Files
deer-flow/backend/packages/harness/deerflow/config/agents_config.py
greatmengqi 3e6a34297d refactor(config): eliminate global mutable state — explicit parameter passing on top of main
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).
2026-04-26 21:45:02 +08:00

138 lines
4.1 KiB
Python

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