# How to integrate Influxdb cloud MCP with LangChain

```json
{
  "title": "How to integrate Influxdb cloud MCP with LangChain",
  "toolkit": "Influxdb cloud",
  "toolkit_slug": "influxdb_cloud",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/influxdb_cloud/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/influxdb_cloud/framework/langchain.md",
  "updated_at": "2026-05-12T10:15:53.118Z"
}
```

## 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 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.

## Also integrate Influxdb cloud with

- [OpenAI Agents SDK](https://composio.dev/toolkits/influxdb_cloud/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/influxdb_cloud/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/influxdb_cloud/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/influxdb_cloud/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/influxdb_cloud/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/influxdb_cloud/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/influxdb_cloud/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/influxdb_cloud/framework/cli)
- [Google ADK](https://composio.dev/toolkits/influxdb_cloud/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/influxdb_cloud/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/influxdb_cloud/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/influxdb_cloud/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/influxdb_cloud/framework/crew-ai)

## 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

| Tool slug | Name | Description |
|---|---|---|
| `INFLUXDB_CLOUD_ADD_DASHBOARD_CELL` | Add Dashboard Cell | Tool to add a cell to a dashboard. Use when you want to add or copy a cell to an existing dashboard after verifying the dashboard exists. |
| `INFLUXDB_CLOUD_DELETE_USER` | Delete User | Delete a user from InfluxDB Cloud by their user ID. This action permanently removes a user from the InfluxDB Cloud organization. Requires an operator token with write:users permission to execute successfully. Use this when you need to remove a user's access to the InfluxDB Cloud organization. |
| `INFLUXDB_CLOUD_GENERATE_QUERY_AST` | Generate Flux Query AST | Generates an Abstract Syntax Tree (AST) from a Flux query script. Use this tool to analyze the structure of a Flux query and validate its syntax. The AST shows the parsed structure but does not validate semantic correctness (e.g., whether buckets or fields exist). |
| `INFLUXDB_CLOUD_GET_DBRP` | Get DBRP Mapping | Retrieve a Database and Retention Policy (DBRP) mapping by ID from InfluxDB Cloud. DBRP mappings enable InfluxDB 1.x query compatibility by mapping old database/retention policy names to InfluxDB 2.x buckets. Use this action to: - Verify which bucket a 1.x database/retention policy maps to - Check if a mapping is the default for its database - Inspect mapping configuration before updating or querying data with 1.x APIs Prerequisites: You must have a valid DBRP mapping ID (obtain via listing DBRP mappings or from previous create operations). |
| `INFLUXDB_CLOUD_LIST_ROUTES` | List Routes | Lists all available InfluxDB v2 API endpoints and routes. This action queries the root API endpoint (GET /api/v2) to retrieve a comprehensive map of all available API resources and their corresponding URLs. Use this to discover what endpoints are available in your InfluxDB Cloud instance, including resources for data management (buckets, write, delete, query), user management (users, orgs, authorizations), monitoring (checks, tasks, dashboards), and configuration (labels, variables, Telegraf). The response includes both simple route strings (e.g., "/api/v2/buckets") and nested route objects (e.g., query routes with analyze, ast, suggestions endpoints). Authentication: Requires a valid authorization token in the metadata headers. |
| `INFLUXDB_CLOUD_SIGNIN` | Sign In | Authenticates a user with username and password to create a session with InfluxDB Cloud. Returns a session cookie that can be used for subsequent API requests instead of token-based authentication. Use this when you need to authenticate with user credentials rather than API tokens, or when establishing a user session for operations that require session-based authentication. |
| `INFLUXDB_CLOUD_SIGNOUT` | Sign Out | Tool to expire a user session using a session cookie. Use when ending an authenticated session after signin. |
| `INFLUXDB_CLOUD_UPDATE_DBRP` | Update DBRP | Tool to update a DBRP mapping's default and retention policy. Use when modifying an existing DBRP mapping after initial creation. |
| `INFLUXDB_CLOUD_WRITE_DATA` | Write Line Protocol Data | Writes time-series data in line protocol format to an InfluxDB Cloud bucket. Use this tool to ingest metrics, sensor data, or any time-series measurements into InfluxDB. The data must be formatted according to InfluxDB line protocol specification. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Influxdb cloud MCP server is an implementation of the Model Context Protocol that connects your AI agent to Influxdb cloud. It provides structured and secure access so your agent can perform Influxdb cloud operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

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
```python
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")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

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

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['influxdb_cloud']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
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)
```

```typescript
const client = new MultiServerMCPClient({
    "influxdb_cloud-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
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")
```

```typescript
let conversationHistory: any[] = [];

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

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
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())
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['influxdb_cloud']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "influxdb_cloud-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Influxdb cloud related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## 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](https://composio.dev/toolkits/influxdb_cloud/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/influxdb_cloud/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/influxdb_cloud/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/influxdb_cloud/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/influxdb_cloud/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/influxdb_cloud/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/influxdb_cloud/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/influxdb_cloud/framework/cli)
- [Google ADK](https://composio.dev/toolkits/influxdb_cloud/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/influxdb_cloud/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/influxdb_cloud/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/influxdb_cloud/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/influxdb_cloud/framework/crew-ai)

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## Frequently Asked Questions

### 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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