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).

frontend/src/content/zh/tutorials/create-your-first-harness.mdx

@@ -0,0 +1,88 @@
+---
+title: 创建你的第一个 Harness
+description: 本教程介绍如何以编程方式使用 DeerFlow Harness Python SDK——直接在你的 Python 代码中导入和使用 DeerFlow，而不是通过 Web 界面。
+---
+
+# 创建你的第一个 Harness
+
+本教程介绍如何以编程方式使用 DeerFlow Harness Python SDK——直接在你的 Python 代码中导入和使用 DeerFlow，而不是通过 Web 界面。
+
+## 前置条件
+
+- Python 3.12+
+- 已安装 `uv`
+- 已克隆 DeerFlow 仓库
+
+## 安装
+
+```bash
+cd deer-flow/backend
+uv sync
+```
+
+## 创建配置
+
+创建一个最小的 `config.yaml`：
+
+```yaml
+config_version: 6
+
+models:
+  - name: gpt-4o
+    use: langchain_openai:ChatOpenAI
+    model: gpt-4o
+    api_key: $OPENAI_API_KEY
+
+sandbox:
+  use: deerflow.sandbox.local:LocalSandboxProvider
+
+tools:
+  - use: deerflow.community.ddg_search.tools:web_search_tool
+  - use: deerflow.sandbox.tools:read_file_tool
+  - use: deerflow.sandbox.tools:write_file_tool
+```
+
+## 编写代码
+
+创建 `my_agent.py`：
+
+```python
+import asyncio
+import os
+from deerflow.client import DeerFlowClient
+from deerflow.config import load_config
+
+# 设置 API Key
+os.environ["OPENAI_API_KEY"] = "sk-..."
+
+# 加载配置
+load_config()
+
+client = DeerFlowClient()
+
+async def main():
+    async for event in client.astream(
+        thread_id="my-first-thread",
+        message="用 Python 写一个斐波那契数列函数，包含文档字符串",
+        config={
+            "configurable": {
+                "model_name": "gpt-4o",
+            }
+        },
+    ):
+        print(event)
+
+asyncio.run(main())
+```
+
+## 运行
+
+```bash
+cd backend
+uv run python my_agent.py
+```
+
+## 下一步
+
+- [使用工具和技能](/docs/tutorials/use-tools-and-skills)
+- [快速上手](/docs/harness/quick-start)