How to integrate Jam MCP with LangChain

This guide walks you through connecting Jam to LangChain using the Composio tool router. By the end, you'll have a working Jam agent that can list your bug reports, analyze videos, inspect console logs, trace network requests, and read transcripts through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Jam account through Composio's Jam MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Jam logoJam
Api Key

Jam is a browser bug reporting and recording platform for capturing issues, console logs, network data, and repro context. Teams use Jam to inspect, organize, and collaborate on bugs faster without long back-and-forth threads.

16 Tools

Introduction

This guide walks you through connecting Jam to LangChain using the Composio tool router. By the end, you'll have a working Jam agent that can list your bug reports, analyze videos, inspect console logs, trace network requests, and read transcripts through natural language commands.

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

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

Also integrate Jam with

TL;DR

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

The Jam MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Jam account. It provides structured and secure access to your browser bug reports, recordings, and debugging context, so your agent can analyze reported issues, inspect technical evidence, trace user interactions, review visual details, and browse workspace content on your behalf.

  • Bug report investigation: Have your agent review a Jam’s core details, reported issue, user goal, feedback, and technical indicators to understand what went wrong.
  • Console and network analysis: Let the agent inspect console messages, stack traces, failed network requests, HTTP traffic, and WebSocket activity captured with a Jam.
  • User journey reconstruction: Direct your agent to trace clicks, form inputs, submissions, navigation, and scrolling in chronological order.
  • Recording and screenshot review: Instruct your agent to examine screenshots, sample video frames, analyze recordings, and read available speech transcripts for visual and spoken context.
  • Workspace discovery: Have your agent search Jams, folders, members, recording links, submitted reports, and connected recording domains across your workspace.

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 Jam 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 Jam 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: ['jam']
    }
);

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

Configure the agent with the MCP URL

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

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

Analyze Jam Video

Extract structured user goals, reported issues, feedback, visual observations, interactions, and technical indicators from a video Jam.

Get Console Logs

Get captured browser console errors, warnings, information, debug output, and stack traces from a Jam.

Get Jam Details

Get the core details and debugging context for a Jam.

Get Jam Metadata

Get custom application debugging context attached through the jam.

Get Network Requests

Get captured HTTP requests and WebSocket frames from a Jam, with filters for failures, hosts, methods, content types, and response bodies.

Get Recording Link

Get one Recording Link's settings and submitted-Jam count by public ID.

Get Jam Screenshots

Get screenshots and image attachments from a screenshot-type Jam for visual inspection.

Get User Events

Get the chronological user interaction timeline from a Jam, including clicks, inputs, submissions, navigation, and scrolling.

Get Jam Video Frames

Get still frames from a video Jam as an overview grid, exact timestamps, or an evenly sampled time window.

Get Jam Video Transcript

Get the timestamped WebVTT speech transcript from a microphone-enabled video Jam when available; otherwise return Jam's explanation that it is pending or unavailable.

List Jam Folders

Browse or search one page of workspace folders and return their IDs, names, Jam counts, and timestamps; pass next_cursor as cursor to continue.

List Jams

Search and browse workspace Jams by text, type, folder, author, source URL, or creation date.

List Jam Members

Browse or search one page of workspace members by name or email for author discovery and attribution; pass next_cursor as cursor to continue.

List Recording Domains

List the workspace's connected recording domains and whether each is verified to capture console and network logs.

List Recording Link Jams

List Jams submitted through one Recording Link, newest first.

List Recording Links

List workspace Recording Links newest first, optionally including revoked links.

FAQ

Frequently asked questions

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

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

Start with Jam.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Jam tool your agent needs.Free to start.

Start building