• Prerequisites
• Create the Python Files
• Install the LangChain and LangGraph Packages
• Run the Python Script

LangChain is a framework for developing applications powered by large language models (LLMs).

Prerequisites

Before you can configure and use LangChain with Connect AI, you must first connect a data source to your Connect AI account. See Sources for more information.

You must also generate a Personal Access Token (PAT) on the Settings page. Copy this down, as it acts as your password during authentication. You must also generate an OAuth JWT bearer token. Copy this down, as it acts as your password during authentication.

You need Python >= 3.10 to install the LangChain and LangGraph packages.

Create the Python Files

1. Create a folder for LangChain MCP.

2. Create two Python files within the folder: config.py and langchain.py.

3. In config.py, create a class Config to define your MCP server authentication and URL. You need to provide your Base64-encoded Connect AI username and PAT (obtained in the prerequisites):In config.py, create a class Config to define your MCP server authentication and URL. MCP_AUTH should be set to "OAUTH_JWT_TOKEN", not EMAIL:PAT:

   class Config:
         MCP_BASE_URL = "https://mcp.cloud.cdata.com/mcp"   #MCP Server URL
         MCP_AUTH = "base64encoded(EMAIL:PAT)"   #Base64 encoded Connect AI Email:PAT
   

4. In langchain.py, set up your MCP server and MCP client to call the tools and prompts:

    """
    Integrates a LangChain ReAct agent with CData Connect AI MCP server.
    The script demonstrates fetching, filtering, and using tools with an LLM for agent-based reasoning.
    """
    import asyncio
   
    from langchain_mcp_adapters.client import MultiServerMCPClient
    from langchain_openai import ChatOpenAI
    from langgraph.prebuilt import create_react_agent
    from config import Config
   
    async def main():
        # Initialize MCP client with one or more server URLs    
        mcp_client = MultiServerMCPClient(
            connections={
                "default": {  # you can name this anything
                "transport": "streamable_http",
                "url": Config.MCP_BASE_URL,
                "headers": {"Authorization": f"Basic {Config.MCP_AUTH}"},
            }
        }
    )
   
        # Load remote MCP tools exposed by the server
        all_mcp_tools = await mcp_client.get_tools()
        print("Discovered MCP tools:", [tool.name for tool in all_mcp_tools])
   
        # Create and run the ReAct style agent
        llm = ChatOpenAI(
            model="gpt-4o", 
            temperature=0.2,
            api_key="YOUR_OPEN_API_KEY"   #Use your OpenAPI Key here. This can be found here: https://platform.openai.com/.
        )
        agent = create_react_agent(llm, all_mcp_tools)
   
        user_prompt = "Tell me how many sales I had in Q1 for the current fiscal year."   #Change prompts as per need
        print(f"\nUser prompt: {user_prompt}")
   
        # Send a prompt asking the agent to use the MCP tools
        response = await agent.ainvoke(
            { "messages": [{ "role": "user", "content": (user_prompt),}]}
        )
   
        # Print out the agent’s final response
        final_msg = response["messages"][-1].content
        print("Agent final response:", final_msg)
   
    if __name__ == "__main__":
        asyncio.run(main())
   

Install the LangChain and LangGraph Packages

Run pip install langchain-mcp-adapters langchain-openai langgraph in your project terminal.

Run the Python Script

1. When the installation finishes, run python langchain.py to execute the script.

2. The script discovers the Connect AI MCP tools needed for the LLM to query the connected data.

3. Supply a prompt for the agent. The agent provides a response.