From 87a7f3f9df18e2d90267fcf23a65680f5c74a673 Mon Sep 17 00:00:00 2001 From: hotwa Date: Mon, 1 Sep 2025 11:59:17 +0800 Subject: [PATCH] add mcp use method --- examples/mcp_modes/README.md | 214 +++++++++++++++++++++ examples/mcp_modes/config/mcp_servers.json | 13 ++ examples/mcp_modes/config/mcp_servers.yaml | 10 + examples/mcp_modes/direct_tool_loop.py | 52 +++++ examples/mcp_modes/plan_and_execute.py | 61 ++++++ examples/mcp_modes/react_agent.py | 33 ++++ langgraph_qwen/mcp.py | 176 +++++++++++++++++ 7 files changed, 559 insertions(+) create mode 100644 examples/mcp_modes/README.md create mode 100644 examples/mcp_modes/config/mcp_servers.json create mode 100644 examples/mcp_modes/config/mcp_servers.yaml create mode 100644 examples/mcp_modes/direct_tool_loop.py create mode 100644 examples/mcp_modes/plan_and_execute.py create mode 100644 examples/mcp_modes/react_agent.py create mode 100644 langgraph_qwen/mcp.py diff --git a/examples/mcp_modes/README.md b/examples/mcp_modes/README.md new file mode 100644 index 0000000..5650364 --- /dev/null +++ b/examples/mcp_modes/README.md @@ -0,0 +1,214 @@ +# MCP 集成三种用法总览 + +本节汇总了在 **LangGraph + Qwen 自定义模型（`ChatQwenOpenAICompat`）** 下使用 MCP 工具的三种常见模式：**ReAct 代理**、**直接工具循环**、**计划-执行（Plan & Execute）**。每种模式都支持从 **代码**、**配置文件（JSON/YAML）** 或 **环境变量** 加载 MCP 服务器；既可连接本地 `stdio`（如 `npx`/`python` 启动的 MCP server），也可连接远程 **HTTP streamable** 服务器。 + +--- + +## 配置来源与优先级 + +可通过任意方式声明 MCP 服务器，内部合并优先级如下（后者覆盖前者同名项）： + +1. **配置文件**：`config_path` 或 `MCP_CONFIG_PATH`（JSON/YAML） +2. **环境变量**：`MCP_SERVERS_JSON`（JSON 字符串） +3. **代码传入**：`servers: Dict[str, Dict[str, Any]]` +4. **兜底**：本地 `weather` HTTP server（`http://127.0.0.1:8000/mcp/`，`transport=streamable_http`） + +**示例：JSON 配置（`mcp_servers.json`）** +```json +{ + "servers": { + "weather": { + "url": "http://127.0.0.1:8000/mcp/", + "transport": "streamable_http" + }, + "airbnb": { + "command": "npx", + "args": ["-y", "@openbnb/mcp-server-airbnb"], + "transport": "stdio" + } + } +} +``` + +**示例：YAML 配置（`mcp_servers.yaml`）** +```yaml +servers: + weather: + url: http://127.0.0.1:8000/mcp/ + transport: streamable_http + playwright: + command: npx + args: ["@playwright/mcp@latest"] + transport: stdio + env: + DISPLAY: ":1" +``` + +> 环境变量（任选）： +> - `MCP_CONFIG_PATH=./mcp_servers.yaml` +> - `MCP_SERVERS_JSON='{"weather":{"url":"http://127.0.0.1:8000/mcp/","transport":"streamable_http"}}'` + +--- + +## 用法一：ReAct 代理（最简单、默认推荐） + +**特点** + +- 一行创建：自动把 MCP 工具注入 LangGraph 的 **ReAct** 代理（`ToolNode` 自动执行工具）。 +- 支持 **多次工具调用**；通过 `config={"recursion_limit": N}` **显式限制步数**。 +- 适合“让模型自由决策何时调用哪个工具”的通用智能体场景。 + +**典型场景** + +- 对话检索、计划+查证、简单自动化任务编排。 +- 工具数量较多、先不想自己写调用逻辑。 + +**最小示例** +```python +from langchain_core.messages import HumanMessage +from langgraph_qwen.mcp import create_qwen_agent_with_mcp_async + +SERVERS = { + "weather": {"url": "http://127.0.0.1:8000/mcp/", "transport": "streamable_http"}, + # "airbnb": {"command":"npx","args":["-y","@openbnb/mcp-server-airbnb"],"transport":"stdio"}, +} + +agent = await create_qwen_agent_with_mcp_async( + servers=SERVERS, # 或者传 config_path / 用 MCP_SERVERS_JSON + tool_choice="auto", # 也可 "none" 做两阶段（先思考再注入） +) + +res = await agent.ainvoke( + {"messages": [HumanMessage(content="列出可用工具，演示一次调用并总结。")]}, + config={"recursion_limit": 6} # ★ 控制最大工具交互步数 +) +print(res["messages"][-1].content) +``` + +--- + +## 用法二：直接工具循环（完全可控） + +**特点** + +- 不用 LangGraph 的 ToolNode，由你在循环里**手动执行**工具（识别 `AIMessage.tool_calls`，再调用 `tool.invoke/ainvoke`，最后用 `ToolMessage` 回传）。 +- 对**每一步**是否调用工具、如何合并结果、失败如何重试，有 **100%** 控制权。 +- 适合需要严格可控的业务流程、精细化容错与审计。 + +**典型场景** + +- 合规/金融/科研等对工具副作用与审计有严格要求的系统。 +- 多工具“串行+并行”的复杂编排、**阶段性切换工具集**。 + +**最小示例** +```python +from langchain_core.messages import HumanMessage, ToolMessage +from langgraph_qwen.chat_model import ChatQwenOpenAICompat +from langgraph_qwen.mcp import load_mcp_tools + +tools = await load_mcp_tools(servers={ + "math": {"command":"python","args":["/abs/path/to/math_server.py"],"transport":"stdio"}, + "weather":{"url":"http://127.0.0.1:8000/mcp/","transport":"streamable_http"}, +}) + +model = ChatQwenOpenAICompat(temperature=0).bind_tools(tools).bind(tool_choice="auto") +tool_map = {t.name: t for t in tools} + +msgs = [HumanMessage(content="先算 12*(3+5)，再查北京天气，最后总结。")] +for _ in range(8): # ★ 最大步骤 + ai = await model.ainvoke(msgs) + msgs.append(ai) + calls = getattr(ai, "tool_calls", []) or ai.additional_kwargs.get("tool_calls", []) + if not calls: + break + for call in calls: + name, args, call_id = call["name"], call.get("args", {}), call.get("id") or "" + tool = tool_map.get(name) + if not tool: + msgs.append(ToolMessage(tool_call_id=call_id, content=f"Unknown tool: {name}")) + continue + out = await tool.ainvoke(args) if hasattr(tool, "ainvoke") else tool.invoke(args) + msgs.append(ToolMessage(tool_call_id=call_id, content=str(out))) + +final = await model.ainvoke(msgs) +print(final.content) +``` + +--- + +## 用法三：计划-执行（Plan & Execute，动态工具集） + +**特点** + +- 第一步用模型把任务拆解为**多个步骤**（Planner）。 +- 对每个步骤，可**动态选择/切换工具集**（可按阶段加载不同的 MCP 配置）。 +- 执行器部分类似“直接工具循环”，可精细控制每步最多调用几次工具。 + +**典型场景** + +- 旅行/采购/研究类 **多阶段任务**：先找资源，再比较价格，再路线规划，再汇总。 +- 不同阶段接入不同 MCP 工具（如：Airbnb → Google Maps → Weather）。 + +**最小示例（摘录）** +```python +from langchain_core.messages import HumanMessage +from langgraph_qwen.chat_model import ChatQwenOpenAICompat +from langgraph_qwen.mcp import load_mcp_tools + +# 规划 +planner = ChatQwenOpenAICompat(temperature=0) +steps_ai = await planner.ainvoke([HumanMessage(content="把任务拆成可执行步骤（每行一步）：...")]) +steps = [s for s in str(steps_ai.content).splitlines() if s][:8] + +# 每步执行（动态加载工具集） +for i, step in enumerate(steps, 1): + tools = await load_mcp_tools(servers=( + {"weather": {"url":"http://127.0.0.1:8000/mcp/","transport":"streamable_http"}} + if i % 2 == 0 else + {"airbnb": {"command":"npx","args":["-y","@openbnb/mcp-server-airbnb"],"transport":"stdio"}} + )) + model = ChatQwenOpenAICompat(temperature=0).bind_tools(tools).bind(tool_choice="auto") + # … 按“直接工具循环”方式执行，给每步设 max_tool_steps_per_step +``` + +--- + +## 选择建议 + +- **先用 ReAct**：如果你需要最快跑通“让模型自己决定如何用工具”的智能体，且可接受自动化行为 → **用法一**。 +- **需要强控/审计/容错**：你希望精确掌控每次工具调用、失败重试、输出格式 → **用法二**。 +- **多阶段任务**：需要“先规划、再按阶段注入不同工具” → **用法三**。 + +--- + +## 常见问题与提示 + +- **工具是异步还是同步？** + `langchain-mcp-adapters` 返回的工具通常是 `StructuredTool`/`BaseTool` 封装，可能只实现 `ainvoke`（异步）。 + 调用前建议检测：`await tool.ainvoke(args) if hasattr(tool, "ainvoke") else tool.invoke(args)`。 + +- **连接失败 (`ConnectError`)** + 大多是 HTTP MCP 服务器没启动/端口不对；请确认 `url` 可访问，或本地 `stdio` 的 `command/args` 正确。 + +- **工具模式兼容** + 后端（如 vLLM / llama.cpp / llama-box）对 `tools/tool_choice` 的支持程度有差异。若遇到 5xx/模板错误： + 1) 先用最小工具 schema（`type=object`，`properties` 简单）验证； + 2) 暂时将 `tool_choice="none"` 做“两阶段”：先思考生成计划，再注入目标工具并允许调用。 + +- **代理与鉴权** + - 如需禁用系统代理：`QWEN_HTTP_TRUST_ENV=0`（适配器会传给 `httpx`）。 + - 自定义鉴权头/前缀：`QWEN_AUTH_HEADER`（默认 `Authorization`）、`QWEN_AUTH_SCHEME`（默认 `Bearer`，设空即裸 Key）。 + +- **配置复用** + 建议把多套 MCP 服务器写在一个 `mcp_servers.yaml`，运行时以 `config_path` 选择，或用 `MCP_SERVERS_JSON` 动态注入，配合三种模式灵活切换。 + +--- + +## 相关 API（来自 `langgraph_qwen/mcp.py`） + +- `resolve_servers_config(servers=None, config_path=None) -> Dict`: 合并并解析配置。 +- `load_mcp_tools(servers=None, config_path=None) -> List[Tool]`: 异步加载 MCP 工具。 +- `create_qwen_agent_with_mcp_async(..., tool_choice="auto")`: **ReAct** 代理（异步）。 +- `create_qwen_agent_with_mcp(..., tool_choice="auto")`: **ReAct** 代理（同步包装；在异步环境请用上面的异步接口）。 + +> 所有模式均依赖：`pip install langchain-mcp-adapters`。工具服务端可混合 `streamable_http` 与 `stdio`（本地 `npx/python/node` 等）。 diff --git a/examples/mcp_modes/config/mcp_servers.json b/examples/mcp_modes/config/mcp_servers.json new file mode 100644 index 0000000..ce1bf33 --- /dev/null +++ b/examples/mcp_modes/config/mcp_servers.json @@ -0,0 +1,13 @@ +{ + "servers": { + "weather": { + "url": "http://127.0.0.1:8000/mcp/", + "transport": "streamable_http" + }, + "airbnb": { + "command": "npx", + "args": ["-y", "@openbnb/mcp-server-airbnb"], + "transport": "stdio" + } + } +} diff --git a/examples/mcp_modes/config/mcp_servers.yaml b/examples/mcp_modes/config/mcp_servers.yaml new file mode 100644 index 0000000..8f08788 --- /dev/null +++ b/examples/mcp_modes/config/mcp_servers.yaml @@ -0,0 +1,10 @@ +servers: + weather: + url: http://127.0.0.1:8000/mcp/ + transport: streamable_http + playwright: + command: npx + args: ["@playwright/mcp@latest"] + transport: stdio + env: + DISPLAY: ":1" diff --git a/examples/mcp_modes/direct_tool_loop.py b/examples/mcp_modes/direct_tool_loop.py new file mode 100644 index 0000000..baae251 --- /dev/null +++ b/examples/mcp_modes/direct_tool_loop.py @@ -0,0 +1,52 @@ +# examples/mcp_modes/direct_tool_loop.py +import asyncio +from langchain_core.messages import HumanMessage, ToolMessage +from langgraph_qwen.chat_model import ChatQwenOpenAICompat +from langgraph_qwen.mcp import load_mcp_tools + +PHASE1_SERVERS = { + "math": {"command":"python","args":["/abs/path/to/math_server.py"],"transport":"stdio"} +} +PHASE2_SERVERS = { + "weather": {"url":"http://127.0.0.1:8000/mcp/","transport":"streamable_http"} +} + +async def main(max_steps: int = 8): + # ★ 比如不同阶段加载不同工具集 + tools_phase1 = await load_mcp_tools(servers=PHASE1_SERVERS) + tools_phase2 = await load_mcp_tools(servers=PHASE2_SERVERS) + + model = ChatQwenOpenAICompat(temperature=0).bind_tools( + tools_phase1 + tools_phase2 + ).bind(tool_choice="auto") + + tool_map = {t.name: t for t in (tools_phase1 + tools_phase2)} + msgs = [HumanMessage(content="先用数学工具算 12*(3+5)，再查北京天气，最后给一句总结。")] + + for _ in range(max_steps): + ai = await model.ainvoke(msgs) + msgs.append(ai) + calls = getattr(ai, "tool_calls", []) or ai.additional_kwargs.get("tool_calls", []) + if not calls: + break + for call in calls: + name, args, call_id = call.get("name"), call.get("args", {}), call.get("id") or "" + tool = tool_map.get(name) + if not tool: + msgs.append(ToolMessage(tool_call_id=call_id, content=f"Unknown tool: {name}")) + continue + try: + if hasattr(tool, "ainvoke"): + out = await tool.ainvoke(args) + else: + out = tool.invoke(args) + msgs.append(ToolMessage(tool_call_id=call_id, content=str(out))) + except Exception as e: + msgs.append(ToolMessage(tool_call_id=call_id, content=f"Error: {e}")) + + final = await model.ainvoke(msgs) + print("=== Final ===") + print(final.content) + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/mcp_modes/plan_and_execute.py b/examples/mcp_modes/plan_and_execute.py new file mode 100644 index 0000000..e5808ea --- /dev/null +++ b/examples/mcp_modes/plan_and_execute.py @@ -0,0 +1,61 @@ +# examples/mcp_modes/plan_and_execute.py +import os, asyncio +from typing import List +from langchain_core.messages import HumanMessage, ToolMessage +from langgraph_qwen.chat_model import ChatQwenOpenAICompat +from langgraph_qwen.mcp import load_mcp_tools + +async def plan(task: str) -> List[str]: + planner = ChatQwenOpenAICompat(temperature=0) + ai = await planner.ainvoke([HumanMessage(content=f"把任务拆成可执行步骤（每行一步）：\n{task}")]) + steps = [s.strip() for s in str(ai.content).splitlines() if s.strip()] + return steps[:8] + +async def tools_for_step(step_idx: int): + # ★ 示例：偶数步用 weather，奇数步用 play/test 工具；也可来自文件 + if step_idx % 2 == 0: + return await load_mcp_tools(servers={ + "weather": {"url":"http://127.0.0.1:8000/mcp/","transport":"streamable_http"} + }) + else: + cfg = os.getenv("MCP_CONFIG_PATH") # 比如 ./mcp_servers.yaml + return await load_mcp_tools(config_path=cfg) + +async def execute(steps: List[str], max_tool_steps_per_step: int = 4): + msgs = [] + for i, step in enumerate(steps, 1): + tools = await tools_for_step(i) + tool_map = {t.name: t for t in tools} + model = ChatQwenOpenAICompat(temperature=0).bind_tools(tools).bind(tool_choice="auto") + + msgs.append(HumanMessage(content=f"执行第{i}步：{step}")) + for _ in range(max_tool_steps_per_step): + ai = await model.ainvoke(msgs) + msgs.append(ai) + calls = getattr(ai, "tool_calls", []) or ai.additional_kwargs.get("tool_calls", []) + if not calls: + break + for call in calls: + name, args, call_id = call.get("name"), call.get("args", {}), call.get("id") or "" + tool = tool_map.get(name) + if not tool: + msgs.append(ToolMessage(tool_call_id=call_id, content=f"Unknown tool: {name}")) + continue + try: + out = await tool.ainvoke(args) if hasattr(tool, "ainvoke") else tool.invoke(args) + msgs.append(ToolMessage(tool_call_id=call_id, content=str(out))) + except Exception as e: + msgs.append(ToolMessage(tool_call_id=call_id, content=f"Error: {e}")) + + final = await ChatQwenOpenAICompat(temperature=0).ainvoke( + msgs + [HumanMessage(content="请汇总执行结果，简洁给出结论。")] + ) + print("=== Final ===") + print(final.content) + +async def main(): + steps = await plan("在巴塞罗那找一个带游泳池的民宿，然后搜索附近的餐厅和景点") + await execute(steps, max_tool_steps_per_step=4) + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/mcp_modes/react_agent.py b/examples/mcp_modes/react_agent.py new file mode 100644 index 0000000..41a45fa --- /dev/null +++ b/examples/mcp_modes/react_agent.py @@ -0,0 +1,33 @@ +# examples/mcp_modes/react_agent.py +import os, asyncio +from langchain_core.messages import HumanMessage +from langgraph_qwen.mcp import create_qwen_agent_with_mcp_async + +# 方式 A：直接在代码里声明服务器（HTTP + 本地 stdio 混用） +SERVERS = { + "weather": { + "url": "http://127.0.0.1:8000/mcp/", + "transport": "streamable_http", + }, + # "airbnb": {"command":"npx","args":["-y","@openbnb/mcp-server-airbnb"],"transport":"stdio"}, +} + +# 方式 B：集中配置文件（JSON/YAML），二选一： +CONFIG_PATH = os.getenv("MCP_CONFIG_PATH") # 例如 ./mcp_servers.json 或 ./mcp_servers.yaml + +async def main(): + # 任选其一：传 servers，或传 config_path，或两者都传（servers 会覆盖同名） + agent = await create_qwen_agent_with_mcp_async( + servers=SERVERS, + config_path=CONFIG_PATH, + tool_choice="auto", + ) + res = await agent.ainvoke( + {"messages": [HumanMessage(content="列出可用工具，然后任选一个演示调用，并用简洁中文总结。")]}, + config={"recursion_limit": 6}, # ★ 显式控制 ReAct 工具交互最大步数 + ) + print("=== Final ===") + print(res["messages"][-1].content) + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_qwen/mcp.py b/langgraph_qwen/mcp.py new file mode 100644 index 0000000..f339be3 --- /dev/null +++ b/langgraph_qwen/mcp.py @@ -0,0 +1,176 @@ +# langgraph_qwen/mcp.py +from __future__ import annotations + +import os +import json +import asyncio +from pathlib import Path +from typing import Any, Dict, List, Optional + +from langgraph.prebuilt import create_react_agent +from .chat_model import ChatQwenOpenAICompat + +__all__ = [ + "load_mcp_tools", + "create_qwen_agent_with_mcp_async", + "create_qwen_agent_with_mcp", + "resolve_servers_config", +] + +def _env(name: str, default: str = "") -> str: + return os.getenv(name) or default + +# ---------- 配置加载 & 合并 ---------- + +def _read_config_file(path: str) -> Dict[str, Dict[str, Any]]: + p = Path(path) + if not p.exists(): + raise FileNotFoundError(f"MCP config file not found: {path}") + + ext = p.suffix.lower() + if ext in {".json", ""}: + data = json.loads(p.read_text(encoding="utf-8")) + elif ext in {".yml", ".yaml"}: + try: + import yaml # type: ignore + except Exception as e: + raise RuntimeError("需要解析 YAML：请安装 `pip install pyyaml`") from e + data = yaml.safe_load(p.read_text(encoding="utf-8")) + else: + raise ValueError(f"Unsupported config extension: {ext}") + + # 支持两种结构： + # 1) {"servers": { ... }} + # 2) { ... } 直接就是 servers 映射 + if isinstance(data, dict) and "servers" in data and isinstance(data["servers"], dict): + return data["servers"] + if isinstance(data, dict): + return data + raise ValueError("Invalid config structure: expect a dict (optionally under key 'servers').") + +def _servers_from_env_json() -> Optional[Dict[str, Dict[str, Any]]]: + raw = os.getenv("MCP_SERVERS_JSON") + if not raw: + return None + try: + data = json.loads(raw) + if isinstance(data, dict) and data: + return data # 直接就是 servers 映射 + except Exception: + pass + return None + +def _fallback_servers() -> Dict[str, Dict[str, Any]]: + # 最后兜底：单一 HTTP 测试服务（本地 FastAPI/fastmcp-http） + return { + "weather": { + "url": _env("WEATHER_MCP_URL", "http://127.0.0.1:8000/mcp/"), + "transport": _env("WEATHER_TRANSPORT", "streamable_http"), + } + } + +def resolve_servers_config( + servers: Optional[Dict[str, Dict[str, Any]]] = None, + config_path: Optional[str] = None, +) -> Dict[str, Dict[str, Any]]: + """ + 统一解析 servers 配置的入口，优先级（后者覆盖前者）： + 1) 文件（config_path 或 MCP_CONFIG_PATH） + 2) 环境变量 MCP_SERVERS_JSON + 3) 代码传入的 servers（作为最终覆盖） + 4) 兜底本地 weather HTTP 服务器 + """ + merged: Dict[str, Dict[str, Any]] = {} + + # 1) 文件 + path = config_path or os.getenv("MCP_CONFIG_PATH") + if path: + try: + merged.update(_read_config_file(path)) + except Exception as e: + raise RuntimeError(f"读取 MCP 配置文件失败：{e}") + + # 2) 环境变量（JSON 字符串） + env_servers = _servers_from_env_json() + if env_servers: + merged.update(env_servers) + + # 3) 代码传入（最后覆盖） + if servers: + merged.update(servers) + + # 4) 兜底 + if not merged: + merged = _fallback_servers() + return merged + +# ---------- MCP 工具加载 ---------- + +async def load_mcp_tools( + servers: Optional[Dict[str, Dict[str, Any]]] = None, + config_path: Optional[str] = None, +) -> List[Any]: + """ + 异步加载 MCP 工具（支持 HTTP streamable 与 本地 stdio/npx）。 + 依赖：langchain-mcp-adapters + """ + try: + from langchain_mcp_adapters.client import MultiServerMCPClient # type: ignore + except Exception as e: + raise RuntimeError("请安装：`uv pip install -e '.[mcp-adapters]'` 或 `pip install langchain-mcp_adapters`") from e + + resolved_servers = resolve_servers_config(servers=servers, config_path=config_path) + client = MultiServerMCPClient(resolved_servers) + + tools = await client.get_tools() + + # 关闭会话（兼容不同版本） + try: + if hasattr(client, "close") and callable(getattr(client, "close")): + await client.close() # type: ignore + elif hasattr(client, "close_all_sessions") and callable(getattr(client, "close_all_sessions")): + await client.close_all_sessions() # type: ignore + except Exception: + pass + return tools + +# ---------- Agent 工厂 ---------- + +async def create_qwen_agent_with_mcp_async( + *, + servers: Optional[Dict[str, Dict[str, Any]]] = None, + config_path: Optional[str] = None, + tool_choice: str = "auto", + model: Optional[ChatQwenOpenAICompat] = None, +): + """ + 异步工厂：加载 MCP 工具 -> 绑定到 Qwen 模型 -> 创建 LangGraph ReAct agent + """ + tools = await load_mcp_tools(servers=servers, config_path=config_path) + model = (model or ChatQwenOpenAICompat(temperature=0)).bind_tools(tools).bind(tool_choice=tool_choice) + return create_react_agent(model, tools) + +def _run_coro_sync(coro): + try: + asyncio.get_running_loop() + except RuntimeError: + return asyncio.run(coro) + raise RuntimeError("检测到事件循环，改用：`await create_qwen_agent_with_mcp_async(...)`") + +def create_qwen_agent_with_mcp( + *, + model: Optional[ChatQwenOpenAICompat] = None, + servers: Optional[Dict[str, Dict[str, Any]]] = None, + config_path: Optional[str] = None, + tool_choice: str = "auto", +): + """ + 同步工厂：内部临时事件循环拉工具，再创建 ReAct agent。 + 用于 quickstart/脚本场景；在异步框架里请用 create_qwen_agent_with_mcp_async。 + """ + model = model or ChatQwenOpenAICompat(temperature=0) + return _run_coro_sync( + create_qwen_agent_with_mcp_async( + servers=servers, config_path=config_path, tool_choice=tool_choice, model=model + ) + )