How to integrate Wit ai MCP with Mastra AI

This guide walks you through connecting Wit ai to Mastra AI 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 Mastra AI 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 Mastra AI 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 Mastra AI 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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TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Wit ai tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Wit ai tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Wit ai agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Wit ai through MCP.
3

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_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
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models
5

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session
6

Create a Tool Router session for Wit ai

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["wit_ai"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Wit ai MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "wit_ai" for Wit ai access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Wit ai toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "wit_ai-mastra-agent",
    instructions: "You are an AI agent with Wit ai tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM
9

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

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;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        wit_ai: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Wit ai toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Wit ai and Mastra AI:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["wit_ai"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      wit_ai: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "wit_ai-mastra-agent",
    instructions: "You are an AI agent with Wit ai tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { wit_ai: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Wit ai through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-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

LangChain logo

LangChain

Use Wit ai MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Wit ai MCP with Vercel AI SDK

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. Mastra AI 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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