* feat(config): add when_thinking_disabled support for model configs
Allow users to explicitly configure what parameters are sent to the
model when thinking is disabled, via a new `when_thinking_disabled`
field in model config. This mirrors the existing `when_thinking_enabled`
pattern and takes full precedence over the hardcoded disable behavior
when set. Backwards compatible — existing configs work unchanged.
Closes#1675
* fix(config): address copilot review — gate when_thinking_disabled independently
- Switch truthiness check to `is not None` so empty dict overrides work
- Restructure disable path so when_thinking_disabled is gated independently
of has_thinking_settings, allowing it to work without when_thinking_enabled
- Update test to reflect new behavior
* feat: implement full checkpoint rollback on user cancellation
- Capture pre-run checkpoint snapshot including checkpoint state, metadata, and pending_writes
- Add _rollback_to_pre_run_checkpoint() function to restore thread state
- Implement _call_checkpointer_method() helper to support both async and sync checkpointer methods
- Rollback now properly restores checkpoint, metadata, channel_versions, and pending_writes
- Remove obsolete TODO comment (Phase 2) as rollback is now complete
This resolves the TODO(Phase 2) comment and enables full thread state
restoration when a run is cancelled by the user.
* fix: address rollback review feedback
* fix: strengthen checkpoint rollback validation and error handling
- Validate restored_config structure and checkpoint_id before use
- Raise RuntimeError on malformed pending_writes instead of silent skip
- Normalize None checkpoint_ns to empty string instead of "None"
- Move delete_thread to only execute when pre_run_snapshot is None
- Add docstring noting non-atomic rollback as known limitation
This addresses review feedback on PR #1867 regarding data integrity
in the checkpoint rollback implementation.
* test: add comprehensive coverage for checkpoint rollback edge cases
- test_rollback_restores_snapshot_without_deleting_thread
- test_rollback_deletes_thread_when_no_snapshot_exists
- test_rollback_raises_when_restore_config_has_no_checkpoint_id
- test_rollback_normalizes_none_checkpoint_ns_to_root_namespace
- test_rollback_raises_on_malformed_pending_write_not_a_tuple
- test_rollback_raises_on_malformed_pending_write_non_string_channel
- test_rollback_propagates_aput_writes_failure
Covers all scenarios from PR #1867 review feedback.
* test: format rollback worker tests
* fix(sandbox): add startup reconciliation to prevent orphaned container leaks
Sandbox containers were never cleaned up when the managing process restarted,
because all lifecycle tracking lived in in-memory dictionaries. This adds
startup reconciliation that enumerates running containers via `docker ps` and
either destroys orphans (age > idle_timeout) or adopts them into the warm pool.
Closes#1972
* fix(sandbox): address Copilot review — adopt-all strategy, improved error handling
- Reconciliation now adopts all containers into warm pool unconditionally,
letting the idle checker decide cleanup. Avoids destroying containers
that another concurrent process may still be using.
- list_running() logs stderr on docker ps failure and catches
FileNotFoundError/OSError.
- Signal handler test restores SIGTERM/SIGINT in addition to SIGHUP.
- E2E test docstring corrected to match actual coverage scope.
* fix(sandbox): address maintainer review — batch inspect, lock tightening, import hygiene
- _reconcile_orphans(): merge check-and-insert into a single lock acquisition
per container to eliminate the TOCTOU window.
- list_running(): batch the per-container docker inspect into a single call.
Total subprocess calls drop from 2N+1 to 2 (one ps + one batch inspect).
Parse port and created_at from the inspect JSON payload.
- Extract _parse_docker_timestamp() and _extract_host_port() as module-level
pure helpers and test them directly.
- Move datetime/json imports to module top level.
- _make_provider_for_reconciliation(): document the __new__ bypass and the
lockstep coupling to AioSandboxProvider.__init__.
- Add assertion that list_running() makes exactly ONE inspect call.
When a model config includes `reasoning_effort` as an extra YAML field
(ModelConfig uses `extra="allow"`), and the thinking-disabled code path
also injects `reasoning_effort="minimal"` into kwargs, the previous
`model_class(**kwargs, **model_settings_from_config)` call raises:
TypeError: got multiple values for keyword argument 'reasoning_effort'
Fix by merging the two dicts before instantiation, giving runtime kwargs
precedence over config values: `{**model_settings_from_config, **kwargs}`.
Fixes#1977
Co-authored-by: octo-patch <octo-patch@github.com>
* fix(middleware): handle string-serialized options in ClarificationMiddleware (#1995)
Some models (e.g. Qwen3-Max) serialize array tool parameters as JSON
strings instead of native arrays. Add defensive type checking in
_format_clarification_message() to deserialize string options before
iteration, preventing per-character rendering.
* fix(middleware): normalize options after JSON deserialization
Address Copilot review feedback:
- Add post-deserialization normalization so options is always a list
(handles json.loads returning a scalar string, dict, or None)
- Add test for JSON-encoded scalar string ("development")
- Fix test_json_string_with_mixed_types to use actual mixed types
* feat(community): add Exa search as community tool provider
Add Exa (exa.ai) as a new community search provider alongside Tavily,
Firecrawl, InfoQuest, and Jina AI. Exa is an AI-native search engine
with neural, keyword, and auto search types.
New files:
- community/exa/tools.py: web_search_tool and web_fetch_tool
- tests/test_exa_tools.py: 10 unit tests with mocked Exa client
Changes:
- pyproject.toml: add exa-py dependency
- config.example.yaml: add commented-out Exa configuration examples
Usage: set `use: deerflow.community.exa.tools:web_search_tool` in
config.yaml and provide EXA_API_KEY.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(community): address PR review comments for Exa tools
- Make _get_exa_client() accept tool_name param so web_fetch reads its own config
- Remove __init__.py to match namespace package pattern of other providers
- Add duplicate tool name warning in config.example.yaml
- Add regression tests for web_fetch config resolution
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Update revision in uv.lock to 3
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix(backend): use timezone-aware UTC in memory modules
Replace datetime.utcnow() with datetime.now(timezone.utc) and a shared
utc_now_iso_z() helper so persisted ISO timestamps keep the trailing Z
suffix without triggering Python 3.12+ deprecation warnings.
Made-with: Cursor
* refactor(backend): use removesuffix for utc_now_iso_z suffix
Makes the +00:00 -> Z transform explicit for the trailing offset only
(Copilot review on PR #1992).
Made-with: Cursor
* style(backend): satisfy ruff UP017 with datetime.UTC in memory queue
Made-with: Cursor
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* 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>
* fix(backend): make loop detection hash tool calls by stable keys
The loop detection middleware previously hashed full tool call arguments,
which made repeated calls look different when only non-essential argument
details changed. In particular, `read_file` calls with nearby line ranges
could bypass repetition detection even when the agent was effectively
reading the same file region again and again.
- Hash tool calls using stable keys instead of the full raw args payload
- Bucket `read_file` line ranges so nearby reads map to the same region key
- Prefer stable identifiers such as `path`, `url`, `query`, or `command`
before falling back to JSON serialization of args
- Keep hashing order-independent so the same tool call set produces the
same hash regardless of call order
Fixes#1905
* fix(backend): harden loop detection hash normalization
- Normalize and parse stringified tool args defensively
- Expand stable key derivation to include pattern, glob, and cmd
- Normalize reversed read_file ranges before bucketing
Fixes#1905
* fix(backend): harden loop detection tool format
* exclude write_file and str_replace from the stable-key path — writing different content to the same file shouldn't be flagged.
---------
Co-authored-by: JeffJiang <for-eleven@hotmail.com>
* fix(subagents): add cooperative cancellation for subagent threads
Subagent tasks run inside ThreadPoolExecutor threads with their own
event loop (asyncio.run). When a user clicks stop, RunManager cancels
the parent asyncio.Task, but Future.cancel() cannot terminate a running
thread and asyncio.Event does not propagate across event loops. This
causes subagent threads to keep executing (writing files, calling LLMs)
even after the user explicitly stops the run.
Fix: add a threading.Event (cancel_event) to SubagentResult and check
it cooperatively in _aexecute()'s astream iteration loop. On cancel,
request_cancel_background_task() sets the event, and the thread exits
at the next iteration boundary.
Changes:
- executor.py: Add cancel_event field to SubagentResult, check it in
_aexecute loop, set it on timeout, add request_cancel_background_task
- task_tool.py: Call request_cancel_background_task on CancelledError
* fix(subagents): guard cancel status and add pre-check before astream
- Only overwrite status to FAILED when still RUNNING, preserving
TIMED_OUT set by the scheduler thread.
- Add cancel_event pre-check before entering the astream loop so
cancellation is detected immediately when already signalled.
* fix(subagents): guard status updates with lock to prevent race condition
Wrap the check-and-set on result.status in _aexecute with
_background_tasks_lock so the timeout handler in execute_async
cannot interleave between the read and write.
* fix(subagents): add dedicated CANCELLED status for user cancellation
Introduce SubagentStatus.CANCELLED to distinguish user-initiated
cancellation from actual execution failures. Update _aexecute,
task_tool polling, cleanup terminal-status sets, and test fixtures.
* test(subagents): add cancellation tests and fix timeout regression test
- Add dedicated TestCooperativeCancellation test class with 6 tests:
- Pre-set cancel_event prevents astream from starting
- Mid-stream cancel_event returns CANCELLED immediately
- request_cancel_background_task() sets cancel_event correctly
- request_cancel on nonexistent task is a no-op
- Real execute_async timeout does not overwrite CANCELLED (deterministic
threading.Event sync, no wall-clock sleeps)
- cleanup_background_task removes CANCELLED tasks
- Add task_tool cancellation coverage:
- test_cancellation_calls_request_cancel: assert CancelledError path
calls request_cancel_background_task(task_id)
- test_task_tool_returns_cancelled_message: assert CANCELLED polling
branch emits task_cancelled event and returns expected message
- Fix pre-existing test infrastructure issue: add deerflow.sandbox.security
to _MOCKED_MODULE_NAMES (fixes ModuleNotFoundError for all executor tests)
- Add RUNNING guard to timeout handler in executor.py to prevent
TIMED_OUT from overwriting CANCELLED status
- Add cooperative cancellation granularity comment documenting that
cancellation is only detected at astream iteration boundaries
---------
Co-authored-by: lulusiyuyu <lulusiyuyu@users.noreply.github.com>
* 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(sandbox): add L2 input sanitisation to SandboxAuditMiddleware
Add _validate_input() to reject malformed bash commands before regex
classification: empty commands, oversized commands (>10 000 chars), and
null bytes that could cause detection/execution layer inconsistency.
* fix(sandbox): address Copilot review — type guard, log truncation, reject reason
- Coerce None/non-string command to str before validation
- Truncate oversized commands in audit logs to prevent log amplification
- Propagate reject_reason through _pre_process() to block message
- Remove L2 label from comments and test class names
* fix(sandbox): isinstance type guard + async input sanitisation tests
Address review comments:
- Replace str() coercion with isinstance(raw_command, str) guard so
non-string truthy values (0, [], False) fall back to empty string
instead of passing validation as "0"/"[]"/"False".
- Add TestInputSanitisationBlocksInAwrapToolCall with 4 async tests
covering empty, null-byte, oversized, and None command via
awrap_tool_call path.
support for vLLM 0.19.0 OpenAI-compatible chat endpoints and fixes the Qwen reasoning toggle so flash mode can actually disable thinking.
Co-authored-by: NmanQAQ <normangyao@qq.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
ls_tool was the only sandbox tool without output size limits, allowing
multi-MB results from large directories to blow up the model context
window. Add head-truncation (configurable via ls_output_max_chars,
default 20000) consistent with existing bash and read_file truncation.
Closes#1887
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* fix(memory): case-insensitive fact deduplication and positive reinforcement detection
Two fixes to the memory system:
1. _fact_content_key() now lowercases content before comparison, preventing
semantically duplicate facts like "User prefers Python" and "user prefers
python" from being stored separately.
2. Adds detect_reinforcement() to MemoryMiddleware (closes#1719), mirroring
detect_correction(). When users signal approval ("yes exactly", "perfect",
"完全正确", etc.), the memory updater now receives reinforcement_detected=True
and injects a hint prompting the LLM to record confirmed preferences and
behaviors with high confidence.
Changes across the full signal path:
- memory_middleware.py: _REINFORCEMENT_PATTERNS + detect_reinforcement()
- queue.py: reinforcement_detected field in ConversationContext and add()
- updater.py: reinforcement_detected param in update_memory() and
update_memory_from_conversation(); builds reinforcement_hint alongside
the existing correction_hint
Tests: 11 new tests covering deduplication, hint injection, and signal
detection (Chinese + English patterns, window boundary, conflict with correction).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(memory): address Copilot review comments on reinforcement detection
- Tighten _REINFORCEMENT_PATTERNS: remove 很好, require punctuation/end-of-string boundaries on remaining patterns, split this-is-good into stricter variants
- Suppress reinforcement_detected when correction_detected is true to avoid mixed-signal noise
- Use casefold() instead of lower() for Unicode-aware fact deduplication
- Add missing test coverage for reinforcement_detected OR merge and forwarding in queue
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* Rename BACKEND_TODO.md to TODO.md in documentation
* Update MCP Setup Guide link in CONTRIBUTING.md
* Update reference to config.yaml path in documentation
* Fix config file path in TITLE_GENERATION_IMPLEMENTATION.md
Updated the path to the example config file in the documentation.
* fix(docker): use multi-stage build to remove build-essential from runtime image
The build-essential toolchain (~200 MB) was only needed for compiling
native Python extensions during `uv sync` but remained in the final
image, increasing size and attack surface. Split the Dockerfile into
a builder stage (with build-essential) and a clean runtime stage that
copies only the compiled artifacts, Node.js, Docker CLI, and uv.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(docker): add dev stage and pin docker:cli per review feedback
Address Copilot review comments:
- Add a `dev` build stage (FROM builder) that retains build-essential
so startup-time `uv sync` in dev containers can compile from source
- Update docker-compose-dev.yaml to use `target: dev` for gateway and
langgraph services
- Keep the clean runtime stage (no build-essential) as the default
final stage for production builds
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
sandbox_from_runtime() and ensure_sandbox_initialized() write
sandbox_id into runtime.context after acquiring a sandbox. When
lazy_init=True and no context is supplied to the graph run,
runtime.context is None (the LangGraph default), causing a TypeError
on the assignment.
Add `if runtime.context is not None` guards at all three write sites.
Reads already had equivalent guards (e.g. `runtime.context.get(...) if
runtime.context else None`); this brings writes into line.
Previously, the list endpoint always returned soul=null because
_agent_config_to_response() was called without include_soul=True.
This caused confusion since PUT /api/agents/{name} and GET /api/agents/{name}
both returned the soul content, but the list endpoint silently omitted it.
Co-authored-by: octo-patch <octo-patch@users.noreply.github.com>
* feat(uploads): guide agent to use grep/glob/read_file for uploaded documents
Add workflow guidance to the <uploaded_files> context block so the agent
knows to use grep and glob (added in #1784) alongside read_file when
working with uploaded documents, rather than falling back to web search.
This is the final piece of the three-PR PDF agentic search pipeline:
- PR1 (#1727): pymupdf4llm converter produces structured Markdown with headings
- PR2 (#1738): document outline injected into agent context with line numbers
- PR3 (this): agent guided to use outline + grep + read_file workflow
* feat(uploads): add file-first priority and fallback guidance to uploaded_files context
* fix(uploads): handle split-bold headings and ** ** artefacts in extract_outline
- Add _clean_bold_title() to merge adjacent bold spans (** **) produced
by pymupdf4llm when bold text crosses span boundaries
- Add _SPLIT_BOLD_HEADING_RE (Style 3) to recognise **<num>** **<title>**
headings common in academic papers; excludes pure-number table headers
and rows with more than 4 bold blocks
- When outline is empty, read first 5 non-empty lines of the .md as a
content preview and surface a grep hint in the agent context
- Update _format_file_entry to render the preview + grep hint instead of
silently omitting the outline section
- Add 3 new extract_outline tests and 2 new middleware tests (65 total)
* fix(uploads): address Copilot review comments on extract_outline regex
- Replace ASCII [A-Za-z] guard with negative lookahead to support non-ASCII
titles (e.g. **1** **概述**); pure-numeric/punctuation blocks still excluded
- Replace .+ with [^*]+ and cap repetition at {0,2} (four blocks total) to
keep _SPLIT_BOLD_HEADING_RE linear and avoid ReDoS on malformed input
- Remove now-redundant len(blocks) <= 4 code-level check (enforced by regex)
- Log debug message with exc_info when preview extraction fails
* feat(uploads): guide agent to use grep/glob/read_file for uploaded documents
Add workflow guidance to the <uploaded_files> context block so the agent
knows to use grep and glob (added in #1784) alongside read_file when
working with uploaded documents, rather than falling back to web search.
This is the final piece of the three-PR PDF agentic search pipeline:
- PR1 (#1727): pymupdf4llm converter produces structured Markdown with headings
- PR2 (#1738): document outline injected into agent context with line numbers
- PR3 (this): agent guided to use outline + grep + read_file workflow
* feat(uploads): add file-first priority and fallback guidance to uploaded_files context
* fix: inject longTermBackground into memory prompt
The format_memory_for_injection function only processed recentMonths and
earlierContext from the history section, silently dropping longTermBackground.
The LLM writes longTermBackground correctly and it persists to memory.json,
but it was never injected into the system prompt — making the user's
long-term background invisible to the AI.
Add the missing field handling and a regression test.
* fix(middleware): handle list-type AIMessage.content in LoopDetectionMiddleware
LangChain AIMessage.content can be str | list. When using providers that
return structured content blocks (e.g. Anthropic thinking mode, certain
OpenAI-compatible gateways), content is a list of dicts like
[{"type": "text", "text": "..."}].
The hard_limit branch in _apply() concatenated content with a string via
(last_msg.content or "") + f"\n\n{_HARD_STOP_MSG}", which raises
TypeError when content is a non-empty list (list + str is invalid).
Add _append_text() static method that:
- Returns the text directly when content is None
- Appends a {"type": "text"} block when content is a list
- Falls back to string concatenation when content is a str
This is consistent with how other modules in the project already handle
list content (client.py._extract_text, memory_middleware, executor.py).
* test(middleware): add unit tests for _append_text and list content hard stop
Add regression tests to verify LoopDetectionMiddleware handles list-type
AIMessage.content correctly during hard stop:
- TestAppendText: unit tests for the new _append_text() static method
covering None, str, list (including empty list) content types
- TestHardStopWithListContent: integration tests verifying hard stop
works correctly with list content (Anthropic thinking mode), None
content, and str content
Requested by reviewer in PR #1823.
* fix(middleware): improve _append_text robustness and test isolation
- Add explicit isinstance(content, str) check with fallback for
unexpected types (coerce to str) to prevent TypeError on edge cases
- Deep-copy list content in _make_state() test helper to prevent
shared mutable references across test iterations
- Add test_unexpected_type_coerced_to_str: verify fallback for
non-str/list/None content types
- Add test_list_content_not_mutated_in_place: verify _append_text
does not modify the original list
* style: fix ruff format whitespace in test file
---------
Co-authored-by: ppyt <14163465+ppyt@users.noreply.github.com>
* feat(uploads): add pymupdf4llm PDF converter with auto-fallback and async offload
- Introduce pymupdf4llm as an optional PDF converter with better heading
detection and table preservation than MarkItDown
- Auto mode: prefer pymupdf4llm when installed; fall back to MarkItDown
when output is suspiciously sparse (image-based / scanned PDFs)
- Sparsity check uses chars-per-page (< 50 chars/page) rather than an
absolute threshold, correctly handling both short and long documents
- Large files (> 1 MB) are offloaded to asyncio.to_thread() to avoid
blocking the event loop (related: #1569)
- Add UploadsConfig with pdf_converter field (auto/pymupdf4llm/markitdown)
- Add pymupdf4llm as optional dependency: pip install deerflow-harness[pymupdf]
- Add 14 unit tests covering sparsity heuristic, routing logic, and async path
* fix(uploads): address Copilot review comments on PDF converter
- Fix docstring: MIN_CHARS_PYMUPDF -> _MIN_CHARS_PER_PAGE (typo)
- Fix file handle leak: wrap pymupdf.open in try/finally to ensure doc.close()
- Fix silent fallback gap: _convert_pdf_with_pymupdf4llm now catches all
conversion exceptions (not just ImportError), so encrypted/corrupt PDFs
fall back to MarkItDown instead of propagating
- Tighten type: pdf_converter field changed from str to Literal[auto|pymupdf4llm|markitdown]
- Normalize config value: _get_pdf_converter() strips and lowercases the raw
config string, warns and falls back to 'auto' on unknown values
* feat(uploads): inject document outline into agent context for converted files
Extract headings from converted .md files and inject them into the
<uploaded_files> context block so the agent can navigate large documents
by line number before reading.
- Add `extract_outline()` to `file_conversion.py`: recognises standard
Markdown headings (#/##/###) and SEC-style bold structural headings
(**ITEM N. BUSINESS**, **PART II**); caps at 50 entries; excludes
cover-page boilerplate (WASHINGTON DC, CURRENT REPORT, SIGNATURES)
- Add `_extract_outline_for_file()` helper in `uploads_middleware.py`:
looks for a sibling `.md` file produced by the conversion pipeline
- Update `UploadsMiddleware._create_files_message()` to render the outline
under each file entry with `L{line}: {title}` format and a `read_file`
prompt for range-based reading
- Tests: 10 new tests for `extract_outline()`, 4 new tests for outline
injection in `UploadsMiddleware`; existing test updated for new `outline`
field in `uploaded_files` state
Partially addresses #1647 (agent ignores uploaded files).
* fix(uploads): stream outline file reads and strip inline bold from heading titles
- Switch extract_outline() from read_text().splitlines() to open()+line iteration
so large converted documents are not loaded into memory on every agent turn;
exits as soon as MAX_OUTLINE_ENTRIES is reached (Copilot suggestion)
- Strip **...** wrapper from standard Markdown heading titles before appending
to outline so agent context stays clean (e.g. "## **Overview**" → "Overview")
(Copilot suggestion)
- Remove unused pathlib.Path import and fix import sort order in test_file_conversion.py
to satisfy ruff CI lint
* fix(uploads): show truncation hint when outline exceeds MAX_OUTLINE_ENTRIES
When extract_outline() hits the cap it now appends a sentinel entry
{"truncated": True} instead of silently dropping the rest of the headings.
UploadsMiddleware reads the sentinel and renders a hint line:
... (showing first 50 headings; use `read_file` to explore further)
Without this the agent had no way to know the outline was incomplete and
would treat the first 50 headings as the full document structure.
* fix(uploads): fall back to configurable.thread_id when runtime.context lacks thread_id
runtime.context does not always carry thread_id (depends on LangGraph
invocation path). ThreadDataMiddleware already falls back to
get_config().configurable.thread_id — apply the same pattern so
UploadsMiddleware can resolve the uploads directory and attach outlines
in all invocation paths.
* style: apply ruff format
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix(uploads): fall back to configurable.thread_id when runtime.context lacks thread_id
runtime.context does not always carry thread_id depending on the
LangGraph invocation path. When absent, uploads_dir resolved to None
and the entire outline/historical-files attachment was silently skipped.
Apply the same fallback pattern already used by ThreadDataMiddleware:
try get_config().configurable.thread_id, with a RuntimeError guard for
test environments where get_config() is called outside a runnable context.
Discovered via live integration testing (curl against local LangGraph).
Unit tests inject uploads_dir directly and would not catch this.
* style: apply ruff format to uploads_middleware.py
When MemoryStreamBridge queue reaches capacity, publish_end() previously
used the same 30s timeout + drop strategy as regular events. If the END
sentinel was dropped, subscribe() would loop forever waiting for it,
causing the SSE connection to hang indefinitely and leaking _queues and
_counters resources for that run_id.
Changes:
- publish_end() now evicts oldest regular events when queue is full to
guarantee END sentinel delivery — the sentinel is the only signal that
allows subscribers to terminate
- Added per-run drop counters (_dropped_counts) with dropped_count() and
dropped_total properties for observability
- cleanup() and close() now clear drop counters
- publish() logs total dropped count per run for easier debugging
Tests:
- test_end_sentinel_delivered_when_queue_full: verifies END arrives even
with a completely full queue
- test_end_sentinel_evicts_oldest_events: verifies eviction behavior
- test_end_sentinel_no_eviction_when_space_available: no side effects
when queue has room
- test_concurrent_tasks_end_sentinel: 4 concurrent producer/consumer
pairs all terminate properly
- test_dropped_count_tracking, test_dropped_total,
test_cleanup_clears_dropped_counts, test_close_clears_dropped_counts:
drop counter coverage
Closes#1689
Co-authored-by: voidborne-d <voidborne-d@users.noreply.github.com>
* fix: use SystemMessage+HumanMessage for follow-up question generation (fixes#1697)
Some models (e.g. MiniMax-M2.7) require the system prompt and user
content to be passed as separate message objects rather than a single
combined string. Invoking with a plain string sends everything as a
HumanMessage, which causes these models to ignore the generation
instructions and fail to produce valid follow-up questions.
* test: verify model is invoked with SystemMessage and HumanMessage
