How to integrate Zoho bigin MCP with LangChain

This guide walks you through connecting Zoho bigin to LangChain using the Composio tool router. By the end, you'll have a working Zoho bigin agent that can add new contact to sales pipeline, list all open deals this week, tag recent leads as 'hot prospects' through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Zoho bigin account through Composio's Zoho bigin MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Zoho Bigin is a simple CRM designed for small businesses to manage pipelines and customer relationships. It helps you organize deals, track progress, and streamline sales processes in one place.

54 Tools

Introduction

This guide walks you through connecting Zoho bigin to LangChain using the Composio tool router. By the end, you'll have a working Zoho bigin agent that can add new contact to sales pipeline, list all open deals this week, tag recent leads as 'hot prospects' through natural language commands.

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

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

Also integrate Zoho bigin with

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

The Zoho bigin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zoho bigin account. It provides structured and secure access to your CRM pipeline data, so your agent can manage contacts, track deals, organize records, handle attachments, and streamline your small business workflows—all on your behalf.

  • Automated record management: Add, update, or delete records in any Zoho bigin module to keep your CRM data accurate and up to date.
  • Tagging and categorization: Create new tags or apply them to records, making it easy to segment contacts, deals, or companies for better organization.
  • Attachment handling: Retrieve, download, or delete attachments associated with your records, letting your agent manage files and documents with ease.
  • Module and data discovery: List available modules and fetch records with sorting, filtering, and pagination—perfect for quickly surfacing the data you need.
  • Deleted records auditing: Access and review recently deleted records for auditing or restoration, helping you maintain data integrity and recover lost information.

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 Zoho bigin 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 Zoho bigin 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: ['zoho_bigin']
    }
);

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

Configure the agent with the MCP URL

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

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

Add Records

Tool to add new records to a module.

Add Tags to Records

Tool to add tags to a specific record in a module.

Create Bulk Read Job

Tool to create a bulk read job for exporting large amounts of data asynchronously.

Create Notes

Tool to create notes and associate them with records in Zoho Bigin.

Create Record Notes

Tool to create new notes for a specific record.

Create Tags

Tool to create tags for a module.

Delete Attachment

Tool to delete an attachment from a record.

Delete Note

Tool to delete a note from a specific record.

Delete Notes

Tool to delete multiple notes from Zoho Bigin.

Delete Record

Tool to delete a specific record from a module.

Delete Record Photo

Tool to delete a profile photo from a record.

Delete Records

Tool to delete records from a module.

Delink Related Records

Tool to delete the association between a module record and related list records.

Disable Notifications

Tool to disable instant notifications for one or more channels.

Download Attachment

Tool to download an attachment from a record.

Download Bulk Read Result

Tool to download the bulk read job result in ZIP format (containing CSV or ICS export).

Download Record Photo

Tool to download the profile photo associated with a specific record.

Enable Notifications

Tool to enable instant webhook notifications for module events in Bigin.

Get All Notes

Tool to retrieve the list of notes associated with records.

Get Attachments

Tool to retrieve attachments for a record.

Get Bulk Read Job Status

Tool to retrieve the details of a bulk read job performed earlier.

Get Custom View

Tool to get the metadata of a specific custom view configured in a module.

Get Custom Views

Tool to retrieve the list of custom views available for a module.

Get Deleted Records

Tool to get a list of deleted records in a module.

Get Fields

Tool to retrieve field metadata for a Bigin module.

Get Layout

Tool to retrieve details of a specific layout by layout ID.

Get Layouts

Tool to retrieve the list of layouts available for a module.

Get Module Metadata

Tool to retrieve metadata of a specific module by its API name.

Get Modules

Tool to retrieve a list of all modules.

Get Notification Details

Tool to retrieve information about enabled notifications.

Get Organization

Tool to retrieve organization details including name, ID, currency, time zone, and other settings.

Get Profiles

Tool to retrieve the list of available profiles and their properties in an organization.

Get Record

Tool to retrieve details of a specific record in a module using the record ID.

Get Record Notes

Tool to retrieve the list of notes associated with a specific record.

Get Records

Tool to retrieve records from a Bigin module.

Get Records Count

Tool to get the count of records in a Bigin module.

Get Related Lists Metadata

Tool to retrieve the list of related lists metadata for a module.

Get Related Records

Tool to retrieve related records associated with a specific record in a module.

Get Roles

Tool to retrieve the list of available roles and their properties in an organization.

Get Team Pipeline Records

Tool to retrieve pipeline records from Team Pipelines in Zoho Bigin.

Get User

Tool to retrieve details of a specific user using the user identification.

Get Users

Tool to retrieve the list of users in the organization.

Search Records

Tool to search for records in a Bigin module using various criteria.

Update Note

Tool to update an existing note for a specific record in a module.

Update Notification Details

Tool to update notification channel details in Zoho Bigin.

Update Notification Info

Tool to update specific notification information without losing existing data.

Update Records

Tool to update existing records in a module.

Update Related Records

Tool to update related records associated with a specific record in a module.

Update User

Tool to update details of an existing user by user ID.

Update Users

Tool to update details of multiple users in an organization.

Upload Attachment

Tool to upload an attachment to a record.

Upload Organization Photo

Tool to upload or update the brand logo or image for the current organization.

Upload Record Photo

Tool to upload a photo/image to a specific record (e.

Upsert Records

Tool to insert or update records in a module based on unique field values.

FRAMEWORKS

How to build Zoho bigin MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Zoho bigin MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Zoho bigin MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Zoho bigin MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Zoho bigin MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Zoho bigin MCP with Claude Cowork

Codex logo

Codex

Use Zoho bigin MCP with Codex

OpenClaw logo

OpenClaw

Use Zoho bigin MCP with OpenClaw

Hermes logo

Hermes

Use Zoho bigin MCP with Hermes

CLI logo

CLI

Use Zoho bigin MCP with CLI

Google ADK logo

Google ADK

Use Zoho bigin MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Zoho bigin MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Zoho bigin MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Zoho bigin MCP with LlamaIndex

CrewAI logo

CrewAI

Use Zoho bigin MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, absolutely. You can configure which Zoho bigin 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 Zoho bigin 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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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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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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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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