test_sum_int_with_agent_streamable_http.py•8.66 kB
#!/usr/bin/env python3
"""
通过agent使用MCP的测试脚本 (Streamable HTTP方式)
"""
import asyncio
import sys
from pathlib import Path
import os
from dotenv import load_dotenv
import logging
import subprocess
import time
import httpx
# 添加src目录到Python路径
sys.path.insert(0, str(Path(__file__).parent.parent / "src" / "mcp_server"))
# 尝试加载.env文件
env_path = Path(__file__).parent.parent / ".env"
if env_path.exists():
load_dotenv(dotenv_path=env_path)
# 设置日志级别以显示更多LangChain内部信息
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
try:
from langchain_mcp_adapters.tools import load_mcp_tools
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from langgraph.prebuilt import create_react_agent
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
LANGCHAIN_AVAILABLE = True
except ImportError as e:
LANGCHAIN_AVAILABLE = False
print(f"Warning: LangChain dependencies not found. Install with: pip install langchain langchain-openai langchain-mcp-adapters langgraph")
print(f"Import error: {e}")
async def wait_for_server(url: str, timeout: int = 30) -> bool:
"""等待服务器启动"""
start_time = time.time()
while time.time() - start_time < timeout:
try:
async with httpx.AsyncClient() as client:
response = await client.get(url)
# Streamable HTTP服务器会返回405 (Method Not Allowed)而不是200
# 因为它只接受POST请求，所以检查状态码是否不是502
if response.status_code != 502:
return True
except Exception:
pass
await asyncio.sleep(1)
return False
if LANGCHAIN_AVAILABLE:
async def test_agent_with_mcp_streamable_http():
"""测试通过agent使用MCP (Streamable HTTP方式)"""
# 从环境变量获取配置
base_url = os.environ.get("LLM_BASE_URL")
api_key = os.environ.get("LLM_API_KEY")
model = os.environ.get("LLM_MODEL")
mcp_port = os.environ.get("MCP_SERVER_PORT", "8000")
# 检查环境变量是否设置
if not base_url:
raise ValueError("LLM_BASE_URL environment variable is not set")
if not api_key:
raise ValueError("LLM_API_KEY environment variable is not set")
if not model:
raise ValueError("LLM_MODEL environment variable is not set")
# 启动MCP服务器 (Streamable HTTP方式)
print("Starting MCP server with Streamable HTTP transport...")
server_process = subprocess.Popen([
sys.executable, "src/mcp_server/sum_int.py", "streamable-http"
], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
# 等待服务器启动
server_url = f"http://127.0.0.1:{mcp_port}/mcp"
if not await wait_for_server(server_url):
server_process.terminate()
raise RuntimeError("MCP server failed to start within timeout")
print(f"MCP server started at {server_url}")
try:
# 使用指定的LLM配置
try:
model = ChatOpenAI(
model=model,
base_url=base_url,
api_key=api_key,
temperature=0 # 降低随机性以获得更一致的结果
)
except Exception as e:
print(f"Failed to initialize model: {e}")
raise
# 使用Streamable HTTP客户端连接到MCP服务器
async with streamablehttp_client(server_url) as (read, write, _):
async with ClientSession(read, write) as session:
# 初始化连接
await session.initialize()
# 加载MCP工具
tools = await load_mcp_tools(session)
print(f"Loaded tools: {[tool.name for tool in tools]}")
# 创建agent
agent = create_react_agent(model, tools)
# 测试问题列表
test_questions = [
"What is 15 plus 25?",
"Calculate the sum of 123 and 456"
]
expected_results = [40, 579]
for i, question in enumerate(test_questions):
print(f"\n{'='*50}")
print(f"Testing question {i+1}: {question}")
print(f"{'='*50}")
try:
# 显示发送给agent的消息
print(f"\n[User Message] Sending to agent:")
print(f" Content: {question}")
# 调用agent
response = await agent.ainvoke({
"messages": [HumanMessage(content=question)]
})
# 显示完整的对话历史
print(f"\n[Agent Messages] Full conversation history:")
for j, msg in enumerate(response['messages']):
if isinstance(msg, HumanMessage):
print(f" Message {j+1} [Human]: {msg.content}")
elif isinstance(msg, AIMessage):
print(f" Message {j+1} [AI]: {msg.content}")
if hasattr(msg, 'tool_calls') and msg.tool_calls:
print(f" Tool Calls: {msg.tool_calls}")
elif isinstance(msg, ToolMessage):
print(f" Message {j+1} [Tool]: {msg.content}")
# 输出最终响应
agent_response = response['messages'][-1].content
print(f"\n[Final Response] Agent final response:")
print(f" {agent_response}")
# 验证结果是否包含预期值
if str(expected_results[i]) in agent_response:
print(f"\n✓ Test passed: Response contains expected result {expected_results[i]}")
else:
print(f"\n⚠ Warning: Response may not contain expected result {expected_results[i]}")
except Exception as e:
print(f"Error processing question '{question}': {e}")
import traceback
traceback.print_exc()
print(f"\n{'='*50}")
print("Agent with MCP Tests Completed")
print(f"{'='*50}")
finally:
# 终止服务器进程
server_process.terminate()
try:
server_process.wait(timeout=5)
except subprocess.TimeoutExpired:
server_process.kill()
def main():
"""主函数"""
if not LANGCHAIN_AVAILABLE:
print("Cannot run agent tests without LangChain dependencies.")
print("Install dependencies with: pip install langchain langchain-openai langchain-mcp-adapters langgraph")
return
print("=== Agent with MCP Integration Test (Streamable HTTP) ===")
base_url = os.environ.get("LLM_BASE_URL")
model = os.environ.get("LLM_MODEL")
print(f"Using API base URL: {base_url}")
print(f"Using model: {model}")
try:
asyncio.run(test_agent_with_mcp_streamable_http())
except Exception as e:
print(f"Test failed with error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
main()
else:
def main():
print("Cannot run agent tests without LangChain dependencies.")
print("Install dependencies with: pip install langchain langchain-openai langchain-mcp-adapters langgraph")
if __name__ == "__main__":
main()