How to integrate Jigsawstack MCP with LangChain

This guide walks you through connecting Jigsawstack to LangChain using the Composio tool router. By the end, you'll have a working Jigsawstack agent that can generate a logo from this business idea, analyze customer review sentiment for this product, convert this sales script into an audio file through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Jigsawstack account through Composio's Jigsawstack MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Jigsawstack is a platform providing custom small AI models on scalable infrastructure. It's built for developers who need rapid, efficient deployment of AI-powered features.

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Introduction

This guide walks you through connecting Jigsawstack to LangChain using the Composio tool router. By the end, you'll have a working Jigsawstack agent that can generate a logo from this business idea, analyze customer review sentiment for this product, convert this sales script into an audio file through natural language commands.

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

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

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

The Jigsawstack MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Jigsawstack account. It provides structured and secure access to Jigsawstack's suite of custom AI models, so your agent can perform actions like generating images, analyzing sentiment, converting text to speech, and running smart web searches on your behalf.

  • AI-powered image generation: Instantly create custom images from any text prompt, perfect for visual content, ideation, or creative tasks.
  • Text sentiment analysis: Have your agent classify the emotional tone of written content, detecting positive, negative, or neutral sentiment for feedback, moderation, or analytics.
  • Natural text-to-speech synthesis: Convert any text into clear, natural-sounding audio files, enabling voice experiences or accessibility features in your workflows.
  • Enhanced web search with AI summaries: Perform smart, geo-aware web searches and get concise, AI-generated overviews for quick research and information gathering.

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 Jigsawstack 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 Jigsawstack 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: ['jigsawstack']
    }
);

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

Configure the agent with the MCP URL

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

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

Check Image for NSFW Content

Tool to detect NSFW content in images.

Check Profanity

Tool to check text for profanity and inappropriate language.

Check Spam

Tool to perform spam check analysis on text.

Check Spelling

Tool to check and correct spelling errors in text.

Classify Content

Tool to classify text and image datasets using custom labels.

Convert HTML to Image or PDF

Tool to convert HTML to images (PNG/JPEG/WEBP) or PDF, or capture website screenshots.

Create Embedding V2

Tool to generate enhanced vector embeddings with speaker fingerprint support using the v2 model.

Create Prediction

Tool to forecast time series data using AI-powered prediction.

Create Prompt

Tool to create a new prompt in the Prompt Engine for reusable LLM interactions.

Create Voice Clone

Tool to create a cloned voice for text-to-speech synthesis.

Detect Objects in Image

Tool to recognize and identify objects within an image using computer vision AI.

Extract Data with Vision OCR

Tool to recognize, describe and retrieve data within images with great accuracy using Vision OCR.

Get Search Suggestions

Tool to get real-time search suggestions for a given query.

Get Sentiment

Tool to retrieve sentiment analysis via GET request.

Generate Image from Prompt

Tool to generate images from text prompts.

List Prompts

Tool to list all prompts stored in the Prompt Engine.

Run Prompt By ID

Tool to execute a stored prompt using its prompt engine ID.

Scrape Website

Tool to scrape any website and extract structured data using AI-powered element prompts or CSS selectors.

Sentiment Analysis

Tool to analyze text sentiment.

Summarize Text

Tool to generate concise, intelligent summaries of text or documents with AI.

Text to Speech

Tool to convert text to natural-sounding speech.

Translate Text

Tool to translate text from one language to another.

Web Search

Tool to perform AI-powered web search with AI overview and geo-aware results.

FRAMEWORKS

How to build Jigsawstack MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Jigsawstack MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Jigsawstack MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Jigsawstack MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Jigsawstack MCP with Claude Cowork

Codex logo

Codex

Use Jigsawstack MCP with Codex

OpenClaw logo

OpenClaw

Use Jigsawstack MCP with OpenClaw

Hermes logo

Hermes

Use Jigsawstack MCP with Hermes

CLI logo

CLI

Use Jigsawstack MCP with CLI

Google ADK logo

Google ADK

Use Jigsawstack MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Jigsawstack MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Jigsawstack MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Jigsawstack MCP with LlamaIndex

CrewAI logo

CrewAI

Use Jigsawstack MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, absolutely. You can configure which Jigsawstack 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 Jigsawstack 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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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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