearly surfaced in the UI, the frontend could not display the actual provider model ID, and MiniMax reasoning output could leak into final assistant content as inline <think>...</think>. The change adds a MiniMax-specific adapter, exposes real model IDs end-to-end, and adds a frontend fallback for historical messages. Co-authored-by: Willem Jiang <willem.jiang@gmail.com> |
||
|
DanielWalnut
|
76803b826f |
refactor: split backend into harness (deerflow.*) and app (app.*) (#1131)
* refactor: extract shared utils to break harness→app cross-layer imports Move _validate_skill_frontmatter to src/skills/validation.py and CONVERTIBLE_EXTENSIONS + convert_file_to_markdown to src/utils/file_conversion.py. This eliminates the two reverse dependencies from client.py (harness layer) into gateway/routers/ (app layer), preparing for the harness/app package split. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor: split backend/src into harness (deerflow.*) and app (app.*) Physically split the monolithic backend/src/ package into two layers: - **Harness** (`packages/harness/deerflow/`): publishable agent framework package with import prefix `deerflow.*`. Contains agents, sandbox, tools, models, MCP, skills, config, and all core infrastructure. - **App** (`app/`): unpublished application code with import prefix `app.*`. Contains gateway (FastAPI REST API) and channels (IM integrations). Key changes: - Move 13 harness modules to packages/harness/deerflow/ via git mv - Move gateway + channels to app/ via git mv - Rename all imports: src.* → deerflow.* (harness) / app.* (app layer) - Set up uv workspace with deerflow-harness as workspace member - Update langgraph.json, config.example.yaml, all scripts, Docker files - Add build-system (hatchling) to harness pyproject.toml - Add PYTHONPATH=. to gateway startup commands for app.* resolution - Update ruff.toml with known-first-party for import sorting - Update all documentation to reflect new directory structure Boundary rule enforced: harness code never imports from app. All 429 tests pass. Lint clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * chore: add harness→app boundary check test and update docs Add test_harness_boundary.py that scans all Python files in packages/harness/deerflow/ and fails if any `from app.*` or `import app.*` statement is found. This enforces the architectural rule that the harness layer never depends on the app layer. Update CLAUDE.md to document the harness/app split architecture, import conventions, and the boundary enforcement test. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add config versioning with auto-upgrade on startup When config.example.yaml schema changes, developers' local config.yaml files can silently become outdated. This adds a config_version field and auto-upgrade mechanism so breaking changes (like src.* → deerflow.* renames) are applied automatically before services start. - Add config_version: 1 to config.example.yaml - Add startup version check warning in AppConfig.from_file() - Add scripts/config-upgrade.sh with migration registry for value replacements - Add `make config-upgrade` target - Auto-run config-upgrade in serve.sh and start-daemon.sh before starting services - Add config error hints in service failure messages Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix comments * fix: update src.* import in test_sandbox_tools_security to deerflow.* Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: handle empty config and search parent dirs for config.example.yaml Address Copilot review comments on PR #1131: - Guard against yaml.safe_load() returning None for empty config files - Search parent directories for config.example.yaml instead of only looking next to config.yaml, fixing detection in common setups Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: correct skills root path depth and config_version type coercion - loader.py: fix get_skills_root_path() to use 5 parent levels (was 3) after harness split, file lives at packages/harness/deerflow/skills/ so parent×3 resolved to backend/packages/harness/ instead of backend/ - app_config.py: coerce config_version to int() before comparison in _check_config_version() to prevent TypeError when YAML stores value as string (e.g. config_version: "1") - tests: add regression tests for both fixes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix: update test imports from src.* to deerflow.*/app.* after harness refactor Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |