How to integrate SalesRobot MCP with LangChain

This guide walks you through connecting SalesRobot to LangChain using the Composio tool router. By the end, you'll have a working SalesRobot agent that can list your campaigns, review prospect status, add prospects, update prospects, and send messages through natural language commands. This guide will help you understand how to give your LangChain agent real control over a SalesRobot account through Composio's SalesRobot MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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SalesRobot is a sales engagement platform for LinkedIn outreach, campaign management, prospecting, and messaging. It helps sales teams automate personalized outreach workflows and manage replies at scale.

10 Tools

Introduction

This guide walks you through connecting SalesRobot to LangChain using the Composio tool router. By the end, you'll have a working SalesRobot agent that can list your campaigns, review prospect status, add prospects, update prospects, and send messages through natural language commands.

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

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

Also integrate SalesRobot with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The SalesRobot MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your SalesRobot account. It provides structured and secure access to your LinkedIn outreach campaigns, prospects, conversations, profiles, and posts, so your agent can manage prospects, review campaigns, track outreach, research profiles, and send messages on your behalf.

  • Campaign and prospect management: Have your agent list campaigns, review their prospects, add a LinkedIn prospect to a campaign, and update existing prospect details.
  • Outreach progress tracking: Let the agent check each prospect's sequence progress and message status within a campaign.
  • LinkedIn profile research: Direct your agent to retrieve person or company profiles and review their recent LinkedIn posts.
  • Inbox and messaging support: Instruct your agent to find relevant inbox conversations and send a message in an existing LinkedIn conversation.
  • Connected account visibility: Have your agent list the LinkedIn accounts connected to SalesRobot before running campaign, prospect, or messaging tasks.

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 SalesRobot 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 SalesRobot 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: ['salesrobot']
    }
);

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

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "salesrobot-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 SalesRobot MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available SalesRobot 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 SalesRobot 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 SalesRobot 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: ['salesrobot']
        }
    );

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

Add Prospect to Campaign

Add one identified LinkedIn prospect to a SalesRobot campaign.

Get LinkedIn Profile

Retrieve a LinkedIn person or company profile through a connected SalesRobot LinkedIn account.

Get Prospect Sequence Status

Get sequence-step execution and message status for one prospect, optionally within a campaign.

List Campaign Prospects

List and filter prospects for a campaign or across campaigns on one connected LinkedIn account.

List Campaigns

List and filter campaigns for one connected LinkedIn account, returning campaign UUIDs for prospect and status workflows.

List Inbox Threads

List and filter synchronized SalesRobot inbox threads for one connected LinkedIn account.

List LinkedIn Accounts

List LinkedIn accounts connected to SalesRobot and return the account UUIDs required by most other tools.

List LinkedIn Posts

List recent posts for a LinkedIn person or company through a connected SalesRobot LinkedIn account.

Send Message

Send one message in an existing SalesRobot LinkedIn conversation.

Update Prospect

Update profile, contact, and custom field data for one existing SalesRobot prospect.

FRAMEWORKS

How to build SalesRobot MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use SalesRobot MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use SalesRobot MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use SalesRobot MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use SalesRobot MCP with Claude Cowork

Codex logo

Codex

Use SalesRobot MCP with Codex

OpenClaw logo

OpenClaw

Use SalesRobot MCP with OpenClaw

Hermes logo

Hermes

Use SalesRobot MCP with Hermes

CLI logo

CLI

Use SalesRobot MCP with CLI

Google ADK logo

Google ADK

Use SalesRobot MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use SalesRobot MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use SalesRobot MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use SalesRobot MCP with LlamaIndex

CrewAI logo

CrewAI

Use SalesRobot MCP with CrewAI

FAQ

Frequently asked questions

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

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

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