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* 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.
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
import logging
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import yaml
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from langchain_core.messages import ToolMessage
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from langchain_core.tools import tool
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from langgraph.types import Command
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from deerflow.config.agents_config import validate_agent_name
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from deerflow.config.paths import get_paths
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from deerflow.runtime.user_context import resolve_runtime_user_id
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from deerflow.tools.types import Runtime
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logger = logging.getLogger(__name__)
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@tool(parse_docstring=True)
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def setup_agent(
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soul: str,
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description: str,
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runtime: Runtime,
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skills: list[str] | None = None,
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) -> Command:
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"""Setup the custom DeerFlow agent.
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Args:
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soul: Full SOUL.md content defining the agent's personality and behavior.
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description: One-line description of what the agent does.
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skills: Optional list of skill names this agent should use. None means use all enabled skills, empty list means no skills.
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"""
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agent_name: str | None = runtime.context.get("agent_name") if runtime.context else None
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agent_dir = None
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is_new_dir = False
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try:
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agent_name = validate_agent_name(agent_name)
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paths = get_paths()
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if agent_name:
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# Custom agents are persisted under the current user's bucket so
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# different users do not see each other's agents.
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user_id = resolve_runtime_user_id(runtime)
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agent_dir = paths.user_agent_dir(user_id, agent_name)
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else:
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# Default agent (no agent_name): SOUL.md lives at the global base dir.
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agent_dir = paths.base_dir
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is_new_dir = not agent_dir.exists()
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agent_dir.mkdir(parents=True, exist_ok=True)
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if agent_name:
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# If agent_name is provided, we are creating a custom agent in the agents/ directory
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config_data: dict = {"name": agent_name}
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if description:
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config_data["description"] = description
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if skills is not None:
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config_data["skills"] = skills
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config_file = agent_dir / "config.yaml"
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with open(config_file, "w", encoding="utf-8") as f:
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yaml.dump(config_data, f, default_flow_style=False, allow_unicode=True)
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soul_file = agent_dir / "SOUL.md"
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soul_file.write_text(soul, encoding="utf-8")
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logger.info(f"[agent_creator] Created agent '{agent_name}' at {agent_dir}")
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return Command(
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update={
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"created_agent_name": agent_name,
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"messages": [ToolMessage(content=f"Agent '{agent_name}' created successfully!", tool_call_id=runtime.tool_call_id)],
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}
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)
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except Exception as e:
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import shutil
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if agent_name and is_new_dir and agent_dir is not None and agent_dir.exists():
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# Cleanup the custom agent directory only if it was newly created during this call
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shutil.rmtree(agent_dir)
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logger.error(f"[agent_creator] Failed to create agent '{agent_name}': {e}", exc_info=True)
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return Command(update={"messages": [ToolMessage(content=f"Error: {e}", tool_call_id=runtime.tool_call_id)]})
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