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).

config.example.yaml

@@ -788,42 +788,79 @@ agents_api:
 # ============================================================================
 # Allow the agent to autonomously create and improve skills in skills/custom/.
 skill_evolution:
-  enabled: false                # Set to true to allow agent-managed writes under skills/custom
-  moderation_model_name: null  # Model for LLM-based security scanning (null = use default model)
+  enabled: false # Set to true to allow agent-managed writes under skills/custom
+  moderation_model_name: null # Model for LLM-based security scanning (null = use default model)
 
 # ============================================================================
-# Checkpointer Configuration
+# Checkpointer Configuration (DEPRECATED — use `database` instead)
 # ============================================================================
-# Configure state persistence for the embedded DeerFlowClient.
-# The LangGraph Server manages its own state persistence separately
-# via the server infrastructure (this setting does not affect it).
+# Legacy standalone checkpointer config. Kept for backward compatibility.
+# Prefer the unified `database` section below, which drives BOTH the
+# LangGraph checkpointer AND DeerFlow application data (runs, feedback,
+# events) from a single backend setting.
 #
-# When configured, DeerFlowClient will automatically use this checkpointer,
-# enabling multi-turn conversations to persist across process restarts.
+# If both `checkpointer` and `database` are present, `checkpointer`
+# takes precedence for LangGraph state persistence only.
 #
-# Supported types:
-#   memory   - In-process only. State is lost when the process exits. (default)
-#   sqlite   - File-based SQLite persistence. Survives restarts.
-#              Requires: uv add langgraph-checkpoint-sqlite
-#   postgres - PostgreSQL persistence. Suitable for multi-process deployments.
-#              Requires: uv add langgraph-checkpoint-postgres psycopg[binary] psycopg-pool
-#
-# Examples:
-#
-# In-memory (default when omitted — no persistence):
 # checkpointer:
-#   type: memory
+#   type: sqlite
+#   connection_string: checkpoints.db
 #
-# SQLite (file-based, single-process):
-checkpointer:
-  type: sqlite
-  connection_string: checkpoints.db
-#
-# PostgreSQL (multi-process, production):
 # checkpointer:
 #   type: postgres
 #   connection_string: postgresql://user:password@localhost:5432/deerflow
 
+# ============================================================================
+# Database
+# ============================================================================
+# Unified storage backend for LangGraph checkpointer and DeerFlow
+# application data (runs, threads metadata, feedback, etc.).
+#
+# backend: memory  -- No persistence, data lost on restart (default)
+# backend: sqlite  -- Single-node deployment, files in sqlite_dir
+# backend: postgres -- Production multi-node deployment
+#
+# SQLite mode uses a single deerflow.db file with WAL journal mode
+# for both checkpointer and application data.
+#
+# Postgres mode: put your connection URL in .env as DATABASE_URL,
+# then reference it here with $DATABASE_URL.
+# Install the driver first:
+#   Local:  uv sync --extra postgres
+#   Docker: UV_EXTRAS=postgres docker compose build
+#
+# NOTE: When both `checkpointer` and `database` are configured,
+# `checkpointer` takes precedence for LangGraph state persistence.
+# If you use `database`, you can remove the `checkpointer` section.
+# database:
+#   backend: sqlite
+#   sqlite_dir: .deer-flow/data
+#
+# database:
+#   backend: postgres
+#   postgres_url: $DATABASE_URL
+database:
+  backend: sqlite
+  sqlite_dir: .deer-flow/data
+
+# ============================================================================
+# Run Events Configuration
+# ============================================================================
+# Storage backend for run events (messages + execution traces).
+#
+# backend: memory  -- No persistence, data lost on restart (default)
+# backend: db      -- SQL database via ORM, full query capability (production)
+# backend: jsonl   -- Append-only JSONL files (lightweight single-node persistence)
+#
+# run_events:
+#   backend: memory
+#   max_trace_content: 10240    # Truncation threshold for trace content (db backend, bytes)
+#   track_token_usage: true     # Accumulate token counts to RunRow
+run_events:
+  backend: memory
+  max_trace_content: 10240
+  track_token_usage: true
+
 # ============================================================================
 # IM Channels Configuration
 # ============================================================================