* 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>
Escape shell variables to prevent Docker Compose from attempting
substitution at parse time. Rename allow_blocking_flag to allow_blocking
for consistency with dev version.
Fixes the 'allow_blocking_flag not set' warning and enables --allow-blocking
flag to work correctly.
* 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>
Add three new public skills to enhance DeerFlow's content creation capabilities:
- **academic-paper-review**: Structured peer-review-quality analysis of
research papers following top-venue review standards (NeurIPS, ICML, ACL).
Covers methodology assessment, contribution evaluation, literature
positioning, and constructive feedback with a 3-phase workflow.
- **code-documentation**: Professional documentation generation for software
projects, including README generation, API reference docs, architecture
documentation with Mermaid diagrams, and inline code documentation
supporting Python, TypeScript, Go, Rust, and Java conventions.
- **newsletter-generation**: Curated newsletter creation with research
workflow, supporting daily digest, weekly roundup, deep-dive, and industry
briefing formats. Includes audience-specific tone adaptation and
multi-source content curation.
All skills:
- Follow the existing SKILL.md frontmatter convention (name + description)
- Pass the official _validate_skill_frontmatter() validation
- Use hyphen-case naming consistent with existing skills
- Contain only allowed frontmatter properties
- Include comprehensive examples, quality checklists, and output templates
* 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
Server-rendered data-variant={undefined} didn't match client hydration.
Now only render data-variant and data-size when explicitly set.
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: JeffJiang <for-eleven@hotmail.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: add missing DEER_FLOW_CONFIG_PATH and DEER_FLOW_EXTENSIONS_CONFIG_PATH env vars to gateway service (fixes#1829)
The gateway service was missing these two environment variables that tell
it where to find the config files inside the container. Without them,
the gateway reads DEER_FLOW_CONFIG_PATH from the host's .env file (set
to a host filesystem path), which is not accessible inside the container,
causing FileNotFoundError on startup. The langgraph service already had
these variables set correctly.
* fix: remove nginx Plus-only zone/resolve directives from nginx.conf (fixes#1744)
The `zone` and `resolve` parameters in upstream server directives are
nginx Plus features not available in the standard `nginx:alpine` image.
This caused nginx to fail at startup with:
[emerg] invalid parameter "resolve" in /etc/nginx/nginx.conf:25
Remove these directives so the config is compatible with open-source nginx.
Docker's internal DNS (127.0.0.11, already configured via `resolver`) handles
service name resolution. The `resolver` directive is kept for the provisioner
location which uses variable-based proxy_pass for optional-service support.
The gateway service was missing these two environment variables that tell
it where to find the config files inside the container. Without them,
the gateway reads DEER_FLOW_CONFIG_PATH from the host's .env file (set
to a host filesystem path), which is not accessible inside the container,
causing FileNotFoundError on startup. The langgraph service already had
these variables set correctly.
* 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
* Add explicit save action for agent creation
* Hide internal save prompts and retry agent reads
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
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.
Co-authored-by: ppyt <14163465+ppyt@users.noreply.github.com>
