How to integrate Linkedin MCP with LangChain

This guide walks you through connecting Linkedin to LangChain using the Composio tool router. By the end, you'll have a working Linkedin agent that can share a new post about our product launch, delete your last published linkedin post, fetch company pages i can manage through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Linkedin account through Composio's Linkedin MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections.

22 Tools

Introduction

This guide walks you through connecting Linkedin to LangChain using the Composio tool router. By the end, you'll have a working Linkedin agent that can share a new post about our product launch, delete your last published linkedin post, fetch company pages i can manage through natural language commands.

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

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

Also integrate Linkedin with

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Linkedin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Linkedin account. It provides structured and secure access to your LinkedIn profile and company pages, so your agent can post updates, fetch your profile, manage company info, and even delete posts on your behalf.

  • Automated LinkedIn posting: Let your agent create and share new posts from your profile or managed company pages, keeping your network engaged without manual effort.
  • Profile information retrieval: Instantly fetch your LinkedIn profile details, including author ID and headline, for use in resumes, reporting, or personalized content generation.
  • Company page management: Retrieve a list of organizations you manage, making it easy for your agent to post or gather company info for employer branding and outreach.
  • Content cleanup and moderation: Direct your agent to delete specific LinkedIn posts (by share ID) to maintain a professional, up-to-date presence.

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:

  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

Step by step10 STEPS
1

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
2

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.
3

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
4

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
5

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 Linkedin functionality through MCP
6

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 Linkedin tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

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

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Linkedin 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 Linkedin tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "linkedin-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 Linkedin MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Linkedin tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

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 Linkedin 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
10

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 Linkedin 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: ['linkedin']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "linkedin-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 Linkedin 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 Linkedin 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 Linkedin action and event your agent gets out of the box.

Create article or URL share

Tool to create an article or URL share on LinkedIn using the UGC Posts API.

Create comment on LinkedIn post

Tool to create a first-level or nested comment on a LinkedIn share, UGC post, or parent comment via the Social Actions Comments API.

Create a LinkedIn post

Creates a new post on LinkedIn for the authenticated user or an organization they manage.

Delete LinkedIn Post

Deletes a specific LinkedIn post (share) by its unique `share_id`, which must correspond to an existing share.

Delete Post

Delete a LinkedIn post using the Posts API REST endpoint.

Delete UGC Post (Legacy)

Delete a UGC post using the legacy UGC Post API endpoint.

Get ad targeting facets

Tool to retrieve available ad targeting facets from LinkedIn Marketing API.

Get audience counts

Retrieves audience size counts for specified targeting criteria.

Get company info

Retrieves organizations where the authenticated user has specific roles (ACLs), to determine their management or content posting capabilities for LinkedIn company pages.

Get image details

Tool to retrieve details of a LinkedIn image using its URN.

Get images

Tool to retrieve image metadata including download URLs, status, and dimensions from LinkedIn's Images API.

Get my info

Fetches the authenticated LinkedIn user's profile information including name, headline, profile picture, and other profile details.

Get network size

Tool to retrieve the follower count for a LinkedIn organization.

Get organization page statistics

Tool to retrieve page statistics for a LinkedIn organization page.

Get person profile

Retrieves a LinkedIn member's profile information by their person ID.

Get post content

Tool to retrieve detailed post content including text, images, videos, and metadata from LinkedIn by post URN.

Get share statistics

Retrieves share statistics for a LinkedIn organization, including impressions, clicks, likes, comments, and shares.

Get videos

Retrieves video metadata from LinkedIn Marketing API.

Initialize image upload

Tool to initialize an image upload to LinkedIn and return a presigned upload URL plus the resulting image URN.

List reactions on entity

Retrieves reactions (likes, celebrations, etc.

Register image upload

Tool to initialize a native LinkedIn image upload for feed shares and return a presigned upload URL plus the resulting digital media asset URN.

Search ad targeting entities

Search for ad targeting entities using typeahead search.

FRAMEWORKS

How to build Linkedin MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Linkedin MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Linkedin MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Linkedin MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Linkedin MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Linkedin MCP with Claude Cowork

Codex logo

Codex

Use Linkedin MCP with Codex

Cursor logo

Cursor

Use Linkedin MCP with Cursor

OpenClaw logo

OpenClaw

Use Linkedin MCP with OpenClaw

Hermes logo

Hermes

Use Linkedin MCP with Hermes

CLI logo

CLI

Use Linkedin MCP with CLI

Google ADK logo

Google ADK

Use Linkedin MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Linkedin MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Linkedin MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Linkedin MCP with LlamaIndex

CrewAI logo

CrewAI

Use Linkedin MCP with CrewAI

OpenCode logo

OpenCode

Use Linkedin MCP with OpenCode

VS Code logo

VS Code

Use Linkedin MCP with VS Code

FAQ

Frequently asked questions

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

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

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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.

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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.

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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.

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