# How to integrate Wit ai MCP with LangChain

```json
{
  "title": "How to integrate Wit ai MCP with LangChain",
  "toolkit": "Wit ai",
  "toolkit_slug": "wit_ai",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/wit_ai/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/wit_ai/framework/langchain.md",
  "updated_at": "2026-05-12T10:30:24.788Z"
}
```

## 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 SDK](https://composio.dev/toolkits/wit_ai/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/wit_ai/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/wit_ai/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/wit_ai/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/wit_ai/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/wit_ai/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/wit_ai/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/wit_ai/framework/cli)
- [Google ADK](https://composio.dev/toolkits/wit_ai/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/wit_ai/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/wit_ai/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/wit_ai/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/wit_ai/framework/crew-ai)

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `WIT_AI_ADD_ENTITY_KEYWORD` | Add Entity Keyword | Tool to add a keyword with optional synonyms to a Wit.ai entity. Use when extending entity values. |
| `WIT_AI_ADD_KEYWORD_SYNONYM` | Add Keyword Synonym | Tool to add a new synonym to a keyword in an entity. Use when expanding entity keyword recognition with additional terms. |
| `WIT_AI_ADD_TRAIT_VALUE` | Add Value to Trait | Tool to add a new value to an existing trait in Wit.ai. Use when extending trait vocabulary with additional values. |
| `WIT_AI_CREATE_APP` | Create Wit.ai App | Tool to create a new app in Wit.ai. Use when you need to programmatically initialize an application before training or importing data. |
| `WIT_AI_CREATE_ENTITY` | Create Wit.ai Entity | Tool to create a new entity in Wit.ai. Use when defining custom entity types for natural language understanding. |
| `WIT_AI_CREATE_INTENT` | Create Wit.ai Intent | Tool to create a new intent in Wit.ai. Use when you need to define a new intent for natural language understanding. |
| `WIT_AI_CREATE_TRAIT` | Create Wit.ai Trait | Tool to create a new trait in Wit.ai. Use when defining custom entity attribute matching behavior. |
| `WIT_AI_CREATE_UTTERANCES` | Create Wit.ai Training Utterances | Tool to add training utterances (samples with annotations) to your Wit.ai app. Use when you need to train your model with labeled examples. Rate limit: 200 samples per minute. |
| `WIT_AI_DELETE_APP` | Delete App | Tool to delete a specific app from wit.ai. Use when you need to remove an existing app by its ID after confirming its existence. |
| `WIT_AI_DELETE_ENTITY` | Delete Entity | Tool to permanently delete an entity by name. Use when you need to remove an existing entity from the wit.ai app. |
| `WIT_AI_DELETE_ENTITY_KEYWORD` | Delete Entity Keyword | Tool to delete a keyword from a keywords entity in wit.ai. Use when you need to remove a specific keyword from an entity. |
| `WIT_AI_DELETE_ENTITY_ROLE` | Delete Entity Role | Tool to delete a specific role from an entity in wit.ai. Use when you need to remove a role association from an entity. |
| `WIT_AI_DELETE_INTENT` | Delete Intent | Tool to permanently delete an intent by name. Use when you need to remove an intent from the app. |
| `WIT_AI_DELETE_KEYWORD_SYNONYM` | Delete Keyword Synonym | Tool to delete a synonym from a keyword in an entity. Use when you need to remove a specific synonym mapping from an entity keyword. |
| `WIT_AI_DELETE_UTTERANCES` | Delete Utterances | Tool to delete validated utterances (training samples) from your Wit.ai app. Use when you need to remove specific training data. |
| `WIT_AI_DETECT_LANGUAGE` | Wit.ai Detect Language | Tool to detect the language of a given text input. Returns detected locales with confidence scores. Use when you need to identify the language of user-provided text. |
| `WIT_AI_EXPORT_APP` | Export App Data | Tool to export Wit.ai app data as a backup ZIP file. Returns a download URL for the backup file containing all app data. |
| `WIT_AI_GET_APP` | Get App Details | Tool to retrieve metadata and settings of a Wit.ai app. Use when you need to fetch complete app details by app ID after authenticating. |
| `WIT_AI_GET_ENTITY` | Get Entity Details | Tool to retrieve details of a specific entity including keywords and roles. Use when you need entity metadata by ID or name. |
| `WIT_AI_GET_INTENT` | Get Intent Details | Tool to retrieve details of a specific intent. Use when you need full intent metadata given its ID. |
| `WIT_AI_GET_INTENTS` | Get Intents | Tool to list all intents in a Wit.ai app. Use after authenticating to retrieve defined intents. |
| `WIT_AI_GET_MESSAGE` | Wit.ai Get Message | Tool to analyze a text message and extract its intent, entities, and traits. Use when you need structured meaning from user input. |
| `WIT_AI_GET_TRAIT` | Get Trait Details | Tool to retrieve details of a specific trait. Use when you have the trait ID and need its full metadata. |
| `WIT_AI_GET_TRAITS` | List Traits | Tool to list all traits in a Wit.ai app. Use after authenticating to retrieve defined traits. |
| `WIT_AI_GET_VOICE` | Get Voice Details | Tool to retrieve details for a specific text-to-speech voice. Use when you need information about available styles and parameters for a voice. |
| `WIT_AI_LIST_APPS` | List Wit.ai Apps | Tool to retrieve the list of all Wit.ai apps for the authenticated user. Use when you need to fetch apps with pagination support. |
| `WIT_AI_LIST_APP_TAGS` | List App Tags | Tool to retrieve all tag groups (versions) for a Wit.ai app. Use when you need to list available versions or snapshots of an app's state. |
| `WIT_AI_LIST_ENTITIES` | List Entities | Tool to list all entities in a Wit.ai app. Use after authenticating to retrieve defined entities. |
| `WIT_AI_LIST_UTTERANCES` | List Utterances | Tool to retrieve training utterances (samples) from a Wit.ai app. Use when you need to view or analyze the app's training data. Supports filtering by intents, entities, and traits. |
| `WIT_AI_LIST_VOICES` | List Voices | Tool to retrieve all available text-to-speech voices grouped by locale. Use when you need to discover which voices are available for speech synthesis. |
| `WIT_AI_PUT_APP` | Update Wit.ai App | Tool to update an existing Wit.ai app. Use when you need to modify app settings after creation. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Wit ai MCP server is an implementation of the Model Context Protocol that connects your AI agent to Wit ai. It provides structured and secure access so your agent can perform Wit ai operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
```

```typescript
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();
```

### 5. Initialize Composio client

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
```python
async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

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
```python
# Create Tool Router session for Wit ai
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['wit_ai']
)

url = session.mcp.url
```

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

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "wit_ai-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
```

```typescript
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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Wit ai related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
```

```typescript
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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['wit_ai']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "wit_ai-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Wit ai related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
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.

## How to build Wit ai MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/wit_ai/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/wit_ai/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/wit_ai/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/wit_ai/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/wit_ai/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/wit_ai/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/wit_ai/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/wit_ai/framework/cli)
- [Google ADK](https://composio.dev/toolkits/wit_ai/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/wit_ai/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/wit_ai/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/wit_ai/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/wit_ai/framework/crew-ai)

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Wit ai MCP?

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.

### Can I use Tool Router MCP with LangChain?

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.

### Can I manage the permissions and scopes for Wit ai while using Tool Router?

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.

### How safe is my data with Composio Tool Router?

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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[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)