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Prerequisites

Before you can configure and use LangChain with Connect AI, you must first do the following:
  • Connect a data source to your Connect AI account. See Sources for more information.
  • Generate a Personal Access Token (PAT) on the Settings page. Copy this down, as it acts as your password during authentication.
  • Obtain an OpenAI API key: https://platform.openai.com/.
  • Make sure you have Python >= 3.10 in order to install the LangChain and LangGraph packages.

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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):
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
1
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())

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Install the LangChain and LangGraph Packages

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

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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.
LangChain Terminal