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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).
169 lines
6.9 KiB
Python
169 lines
6.9 KiB
Python
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# --- Phase 2 config-refactor test helper ---
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# Memory APIs now take MemoryConfig / AppConfig explicitly. Tests construct a
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# minimal config once and reuse it across call sites.
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from deerflow.config.app_config import AppConfig as _TestAppConfig
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from deerflow.config.memory_config import MemoryConfig as _TestMemoryConfig
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from deerflow.config.sandbox_config import SandboxConfig as _TestSandboxConfig
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_TEST_MEMORY_CONFIG = _TestMemoryConfig(enabled=True)
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_TEST_APP_CONFIG = _TestAppConfig(sandbox=_TestSandboxConfig(use="test"), memory=_TEST_MEMORY_CONFIG)
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# -------------------------------------------
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"""Tests for per-user memory storage isolation."""
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import pytest
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from pathlib import Path
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from unittest.mock import patch
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from deerflow.agents.memory.storage import FileMemoryStorage, create_empty_memory
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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.sandbox_config import SandboxConfig
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def _mock_app_config() -> AppConfig:
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"""Build a minimal AppConfig with default (empty) memory storage_path."""
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return AppConfig(sandbox=SandboxConfig(use="test"), memory=MemoryConfig(storage_path=""))
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@pytest.fixture
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def base_dir(tmp_path: Path) -> Path:
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return tmp_path
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@pytest.fixture
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def storage() -> FileMemoryStorage:
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return FileMemoryStorage(_TEST_MEMORY_CONFIG)
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class TestUserIsolatedStorage:
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def test_save_and_load_per_user(self, storage: FileMemoryStorage, base_dir: Path):
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from deerflow.config.paths import Paths
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paths = Paths(base_dir)
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with patch("deerflow.agents.memory.storage.get_paths", return_value=paths):
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memory_a = create_empty_memory()
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memory_a["user"]["workContext"]["summary"] = "User A context"
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storage.save(memory_a, user_id="alice")
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memory_b = create_empty_memory()
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memory_b["user"]["workContext"]["summary"] = "User B context"
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storage.save(memory_b, user_id="bob")
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loaded_a = storage.load(user_id="alice")
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loaded_b = storage.load(user_id="bob")
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assert loaded_a["user"]["workContext"]["summary"] == "User A context"
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assert loaded_b["user"]["workContext"]["summary"] == "User B context"
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def test_user_memory_file_location(self, base_dir: Path):
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from deerflow.config.paths import Paths
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paths = Paths(base_dir)
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with patch("deerflow.agents.memory.storage.get_paths", return_value=paths):
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s = FileMemoryStorage(_TEST_MEMORY_CONFIG)
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memory = create_empty_memory()
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s.save(memory, user_id="alice")
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expected_path = base_dir / "users" / "alice" / "memory.json"
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assert expected_path.exists()
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def test_cache_isolated_per_user(self, base_dir: Path):
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from deerflow.config.paths import Paths
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paths = Paths(base_dir)
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with patch("deerflow.agents.memory.storage.get_paths", return_value=paths):
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s = FileMemoryStorage(_TEST_MEMORY_CONFIG)
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memory_a = create_empty_memory()
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memory_a["user"]["workContext"]["summary"] = "A"
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s.save(memory_a, user_id="alice")
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memory_b = create_empty_memory()
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memory_b["user"]["workContext"]["summary"] = "B"
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s.save(memory_b, user_id="bob")
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loaded_a = s.load(user_id="alice")
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assert loaded_a["user"]["workContext"]["summary"] == "A"
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def test_no_user_id_uses_legacy_path(self, base_dir: Path):
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from deerflow.config.paths import Paths
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paths = Paths(base_dir)
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with patch("deerflow.agents.memory.storage.get_paths", return_value=paths):
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s = FileMemoryStorage(_TEST_MEMORY_CONFIG)
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memory = create_empty_memory()
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s.save(memory, user_id=None)
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expected_path = base_dir / "memory.json"
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assert expected_path.exists()
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def test_user_and_legacy_do_not_interfere(self, base_dir: Path):
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"""user_id=None (legacy) and user_id='alice' must use different files and caches."""
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from deerflow.config.paths import Paths
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paths = Paths(base_dir)
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with patch("deerflow.agents.memory.storage.get_paths", return_value=paths):
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s = FileMemoryStorage(_TEST_MEMORY_CONFIG)
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legacy_mem = create_empty_memory()
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legacy_mem["user"]["workContext"]["summary"] = "legacy"
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s.save(legacy_mem, user_id=None)
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user_mem = create_empty_memory()
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user_mem["user"]["workContext"]["summary"] = "alice"
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s.save(user_mem, user_id="alice")
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assert s.load(user_id=None)["user"]["workContext"]["summary"] == "legacy"
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assert s.load(user_id="alice")["user"]["workContext"]["summary"] == "alice"
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def test_user_agent_memory_file_location(self, base_dir: Path):
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"""Per-user per-agent memory uses the user_agent_memory_file path."""
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from deerflow.config.paths import Paths
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paths = Paths(base_dir)
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with patch("deerflow.agents.memory.storage.get_paths", return_value=paths):
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s = FileMemoryStorage(_TEST_MEMORY_CONFIG)
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memory = create_empty_memory()
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memory["user"]["workContext"]["summary"] = "agent scoped"
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s.save(memory, "test-agent", user_id="alice")
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expected_path = base_dir / "users" / "alice" / "agents" / "test-agent" / "memory.json"
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assert expected_path.exists()
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def test_cache_key_is_user_agent_tuple(self, base_dir: Path):
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"""Cache keys must be (user_id, agent_name) tuples, not bare agent names."""
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from deerflow.config.paths import Paths
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paths = Paths(base_dir)
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with patch("deerflow.agents.memory.storage.get_paths", return_value=paths):
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s = FileMemoryStorage(_TEST_MEMORY_CONFIG)
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memory = create_empty_memory()
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s.save(memory, user_id="alice")
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# After save, cache should have tuple key
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assert ("alice", None) in s._memory_cache
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def test_reload_with_user_id(self, base_dir: Path):
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"""reload() with user_id should force re-read from the user-scoped file."""
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from deerflow.config.paths import Paths
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paths = Paths(base_dir)
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with patch("deerflow.agents.memory.storage.get_paths", return_value=paths):
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s = FileMemoryStorage(_TEST_MEMORY_CONFIG)
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memory = create_empty_memory()
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memory["user"]["workContext"]["summary"] = "initial"
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s.save(memory, user_id="alice")
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# Load once to prime cache
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s.load(user_id="alice")
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# Write updated content directly to file
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user_file = base_dir / "users" / "alice" / "memory.json"
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import json
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updated = create_empty_memory()
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updated["user"]["workContext"]["summary"] = "updated"
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user_file.write_text(json.dumps(updated))
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# reload should pick up the new content
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reloaded = s.reload(user_id="alice")
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assert reloaded["user"]["workContext"]["summary"] == "updated"
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