How to integrate Influxdb cloud MCP with LangChain

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Introduction

This guide walks you through connecting Influxdb cloud to LangChain using the Composio tool router. By the end, you'll have a working Influxdb cloud agent that can write temperature sensor data to bucket, add cpu usage graph to dashboard, update retention policy for analytics data, list all available api routes for instance through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Influxdb cloud account through Composio's Influxdb cloud MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Influxdb cloud project to Composio
  • Create a Tool Router MCP session for Influxdb cloud
  • Initialize an MCP client and retrieve Influxdb cloud tools
  • Build a LangChain agent that can interact with Influxdb cloud
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Influxdb cloud MCP server, and what's possible with it?

The Influxdb cloud MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your InfluxDB Cloud account. It provides structured and secure access to your time series data, letting your agent run queries, ingest new data, manage dashboards, and update user settings automatically.

  • Real-time data ingestion and writing: Instantly send line protocol data points to your InfluxDB Cloud buckets for seamless time series collection and analytics.
  • Automated dashboard cell management: Direct your agent to add new cells to existing dashboards, making it easy to visualize and monitor the latest metrics or results.
  • Advanced query analysis and validation: Have the agent generate and inspect Flux query Abstract Syntax Trees (AST) to validate and debug your analytics scripts before running them.
  • User and session management: Enable your agent to sign users in or out and even delete users by ID, supporting secure and automated access control.
  • DBRP mapping updates and retrieval: Let your agent fetch or update Database Retention Policy (DBRP) mappings, so you can adapt your data retention and default policies on the fly.

Supported Tools & Triggers

Tools
Add Dashboard CellTool to add a cell to a dashboard.
Delete UserTool to delete a user by ID.
Generate Flux Query ASTTool to generate a Flux query Abstract Syntax Tree (AST).
Get DBRP MappingTool to retrieve a DBRP mapping by ID.
List RoutesTool to list top-level API routes.
Sign InTool to create a user session by authenticating credentials.
Sign OutTool to expire a user session using a session cookie.
Update DBRPTool to update a DBRP mapping's default and retention policy.
Write Line Protocol DataTool to write line protocol data to a bucket.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Influxdb cloud functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Influxdb cloud tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Influxdb cloud
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['influxdb_cloud']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Influxdb cloud tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Influxdb cloud tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "influxdb_cloud-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Influxdb cloud MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Influxdb cloud tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Influxdb cloud related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Influxdb cloud and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['influxdb_cloud']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "influxdb_cloud-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Influxdb cloud related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Influxdb cloud through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Influxdb cloud MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Influxdb cloud MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Influxdb cloud MCP with Claude Agent SDK

Google ADK

Google ADK

Use Influxdb cloud MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Influxdb cloud MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Influxdb cloud MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Influxdb cloud MCP with LlamaIndex

CrewAI

CrewAI

Use Influxdb cloud MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Influxdb cloud MCP?

With a standalone Influxdb cloud MCP server, the agents and LLMs can only access a fixed set of Influxdb cloud tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Influxdb cloud and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Influxdb cloud tools.

Can I manage the permissions and scopes for Influxdb cloud while using Tool Router?

Yes, absolutely. You can configure which Influxdb cloud scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Influxdb cloud data and credentials are handled as safely as possible.

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