How to integrate Botdog MCP with LangChain

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

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Botdog is a LinkedIn outreach platform for managing campaigns, leads, messages, analytics, and sending accounts. It helps sales teams run outbound workflows and track performance from one place.

12 Tools

Introduction

This guide walks you through connecting Botdog to LangChain using the Composio tool router. By the end, you'll have a working Botdog agent that can list your campaigns, add leads, review campaign sequences, schedule messages, and track analytics through natural language commands.

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

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

Also integrate Botdog 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 Botdog project to Composio
  • Create a Tool Router MCP session for Botdog
  • Initialize an MCP client and retrieve Botdog tools
  • Build a LangChain agent that can interact with Botdog
  • 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 Botdog MCP server, and what's possible with it?

The Botdog MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Botdog account. It provides structured and secure access to your campaigns, leads, LinkedIn messaging, outreach analytics, and connected sending accounts, so your agent can review campaigns, add leads, send or schedule messages, monitor results, and check account health on your behalf.

  • Campaign oversight: Have your agent list campaigns, review their status and schedule, and inspect configured workflow steps and branches.
  • Lead management: Direct your agent to find leads by campaign or engagement state and add LinkedIn profiles to an existing campaign.
  • LinkedIn messaging: Instruct your agent to send a message to an existing lead conversation now or schedule it for later.
  • Outreach performance: Let the agent report invitation, message, profile visit, and response results for your team or individual campaigns over a chosen period.
  • Team and account visibility: Have your agent list team members, check team details, and review the connection health of LinkedIn sending accounts.

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 Botdog 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 Botdog 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: ['botdog']
    }
);

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

Configure the agent with the MCP URL

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

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

Add Leads to Campaign

Create 1 to 100 lead records from LinkedIn profiles in an existing campaign.

Get Analytics

Return outreach analytics for a preset period, either as team/campaign totals or as a per-user breakdown.

Get Campaign

Return one campaign's identity, status, owner, and configured dates by campaign ID.

Get Campaign Sequence

Return a campaign's configured workflow steps and branches in graph, tree, readable, or combined form; this does not edit or launch the campaign.

Get Campaign Statistics

Return invitation, message, profile-visit, and response metrics and rates for one campaign.

Get Current Team

Return the name and user count of the Botdog team authenticated by the connected API key.

Get Daily Analytics

Return one outreach-activity row per calendar day plus totals for an explicit date range, optionally filtered by user or campaign.

List LinkedIn Accounts

Return connected LinkedIn sending accounts and their connection health for the authenticated Botdog team.

List Campaigns

Return campaigns in the authenticated Botdog team, optionally filtered by status or owner user IDs.

List Leads

Return one cursor-controlled page of team leads, optionally filtered by campaign, list, campaign state, reply state, unread state, or skip state.

List Team Users

Return Botdog users in the authenticated team so their IDs can be used in campaign and analytics filters.

Send or Schedule Message

Immediately send a LinkedIn message to an existing lead conversation, or schedule delivery when send_at is provided.

FRAMEWORKS

How to build Botdog MCP Agent with another framework

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Use Botdog MCP with OpenAI Agents SDK

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Claude Agent SDK

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Claude Code

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Claude Cowork logo

Claude Cowork

Use Botdog MCP with Claude Cowork

Codex logo

Codex

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OpenClaw logo

OpenClaw

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Hermes logo

Hermes

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CLI logo

CLI

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Google ADK logo

Google ADK

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Vercel AI SDK logo

Vercel AI SDK

Use Botdog MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Botdog MCP with Mastra AI

LlamaIndex logo

LlamaIndex

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CrewAI logo

CrewAI

Use Botdog MCP with CrewAI

FAQ

Frequently asked questions

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

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

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