* fix(threads): clean up local thread data after thread deletion
Delete DeerFlow-managed thread directories after the web UI removes a LangGraph thread.
This keeps local thread data in sync with conversation deletion and adds regression coverage for the cleanup flow.
* fix(threads): address thread cleanup review feedback
Encode thread cleanup URLs in the web client, keep cache updates explicit when no thread search data is cached, and return a generic 500 response from the cleanup endpoint while documenting the sanitized error behavior.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Add GuardrailMiddleware that evaluates every tool call before execution.
Three provider options: built-in AllowlistProvider (zero deps), OAP passport
providers (open standard), or custom providers loaded by class path.
- GuardrailProvider protocol with GuardrailRequest/Decision dataclasses
- GuardrailMiddleware (AgentMiddleware, position 5 in chain)
- AllowlistProvider for simple deny/allow by tool name
- GuardrailsConfig (Pydantic singleton, loaded from config.yaml)
- 25 tests covering allow/deny, fail-closed/open, async, GraphBubbleUp
- Comprehensive docs at backend/docs/GUARDRAILS.md
Closes#1213
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat: add Claude Code OAuth and Codex CLI providers
Port of bytedance/deer-flow#1136 from @solanian's feat/cli-oauth-providers branch.\n\nCarries the feature forward on top of current main without the original CLA-blocked commit metadata, while preserving attribution in the commit message for review.
* fix: harden CLI credential loading
Align Codex auth loading with the current ~/.codex/auth.json shape, make Docker credential mounts directory-based to avoid broken file binds on hosts without exported credential files, and add focused loader tests.
* refactor: tighten codex auth typing
Replace the temporary Any return type in CodexChatModel._load_codex_auth with the concrete CodexCliCredential type after the credential loader was stabilized.
* fix: load Claude Code OAuth from Keychain
Match Claude Code's macOS storage strategy more closely by checking the Keychain-backed credentials store before falling back to ~/.claude/.credentials.json. Keep explicit file overrides and add focused tests for the Keychain path.
* fix: require explicit Claude OAuth handoff
* style: format thread hooks reasoning request
* docs: document CLI-backed auth providers
* fix: address provider review feedback
* fix: harden provider edge cases
* Fix deferred tools, Codex message normalization, and local sandbox paths
* chore: narrow PR scope to OAuth providers
* chore: remove unrelated frontend changes
* chore: reapply OAuth branch frontend scope cleanup
* fix: preserve upload guards with reasoning effort wiring
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* 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>
* 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>
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>
* fix(feishu): support @bot message in topic groups
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* fix(feishu): preserve rich-text formatting and add parser unit tests
* chore(test): remove unused import to fix ruff lint error
* style: auto-format imports to satisfy ruff
---------
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(manager): add bootstrap command to initialize soul.md in correct place
* feat(channels): add /bootstrap command to IM channels
Add a `/bootstrap` command that routes to the chat handler with
`is_bootstrap: True` in the run context, allowing the agent to invoke
its setup/initialization flow (e.g. `setup_agent`).
- The text after `/bootstrap` is forwarded as the chat message; when
omitted a default "Initialize workspace" message is used.
- Feishu channels use the streaming path as with normal chat.
- No changes to ChannelStore — bootstrap is stateless and triggered
purely by the command.
- Update /help output to include /bootstrap.
- Add 5 tests covering: text/no-text variants, Feishu streaming path,
thread creation, and help text.
* Potential fix for pull request finding
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* fix: accept copilot suggestion
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
* fix(harness): allow agent read access to /mnt/skills in local sandbox
Skill files under /mnt/skills/ were blocked by the path validator,
preventing agents from reading skill definitions. This change:
- Refactors `resolve_local_tool_path` into `validate_local_tool_path`,
a pure security gate that no longer resolves paths (left to the sandbox)
- Permits read-only access to the skills container path (/mnt/skills by
default, configurable via config.skills.container_path)
- Blocks write access to skills paths (PermissionError)
- Allows /mnt/skills in bash command path validation
- Adds `LocalSandbox.update_path_mappings` and injects per-thread
user-data mappings into the sandbox so all virtual-path resolution
is handled uniformly by the sandbox layer
- Covers all new behaviour with tests
Fixes#1177
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* refactor(sandbox): unify all virtual path resolution in tools.py
Move skills path resolution from LocalSandbox into tools.py so that all
virtual-to-host path translation (user-data and skills) lives in one
layer. LocalSandbox becomes a pure execution layer that receives only
real host paths — no more path_mappings, _resolve_path, or reverse
resolve logic.
This addresses architecture feedback that path resolution was split
across two layers (tools.py for user-data, LocalSandbox for skills),
making the flow hard to follow.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(sandbox): address Copilot review — cache-on-success and error path masking
- Replace @lru_cache with manual cache-on-success for _get_skills_container_path
and _get_skills_host_path so transient failures at startup don't permanently
disable skills access.
- Add _sanitize_error() helper that masks host filesystem paths in error
messages via mask_local_paths_in_output before returning them to the agent.
- Apply _sanitize_error() to all catch-all (Exception/OSError) handlers in
sandbox tool functions to prevent host path leakage in error output.
- Remove unused lru_cache import.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat(tools): add tool_search for deferred MCP tool loading
When multiple MCP servers are enabled, total tool count can exceed 30-50,
causing context bloat and degraded tool selection accuracy. This adds a
deferred tool loading mechanism controlled by `tool_search.enabled` config.
- Add ToolSearchConfig with single `enabled` field
- Add DeferredToolRegistry with regex search (select:, +keyword, keyword)
- Add tool_search tool returning OpenAI-compatible function JSON
- Add DeferredToolFilterMiddleware to hide deferred schemas from bind_tools
- Add <available-deferred-tools> section to system prompt
- Enable MCP tool_name_prefix to prevent cross-server name collisions
- Add 34 unit tests covering registry, tool, prompt, and middleware
* fix: reset stale deferred registry and bump config_version
- Reset deferred registry upfront in get_available_tools() to prevent
stale tool entries when MCP servers are disabled between calls
- Bump config_version to 2 for new tool_search config field
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(tests): mock get_app_config in prompt section tests for CI
CI has no config.yaml, causing TestDeferredToolsPromptSection to fail
with FileNotFoundError. Add autouse fixture to mock get_app_config.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add citation/reference support to deep research reports (#1141)
- Enhance lead agent system prompt with mandatory citation requirements
after web_search/web_fetch tool usage
- Add citation examples and best practices to GitHub Deep Research skill
- Add citation hints to report template (Executive Summary, Key Analysis)
- Style regular markdown links in frontend for visual distinction
(color, underline, hover effect)
- Fix TitleMiddleware being registered when title generation is disabled
* fix: address PR review comments
- Revert TitleMiddleware conditional registration (agent.py) to avoid
sync/async incompatibility with DeerFlowClient
- Fix markdown link rendering: merge classNames instead of overwriting,
only set target=_blank for external http(s) URLs
- Remove unrelated package.json/pnpm-lock.yaml changes
* fix: use plain markdown links in Sources section for cleaner rendering
Inline citations in report body use [citation:Title](URL) for pill/badge style.
Sources section uses plain [Title](URL) for simple underlined link style.
* fix(frontend): render plain links as underlined text in artifact markdown
Only links with citation: prefix render as Badge pills.
Regular links in Sources section now render as underlined text links.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* 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>
* feat(feishu): stream updates on a single card
* fix(feishu): ensure final message on stream error and warn on missing card ID
- Wrap streaming loop in try/except/finally so a is_final=True outbound
message is always published, even when the LangGraph stream breaks
mid-way. This prevents _running_card_ids memory leaks and ensures the
Feishu card shows a DONE reaction instead of hanging on "Working on it".
- Log a warning when _ensure_running_card gets no message_id back from
the Feishu reply API, making silent fallback to new-card behavior
visible in logs.
- Add test_handle_feishu_stream_error_still_sends_final to cover the
error path.
- Reformat service.py dict comprehension (ruff format, no logic change).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Avoid blocking inbound on Feishu card creation
---------
Co-authored-by: songyaolun <songyaolun@bytedance.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat: add LoopDetectionMiddleware to break repetitive tool call loops
Adds a new AgentMiddleware that detects when the agent is stuck calling
the same tools with the same arguments repeatedly, which currently runs
until the recursion limit kills the run.
Detection: per-thread sliding window of tool call hashes (name + args).
- Warn threshold (default 3): injects a "wrap up" system message
- Hard limit (default 5): strips tool_calls, forcing final text output
Includes 13 unit tests covering hashing, thresholds, window sliding,
reset, and edge cases.
Closes#1055
* fix: address PR #1056 review feedback for LoopDetectionMiddleware
- Remove unused imports (Awaitable, Callable, ModelCallResult,
ModelRequest, ModelResponse, AIMessage) from loop_detection_middleware
- Remove unused pytest import from test file
- Fix _hash_tool_calls sort key: sort by (name, serialized args) for
deterministic hashing when multiple calls share the same tool name
- Revert subagent_enabled default to False in agent.py to match
DeerFlowClient and channel defaults
- Remove unrelated SearxNG tools and Next.js rewrite changes from PR
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address 2nd round review feedback on PR #1056
- Inject loop warning only once per thread (prevents context bloat)
- Add threading.Lock for thread-safe history mutations
- Use runtime.context thread_id instead of workspace_path
- Add LRU eviction for per-thread history (max 100 threads)
- Add 5 new tests covering warn-once, LRU eviction, thread isolation,
fallback thread_id, and lock presence
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: resolve lint errors in loop detection middleware tests
Sort imports (I001) and remove unused _WARNING_MSG import (F401)
to fix ruff lint failures in CI.
* Apply suggestions from code review
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Add MiniMax as an OpenAI-compatible model provider
MiniMax offers high-performance LLMs (M2.5, M2.5-highspeed) with
204K context windows. This commit adds MiniMax as a selectable
provider in the configuration system.
Changes:
- Add MiniMax to SUPPORTED_MODELS with model definitions
- Add MiniMax provider configuration in conf/config.yaml
- Update documentation with MiniMax setup instructions
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Update README to remove MiniMax API details
Removed mention of MiniMax API usage and configuration examples.
---------
Co-authored-by: octo-patch <octo-patch@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix: preserve conversation context in Telegram private chats
In private (1-on-1) chats, set topic_id=None so all messages map to a
single DeerFlow thread per chat instead of creating a new thread for
every message. Also fix _cmd_generic to use topic_id=None in private
chats so /new correctly targets the default thread.
Group chat behavior is unchanged (reply_to or msg_id as topic_id).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: preserve conversation context in Telegram private chats
Fixes#1101
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: mirror _on_text reply logic in _cmd_generic for group chats
_cmd_generic now prefers reply_to_message.message_id over msg_id in
group/supergroup chats, consistent with _on_text. This ensures commands
like /new and /status target the correct conversation thread when sent
as a reply in group chats.
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: JeffJiang <for-eleven@hotmail.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* feat(sandbox): harden local file access and mask host paths
- enforce local sandbox file tools to only accept /mnt/user-data paths
- add path traversal checks against thread workspace/uploads/outputs roots
- preserve requested virtual paths in tool error messages (no host path leaks)
- mask local absolute paths in bash output back to virtual sandbox paths
- update bash tool guidance to prefer thread-local venv + python -m pip
- add regression tests for path mapping, masking, and access restrictions
Fixes#968
* feat(sandbox): restrict risky absolute paths in local bash commands
- validate absolute path usage in local-mode bash commands
- allow only /mnt/user-data virtual paths for user data access
- keep a small allowlist for system executable/device paths
- return clear permission errors for unsafe command paths
- add regression tests for bash path validation rules
* test(sandbox): add success path test for resolve_local_tool_path (#992)
* Initial plan
* test(sandbox): add success path test for resolve_local_tool_path
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: WillemJiang <219644+WillemJiang@users.noreply.github.com>
* fix(sandbox): reject bare virtual root early with clear error in resolve_local_tool_path (#991)
* Initial plan
* fix(sandbox): reject bare virtual root early with clear error in resolve_local_tool_path
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: WillemJiang <219644+WillemJiang@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
---------
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
* fix(tracing): support LANGCHAIN_* env fallback for LangSmith config
- add backward-compatible env parsing in tracing_config.py
- support fallback keys:
LANGCHAIN_TRACING_V2 / LANGCHAIN_TRACING
LANGCHAIN_API_KEY
LANGCHAIN_PROJECT
LANGCHAIN_ENDPOINT
- keep LANGSMITH_* as preferred source when both are present
- add regression tests in test_tracing_config.py
* fix(tracing): correct LANGSMITH_* precedence over LANGCHAIN_* for enabled flag (#1067)
* Initial plan
* fix(tracing): use first-present-wins logic for enabled flag, add precedence docs and test
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: WillemJiang <219644+WillemJiang@users.noreply.github.com>
---------
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
* Refactor sandbox state management and improve Docker integration
- Removed FileSandboxStateStore and SandboxStateStore classes for a cleaner architecture.
- Enhanced LocalContainerBackend to handle port allocation retries and introduced environment variable support for sandbox host configuration.
- Updated Paths class to include host_base_dir for Docker volume mounts and ensured proper permissions for sandbox directories.
- Modified ExtensionsConfig to improve error handling when loading configuration files and adjusted environment variable resolution.
- Updated sandbox configuration to include a replicas option for managing concurrent sandbox containers.
- Improved logging and context management in SandboxMiddleware for better sandbox lifecycle handling.
- Enhanced network port allocation logic to bind to 0.0.0.0 for compatibility with Docker.
- Updated Docker Compose files to ensure proper volume management and environment variable configuration.
- Created scripts to ensure necessary configuration files are present before starting services.
- Cleaned up unused MCP server configurations in extensions_config.example.json.
* Address Copilot review suggestions from PR #1068 (#9)
---------
Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>
* fix(subagents): cleanup background tasks after completion to prevent memory leak
Added cleanup_background_task() function to remove completed subagent results
from the global _background_tasks dict. Found a small issue: completed tasks
were never removed, causing memory to grow indefinitely with each subagent
execution.
Alternative approaches considered:
- Future + SubagentHandle pattern: Not chosen due to requiring refactoring
Chose the simple cleanup approach for minimal code changes while effectively
resolving the memory leak.
Changes:
- Add cleanup_background_task() in executor.py
- Call cleanup in all task_tool return paths (completed, failed, timed out)
* fix(subagents): prevent race condition in background task cleanup
Address Copilot review feedback on memory leak fix:
- Add terminal state check in cleanup_background_task() to only remove
tasks that are COMPLETED/FAILED/TIMED_OUT or have completed_at set
- Remove cleanup call from polling safety-timeout branch in task_tool
since the task may still be running
- Add comprehensive tests for cleanup behavior including:
- Verification that cleanup is called on terminal states
- Verification that cleanup is NOT called on polling timeout
- Tests for terminal state check logic in executor
This prevents KeyError when the background executor tries to update
a task that was prematurely removed from _background_tasks.
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
* fix(checkpointer): return InMemorySaver instead of None when not configured (#1016)
* fix(checkpointer): also fix get_checkpointer() to return InMemorySaver
Make all three checkpointer functions consistent:
- make_checkpointer() (async) → InMemorySaver
- checkpointer_context() (sync) → InMemorySaver
- get_checkpointer() (sync singleton) → InMemorySaver
This ensures DeerFlowClient always has a valid checkpointer.
* fix: address CI failure and Copilot review feedback
- Fix import order in test_checkpointer_none_fix.py (I001 ruff error)
- Fix type annotation: _checkpointer should be Checkpointer | None
- Update docstring: change "None if not configured" to "InMemorySaver if not configured"
- Ensure app config is loaded before checking checkpointer config to prevent incorrect InMemorySaver fallback
---------
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
