mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-05-22 16:06:50 +00:00
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).
This commit is contained in:
greatmengqi
@@ -8,7 +8,6 @@ import pytest
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from deerflow.agents.lead_agent import agent as lead_agent_module
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from deerflow.config.app_config import AppConfig
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from deerflow.config.memory_config import MemoryConfig
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from deerflow.config.model_config import ModelConfig
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from deerflow.config.sandbox_config import SandboxConfig
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from deerflow.config.summarization_config import SummarizationConfig
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@@ -33,7 +32,7 @@ def _make_model(name: str, *, supports_thinking: bool) -> ModelConfig:
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)
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def test_resolve_model_name_falls_back_to_default(monkeypatch, caplog):
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def test_resolve_model_name_falls_back_to_default(caplog):
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app_config = _make_app_config(
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[
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_make_model("default-model", supports_thinking=False),
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@@ -41,16 +40,14 @@ def test_resolve_model_name_falls_back_to_default(monkeypatch, caplog):
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]
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)
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monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
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with caplog.at_level("WARNING"):
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resolved = lead_agent_module._resolve_model_name("missing-model")
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resolved = lead_agent_module._resolve_model_name(app_config, "missing-model")
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assert resolved == "default-model"
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assert "fallback to default model 'default-model'" in caplog.text
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def test_resolve_model_name_uses_default_when_none(monkeypatch):
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def test_resolve_model_name_uses_default_when_none():
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app_config = _make_app_config(
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[
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_make_model("default-model", supports_thinking=False),
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@@ -58,23 +55,19 @@ def test_resolve_model_name_uses_default_when_none(monkeypatch):
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]
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)
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monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
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resolved = lead_agent_module._resolve_model_name(None)
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resolved = lead_agent_module._resolve_model_name(app_config, None)
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assert resolved == "default-model"
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def test_resolve_model_name_raises_when_no_models_configured(monkeypatch):
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def test_resolve_model_name_raises_when_no_models_configured():
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app_config = _make_app_config([])
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monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
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with pytest.raises(
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ValueError,
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match="No chat models are configured",
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):
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lead_agent_module._resolve_model_name("missing-model")
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lead_agent_module._resolve_model_name(app_config, "missing-model")
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def test_make_lead_agent_disables_thinking_when_model_does_not_support_it(monkeypatch):
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@@ -82,13 +75,12 @@ def test_make_lead_agent_disables_thinking_when_model_does_not_support_it(monkey
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import deerflow.tools as tools_module
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monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
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monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
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monkeypatch.setattr(lead_agent_module, "_build_middlewares", lambda config, model_name, agent_name=None: [])
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monkeypatch.setattr(lead_agent_module, "_build_middlewares", lambda app_config, config, model_name, agent_name=None: [])
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captured: dict[str, object] = {}
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def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None):
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def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None, app_config=None):
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captured["name"] = name
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captured["thinking_enabled"] = thinking_enabled
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captured["reasoning_effort"] = reasoning_effort
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@@ -105,7 +97,8 @@ def test_make_lead_agent_disables_thinking_when_model_does_not_support_it(monkey
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"is_plan_mode": False,
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"subagent_enabled": False,
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}
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}
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},
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app_config=app_config,
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)
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assert captured["name"] == "safe-model"
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@@ -113,74 +106,6 @@ def test_make_lead_agent_disables_thinking_when_model_does_not_support_it(monkey
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assert result["model"] is not None
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def test_make_lead_agent_reads_runtime_options_from_context(monkeypatch):
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app_config = _make_app_config(
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[
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_make_model("default-model", supports_thinking=False),
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_make_model("context-model", supports_thinking=True),
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]
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)
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import deerflow.tools as tools_module
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get_available_tools = MagicMock(return_value=[])
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monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
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monkeypatch.setattr(tools_module, "get_available_tools", get_available_tools)
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monkeypatch.setattr(lead_agent_module, "_build_middlewares", lambda config, model_name, agent_name=None: [])
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captured: dict[str, object] = {}
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def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None):
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captured["name"] = name
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captured["thinking_enabled"] = thinking_enabled
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captured["reasoning_effort"] = reasoning_effort
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return object()
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monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
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monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
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result = lead_agent_module.make_lead_agent(
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{
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"context": {
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"model_name": "context-model",
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"thinking_enabled": False,
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"reasoning_effort": "high",
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"is_plan_mode": True,
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"subagent_enabled": True,
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"max_concurrent_subagents": 7,
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}
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}
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)
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assert captured == {
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"name": "context-model",
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"thinking_enabled": False,
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"reasoning_effort": "high",
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}
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get_available_tools.assert_called_once_with(model_name="context-model", groups=None, subagent_enabled=True)
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assert result["model"] is not None
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def test_make_lead_agent_rejects_invalid_bootstrap_agent_name(monkeypatch):
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app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
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monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
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with pytest.raises(ValueError, match="Invalid agent name"):
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lead_agent_module.make_lead_agent(
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{
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"configurable": {
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"model_name": "safe-model",
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"thinking_enabled": False,
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"is_plan_mode": False,
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"subagent_enabled": False,
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"is_bootstrap": True,
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"agent_name": "../../../tmp/evil",
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}
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}
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)
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def test_build_middlewares_uses_resolved_model_name_for_vision(monkeypatch):
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app_config = _make_app_config(
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[
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@@ -197,11 +122,10 @@ def test_build_middlewares_uses_resolved_model_name_for_vision(monkeypatch):
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]
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)
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monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
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monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda: None)
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monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda _ac: None)
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monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
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middlewares = lead_agent_module._build_middlewares({"configurable": {"model_name": "stale-model", "is_plan_mode": False, "subagent_enabled": False}}, model_name="vision-model", custom_middlewares=[MagicMock()])
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middlewares = lead_agent_module._build_middlewares(app_config, {"configurable": {"model_name": "stale-model", "is_plan_mode": False, "subagent_enabled": False}}, model_name="vision-model", custom_middlewares=[MagicMock()])
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assert any(isinstance(m, lead_agent_module.ViewImageMiddleware) for m in middlewares)
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# verify the custom middleware is injected correctly
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@@ -209,73 +133,27 @@ def test_build_middlewares_uses_resolved_model_name_for_vision(monkeypatch):
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def test_create_summarization_middleware_uses_configured_model_alias(monkeypatch):
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monkeypatch.setattr(
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lead_agent_module,
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"get_summarization_config",
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lambda: SummarizationConfig(enabled=True, model_name="model-masswork"),
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)
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monkeypatch.setattr(lead_agent_module, "get_memory_config", lambda: MemoryConfig(enabled=False))
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app_config = _make_app_config([_make_model("default", supports_thinking=False)])
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patched = app_config.model_copy(update={"summarization": SummarizationConfig(enabled=True, model_name="model-masswork")})
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from unittest.mock import MagicMock
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captured: dict[str, object] = {}
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fake_model = object()
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fake_model = MagicMock()
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fake_model.with_config.return_value = fake_model
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def _fake_create_chat_model(*, name=None, thinking_enabled, reasoning_effort=None):
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def _fake_create_chat_model(*, name=None, thinking_enabled, reasoning_effort=None, app_config=None):
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captured["name"] = name
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captured["thinking_enabled"] = thinking_enabled
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captured["reasoning_effort"] = reasoning_effort
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return fake_model
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monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
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monkeypatch.setattr(lead_agent_module, "DeerFlowSummarizationMiddleware", lambda **kwargs: kwargs)
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monkeypatch.setattr(lead_agent_module, "SummarizationMiddleware", lambda **kwargs: kwargs)
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middleware = lead_agent_module._create_summarization_middleware()
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middleware = lead_agent_module._create_summarization_middleware(patched)
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assert captured["name"] == "model-masswork"
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assert captured["thinking_enabled"] is False
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assert middleware["model"] is fake_model
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def test_create_summarization_middleware_registers_memory_flush_hook_when_memory_enabled(monkeypatch):
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monkeypatch.setattr(
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lead_agent_module,
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"get_summarization_config",
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lambda: SummarizationConfig(enabled=True),
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)
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monkeypatch.setattr(lead_agent_module, "get_memory_config", lambda: MemoryConfig(enabled=True))
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monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
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captured: dict[str, object] = {}
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def _fake_middleware(**kwargs):
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captured.update(kwargs)
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return kwargs
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monkeypatch.setattr(lead_agent_module, "DeerFlowSummarizationMiddleware", _fake_middleware)
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lead_agent_module._create_summarization_middleware()
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assert captured["before_summarization"] == [lead_agent_module.memory_flush_hook]
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def test_create_summarization_middleware_passes_skill_read_tool_names(monkeypatch):
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app_config = _make_app_config([_make_model("default-model", supports_thinking=False)])
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monkeypatch.setattr(
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lead_agent_module,
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"get_summarization_config",
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lambda: SummarizationConfig(enabled=True, skill_file_read_tool_names=["read_file", "cat"]),
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)
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monkeypatch.setattr(lead_agent_module, "get_memory_config", lambda: MemoryConfig(enabled=False))
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monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
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monkeypatch.setattr(lead_agent_module, "create_chat_model", lambda **kwargs: object())
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captured: dict[str, object] = {}
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def _fake_middleware(**kwargs):
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captured.update(kwargs)
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return kwargs
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monkeypatch.setattr(lead_agent_module, "DeerFlowSummarizationMiddleware", _fake_middleware)
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lead_agent_module._create_summarization_middleware()
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assert captured["skill_file_read_tool_names"] == ["read_file", "cat"]
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fake_model.with_config.assert_called_once_with(tags=["middleware:summarize"])
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