How to integrate 0CodeKit MCP with LangChain

This guide walks you through connecting 0CodeKit to LangChain using the Composio tool router. By the end, you'll have a working 0CodeKit agent that can convert json data into csv file, generate qr code for checkout link, extract text from uploaded pdf through natural language commands. This guide will help you understand how to give your LangChain agent real control over a 0CodeKit account through Composio's 0CodeKit MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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0CodeKit is a utility API platform for AI, document, image, and data workflows. Use it to convert, validate, generate, store, and automate data without custom backend code.

8 Tools

Introduction

This guide walks you through connecting 0CodeKit to LangChain using the Composio tool router. By the end, you'll have a working 0CodeKit agent that can convert json data into csv file, generate qr code for checkout link, extract text from uploaded pdf through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a 0CodeKit account through Composio's 0CodeKit MCP server.

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

Also integrate 0CodeKit 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 0CodeKit project to Composio
  • Create a Tool Router MCP session for 0CodeKit
  • Initialize an MCP client and retrieve 0CodeKit tools
  • Build a LangChain agent that can interact with 0CodeKit
  • 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 0CodeKit MCP server, and what's possible with it?

The 0CodeKit MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your 0CodeKit account. It provides structured and secure access to text, validation, conversion, date, and calculation utilities, so your agent can validate emails, find keywords, parse campaign links, hash messages, and calculate BMI on your behalf.

  • Email validation: Have your agent check whether an email address is valid and retrieve the corrected address when available.
  • Text search and hashing: Let the agent locate keywords and their positions in text, or generate a cryptographic digest or HMAC for a message.
  • Campaign link analysis: Direct your agent to extract standard UTM campaign details from a URL.
  • Country and date checks: Instruct your agent to convert country names and ISO codes, or determine whether a date falls on a weekend.
  • Health estimates and credit checks: Have your agent calculate BMI with related weight, energy, and nutrient estimates, or check your remaining 0CodeKit credits.

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 0CodeKit 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 0CodeKit 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: ['0codekit']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to 0CodeKit 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 0CodeKit tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "0codekit-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 0CodeKit MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available 0CodeKit 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 0CodeKit 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 0CodeKit 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: ['0codekit']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "0codekit-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 0CodeKit 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 0CodeKit 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 0CodeKit action and event your agent gets out of the box.

Calculate BMI

Calculate body mass index and the provider's related desirable-weight, daily-energy, and nutrient estimates from weight in kilograms and height in centimeters.

Check Weekend

Determine whether a date is a weekend and return its weekday name and day number.

Convert Nation and ISO Code

Convert either an English country name to its ISO code or an ISO code to its English country name.

Find Keywords

Search text for one or more keywords and report whether each occurs and the zero-based positions of matches.

Get Remaining Credits

Return the number of 0CodeKit credits remaining for the connected API key.

Hash Message

Generate a cryptographic digest or HMAC for a text message using a supported algorithm.

Parse UTM Parameters

Extract standard UTM campaign parameters from a URL.

Validate Email

Check an email address using 0CodeKit and return whether it is valid plus the provider's corrected-address value.

FRAMEWORKS

How to build 0CodeKit MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use 0CodeKit MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use 0CodeKit MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use 0CodeKit MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use 0CodeKit MCP with Claude Cowork

Codex logo

Codex

Use 0CodeKit MCP with Codex

OpenClaw logo

OpenClaw

Use 0CodeKit MCP with OpenClaw

Hermes logo

Hermes

Use 0CodeKit MCP with Hermes

CLI logo

CLI

Use 0CodeKit MCP with CLI

Google ADK logo

Google ADK

Use 0CodeKit MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use 0CodeKit MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use 0CodeKit MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use 0CodeKit MCP with LlamaIndex

CrewAI logo

CrewAI

Use 0CodeKit MCP with CrewAI

FAQ

Frequently asked questions

With a standalone 0CodeKit MCP server, the agents and LLMs can only access a fixed set of 0CodeKit tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from 0CodeKit 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 0CodeKit tools.

Yes, absolutely. You can configure which 0CodeKit 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 0CodeKit data and credentials are handled as safely as possible.

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