How to integrate Wit ai MCP with LangChain

This guide walks you through connecting Wit ai to LangChain using the Composio tool router. By the end, you'll have a working Wit ai agent that can analyze user message for intent and entities, list all custom traits in your wit app, get details of the 'bookflight' intent through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Wit ai account through Composio's Wit ai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Wit.ai is a natural language processing platform that turns text or speech into structured data. It's perfect for building voice and chat interfaces that truly understand users.

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Introduction

This guide walks you through connecting Wit ai to LangChain using the Composio tool router. By the end, you'll have a working Wit ai agent that can analyze user message for intent and entities, list all custom traits in your wit app, get details of the 'bookflight' intent through natural language commands.

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

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

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

The Wit ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Wit ai account. It provides structured and secure access to your NLP resources, so your agent can create and manage apps, analyze natural language, organize intents and traits, and update configurations on your behalf.

  • Instant natural language analysis: Let your agent extract intents, entities, and traits from any text message using Wit.ai’s advanced NLP engine.
  • Automated app management: Easily create, update, or delete Wit.ai apps, enabling rapid deployment and maintenance of your language models.
  • Intent and trait organization: Have your agent list, retrieve details, or update all defined intents and traits, keeping your language understanding models organized and up to date.
  • Full app metadata access: Fetch comprehensive app settings and metadata for better monitoring, debugging, or auditing of your NLP solutions.
  • Seamless entity and trait customization: Programmatically add or configure traits for tailored entity recognition and improved intent matching.

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 Wit ai 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 Wit ai 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: ['wit_ai']
    }
);

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

Configure the agent with the MCP URL

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

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "wit_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 Wit ai 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 Wit ai 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 Wit ai action and event your agent gets out of the box.

Add Entity Keyword

Tool to add a keyword with optional synonyms to a Wit.

Add Keyword Synonym

Tool to add a new synonym to a keyword in an entity.

Add Value to Trait

Tool to add a new value to an existing trait in Wit.

Create Wit.ai App

Tool to create a new app in Wit.

Create Wit.ai Entity

Tool to create a new entity in Wit.

Create Wit.ai Intent

Tool to create a new intent in Wit.

Create Wit.ai Trait

Tool to create a new trait in Wit.

Create Wit.ai Training Utterances

Tool to add training utterances (samples with annotations) to your Wit.

Delete App

Tool to delete a specific app from wit.

Delete Entity

Tool to permanently delete an entity by name.

Delete Entity Keyword

Tool to delete a keyword from a keywords entity in wit.

Delete Entity Role

Tool to delete a specific role from an entity in wit.

Delete Intent

Tool to permanently delete an intent by name.

Delete Keyword Synonym

Tool to delete a synonym from a keyword in an entity.

Delete Utterances

Tool to delete validated utterances (training samples) from your Wit.

Wit.ai Detect Language

Tool to detect the language of a given text input.

Export App Data

Tool to export Wit.

Get App Details

Tool to retrieve metadata and settings of a Wit.

Get Entity Details

Tool to retrieve details of a specific entity including keywords and roles.

Get Intent Details

Tool to retrieve details of a specific intent.

Get Intents

Tool to list all intents in a Wit.

Wit.ai Get Message

Tool to analyze a text message and extract its intent, entities, and traits.

Get Trait Details

Tool to retrieve details of a specific trait.

List Traits

Tool to list all traits in a Wit.

Get Voice Details

Tool to retrieve details for a specific text-to-speech voice.

List Wit.ai Apps

Tool to retrieve the list of all Wit.

List App Tags

Tool to retrieve all tag groups (versions) for a Wit.

List Entities

Tool to list all entities in a Wit.

List Utterances

Tool to retrieve training utterances (samples) from a Wit.

List Voices

Tool to retrieve all available text-to-speech voices grouped by locale.

Update Wit.ai App

Tool to update an existing Wit.

FRAMEWORKS

How to build Wit ai MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Wit ai MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Wit ai MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Wit ai MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Wit ai MCP with Claude Cowork

Codex logo

Codex

Use Wit ai MCP with Codex

OpenClaw logo

OpenClaw

Use Wit ai MCP with OpenClaw

Hermes logo

Hermes

Use Wit ai MCP with Hermes

CLI logo

CLI

Use Wit ai MCP with CLI

Google ADK logo

Google ADK

Use Wit ai MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Wit ai MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Wit ai MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Wit ai MCP with LlamaIndex

CrewAI logo

CrewAI

Use Wit ai MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, absolutely. You can configure which Wit ai 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 Wit ai 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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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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