How to integrate JoggAI MCP with LangChain

This guide walks you through connecting JoggAI to LangChain using the Composio tool router. By the end, you'll have a working JoggAI agent that can generate your scripts, animate avatars, create product videos, sync lips to audio, and manage webhooks through natural language commands. This guide will help you understand how to give your LangChain agent real control over a JoggAI account through Composio's JoggAI MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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JoggAI

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JoggAI is an AI video creation platform for avatar, product, lip-sync, template, and translated videos. It helps teams turn scripts, products, and media assets into polished videos through APIs.

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Introduction

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

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

Also integrate JoggAI with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

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

The JoggAI MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your JoggAI account. It provides structured and secure access to your avatars, products, videos, templates, scripts, and supporting media assets, so your agent can create videos, generate avatars, translate content, track production tasks, and manage webhooks on your behalf.

Avatar and lip-sync video creation: Have your agent create talking-avatar and lip-sync videos, generate photo or product avatars, and animate selected avatar images with speech and motion.
Product video production: Let the agent create or update product details, generate scripts and visual previews, then render selected concepts into finished product videos.
Template-based video rendering: Direct your agent to produce videos from JoggAI templates using text, scripts, images, videos, avatars, voices, captions, and music.
Video translation and dubbing: Instruct your agent to find supported languages and translate videos with options for dubbing, lip sync, subtitles, audio-only output, and adjusted duration.
Media tracking and notifications: Have your agent check video and avatar generation progress, browse available voices, music, templates, and visual styles, review account credits, and manage webhook notifications.

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:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS

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

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.

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

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

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 JoggAI functionality through MCP

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 JoggAI tools
Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['jogg_ai']
    }
);

const url = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to JoggAI 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 JoggAI tools as needed

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "jogg_ai-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 JoggAI MCP server via HTTP
The client is configured with a name and the URL from our Tool Router session
getTools() retrieves all available JoggAI tools that the agent can use
We're creating a LangChain agent using the GPT-5 model

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 JoggAI 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

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 JoggAI 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: ['jogg_ai']
        }
    );

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

Animate Photo Avatar

Create a reusable animated avatar from a photo-avatar image.

Animate Product Avatar

Add motion and a voice to a selected JoggAI product-avatar image generation.

Create Asset Upload URL

Create a signed URL for uploading an asset to JoggAI.

Create Lip Sync Video

Start an asynchronous lip-sync video from publicly reachable video and audio URLs.

Create Product

Create product information from exactly one source: a product page URL, or manually supplied product details.

Create Product Video

Start a product video using a saved product and nested video, avatar, voice, audio, and script settings.

Create Product Video Previews

Generate product-video previews for one or more visual styles.

Create Talking Avatar Video

Start a talking-avatar video using an avatar and either a text script or hosted audio.

Create Template Video

Start rendering a JoggAI v2 template with typed text, script, image, or video variables and optional avatar, voice, captions, and music settings.

Create Webhook

Create a JoggAI webhook endpoint with a callback URL, delivery status, and event subscriptions.

Delete Webhook

Permanently delete one JoggAI webhook endpoint.

Generate AI Scripts

Start asynchronous AI script generation from a saved product or inline product details.

Generate Photo Avatar Image

Start AI photo-avatar image generation using JoggAI's exact demographic, style, model, and aspect-ratio modes.

Generate Product Avatar Images

Start asynchronous generation of one to four product-avatar images from a public product image and an existing avatar, reference image, or description.

Get Account and Credit Balance

Return the connected JoggAI user and remaining credit balance.

Get AI Script Task

Check an AI script generation task and return generated scripts when completed.

Get Lip Sync Video

Check a lip-sync task and return its output URLs and duration when successful.

Get Photo Avatar

Check photo-avatar motion generation and return the reusable avatar details when complete.

Get Photo Avatar Image

Check photo-avatar image generation and return the generated image URLs and keys when ready.

Get Product Avatar

Check product-avatar motion generation and return the reusable avatar when complete.

Get Product Avatar Image Task

Check a product-avatar image generation batch and return its status summary and generated image candidates.

Get Product Video

Check one product video and return its generated media when available.

Get Talking Avatar Video

Check a talking-avatar video and return its media URLs when completed.

Get Template Video

Check a template-rendered video and return its output details when complete.

Get Video Translation

Check a video translation and return translated media when complete.

List Avatars

List public, instant, photo, or product avatars available for JoggAI video generation.

List Creation Assets

Browse one music, template, visual-style, or voice catalog used to create JoggAI media.

List Product Previews

List generated JoggAI product-video previews and their statuses one page at a time.

List Product Videos

List product videos with status and sort filters, one bounded page at a time.

List Translation Languages

Return currently supported lowercase target-language identifiers for JoggAI video translation.

List Webhook Events

Return event identifiers that can be subscribed to by a JoggAI webhook.

List Webhooks

List webhook endpoints registered on the connected JoggAI account.

Render Product Video Preview

Render one selected product preview into a final video.

Translate Video

Start asynchronous video translation with optional audio-only translation, dubbing, lip sync, subtitles, and dynamic duration.

Update Product

Update an existing JoggAI product's descriptive fields.

Update Webhook

Fully replace an existing JoggAI webhook's callback URL, delivery status, and event subscriptions.

FRAMEWORKS

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OpenClaw

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Hermes

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CLI

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Google ADK

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Vercel AI SDK

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Mastra AI

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CrewAI

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FAQ

Frequently asked questions

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

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

Start with JoggAI.It takes 30 seconds.

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

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Customer stories

Teams ship real agents on Composio

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

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.

Abhi Arya

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.

Nirman Dave

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.

Ryan Yu

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.

Tomisin Jenrola

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.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate JoggAI MCP with LangChain

Connect JoggAI without auth hassles

Introduction

Also integrate JoggAI with

TL;DR

What is LangChain?

What is the JoggAI MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Initialize Composio client

Create a Tool Router session

Configure the agent with the MCP URL

Set up interactive chat interface

Run the application

Complete Code

Conclusion

Supported Tools

How to build JoggAI MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

AltTextLab

Apiframe

Arize AX

AssemblyAI

Frequently asked questions

What are the differences in Tool Router MCP and JoggAI MCP?+

Can I use Tool Router MCP with LangChain?+

Can I manage the permissions and scopes for JoggAI while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with JoggAI.It takes 30 seconds.

Teams ship real agents on Composio