HuangtingFlux × LangChain

$ pip install langchain-mcp-adapters langchain-openai

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_react_agent
from langchain_openai import ChatOpenAI

async def main():
    # Connect to HuangtingFlux MCP server
    client = MultiServerMCPClient(
        {
            "huangting": {
                "transport": "http",
                "url": "https://mcp.huangting.ai/mcp",
            }
        }
    )

    # Load the three-stage SOP tools:
    # start_task, report_step_result, finalize_and_report, get_network_stats
    tools = await client.get_tools()
    print(f"Loaded tools: {[t.name for t in tools]}")

    llm = ChatOpenAI(model="gpt-4o", temperature=0)
    agent = create_react_agent(llm, tools)

    # Agent will automatically follow the Huangting Protocol three-stage SOP
    response = await agent.ainvoke({
        "messages": [{
            "role": "user",
            "content": "Analyze the core differences between LangChain and CrewAI frameworks."
        }]
    })
    print(response)

if __name__ == "__main__":
    asyncio.run(main())

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_mcp_adapters.tools import load_mcp_tools
from langchain.agents import create_react_agent
from langchain_openai import ChatOpenAI

async def main():
    client = MultiServerMCPClient({
        "huangting": {
            "transport": "http",
            "url": "https://mcp.huangting.ai/mcp",
        }
    })

    # Use a stateful session to maintain context across tool calls
    async with client.session("huangting") as session:
        tools = await load_mcp_tools(session)
        llm = ChatOpenAI(model="gpt-4o")
        agent = create_react_agent(llm, tools)

        result = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Research the current state of the MCP protocol ecosystem."
            }]
        })
        print(result)

asyncio.run(main())

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async def main():
    client = MultiServerMCPClient({
        "huangting": {
            "transport": "http",
            "url": "https://mcp.huangting.ai/mcp",
        }
    })

    tools = await client.get_tools()
    model = ChatOpenAI(model="gpt-4o")

    # Build a more complex agent graph using LangGraph
    agent = create_react_agent(model, tools)

    result = await agent.ainvoke({
        "messages": [{
            "role": "user",
            "content": "Compare the API pricing strategies of GPT-4o and Claude 3.7."
        }]
    })

    for msg in result["messages"]:
        print(f"[{msg.type}] {msg.content[:200]}")

asyncio.run(main())

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async def main():
    # Connect multiple MCP servers — HuangtingFlux as the SOP optimization layer
    client = MultiServerMCPClient({
        "huangting": {
            "transport": "http",
            "url": "https://mcp.huangting.ai/mcp",
            # No authentication required
        },
        # Add other MCP tool servers here
        # "your_tool_server": {
        #     "transport": "http",
        #     "url": "https://your-tool-server.com/mcp",
        #     "headers": {"Authorization": "Bearer YOUR_TOKEN"},
        # },
    })

    tools = await client.get_tools()
    model = ChatOpenAI(model="gpt-4o")
    agent = create_react_agent(model, tools)

    result = await agent.ainvoke({
        "messages": [{"role": "user", "content": "Start a multi-step research task"}]
    })
    print(result)

asyncio.run(main())