mirror of
https://github.com/bytedance/deer-flow.git
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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).
138 lines
4.1 KiB
Python
138 lines
4.1 KiB
Python
"""Configuration and loaders for custom agents."""
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import logging
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import re
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from typing import Any
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import yaml
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from pydantic import BaseModel, ConfigDict
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from deerflow.config.paths import get_paths
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logger = logging.getLogger(__name__)
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SOUL_FILENAME = "SOUL.md"
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AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
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def validate_agent_name(name: str | None) -> str | None:
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"""Validate a custom agent name before using it in filesystem paths."""
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if name is None:
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return None
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if not isinstance(name, str):
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raise ValueError("Invalid agent name. Expected a string or None.")
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if not AGENT_NAME_PATTERN.fullmatch(name):
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raise ValueError(f"Invalid agent name '{name}'. Must match pattern: {AGENT_NAME_PATTERN.pattern}")
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return name
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class AgentConfig(BaseModel):
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"""Configuration for a custom agent."""
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model_config = ConfigDict(frozen=True)
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name: str
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description: str = ""
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model: str | None = None
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tool_groups: list[str] | None = None
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# skills controls which skills are loaded into the agent's prompt:
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# - None (or omitted): load all enabled skills (default fallback behavior)
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# - [] (explicit empty list): disable all skills
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# - ["skill1", "skill2"]: load only the specified skills
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skills: list[str] | None = None
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def load_agent_config(name: str | None) -> AgentConfig | None:
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"""Load the custom or default agent's config from its directory.
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Args:
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name: The agent name.
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Returns:
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AgentConfig instance.
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Raises:
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FileNotFoundError: If the agent directory or config.yaml does not exist.
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ValueError: If config.yaml cannot be parsed.
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"""
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if name is None:
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return None
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name = validate_agent_name(name)
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agent_dir = get_paths().agent_dir(name)
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config_file = agent_dir / "config.yaml"
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if not agent_dir.exists():
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raise FileNotFoundError(f"Agent directory not found: {agent_dir}")
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if not config_file.exists():
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raise FileNotFoundError(f"Agent config not found: {config_file}")
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try:
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with open(config_file, encoding="utf-8") as f:
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data: dict[str, Any] = yaml.safe_load(f) or {}
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except yaml.YAMLError as e:
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raise ValueError(f"Failed to parse agent config {config_file}: {e}") from e
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# Ensure name is set from directory name if not in file
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if "name" not in data:
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data["name"] = name
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# Strip unknown fields before passing to Pydantic (e.g. legacy prompt_file)
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known_fields = set(AgentConfig.model_fields.keys())
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data = {k: v for k, v in data.items() if k in known_fields}
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return AgentConfig(**data)
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def load_agent_soul(agent_name: str | None) -> str | None:
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"""Read the SOUL.md file for a custom agent, if it exists.
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SOUL.md defines the agent's personality, values, and behavioral guardrails.
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It is injected into the lead agent's system prompt as additional context.
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Args:
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agent_name: The name of the agent or None for the default agent.
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Returns:
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The SOUL.md content as a string, or None if the file does not exist.
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"""
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agent_dir = get_paths().agent_dir(agent_name) if agent_name else get_paths().base_dir
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soul_path = agent_dir / SOUL_FILENAME
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if not soul_path.exists():
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return None
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content = soul_path.read_text(encoding="utf-8").strip()
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return content or None
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def list_custom_agents() -> list[AgentConfig]:
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"""Scan the agents directory and return all valid custom agents.
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Returns:
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List of AgentConfig for each valid agent directory found.
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"""
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agents_dir = get_paths().agents_dir
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if not agents_dir.exists():
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return []
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agents: list[AgentConfig] = []
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for entry in sorted(agents_dir.iterdir()):
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if not entry.is_dir():
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continue
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config_file = entry / "config.yaml"
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if not config_file.exists():
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logger.debug(f"Skipping {entry.name}: no config.yaml")
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continue
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try:
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agent_cfg = load_agent_config(entry.name)
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agents.append(agent_cfg)
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except Exception as e:
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logger.warning(f"Skipping agent '{entry.name}': {e}")
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return agents
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