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38714b6ceb
* refactor: thread app_config through middleware factories Continues the incremental config-refactor sequence (#2611 root, #2612 lead path) one layer deeper into the middleware factories. Two ambient lookups inside _build_runtime_middlewares are eliminated and the LLMErrorHandling band-aid removed: - _build_runtime_middlewares / build_lead_runtime_middlewares / build_subagent_runtime_middlewares now require app_config: AppConfig. - get_guardrails_config() inside the factory is replaced with app_config.guardrails (semantically identical — same default-factory GuardrailsConfig — verified by direct equality check). - LLMErrorHandlingMiddleware.__init__ now requires app_config and reads circuit_breaker fields directly. The class-level circuit_failure_threshold / circuit_recovery_timeout_sec defaults are removed along with the try/except (FileNotFoundError, RuntimeError): pass band-aid — the let-it-crash invariant the rest of the refactor enforces. Caller chain (already-resolved app_config sources): - _build_middlewares in lead_agent/agent.py: reorder so resolved_app_config = app_config or get_app_config() is computed BEFORE build_lead_runtime_middlewares is called, then passed as kwarg. - SubagentExecutor: optional app_config parameter (mirrors the lead-agent pattern); _create_agent does the same `or get_app_config()` fallback at agent-build time, so task_tool callers don't need to plumb app_config through yet (typed-context plumbing for tool runtimes is a separate refactor). Tests: - test_llm_error_handling_middleware: _make_app_config helper using AppConfig(sandbox=SandboxConfig(use="test")) — same minimal-config pattern conftest already uses. Three direct LLMErrorHandlingMiddleware() calls each followed by post-construction circuit_breaker mutation fold cleanly into _build_middleware(circuit_failure_threshold=..., circuit_recovery_timeout_sec=...). Verification: - tests/test_llm_error_handling_middleware.py — 14 passed - tests/test_subagent_executor.py — 28 passed - tests/test_tool_error_handling_middleware.py — 6 passed - tests/test_task_tool_core_logic.py — 18 passed (verifies task_tool unchanged behavior) - Full suite: 2697 passed, 3 skipped. The single intermittent failure in tests/test_client_e2e.py::test_tool_call_produces_events is pre-existing LLM flakiness (the test asserts the model decided to call a tool; reproduces 1/3 on unchanged main as well). * fix: address middleware app config review comments * fix: satisfy app config annotation lint * test: cover explicit app config middleware wiring --------- Co-authored-by: greatmengqi <chenmengqi.0376@bytedance.com>
285 lines
10 KiB
Python
285 lines
10 KiB
Python
"""Tests for lead agent runtime model resolution behavior."""
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from __future__ import annotations
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from unittest.mock import MagicMock
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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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def _make_app_config(models: list[ModelConfig]) -> AppConfig:
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return AppConfig(
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models=models,
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sandbox=SandboxConfig(use="deerflow.sandbox.local:LocalSandboxProvider"),
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)
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def _make_model(name: str, *, supports_thinking: bool) -> ModelConfig:
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return ModelConfig(
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name=name,
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display_name=name,
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description=None,
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use="langchain_openai:ChatOpenAI",
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model=name,
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supports_thinking=supports_thinking,
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supports_vision=False,
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)
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def test_resolve_model_name_falls_back_to_default(monkeypatch, 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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_make_model("other-model", supports_thinking=True),
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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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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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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("other-model", supports_thinking=True),
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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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assert resolved == "default-model"
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def test_resolve_model_name_raises_when_no_models_configured(monkeypatch):
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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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def test_make_lead_agent_disables_thinking_when_model_does_not_support_it(monkeypatch):
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app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
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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, **kwargs: [])
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captured: dict[str, object] = {}
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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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captured["app_config"] = app_config
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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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"configurable": {
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"model_name": "safe-model",
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"thinking_enabled": True,
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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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assert captured["name"] == "safe-model"
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assert captured["thinking_enabled"] is False
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assert captured["app_config"] is app_config
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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, **kwargs: [])
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captured: dict[str, object] = {}
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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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captured["app_config"] = app_config
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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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"app_config": app_config,
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}
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get_available_tools.assert_called_once_with(model_name="context-model", groups=None, subagent_enabled=True, app_config=app_config)
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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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_make_model("stale-model", supports_thinking=False),
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ModelConfig(
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name="vision-model",
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display_name="vision-model",
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description=None,
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use="langchain_openai:ChatOpenAI",
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model="vision-model",
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supports_thinking=False,
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supports_vision=True,
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),
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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 **kwargs: 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(
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{"configurable": {"model_name": "stale-model", "is_plan_mode": False, "subagent_enabled": False}},
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model_name="vision-model",
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custom_middlewares=[MagicMock()],
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app_config=app_config,
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)
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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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assert len(middlewares) > 0 and isinstance(middlewares[-2], MagicMock)
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def test_build_middlewares_passes_explicit_app_config_to_shared_factory(monkeypatch):
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app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
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captured: dict[str, object] = {}
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def _raise_get_app_config():
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raise AssertionError("ambient get_app_config() must not be used when app_config is explicit")
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def _fake_build_lead_runtime_middlewares(*, app_config, lazy_init):
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captured["app_config"] = app_config
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captured["lazy_init"] = lazy_init
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return ["base-middleware"]
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monkeypatch.setattr(lead_agent_module, "get_app_config", _raise_get_app_config)
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monkeypatch.setattr(
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lead_agent_module,
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"build_lead_runtime_middlewares",
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_fake_build_lead_runtime_middlewares,
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)
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monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda **kwargs: 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(
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{"configurable": {"is_plan_mode": False, "subagent_enabled": False}},
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model_name="safe-model",
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app_config=app_config,
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)
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assert captured == {
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"app_config": app_config,
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"lazy_init": True,
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}
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assert middlewares[0] == "base-middleware"
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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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from unittest.mock import MagicMock
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captured: dict[str, 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, 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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captured["app_config"] = app_config
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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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middleware = lead_agent_module._create_summarization_middleware(app_config=_make_app_config([_make_model("model-masswork", supports_thinking=False)]))
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assert captured["name"] == "model-masswork"
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assert captured["thinking_enabled"] is False
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assert captured["app_config"] is not None
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assert middleware["model"] is fake_model
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fake_model.with_config.assert_called_once_with(tags=["middleware:summarize"])
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