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

293 lines
13 KiB
Python

"""update_agent tool — let a custom agent persist updates to its own SOUL.md / config.
Bound to the lead agent only when ``runtime.context['agent_name']`` is set
(i.e. inside an existing custom agent's chat). The default agent does not see
this tool, and the bootstrap flow continues to use ``setup_agent`` for the
initial creation handshake.
The tool writes back to ``{base_dir}/users/{user_id}/agents/{agent_name}/{config.yaml,SOUL.md}``
so an agent created by one user is never visible to (or mutable by) another.
Writes are staged into temp files first; both files are renamed into place only
after both temp files are successfully written, so a partial failure cannot leave
config.yaml updated while SOUL.md still holds stale content.
"""
from __future__ import annotations
import logging
import tempfile
from pathlib import Path
from typing import Annotated, Any
import yaml
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool
from langgraph.types import Command
from pydantic import BeforeValidator
from deerflow.config.agents_config import load_agent_config, preserve_non_managed_fields, validate_agent_name
from deerflow.config.app_config import get_app_config
from deerflow.config.paths import get_paths
from deerflow.runtime.user_context import resolve_runtime_user_id
from deerflow.tools.types import Runtime
logger = logging.getLogger(__name__)
_NULLISH_STRINGS = frozenset({"null", "none", "undefined"})
# Channels whose inbound messages come from untrusted external commenters
# (anyone on a GitHub repo, etc.). The lead-agent factory already drops
# this tool for runs on these channels (see ``_WEBHOOK_CHANNELS`` in
# ``deerflow.agents.lead_agent.agent``); this set is the in-tool mirror
# so a custom factory that re-attaches ``update_agent`` cannot silently
# expose self-mutation over a webhook.
_UNTRUSTED_CHANNELS: frozenset[str] = frozenset({"github"})
def _stage_temp(path: Path, text: str) -> Path:
"""Write ``text`` into a sibling temp file and return its path.
The caller is responsible for ``Path.replace``-ing the temp into the target
once every staged file is ready, or for unlinking it on failure.
"""
path.parent.mkdir(parents=True, exist_ok=True)
fd = tempfile.NamedTemporaryFile(
mode="w",
dir=path.parent,
suffix=".tmp",
delete=False,
encoding="utf-8",
)
try:
fd.write(text)
fd.flush()
fd.close()
return Path(fd.name)
except BaseException:
fd.close()
Path(fd.name).unlink(missing_ok=True)
raise
def _cleanup_temps(temps: list[Path]) -> None:
"""Best-effort removal of staged temp files."""
for tmp in temps:
try:
tmp.unlink(missing_ok=True)
except OSError:
logger.debug("Failed to clean up temp file %s", tmp, exc_info=True)
def _is_nullish_string(value: object) -> bool:
return isinstance(value, str) and value.strip().lower() in _NULLISH_STRINGS
def _normalize_nullish_string(value: object) -> object:
return None if _is_nullish_string(value) else value
OptionalText = Annotated[str | None, BeforeValidator(_normalize_nullish_string)]
OptionalStringList = Annotated[list[str] | None, BeforeValidator(_normalize_nullish_string)]
@tool(parse_docstring=True)
def update_agent(
runtime: Runtime,
soul: OptionalText = None,
description: OptionalText = None,
skills: OptionalStringList = None,
tool_groups: OptionalStringList = None,
model: OptionalText = None,
) -> Command:
"""Persist updates to the current custom agent's SOUL.md and config.yaml.
Use this when the user asks to refine the agent's identity, description,
skill whitelist, tool-group whitelist, or default model. Only the fields
you explicitly pass are updated; omitted fields keep their existing values.
Pass ``soul`` as the FULL replacement SOUL.md content — there is no patch
semantics, so always start from the current SOUL and apply your edits.
Pass ``skills=[]`` to disable all skills for this agent. Omit ``skills``
entirely to keep the existing whitelist. Do not pass literal strings like
``"null"`` / ``"none"`` / ``"undefined"`` for unchanged fields; omit those
fields instead.
Args:
soul: Optional full replacement SOUL.md content.
description: Optional new one-line description.
skills: Optional skill whitelist. ``[]`` = no skills, omit = unchanged.
tool_groups: Optional tool-group whitelist. ``[]`` = empty, omit = unchanged.
model: Optional model override (must match a configured model name).
Returns:
Command with a ToolMessage describing the result. Changes take effect
on the next user turn (when the lead agent is rebuilt with the fresh
SOUL.md and config.yaml).
"""
tool_call_id = runtime.tool_call_id
agent_name_raw: str | None = runtime.context.get("agent_name") if runtime.context else None
channel_name: str | None = runtime.context.get("channel_name") if runtime.context else None
def _err(message: str) -> Command:
return Command(update={"messages": [ToolMessage(content=f"Error: {message}", tool_call_id=tool_call_id, status="error")]})
# Defence in depth — the lead-agent factory already withholds this
# tool from webhook-channel runs (see ``_WEBHOOK_CHANNELS`` in
# ``deerflow.agents.lead_agent.agent``). The same channel set is
# mirrored here so a future code path that re-attaches the tool
# without going through ``_make_lead_agent`` (custom factories,
# tests, etc.) does not silently accept untrusted self-mutation
# requests routed from a webhook.
if channel_name in _UNTRUSTED_CHANNELS:
return _err(f"update_agent is disabled on the {channel_name!r} channel. Self-mutation requests must come from an operator-trusted surface (chat UI or the HTTP API), not a webhook fan-out.")
if soul is None and description is None and skills is None and tool_groups is None and model is None:
return _err('No fields provided. Pass at least one of: soul, description, skills, tool_groups, model. Omit unchanged fields instead of passing null-like strings such as "null", "none", or "undefined".')
try:
agent_name = validate_agent_name(agent_name_raw)
except ValueError as e:
return _err(str(e))
if not agent_name:
return _err("update_agent is only available inside a custom agent's chat. There is no agent_name in the current runtime context, so there is nothing to update. If you are inside the bootstrap flow, use setup_agent instead.")
# Resolve the active user so that updates only affect this user's agent.
# ``resolve_runtime_user_id`` prefers ``runtime.context["user_id"]`` (set by
# the gateway from the auth-validated request) and falls back to the
# contextvar, then DEFAULT_USER_ID. This matches setup_agent so a user
# creating an agent and later refining it always touches the same files,
# even if the contextvar gets lost across an async/thread boundary
# (issue #2782 / #2862 class of bugs).
user_id = resolve_runtime_user_id(runtime)
# Reject an unknown ``model`` *before* touching the filesystem. Otherwise
# ``_resolve_model_name`` silently falls back to the default at runtime
# and the user sees confusing repeated warnings on every later turn.
if model is not None and get_app_config().get_model_config(model) is None:
return _err(f"Unknown model '{model}'. Pass a model name that exists in config.yaml's models section.")
paths = get_paths()
agent_dir = paths.user_agent_dir(user_id, agent_name)
if not agent_dir.exists() and paths.agent_dir(agent_name).exists():
return _err(f"Agent '{agent_name}' only exists in the legacy shared layout and is not scoped to a user. Run scripts/migrate_user_isolation.py to move legacy agents into the per-user layout before updating.")
try:
existing_cfg = load_agent_config(agent_name, user_id=user_id)
except FileNotFoundError:
return _err(f"Agent '{agent_name}' does not exist for the current user. Use setup_agent to create a new agent first.")
except ValueError as e:
return _err(f"Agent '{agent_name}' has an unreadable config: {e}")
if existing_cfg is None:
return _err(f"Agent '{agent_name}' could not be loaded.")
updated_fields: list[str] = []
# Force the on-disk ``name`` to match the directory we are writing into,
# even if ``existing_cfg.name`` had drifted (e.g. from manual yaml edits).
config_data: dict[str, Any] = {"name": agent_name}
new_description = description if description is not None else existing_cfg.description
config_data["description"] = new_description
if description is not None and description != existing_cfg.description:
updated_fields.append("description")
new_model = model if model is not None else existing_cfg.model
if new_model is not None:
config_data["model"] = new_model
if model is not None and model != existing_cfg.model:
updated_fields.append("model")
new_tool_groups = tool_groups if tool_groups is not None else existing_cfg.tool_groups
if new_tool_groups is not None:
config_data["tool_groups"] = new_tool_groups
if tool_groups is not None and tool_groups != existing_cfg.tool_groups:
updated_fields.append("tool_groups")
new_skills = skills if skills is not None else existing_cfg.skills
if new_skills is not None:
config_data["skills"] = new_skills
if skills is not None and skills != existing_cfg.skills:
updated_fields.append("skills")
# Preserve every top-level AgentConfig field that this tool does not
# expose as an argument (currently ``github:``, plus any future field
# added to :class:`AgentConfig`). The same helper is used by the HTTP
# ``PATCH /api/agents/{name}`` route so the two surfaces stay in lockstep.
# Without this, operators who hand-author a ``github:`` block on a custom
# agent would silently lose it the next time the agent self-updates via
# ``update_agent``.
preserved = preserve_non_managed_fields(existing_cfg)
for key, value in preserved.items():
config_data.setdefault(key, value)
config_changed = bool({"description", "model", "tool_groups", "skills"} & set(updated_fields))
# Stage every file we intend to rewrite into a temp sibling. Only after
# *all* temp files exist do we rename them into place — so a failure on
# SOUL.md cannot leave config.yaml already replaced.
pending: list[tuple[Path, Path]] = []
staged_temps: list[Path] = []
try:
agent_dir.mkdir(parents=True, exist_ok=True)
if config_changed:
yaml_text = yaml.dump(config_data, default_flow_style=False, allow_unicode=True, sort_keys=False)
config_target = agent_dir / "config.yaml"
config_tmp = _stage_temp(config_target, yaml_text)
staged_temps.append(config_tmp)
pending.append((config_tmp, config_target))
if soul is not None:
soul_target = agent_dir / "SOUL.md"
soul_tmp = _stage_temp(soul_target, soul)
staged_temps.append(soul_tmp)
pending.append((soul_tmp, soul_target))
updated_fields.append("soul")
# Commit phase. ``Path.replace`` is atomic per file on POSIX/NTFS and
# the staging step above means any earlier failure has already been
# reported. The remaining failure mode is a crash *between* two
# ``replace`` calls, which is reported via the partial-write error
# branch below so the caller knows which files are now on disk.
committed: list[Path] = []
try:
for tmp, target in pending:
tmp.replace(target)
committed.append(target)
except Exception as e:
_cleanup_temps([t for t, _ in pending if t not in committed])
if committed:
logger.error(
"[update_agent] Partial write for agent '%s' (user=%s): committed=%s, failed during rename: %s",
agent_name,
user_id,
[p.name for p in committed],
e,
exc_info=True,
)
return _err(f"Partial update for agent '{agent_name}': {[p.name for p in committed]} were updated, but the rest failed ({e}). Re-run update_agent to retry the remaining fields.")
raise
except Exception as e:
_cleanup_temps(staged_temps)
logger.error("[update_agent] Failed to update agent '%s' (user=%s): %s", agent_name, user_id, e, exc_info=True)
return _err(f"Failed to update agent '{agent_name}': {e}")
if not updated_fields:
return Command(update={"messages": [ToolMessage(content=f"No changes applied to agent '{agent_name}'. The provided values matched the existing config.", tool_call_id=tool_call_id)]})
logger.info("[update_agent] Updated agent '%s' (user=%s) fields: %s", agent_name, user_id, updated_fields)
return Command(
update={
"messages": [
ToolMessage(
content=(f"Agent '{agent_name}' updated successfully. Changed: {', '.join(updated_fields)}. The new configuration takes effect on the next user turn."),
tool_call_id=tool_call_id,
)
]
}
)