How to integrate ActiveMerge MCP with LangChain

This guide walks you through connecting ActiveMerge to LangChain using the Composio tool router. By the end, you'll have a working ActiveMerge agent that can generate invoices from saved templates, merge contract files for client signature, store signed ndas in project folders through natural language commands. This guide will help you understand how to give your LangChain agent real control over a ActiveMerge account through Composio's ActiveMerge MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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ActiveMerge is a document automation platform for generating documents, images, merged files, and e-signature workflows. It helps teams turn templates and stored files into finished, shareable documents faster.

13 Tools

Introduction

This guide walks you through connecting ActiveMerge to LangChain using the Composio tool router. By the end, you'll have a working ActiveMerge agent that can generate invoices from saved templates, merge contract files for client signature, store signed ndas in project folders through natural language commands.

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

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

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

The ActiveMerge MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your ActiveMerge account. It provides structured and secure access to your templates, workflows, generation jobs, credits, and stored files, so your agent can generate documents, create images, run template workflows, track generation progress, and manage generated files on your behalf.

  • Template-based document generation: Have your agent review available document templates and their required placeholders, then generate documents using your case-sensitive data.
  • Automated image creation: Let the agent inspect image templates, create images from your records, and check each generation job for progress, errors, and completed output.
  • Multi-document workflows: Direct your agent to find available workflows, collect their required placeholders, and generate documents for every template in a chosen workflow.
  • Generation tracking and credit checks: Instruct your agent to check remaining credits before starting work, then review document generation history and job status.
  • Generated file management: Have your agent browse stored files, save completed document outputs to permanent storage, and prepare stored files for download.

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 ActiveMerge 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 ActiveMerge 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: ['active_merge']
    }
);

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

Configure the agent with the MCP URL

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

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

Generate Document

Start asynchronous document generation from a template and case-sensitive placeholder data.

Generate Images

Start an asynchronous image-generation job from inline JSON records.

Generate Workflow Documents

Generate documents for every template in an ActiveMerge workflow using case-sensitive placeholder data.

Get Credits

Get total, used, remaining, plan, and prepaid ActiveMerge credits before running credit-consuming generation tools.

Get Document Generations

Get one document-generation job by ID, or list and filter generation history when no job ID is supplied.

Get Document Template Placeholders

Return the case-sensitive placeholder names required by one document template.

Get File Download URL

Create a temporary download URL for a file in ActiveMerge cloud storage.

Get Image Generation

Get an image-generation job's progress, credit usage, errors, and temporary ZIP download URL when complete.

Get Image Templates

List image templates and basic placeholders, or get complete dimensions, positions, defaults, and styles for one template when template_id is supplied.

Get Workflows

List available ActiveMerge workflows, or return the case-sensitive placeholders for one workflow when workflow_id is supplied.

List Document Templates

List document templates available to the connected ActiveMerge account, including the unique template IDs needed for placeholder lookup and generation.

List Stored Files

List the immediate files and folders at the ActiveMerge cloud-storage root, or directly inside parent_folder_id when supplied.

Save Generated Files

Save non-expired output from a completed document-generation job to permanent ActiveMerge cloud storage.

FRAMEWORKS

How to build ActiveMerge MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use ActiveMerge MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use ActiveMerge MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use ActiveMerge MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use ActiveMerge MCP with Claude Cowork

Codex logo

Codex

Use ActiveMerge MCP with Codex

OpenClaw logo

OpenClaw

Use ActiveMerge MCP with OpenClaw

Hermes logo

Hermes

Use ActiveMerge MCP with Hermes

CLI logo

CLI

Use ActiveMerge MCP with CLI

Google ADK logo

Google ADK

Use ActiveMerge MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use ActiveMerge MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use ActiveMerge MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use ActiveMerge MCP with LlamaIndex

CrewAI logo

CrewAI

Use ActiveMerge MCP with CrewAI

FAQ

Frequently asked questions

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

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

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