How to integrate Bolna MCP with LangChain

This guide walks you through connecting Bolna to LangChain using the Composio tool router. By the end, you'll have a working Bolna agent that can list all voice agents available to me, initiate a call using your sales agent, get status of recent agent executions through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Bolna account through Composio's Bolna MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Bolna is an AI platform for building conversational voice agents. It helps businesses automate support and streamline interactions through natural, voice-powered conversations.

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Introduction

This guide walks you through connecting Bolna to LangChain using the Composio tool router. By the end, you'll have a working Bolna agent that can list all voice agents available to me, initiate a call using your sales agent, get status of recent agent executions through natural language commands.

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

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

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

The Bolna MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bolna account. It provides structured and secure access to your Bolna voice agent platform, so your agent can perform actions like listing agents, making phone calls, managing executions, and retrieving analytics on your behalf.

  • Automated voice call initiation: Let your AI agent instantly initiate phone calls using your Bolna conversational agents, streamlining outreach and support tasks.
  • Agent and phone number management: Effortlessly fetch and list all your Bolna agents or phone numbers, making it easy to review and organize your voice assets.
  • Real-time execution monitoring: Retrieve detailed information about specific call executions or monitor all executions for a given agent to track performance and outcomes.
  • Batch processing for agents: List and manage batch operations associated with your agents, supporting bulk workflows and campaign management.
  • Agent cleanup and maintenance: Quickly delete agents or batches that are no longer needed, keeping your Bolna environment organized and up to date.

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 Bolna 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 Bolna 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: ['bolna']
    }
);

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

Configure the agent with the MCP URL

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

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

Add Provider to Bolna

Tool to add a new telephony or voice service provider to your Bolna account.

Copy Bolna Agent

Tool to create a duplicate copy of an existing Bolna voice AI agent.

Create Bolna Voice AI Agent (v2)

Tool to create a new Bolna Voice AI agent using the v2 API.

Create Bolna Batch

Tool to create a new outbound calling batch by uploading a CSV of contacts to obtain a batch_id.

Create Bolna Knowledgebase

Tool to create a new knowledgebase for Voice AI agents to reference during conversations.

Create Template Agent

Tool to create a new Bolna Voice AI agent from a template.

Delete agent by id

Permanently delete a Voice AI agent and all associated data including batches, executions, and configurations

Delete batch by id

Permanently delete a batch campaign by its ID, removing it from the system.

Delete Knowledgebase

Tool to permanently delete a knowledgebase from your Bolna account.

Fetch all batches by agent id

Retrieve all batches associated with a specific Bolna Voice AI agent.

Get all agents

Retrieve all agents configured in your Bolna account Returns a comprehensive list of all voice agents with their configurations including: - Agent metadata (ID, name, type, status) - Task configurations (conversation settings, toolchains) - AI model settings (LLM, transcriber, synthesizer) - Webhook and phone number assignments - System prompts and guardrails This is useful for listing available agents, checking agent configurations, or finding specific agents by their properties.

Get execution by id

Retrieve detailed information about a specific phone call execution by its ID.

Get knowledgebase by ID

Tool to retrieve details of a specific knowledgebase by its ID.

Get User Information

Tool to retrieve information about the authenticated user.

Import Bolna Agent

Tool to import an existing Bolna voice AI agent by its ID.

List agents (paginated)

Tool to retrieve a paginated list of all agents in your Bolna account.

List Knowledgebases

Tool to retrieve all knowledgebases from your Bolna account.

List all phone numbers

Tool to list all phone numbers associated with your Bolna account.

List all providers

Retrieve all providers associated with your Bolna account Returns a list of all configured providers including: - Provider IDs (unique identifiers) - Provider names (e.

List available voices

Tool to list all available voices that can be utilized for Voice AI agents.

Make an outbound phone call from agent

Initiate an outbound phone call using a configured Bolna Voice AI agent.

Remove Provider from Bolna Account

Tool to remove a provider from your Bolna account by its key name.

Retrieve agent by id

Retrieve complete configuration and details for a specific Bolna voice AI agent by its ID.

Retrieve agent execution details

Retrieve detailed information about a specific execution (call/conversation) by an agent, including transcript, costs, duration, status, and telephony data

Retrieve agent execution status

Retrieve all executions for a specific agent with pagination and filtering support.

Retrieve Batch Details by ID

Retrieve comprehensive details about a specific Bolna batch by its ID.

Retrieve batch execution list

Retrieve all executions from a specific batch with pagination support.

Schedule Batch by ID

Schedule a batch to execute at a specific time.

Search available phone numbers

Tool to search for available phone numbers that can be purchased for Bolna Voice agents.

Setup inbound call for agent

Add agent for inbound calls

Stop Agent Calls

Tool to stop all queued or scheduled calls for a specific Voice AI agent.

Stop batch by id

Stop a running batch by its ID.

Update Bolna Voice AI Agent (v2)

Tool to update all settings and configuration of an existing Bolna Voice AI agent using the v2 API.

FRAMEWORKS

How to build Bolna MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Bolna MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Bolna MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Bolna MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Bolna MCP with Claude Cowork

Codex logo

Codex

Use Bolna MCP with Codex

OpenClaw logo

OpenClaw

Use Bolna MCP with OpenClaw

Hermes logo

Hermes

Use Bolna MCP with Hermes

CLI logo

CLI

Use Bolna MCP with CLI

Google ADK logo

Google ADK

Use Bolna MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Bolna MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Bolna MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Bolna MCP with LlamaIndex

CrewAI logo

CrewAI

Use Bolna MCP with CrewAI

FAQ

Frequently asked questions

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

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

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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.

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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.

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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.

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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.

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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.

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