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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).
38 lines
1.5 KiB
Python
38 lines
1.5 KiB
Python
from types import SimpleNamespace
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import pytest
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from deerflow.skills.security_scanner import scan_skill_content
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@pytest.mark.anyio
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async def test_scan_skill_content_passes_run_name_to_model(monkeypatch):
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config = SimpleNamespace(skill_evolution=SimpleNamespace(moderation_model_name=None))
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fake_response = SimpleNamespace(content='{"decision":"allow","reason":"ok"}')
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class FakeModel:
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async def ainvoke(self, *args, **kwargs):
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self.args = args
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self.kwargs = kwargs
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return fake_response
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model = FakeModel()
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monkeypatch.setattr("deerflow.skills.security_scanner.get_app_config", lambda: config)
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monkeypatch.setattr("deerflow.skills.security_scanner.create_chat_model", lambda **kwargs: model)
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result = await scan_skill_content("---\nname: demo-skill\ndescription: demo\n---\n", executable=False)
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assert result.decision == "allow"
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assert model.kwargs["config"] == {"run_name": "security_agent"}
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@pytest.mark.anyio
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async def test_scan_skill_content_blocks_when_model_unavailable(monkeypatch):
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config = SimpleNamespace(skill_evolution=SimpleNamespace(moderation_model_name=None))
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monkeypatch.setattr("deerflow.skills.security_scanner.create_chat_model", lambda **kwargs: (_ for _ in ()).throw(RuntimeError("boom")))
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result = await scan_skill_content(config, "---\nname: demo-skill\ndescription: demo\n---\n", executable=False)
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assert result.decision == "block"
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assert "manual review required" in result.reason
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