* fix: improve sandbox security and preserve multimodal content
* Add unit test modifications for test_injects_uploaded_files_tag_into_list_content
* format updated_content
* Add regression tests for multimodal upload content and host bash default safety
* feat(provisioner): add optional PVC support for sandbox volumes (#1978)
Add SKILLS_PVC_NAME and USERDATA_PVC_NAME env vars to allow sandbox
Pods to use PersistentVolumeClaims instead of hostPath volumes. This
prevents data loss in production when pods are rescheduled across nodes.
When USERDATA_PVC_NAME is set, a subPath of threads/{thread_id}/user-data
is used so a single PVC can serve multiple threads. Falls back to hostPath
when the new env vars are not set, preserving backward compatibility.
* add unit test for provisioner pvc volumes
* refactor: extract shared provisioner_module fixture to conftest.py
Agent-Logs-Url: https://github.com/bytedance/deer-flow/sessions/e7ccf708-c6ba-40e4-844a-b526bdb249dd
Co-authored-by: WillemJiang <219644+WillemJiang@users.noreply.github.com>
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: JeffJiang <for-eleven@hotmail.com>
* feat(blog): implement blog structure with post listing and tagging functionality
* feat(blog): enhance blog layout and post metadata display with new components
* fix(blog): address PR #1962 review feedback and fix lint issues (#14)
* fix: format
---------
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
* 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>
* test(skills): add trigger eval set for systematic-literature-review skill
20 eval queries (10 should-trigger, 10 should-not-trigger) for use with
skill-creator's run_eval.py. Includes real-world SLR queries contributed
by @VANDRANKI (issue #1862 author) and edge cases for routing
disambiguation with academic-paper-review.
* test(skills): add grader expectations for SLR skill evaluation
5 eval cases with 39 expectations covering:
- Standard SLR flow (APA/BibTeX/IEEE format selection)
- Keyword extraction and search behavior
- Subagent dispatch for metadata extraction
- Report structure (themes, convergences, gaps, per-paper annotations)
- Negative case: single-paper routing to academic-paper-review
- Edge case: implicit SLR without explicit keywords
* refactor(skills): shorten SLR description for better trigger rate
Reduce description from 833 to 344 chars. Key changes:
- Lead with "systematic literature review" as primary trigger phrase
- Strengthen single-paper exclusion: "Not for single-paper tasks"
- Remove verbose example patterns that didn't improve routing
Tested with run_eval.py (10 runs/query):
- False positive "best paper on RL": 67% → 20% (improved)
- True positive explicit SLR query: ~30% (unchanged)
Low recall is a routing-layer limitation, not a description issue —
see PR description for full analysis.
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
The /api/langgraph/* endpoints proxy straight to the LangGraph server,
so clients inherit LangGraph's native recursion_limit default of 25
instead of the 100 that build_run_config sets for the Gateway and IM
channel paths. 25 is too low for plan-mode or subagent runs and
reliably triggers GraphRecursionError on the lead agent's final
synthesis step after subagents return.
Set recursion_limit: 100 in the Create Run example and the cURL
snippet, and add a short note explaining the discrepancy so users
following the docs don't hit the 25-step ceiling as a surprise.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(skills): add systematic-literature-review skill for multi-paper SLR workflows
Adds a new skill that produces a structured systematic literature review (SLR)
across multiple academic papers on a topic. Addresses #1862 with a pure skill
approach: no new tools, no architectural changes, no new dependencies.
Skill layout:
- SKILL.md — 4+1 phase workflow (plan, search, extract, synthesize, present)
- scripts/arxiv_search.py — arXiv API client, stdlib only, with a
requests->urllib fallback shim modeled after github-deep-research's
github_api.py
- templates/{apa,ieee,bibtex}.md — citation format templates selected
dynamically in Phase 4, mirroring podcast-generation's templates/ pattern
Design notes:
- Multi-paper synthesis uses the existing `task` tool to dispatch extraction
subagents in parallel. SKILL.md's Phase 3 includes a fixed decision table
for batch splitting to respect the runtime's MAX_CONCURRENT_SUBAGENTS = 3
cap, and explicitly tells the agent to strip the "Task Succeeded. Result: "
prefix before parsing subagent JSON output.
- arXiv only, by design. Semantic Scholar and PubMed adapters would push the
scope toward a standalone MCP server (see #933) and are intentionally out
of scope for this skill.
- Coexists with the existing `academic-paper-review` skill: this skill does
breadth-first synthesis across many papers, academic-paper-review does
single-paper peer review. The two are routed via distinct triggers and
can compose (SLR on many + deep review on 1-2 important ones).
- Hard upper bound of 50 papers, tied to the Phase 3 concurrency strategy.
Larger surveys degrade in synthesis quality and are better split by
sub-topic.
BibTeX template explicitly uses @misc for arXiv preprints (not @article),
which is the most common mistake when generating BibTeX for arXiv papers.
arxiv_search.py was smoke-tested end-to-end against the live arXiv API with
two query shapes (relevance sort, submittedDate sort with category filter);
all returned JSON fields parse correctly (id normalization, Atom namespace
handling, URL encoding for multi-word queries).
* fix(skills): prevent LLM from saving intermediate search results to file
Adds an explicit "do not save" instruction at the end of Phase 2.
Observed during Test 1 with DeepSeek: the model saved search results
to a markdown file before proceeding to Phase 3, wasting 2-3 tool call
rounds and increasing the risk of hitting the graph recursion limit.
The search JSON should stay in context for Phase 3, not be persisted.
* fix(skills): use relevance+start-date instead of submittedDate sorting
Test 2 revealed that arXiv's submittedDate sorting returns the most
recently submitted papers in the category regardless of query relevance.
Searching "diffusion models" with sortBy=submittedDate in cs.CV returned
papers on spatial memory, Navier-Stokes, and photon-counting CT — none
about diffusion models. The LLM then retried with 4 different queries,
wasting tool calls and approaching the recursion limit.
Fix: always sort by relevance; when the user wants "recent" papers,
combine relevance sorting with --start-date to constrain the time window.
Also add an explicit "run the search exactly once" instruction to prevent
the retry loop.
* fix(skills): wrap multi-word arXiv queries in double quotes for phrase matching
Without quotes, `all:diffusion model` is parsed by arXiv's Lucene as
`all:diffusion OR model`, pulling in unrelated papers from physics
(thermal diffusion) and other fields. Wrapping in double quotes forces
phrase matching: `all:"diffusion model"`.
Also fixes date filtering: the previous bug caused 2011 papers to appear
in results despite --start-date 2024-04-09, because the unquoted query
words were OR'd with the date constraint.
Verified: "diffusion models" --category cs.CV --start-date 2024-04-09
now returns only relevant diffusion model papers published after April
2024.
* fix(skills): add query phrasing guide and enforce subagent delegation
Two fixes from Test 2 observations with DeepSeek:
1. Query phrasing: add a table showing good vs bad query examples.
The script wraps multi-word queries in double quotes for phrase
matching, so long queries like "diffusion models in computer vision"
return 0 results. Guide the LLM to use 2-3 core keywords + --category
instead.
2. Subagent enforcement: DeepSeek was extracting metadata inline via
python -c scripts instead of using the task tool. Strengthen Phase 3
to explicitly name the task tool, say "do not extract metadata
yourself", and explain why (token budget, isolation). This is more
direct than the previous natural-language-only approach while still
providing the reasoning behind the constraint.
* fix(skills): strengthen search keyword guidance and subagent enforcement
Address two issues found during end-to-end testing with DeepSeek:
1. Search retry: LLM passed full topic descriptions as queries (e.g.
"diffusion models in computer vision"), which returned 0 results due
to exact phrase matching and triggered retries. Added explicit
instruction to extract 2-3 core keywords before searching.
2. Subagent bypass: LLM used python -c to extract metadata instead of
dispatching via task tool. Added explicit prohibition list (python -c,
bash scripts, inline extraction) with ❌ markers for clarity.
* fix(skills): address Copilot review feedback on SLR skill
- Fix legacy arXiv ID parsing: preserve archive prefix for pre-2007
papers (e.g. hep-th/9901001 instead of just 9901001)
- Fix phase count: "four phases" -> "five phases"
- Add subagent_enabled prerequisite note to SKILL.md Notes section
- Remove PR-specific references ("PR 1") from ieee.md and bibtex.md
templates, replace with workflow-scoped wording
- Fix script header: "stdlib only" -> "no additional dependencies
required", fix relative path to github_api.py reference
- Remove reference to non-existent docs/enhancement/ path in header
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Add langchain-ollama as an optional dependency and provide ChatOllama
config examples, enabling proper thinking/reasoning content preservation
for local Ollama models.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix(frontend): replace invalid "context" select field with "metadata" in threads.search
The LangGraph API server does not support "context" as a select field for
threads/search, causing a 422 Unprocessable Entity error introduced by
commit 60e0abf (#1771).
- Replace "context" with "metadata" in the default select list
- Persist agent_name into thread metadata on creation so search results
carry the agent identity
- Update pathOfThread() to fall back to metadata.agent_name when
context is unavailable from search results
- Add regression tests for metadata-based agent routing
Fixes#2037
Made-with: Cursor
* fix: apply Copilot suggestions
* style: fix the lint error
* 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.
* Fix HTML artifact preview rendering
* Add after screenshot for HTML preview fix
* Add before screenshot for HTML preview fix
* Update before screenshot for HTML preview fix
* Update after screenshot for HTML preview fix
* Update before screenshot to Tsinghua homepage repro
* Update after screenshot to Tsinghua homepage preview
* Address PR review on HTML artifact preview
* Harden HTML artifact preview isolation
Streamdown's streaming safeguard appends closing markers (e.g. `*`) to
text with unmatched markdown syntax. This causes user messages containing
literal `*` (such as `99 * 87`) to display with a spurious trailing
asterisk. Human messages are always complete, so the incomplete-markdown
pre-processing is unnecessary.
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* fix(docker): nginx fails to start on hosts without IPv6
- Detect IPv6 support at runtime and remove `listen [::]` directive
when unavailable, preventing nginx startup failure on non-IPv6 hosts
- Use `exec` to replace shell with nginx as PID 1 for proper signal
handling (graceful shutdown on SIGTERM)
- Reformat command from YAML folded scalar to block scalar (no
functional change)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(docker): harden nginx startup script (Copilot review feedback)
Add `set -e` so envsubst failures exit immediately instead of starting
nginx with an incomplete config. Narrow the sed pattern to match only
the `listen [::]:2026;` directive to avoid accidentally removing future
lines containing [::].
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
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
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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
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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
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Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
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 `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
* 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.
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Co-authored-by: JeffJiang <for-eleven@hotmail.com>
* fix(frontend): resolve layout flickering by migrating workspace sidebar state to cookie
* fix(frontend): unify local settings runtime state to fix state drift
* fix(frontend): only persist thread model on explicit context model updates
* 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
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Co-authored-by: lulusiyuyu <lulusiyuyu@users.noreply.github.com>
* fix(frontend): resolve invalid HTML nesting and tabnabbing vulnerabilities
Fix `<button>` inside `<a>` invalid HTML in artifact components and add
missing `noopener,noreferrer` to `window.open` calls to prevent reverse
tabnabbing.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(frontend): address Copilot review on tabnabbing and double-tab-open
Remove redundant parent onClick on web_fetch ChainOfThoughtStep to
prevent opening two tabs on link click, and explicitly null out
window.opener after window.open() for defensive tabnabbing hardening.
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Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
