How to integrate Extrovert MCP with LangChain

This guide walks you through connecting Extrovert to LangChain using the Composio tool router. By the end, you'll have a working Extrovert agent that can find linkedin prospects, list your campaigns, manage writing styles, manage contexts, and view plan features through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Extrovert account through Composio's Extrovert MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Extrovert

Api Key

Extrovert is a LinkedIn prospect engagement platform for campaigns, monitored topics, writing styles, contexts, and team workflows. It helps teams scale relevant outreach while keeping prospect conversations organized.

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Introduction

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

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

Also integrate Extrovert with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

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

The Extrovert MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Extrovert account. It provides structured and secure access to your campaigns, prospects, writing styles, engagement contexts, team, and workspace plan, so your agent can review campaigns, find prospects, manage messaging guidance, inspect team members, and check plan capabilities on your behalf.

Campaign monitoring: Have your agent list prospect and topic campaigns, then review their status, filters, automation settings, owners, and sharing configuration.
Prospect research: Let the agent browse prospects, inspect LinkedIn profile and engagement details, or check whether a LinkedIn profile already exists in your team's records.
Engagement context management: Direct your agent to create, review, update, or delete company and user background contexts used to guide prospect engagement.
Writing style management: Instruct your agent to create, review, update, or remove writing styles for LinkedIn comments and direct messages.
Team and plan visibility: Have your agent review visible team members and check your workspace plan, available features, API capabilities, and quota usage.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

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

Step-by-step Guide

Step by step10 STEPS

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

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters 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/core is the core agent framework
dotenv/config 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

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();

What's happening:

We're importing LangChain's MCP adapter and Composio SDK
The dotenv/config import loads environment variables from your .env file
This setup prepares the foundation for connecting LangChain with Extrovert functionality through MCP

Initialize Composio client

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()
    });

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 Extrovert tools
Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

const url = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to Extrovert 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 Extrovert tools as needed

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "extrovert-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 });

What's happening:

We're creating a MultiServerMCPClient that connects to our Extrovert MCP server via HTTP
The client is configured with a name and the URL from our Tool Router session
getTools() retrieves all available Extrovert tools that the agent can use
We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Extrovert 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);
    });

What's happening:

We initialize an empty conversationHistory list to maintain context across interactions
A readline interface is used to continuously accept 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 invoke() method with the full conversation history
Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

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

What's happening:

We call the main() function to start the application

Complete Code

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

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: ['extrovert']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "extrovert-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 Extrovert 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 Extrovert 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.

TOOLS

Supported Tools

Every Extrovert action and event your agent gets out of the box.

Create Context

Create a Context for a user.

Create Style

Create a writing Style for a user.

Delete Context

Permanently delete one Context by UUID.

Delete Style

Permanently delete one Style by UUID.

Find Prospect by LinkedIn URL

Check whether a LinkedIn profile URL already exists among the team's prospects and return every matching prospect.

Get Campaign

Load full configuration and state for one campaign by its UUID.

Get Prospect

Load one existing prospect by UUID with LinkedIn profile, campaign and list bindings, flags, connection data, and engagement statistics.

Get Workspace Plan Features

Return the selected workspace's active plan, API capabilities, feature flags, and quota counters.

List Campaigns

Return all Prospects and Topic campaigns visible to the connected user, including their running state, owner, Context, Style, filters, automation, and sharing configuration.

List Contexts

Return all complete Context records owned by a user.

List Prospects

Return one page of existing prospects with campaign and list bindings, LinkedIn profile data, flags, connection state, and engagement statistics.

List Styles

Return all complete writing Styles owned by a user, including learning mode and separate comment and direct-message instructions.

List Team Members

Return users visible in the selected team, including the user IDs required to list or create owner-scoped contexts and styles.

Update Context

Fully replace a Context's name, company background, and user background.

Update Style

Update a Style's required name and learning-mode setting, optionally replacing or clearing its comment and DM instructions.

FRAMEWORKS

How to build Extrovert MCP Agent with another framework

OpenAI Agents SDK

Use Extrovert MCP with OpenAI Agents SDK

Claude Agent SDK

Use Extrovert MCP with Claude Agent SDK

Claude Code

Use Extrovert MCP with Claude Code

Claude Cowork

Use Extrovert MCP with Claude Cowork

Codex

Use Extrovert MCP with Codex

OpenClaw

Use Extrovert MCP with OpenClaw

Hermes

Use Extrovert MCP with Hermes

CLI

Use Extrovert MCP with CLI

Google ADK

Use Extrovert MCP with Google ADK

Vercel AI SDK

Use Extrovert MCP with Vercel AI SDK

Mastra AI

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LlamaIndex

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CrewAI

Use Extrovert MCP with CrewAI

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FAQ

Frequently asked questions

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

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 Extrovert tools.

Yes, absolutely. You can configure which Extrovert 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.

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 Extrovert data and credentials are handled as safely as possible.

Start with Extrovert.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Extrovert tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Extrovert MCP with LangChain

Connect Extrovert without auth hassles

Introduction

Also integrate Extrovert with

TL;DR

What is LangChain?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Initialize Composio client

Create a Tool Router session

Configure the agent with the MCP URL

Set up interactive chat interface

Run the application

Complete Code

Conclusion

Supported Tools

How to build Extrovert MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Bidsketch

Bolna

Botdog

BotPenguin

Frequently asked questions

What are the differences in Tool Router MCP and Extrovert MCP?+

Can I use Tool Router MCP with LangChain?+

Can I manage the permissions and scopes for Extrovert while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Extrovert.It takes 30 seconds.

Teams ship real agents on Composio