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Author SHA1 Message Date
Zheng Feng
dcb2e687d5
feat(channels): add GitHub as a webhook-driven channel (#3754) 
* feat(channels): add GitHub event-driven agents (#3754)

Add a webhook-driven GitHub channel with fail-closed webhook routing, deterministic per-agent PR/issue threads, mention-gated trigger fan-out, GitHub App token injection for sandboxed gh/git commands, and backend/AGENTS.md documentation.

* fix(llm-middleware): classify bare IndexError as transient

Upstream chat providers occasionally return 200 OK with an empty
generations list (observed against Volces "coding" on
ark.cn-beijing.volces.com). When that happens,
langchain_core.language_models.chat_models.ainvoke raises
``IndexError: list index out of range`` at
``llm_result.generations[0][0].message`` and kills the run.

Treat a bare IndexError reaching the middleware as a transient
upstream-payload glitch and route it through the existing
retry/backoff path instead of failing the whole agent run. The
retry budget and backoff schedule are unchanged.

Adds three regression tests covering the classifier and both the
recover-on-retry and exhausted-retries paths.

* fix(runtime): ignore stale LLM fallback markers from prior runs

When a run on a thread ends with the LLM-error-handling middleware emitting
a `deerflow_error_fallback`-marked AIMessage (e.g. after the IndexError
empty-generations classification fix lands), that message is persisted to
the thread's checkpoint as part of the messages channel. LangGraph replays
the full message history in `stream_mode="values"` chunks, so every
subsequent run on the same thread re-streams the stale fallback marker —
and the worker's chunk scanner faithfully picks it up, flipping
`RunStatus.success` to `RunStatus.error` for runs that themselves had
no LLM failure at all.

Snapshot the set of pre-existing message ids from the pre-run checkpoint
and thread it through `_extract_llm_error_fallback_message` /
`_try_extract_from_message` as a filter. Markers on history messages are
ignored; markers on fresh messages produced during this run still trip
the error path. Falls back to an empty set when the checkpointer is
absent or the snapshot can't be captured, preserving the prior behavior
on first-run / no-state paths.

Adds unit tests for the new filter (helper-level and `_collect_pre_existing_message_ids`)
plus an integration test exercising the full `run_agent` path with a stale
history checkpointer.

* fix(channels): make github channel fire-and-forget to avoid httpx.ReadTimeout on long runs

GitHub agent runs (clone -> edit -> test -> push -> PR) routinely exceed
the langgraph_sdk default 300s read deadline. The manager's runs.wait
call kept an HTTP stream open for the entire run lifetime, so the long
run blew up with httpx.ReadTimeout and the outer except branch then
released the dedupe key and emitted a false 'internal error' outbound.

The GitHub channel's outbound send is log-only by design: agents post to
the issue/PR via the gh CLI in the sandbox when they choose to comment
or create a PR. There is nothing for the manager to ferry back, so the
long-poll was pure overhead.

This change adds ChannelRunPolicy.fire_and_forget (default False) and
sets it True for the github channel. When fire_and_forget is True,
_handle_chat dispatches via client.runs.create (short POST, returns
once the run is pending) instead of client.runs.wait, and skips the
response-extraction + outbound-publish block. ConflictError on a busy
thread still trips the standard THREAD_BUSY_MESSAGE path so behavior on
the busy case is preserved for any future non-github fire-and-forget
channel.

Other (non-github) channels are unchanged: their policy defaults
fire_and_forget=False and they continue to dispatch via runs.wait.

Adds 6 regression tests in tests/test_channels.py::TestGithubFireAndForget:
- Default ChannelRunPolicy.fire_and_forget is False.
- The github policy registers fire_and_forget=True.
- github inbound calls runs.create, not runs.wait, with the right kwargs.
- github inbound publishes no outbound on success.
- ConflictError from runs.create still emits THREAD_BUSY_MESSAGE.
- Non-github channels (slack) still dispatch via runs.wait.

* test(lead-agent): accept user_id kwarg in skill-policy test stubs

The two GitHub-channel tests added in #3754 stubbed
_load_enabled_skills_for_tool_policy with a lambda that only accepted
`available_skills` and `app_config`, but the real function (and its call
site in agent.py) also passes `user_id`. This raised TypeError on every
run, failing backend-unit-tests.

Add `user_id=None` to match the three sibling stubs in the same file.

* refactor(gateway): disambiguate context-key set names

The two frozensets _INTERNAL_ONLY_CONTEXT_KEYS and _CONTEXT_ONLY_KEYS
shared a confusable "CONTEXT_ONLY" token in different orders, and the
first broke the _CONTEXT_<X>_KEYS pattern of its sibling
_CONTEXT_CONFIGURABLE_KEYS. Rename to make the distinct axes explicit:

  _CONTEXT_INTERNAL_CALLER_KEYS  - WHO: internal callers (scheduler) only
  _CONTEXT_RUNTIME_ONLY_KEYS     - WHERE: runtime context only, never configurable

Pure rename, no behavior change.
 2026-07-04 22:56:24 +08:00
Eilen Shin
28b1da2172
fix(agents): harden update_agent null-like args (#3237) 
* fix(agents): harden update_agent null-like args

* docs: mention undefined null-like update args

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
 2026-06-04 07:10:59 +08:00
Xinmin Zeng
68d8caec1f
fix(agents): make update_agent honor runtime.context user_id like setup_agent (#2867) 
* fix(agents): make update_agent honor runtime.context user_id like setup_agent

PR #2784 hardened setup_agent to prefer runtime.context["user_id"] (set by
inject_authenticated_user_context from the auth-validated request) over the
contextvar, so an agent created during the bootstrap flow always lands under
users/<auth_uid>/agents/<name>. update_agent was left calling
get_effective_user_id() unconditionally — the same class of bug that produced
issues #2782 / #2862 still applies whenever the contextvar is not available
on the executing task (background work, future cross-process drivers,
checkpoint resume on a different task). In that regime update_agent silently
routes writes to users/default/agents/<name>, corrupting the shared default
bucket and losing the user's edit.

Extract the resolution policy into a shared resolve_runtime_user_id helper
on deerflow.runtime.user_context and route both setup_agent and update_agent
through it so the two halves of the lifecycle stay in lockstep.

Add load-bearing end-to-end tests that drive a real langchain.agents
create_agent graph with a fake LLM, exercising the full pipeline:

  HTTP wire format
    -> app.gateway.services.start_run config-assembly
    -> deerflow.runtime.runs.worker._build_runtime_context
    -> langchain.agents create_agent graph
    -> ToolNode dispatch (sync + async + sub-graph + ContextThreadPoolExecutor)
    -> setup_agent / update_agent

The negative-control tests intentionally land in users/default/ to prove the
positive tests are actually load-bearing rather than vacuously passing.

The new test_update_agent_e2e_user_isolation suite included a test that
failed against main and now passes after this fix.

* style: ruff format on new e2e tests

* test(e2e): real-server HTTP test driving setup_agent through the full ASGI stack

Adds tests/test_setup_agent_http_e2e_real_server.py — a single load-bearing
test that drives the entire FastAPI gateway through starlette.testclient.
TestClient with no mocks above the LLM:

  - lifespan boots (config, sqlite engine, LangGraph runtime, channels)
  - POST /api/v1/auth/register (real password hash, real sqlite write,
    issues access_token + csrf_token cookies)
  - POST /api/threads (real thread_meta + checkpoint creation)
  - POST /api/threads/{id}/runs/stream with the exact wire shape the React
    frontend sends (assistant_id + input + config + context with
    agent_name/is_bootstrap)
  - AuthMiddleware -> CSRFMiddleware -> require_permission ->
    start_run -> inject_authenticated_user_context ->
    asyncio.create_task(run_agent) -> worker._build_runtime_context ->
    Runtime injection -> ToolNode dispatch -> real setup_agent
  - Asserts SOUL.md is under users/<authenticated_uid>/agents/<name>/
    and NOT under users/default/agents/<name>/.

DEER_FLOW_HOME and the sqlite path are redirected into tmp_path so the test
never touches the real .deer-flow directory or developer database. The only
patch above the LLM boundary is replacing create_chat_model with a fake that
emits a single setup_agent tool_call.

This is the "真实验证" answer: it reproduces what curl-against-uvicorn would
do, minus the network socket layer.

* test: address Copilot review on user-isolation e2e tests

- Drop "currently expected to FAIL" wording from update_agent e2e docstring
  and header (Copilot review): the fix is in this PR, the test pins the
  corrected behaviour rather than driving a future change.
- Rephrase the assertion failure messages from "BUG:" to "REGRESSION:" to
  match the test's role on the fixed branch.
- Bound _drain_stream with a wall-clock timeout, a max-bytes cap, and an
  early break on the "event: end" SSE frame (Copilot review). Stops the
  test from hanging on a stuck run or runaway heartbeat loop.
- Replace the misleading "patch both module aliases" comment with an
  explanation of why patching lead_agent.agent.create_chat_model is the
  only correct target (Copilot review): lead_agent rebinds the symbol
  into its own namespace at import time, so patching deerflow.models is
  too late.

* test(refactor): address WillemJiang review on user-isolation e2e tests

- Extract the duplicated FakeToolCallingModel (and a
  build_single_tool_call_model helper) into tests/_agent_e2e_helpers.py.
  All three e2e files now import from the shared module instead of
  redefining the shim locally.
- Convert the manual p.start() / p.stop() try/finally blocks in
  test_update_agent_e2e_user_isolation.py to contextlib.ExitStack so
  patch lifecycle is Pythonic and exception-safe.
- Lift the isolated_app fixture's private-attribute resets into a
  named _reset_process_singletons helper with a comment block
  explaining why each singleton has to be invalidated for true e2e
  isolation, and why raising=False is intentional. Makes the
  fragility visible and the intent self-documenting rather than
  leaving the resets inline as opaque monkeypatch calls.

Net change: -59 lines (143 -> 84) across the three test files, with
every assertion intact. Full suite remains 69 passed / lint clean.

* test(e2e): make real-server test self-supply its config

CI's actions/checkout only ships config.example.yaml (the real config.yaml
is gitignored), so the production config-discovery search
(./config.yaml -> ../config.yaml -> $DEER_FLOW_CONFIG_PATH) finds nothing
and the test fails at lifespan boot with FileNotFoundError. The dev-machine
run passed only because a local config.yaml happened to exist.

Write a minimal AppConfig-valid yaml into tmp_path and pin
DEER_FLOW_CONFIG_PATH to it. The yaml carries just what the schema requires
(a single fake-test-model entry, LocalSandboxProvider, sqlite database).
The LLM never gets instantiated because the test patches create_chat_model
on the lead agent module, so the api_key/base_url stay placeholders.

Verified by hiding the local config.yaml to mirror the CI checkout — the
test now passes in both environments.
 2026-05-12 23:18:54 +08:00
Maz Benoscar
30a5846219
fix(tools): make write_file append discoverable in model-facing schema (#2843) 
* fix: make tool argument behavior discoverable

The write_file tool already supported append=false by default with append=true for end-of-file writes, but the parsed docstring did not describe append in the model-facing schema. This records the overwrite default and append path in the tool description, adds resilient schema regression coverage, and keeps backend sandbox docs aligned.

The regression now also checks that every public parameter in the existing tool schema test matrix has a description. Enabling docstring parsing on setup_agent and update_agent fills the two existing gaps with their existing Args docs instead of duplicating descriptions elsewhere.

Constraint: Issue #2831 asks for a small docstring/schema discoverability fix without changing runtime file-writing behavior
Rejected: Changing write_file defaults | would alter existing overwrite semantics and broaden the fix beyond schema discoverability
Rejected: Exact phrase assertions | too brittle for future docstring rewording while testing the same behavior
Confidence: high
Scope-risk: narrow
Directive: Keep model-facing tool parameters documented through parsed docstrings or equivalent schema descriptions
Tested: cd backend && uv run pytest tests/test_setup_agent_tool.py tests/test_update_agent_tool.py tests/test_tool_args_schema_no_pydantic_warning.py tests/test_sandbox_tools_security.py::test_str_replace_and_append_on_same_path_should_preserve_both_updates -q
Tested: cd backend && uv run ruff check packages/harness/deerflow/sandbox/tools.py packages/harness/deerflow/tools/builtins/setup_agent_tool.py packages/harness/deerflow/tools/builtins/update_agent_tool.py tests/test_tool_args_schema_no_pydantic_warning.py
Not-tested: Full backend test suite
Co-authored-by: OmX <omx@oh-my-codex.dev>

* Fix the lint error

---------

Co-authored-by: OmX <omx@oh-my-codex.dev>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
 2026-05-10 23:09:03 +08:00
He Wang
7de9b5828b
fix(tools): introduce Runtime type alias to eliminate Pydantic serialization warning (#2774) 
* fix(tools): introduce Runtime type alias to eliminate Pydantic serialization warning

Add deerflow/tools/types.py with:

    Runtime = ToolRuntime[dict[str, Any], ThreadState]

Replace every runtime: ToolRuntime[ContextT, ThreadState] and
runtime: ToolRuntime[dict[str, Any], ThreadState] annotation in
sandbox/tools.py, present_file_tool.py, task_tool.py, view_image_tool.py,
and skill_manage_tool.py with the new Runtime alias.

The unbound ContextT TypeVar (default None) caused
PydanticSerializationUnexpectedValue warnings on every tool call because
LangChain's BaseTool._parse_input calls model_dump() on the auto-generated
args_schema while DeerFlow passes a dict as runtime context.
Binding the context to dict[str, Any] aligns Pydantic's serialization
expectations with reality and removes the noise from all run modes.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(tools): extend Runtime alias to setup_agent and update_agent tools

Replace bare ToolRuntime annotations in setup_agent_tool.py and
update_agent_tool.py with the shared Runtime alias introduced in the
previous commit, and add both tools to the Pydantic serialization
warning regression test (13 cases total).

Co-authored-by: Cursor <cursoragent@cursor.com>

* test(tools): loosen Pydantic warning filter to avoid version-specific format

Replace the brittle "field_name='context'" substring check with a looser
"context" match so the assertion stays valid if Pydantic changes its
internal warning format across versions.

Co-authored-by: Cursor <cursoragent@cursor.com>

* test(tools): simplify warning filter and clean up docstring

Remove the "context" substring condition from the Pydantic warning
filter — asserting that no PydanticSerializationUnexpectedValue fires
at all is both simpler and more comprehensive, since the test payload
contains only the tool's own args plus runtime.

Also update the module docstring to remove the version-specific warning
format example that was inconsistent with the looser filter.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-05-08 14:50:33 +08:00
yangzheli
59c4a3f0a4
feat(agent): add custom-agent self-updates with user isolation (#2713) 
* feat(agent): add update_agent tool for in-chat custom-agent self-updates (#2616)

Custom agents had no built-in way to persist updates to their own SOUL.md /
config.yaml from a normal chat — `setup_agent` was only bound during the
bootstrap flow, so when the user asked the agent to refine its description
or personality, the agent would shell out via bash/write_file and the edits
landed in a temporary sandbox/tool workspace instead of
`{base_dir}/agents/{agent_name}/`.

Changes:
- New `update_agent` builtin tool with partial-update semantics (only the
  fields you pass are written) and atomic temp-file + os.replace writes so
  a failed update never corrupts existing SOUL.md / config.yaml.
- Lead agent now binds `update_agent` in the non-bootstrap path whenever
  `agent_name` is set in the runtime context. Default agent (no
  agent_name) and bootstrap flow are unchanged.
- New `<self_update>` system-prompt section is injected for custom agents,
  instructing them to use `update_agent` — and explicitly NOT bash /
  write_file — to persist self-updates.
- Tests: 11 new cases in `tests/test_update_agent_tool.py` covering
  validation (missing/invalid agent_name, unknown agent, no fields),
  partial updates (soul-only, description-only, skills=[] vs omitted),
  no-op detection, atomic-write safety, and AgentConfig round-tripping;
  plus 2 new cases in `tests/test_lead_agent_prompt.py` covering the
  self-update prompt section.
- Docs: updated backend/CLAUDE.md builtin tools list and tools.mdx
  (en/zh) with the new tool description.

* feat(agent): isolate custom agents per user

Store custom agent definitions under the effective user, keep legacy agents readable until migration, and cover API/tool/migration behavior with tests.

Co-authored-by: Cursor <cursoragent@cursor.com>

* feat: consistent write/delete targets & add --user-id to migration

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
 2026-05-05 23:17:42 +08:00