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).
72 lines
2.6 KiB
Python
72 lines
2.6 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.prebuilt import ToolRuntime
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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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logger = logging.getLogger(__name__)
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@tool
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def setup_agent(
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soul: str,
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description: str,
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runtime: ToolRuntime,
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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.agent_name
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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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agent_dir = paths.agent_dir(agent_name) if agent_name else 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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