greatmengqi
3e6a34297d
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
143 lines
4.7 KiB
Python
143 lines
4.7 KiB
Python
"""Configuration for the subagent system loaded from config.yaml."""
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from pydantic import BaseModel, ConfigDict, Field
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class SubagentOverrideConfig(BaseModel):
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"""Per-agent configuration overrides."""
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model_config = ConfigDict(frozen=True)
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timeout_seconds: int | None = Field(
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default=None,
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ge=1,
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description="Timeout in seconds for this subagent (None = use global default)",
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)
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max_turns: int | None = Field(
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default=None,
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ge=1,
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description="Maximum turns for this subagent (None = use global or builtin default)",
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)
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model: str | None = Field(
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default=None,
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min_length=1,
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description="Model name for this subagent (None = inherit from parent agent)",
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)
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skills: list[str] | None = Field(
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default=None,
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description="Skill names whitelist for this subagent (None = inherit all enabled skills, [] = no skills)",
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)
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class CustomSubagentConfig(BaseModel):
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"""User-defined subagent type declared in config.yaml."""
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description: str = Field(
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description="When the lead agent should delegate to this subagent",
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)
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system_prompt: str = Field(
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description="System prompt that guides the subagent's behavior",
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)
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tools: list[str] | None = Field(
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default=None,
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description="Tool names whitelist (None = inherit all tools from parent)",
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)
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disallowed_tools: list[str] | None = Field(
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default_factory=lambda: ["task", "ask_clarification", "present_files"],
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description="Tool names to deny",
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)
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skills: list[str] | None = Field(
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default=None,
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description="Skill names whitelist (None = inherit all enabled skills, [] = no skills)",
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)
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model: str = Field(
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default="inherit",
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description="Model to use - 'inherit' uses parent's model",
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)
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max_turns: int = Field(
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default=50,
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ge=1,
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description="Maximum number of agent turns before stopping",
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)
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timeout_seconds: int = Field(
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default=900,
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ge=1,
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description="Maximum execution time in seconds",
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)
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class SubagentsAppConfig(BaseModel):
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"""Configuration for the subagent system."""
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model_config = ConfigDict(frozen=True)
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timeout_seconds: int = Field(
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default=900,
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ge=1,
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description="Default timeout in seconds for all subagents (default: 900 = 15 minutes)",
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)
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max_turns: int | None = Field(
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default=None,
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ge=1,
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description="Optional default max-turn override for all subagents (None = keep builtin defaults)",
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)
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agents: dict[str, SubagentOverrideConfig] = Field(
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default_factory=dict,
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description="Per-agent configuration overrides keyed by agent name",
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)
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custom_agents: dict[str, CustomSubagentConfig] = Field(
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default_factory=dict,
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description="User-defined subagent types keyed by agent name",
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)
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def get_timeout_for(self, agent_name: str) -> int:
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"""Get the effective timeout for a specific agent.
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Args:
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agent_name: The name of the subagent.
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Returns:
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The timeout in seconds, using per-agent override if set, otherwise global default.
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"""
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override = self.agents.get(agent_name)
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if override is not None and override.timeout_seconds is not None:
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return override.timeout_seconds
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return self.timeout_seconds
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def get_model_for(self, agent_name: str) -> str | None:
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"""Get the model override for a specific agent.
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Args:
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agent_name: The name of the subagent.
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Returns:
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Model name if overridden, None otherwise (subagent will inherit parent model).
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"""
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override = self.agents.get(agent_name)
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if override is not None and override.model is not None:
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return override.model
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return None
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def get_max_turns_for(self, agent_name: str, builtin_default: int) -> int:
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"""Get the effective max_turns for a specific agent."""
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override = self.agents.get(agent_name)
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if override is not None and override.max_turns is not None:
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return override.max_turns
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if self.max_turns is not None:
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return self.max_turns
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return builtin_default
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def get_skills_for(self, agent_name: str) -> list[str] | None:
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"""Get the skills override for a specific agent.
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Args:
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agent_name: The name of the subagent.
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Returns:
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Skill names whitelist if overridden, None otherwise (subagent will inherit all enabled skills).
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"""
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override = self.agents.get(agent_name)
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if override is not None and override.skills is not None:
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return override.skills
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return None
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