# How to integrate Segment MCP with LangChain

```json
{
  "title": "How to integrate Segment MCP with LangChain",
  "toolkit": "Segment",
  "toolkit_slug": "segment",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/segment/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/segment/framework/langchain.md",
  "updated_at": "2026-05-12T10:25:03.502Z"
}
```

## Introduction

This guide walks you through connecting Segment to LangChain using the Composio tool router. By the end, you'll have a working Segment agent that can fetch daily api usage for each source, add metadata labels to a specific source, identify a user and update their traits through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Segment account through Composio's Segment MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Segment with

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

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Segment project to Composio
- Create a Tool Router MCP session for Segment
- Initialize an MCP client and retrieve Segment tools
- Build a LangChain agent that can interact with Segment
- 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 Segment MCP server, and what's possible with it?

The Segment MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Segment account. It provides structured and secure access to your customer data platform, so your agent can perform actions like identifying users, tracking analytics events, managing sources and destinations, and monitoring API usage on your behalf.
- User identification and trait management: Your agent can identify users, set or update their traits, and manage user profiles using Segment's Identify and Group tools.
- Analytics event tracking and batching: Effortlessly track individual or batched analytics events, enabling automated insights and seamless event monitoring across platforms.
- Source and destination administration: Let your agent add labels to sources, delete sources, retrieve detailed destination configurations, and list warehouses connected to a source.
- Alias and merge user identities: Merge anonymous and known identities by aliasing user IDs for more accurate customer journeys and unified profiles.
- Usage monitoring and delivery metrics: Fetch daily API call usage per source and view delivery metrics summaries for destinations to keep tabs on system health and integration performance.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SEGMENT_ADD_LABELS_TO_SOURCE` | Add Labels to Source | Tool to add existing labels to a Source. Use when you have the source ID and want to tag it with metadata labels. |
| `SEGMENT_ALIAS` | Segment Alias | Tool to alias a previous user ID to a new user ID. Use when merging anonymous and known identities. |
| `SEGMENT_BATCH` | Batch Segment Analytics Events | Tool to send multiple analytics calls in a single batch request. Use when you want to reduce HTTP overhead by batching Identify/Track/Page/Screen/Group calls into one request. |
| `SEGMENT_DELETE_SOURCE` | Delete Source | Tool to delete a Segment Source. Use when you need to permanently remove a Source by its ID after confirmation. |
| `SEGMENT_GET_DAILY_PER_SOURCE_API_CALLS_USAGE` | Get Daily Per Source API Calls Usage | Tool to fetch daily API call counts per source for a given period. Use when you need daily breakdown of API usage by source after determining the reporting period. |
| `SEGMENT_GET_DESTINATION` | Get Destination | Tool to retrieve a Destination by ID. Use when you need to fetch the full configuration of a Segment Destination instance by its unique identifier. Falls back US→EU public API and legacy app endpoint; returns minimal envelope on legacy HTML or parse errors. |
| `SEGMENT_GROUP` | Segment Group | Tool to associate an identified user with a group via Segment HTTP Tracking API. Use when grouping users with traits. |
| `SEGMENT_IDENTIFY` | Segment Identify | Tool to identify a user and set/update traits via Segment HTTP Tracking API. |
| `SEGMENT_IMPORT_HISTORICAL_DATA` | Import Historical Data | Tool to import historical data in bulk with support for historical timestamps. Use when you need to backfill or import past events with their original timestamps into Segment. |
| `SEGMENT_LIST_CONNECTED_WAREHOUSES_FROM_SOURCE` | List Connected Warehouses From Source | Tool to list warehouses connected to a Source. Use when you need to retrieve warehouses for a given source ID. |
| `SEGMENT_LIST_DELIVERY_METRICS_SUMMARY_FROM_DESTINATION` | List Delivery Metrics Summary from Destination | Get an event delivery metrics summary from a Destination. Primary attempt uses Segment Public API; fallback to legacy app host if needed. On HTML fallback responses, returns a minimal valid envelope to maintain contract. |
| `SEGMENT_LIST_SCHEMA_SETTINGS_IN_SOURCE` | List Schema Settings in Source | Retrieve schema configuration settings for a Source. |
| `SEGMENT_PAGE` | Segment Page View | Tool to record a page view via Segment HTTP Tracking API. Use when sending page views with optional page name and properties. |
| `SEGMENT_REMOVE_SOURCE_WRITE_KEY` | Remove Source Write Key | Tool to remove a write key from a Source. Use when you need to revoke an existing write key for security or rotation. |
| `SEGMENT_SCREEN` | Segment Screen Event | Tool to record a mobile app screen view. Use when tracking screen views in a mobile app via Segment HTTP Tracking API. |
| `SEGMENT_TRACK` | Segment Track Event | Tool to record a custom user event via Segment HTTP Tracking API. Use when sending events to Segment with valid identity. |
| `SEGMENT_UPDATE_SOURCE` | Update Source | Tool to update a Source's metadata and settings. Use when you need to modify an existing Source after confirming its ID. |

## Supported Triggers

None listed.

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

The Segment MCP server is an implementation of the Model Context Protocol that connects your AI agent to Segment. It provides structured and secure access so your agent can perform Segment 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 Segment 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 Segment 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 Segment tools as needed
```python
# Create Tool Router session for Segment
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['segment']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "segment-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({
    "segment-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 Segment 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 Segment 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=['segment']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "segment-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 Segment 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: ['segment']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "segment-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 Segment 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 Segment 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 Segment MCP Agent with another framework

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

## Related Toolkits

- [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources.
- [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research.
- [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows.
- [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research.
- [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data.
- [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds.
- [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights.
- [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly.
- [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools.
- [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping.
- [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights.
- [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space.
- [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently.
- [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries.
- [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business.
- [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Segment MCP?

With a standalone Segment MCP server, the agents and LLMs can only access a fixed set of Segment tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Segment 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 Segment tools.

### Can I manage the permissions and scopes for Segment while using Tool Router?

Yes, absolutely. You can configure which Segment 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 Segment data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)