mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-21 07:26:50 +00:00
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).
141 lines
5.8 KiB
Python
141 lines
5.8 KiB
Python
from pathlib import Path
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from types import SimpleNamespace
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from deerflow.agents.lead_agent.prompt import get_skills_prompt_section
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from deerflow.config.agents_config import AgentConfig
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from deerflow.skills.types import Skill
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def _make_skill(name: str) -> Skill:
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return Skill(
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name=name,
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description=f"Description for {name}",
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license="MIT",
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skill_dir=Path(f"/tmp/{name}"),
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skill_file=Path(f"/tmp/{name}/SKILL.md"),
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relative_path=Path(name),
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category="public",
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enabled=True,
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)
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_DEFAULT_SKILLS_CONFIG = SimpleNamespace(
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skills=SimpleNamespace(container_path="/mnt/skills"),
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skill_evolution=SimpleNamespace(enabled=False),
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)
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def _evolution_enabled_config() -> SimpleNamespace:
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return SimpleNamespace(
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skills=SimpleNamespace(container_path="/mnt/skills"),
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skill_evolution=SimpleNamespace(enabled=True),
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)
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def test_get_skills_prompt_section_returns_empty_when_no_skills_match(monkeypatch):
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skills = [_make_skill("skill1"), _make_skill("skill2")]
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monkeypatch.setattr("deerflow.agents.lead_agent.prompt._get_enabled_skills", lambda *a, **k: skills)
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result = get_skills_prompt_section(_DEFAULT_SKILLS_CONFIG, available_skills={"non_existent_skill"})
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assert result == ""
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def test_get_skills_prompt_section_returns_empty_when_available_skills_empty(monkeypatch):
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skills = [_make_skill("skill1"), _make_skill("skill2")]
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monkeypatch.setattr("deerflow.agents.lead_agent.prompt._get_enabled_skills", lambda *a, **k: skills)
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result = get_skills_prompt_section(_DEFAULT_SKILLS_CONFIG, available_skills=set())
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assert result == ""
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def test_get_skills_prompt_section_returns_skills(monkeypatch):
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skills = [_make_skill("skill1"), _make_skill("skill2")]
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monkeypatch.setattr("deerflow.agents.lead_agent.prompt._get_enabled_skills", lambda *a, **k: skills)
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result = get_skills_prompt_section(_DEFAULT_SKILLS_CONFIG, available_skills={"skill1"})
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assert "skill1" in result
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assert "skill2" not in result
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assert "[built-in]" in result
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def test_get_skills_prompt_section_returns_all_when_available_skills_is_none(monkeypatch):
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skills = [_make_skill("skill1"), _make_skill("skill2")]
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monkeypatch.setattr("deerflow.agents.lead_agent.prompt._get_enabled_skills", lambda *a, **k: skills)
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result = get_skills_prompt_section(_DEFAULT_SKILLS_CONFIG, available_skills=None)
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assert "skill1" in result
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assert "skill2" in result
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def test_get_skills_prompt_section_includes_self_evolution_rules(monkeypatch):
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skills = [_make_skill("skill1")]
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monkeypatch.setattr("deerflow.agents.lead_agent.prompt._get_enabled_skills", lambda *a, **k: skills)
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result = get_skills_prompt_section(_evolution_enabled_config(), available_skills=None)
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assert "Skill Self-Evolution" in result
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def test_get_skills_prompt_section_includes_self_evolution_rules_without_skills(monkeypatch):
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monkeypatch.setattr("deerflow.agents.lead_agent.prompt._get_enabled_skills", lambda *a, **k: [])
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result = get_skills_prompt_section(_evolution_enabled_config(), available_skills=None)
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assert "Skill Self-Evolution" in result
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def test_get_skills_prompt_section_cache_respects_skill_evolution_toggle(monkeypatch):
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skills = [_make_skill("skill1")]
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monkeypatch.setattr("deerflow.agents.lead_agent.prompt._get_enabled_skills", lambda *a, **k: skills)
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config = _evolution_enabled_config()
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enabled_result = get_skills_prompt_section(config, available_skills=None)
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assert "Skill Self-Evolution" in enabled_result
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disabled_config = SimpleNamespace(
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skills=SimpleNamespace(container_path="/mnt/skills"),
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skill_evolution=SimpleNamespace(enabled=False),
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)
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disabled_result = get_skills_prompt_section(disabled_config, available_skills=None)
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assert "Skill Self-Evolution" not in disabled_result
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def test_make_lead_agent_empty_skills_passed_correctly(monkeypatch):
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from unittest.mock import MagicMock
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from deerflow.agents.lead_agent import agent as lead_agent_module
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# Mock dependencies
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monkeypatch.setattr(lead_agent_module, "_resolve_model_name", lambda app_config=None, x=None: "default-model")
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monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: "model")
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monkeypatch.setattr("deerflow.tools.get_available_tools", lambda **kwargs: [])
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monkeypatch.setattr(lead_agent_module, "_build_middlewares", lambda *args, **kwargs: [])
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monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
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class MockModelConfig:
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supports_thinking = False
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mock_app_config = MagicMock()
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mock_app_config.get_model_config.return_value = MockModelConfig()
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captured_skills = []
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def mock_apply_prompt_template(_app_config, *args, **kwargs):
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captured_skills.append(kwargs.get("available_skills"))
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return "mock_prompt"
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monkeypatch.setattr(lead_agent_module, "apply_prompt_template", mock_apply_prompt_template)
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# Case 1: Empty skills list
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monkeypatch.setattr(lead_agent_module, "load_agent_config", lambda x: AgentConfig(name="test", skills=[]))
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lead_agent_module.make_lead_agent({"configurable": {"agent_name": "test"}}, app_config=mock_app_config)
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assert captured_skills[-1] == set()
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# Case 2: None skills list
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monkeypatch.setattr(lead_agent_module, "load_agent_config", lambda x: AgentConfig(name="test", skills=None))
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lead_agent_module.make_lead_agent({"configurable": {"agent_name": "test"}}, app_config=mock_app_config)
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assert captured_skills[-1] is None
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# Case 3: Some skills list
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monkeypatch.setattr(lead_agent_module, "load_agent_config", lambda x: AgentConfig(name="test", skills=["skill1"]))
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lead_agent_module.make_lead_agent({"configurable": {"agent_name": "test"}}, app_config=mock_app_config)
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assert captured_skills[-1] == {"skill1"}
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