# How to integrate Wit ai MCP with LlamaIndex

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

## Introduction

This guide walks you through connecting Wit ai to LlamaIndex 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 LlamaIndex 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)
- [LangChain](https://composio.dev/toolkits/wit_ai/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/wit_ai/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/wit_ai/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/wit_ai/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Set your OpenAI and Composio API keys
- Install LlamaIndex and Composio packages
- Create a Composio Tool Router session for Wit ai
- Connect LlamaIndex to the Wit ai MCP server
- Build a Wit ai-powered agent using LlamaIndex
- Interact with Wit ai through natural language

## What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.
Key features include:
- ReAct Agent: Reasoning and acting pattern for tool-using agents
- MCP Tools: Native support for Model Context Protocol
- Context Management: Maintain conversation context across interactions
- Async Support: Built for async/await patterns

## 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

Before you begin, make sure you have:
- Python 3.8/Node 16 or higher installed
- A Composio account with the API key
- An OpenAI API key
- A Wit ai account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Wit ai

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

```typescript
npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv
```

### 3. Set environment variables

Create a .env file in your project root:
These credentials will be used to:
- Authenticate with OpenAI's GPT-5 model
- Connect to Composio's Tool Router
- Identify your Composio user session for Wit ai access
```bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id
```

### 4. Import modules

No description provided.
```python
import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")
```

```typescript
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");
```

### 6. Create a Tool Router session and build the agent function

What's happening here:
- We create a Composio client using your API key and configure it with the LlamaIndex provider
- We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, wit ai)
- The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
- LlamaIndex will connect to this endpoint to dynamically discover and use the available Wit ai tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["wit_ai"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Wit ai actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Wit ai actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)
```

```typescript
async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["wit_ai"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Wit ai actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")
```

```typescript
async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}
```

### 8. Define the main entry point

What's happening here:
- We're orchestrating the entire application flow
- The agent gets built with proper error handling
- Then we kick off the interactive chat loop so you can start talking to Wit ai
```python
async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Wit ai, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["wit_ai"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Wit ai actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Wit ai actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["wit_ai"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Wit ai actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();
```

## Conclusion

You've successfully connected Wit ai to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Wit ai tools through an MCP endpoint
- LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
- The agent becomes more capable without increasing prompt size
- Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

## 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)
- [LangChain](https://composio.dev/toolkits/wit_ai/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/wit_ai/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/wit_ai/framework/mastra-ai)
- [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 LlamaIndex?

Yes, you can. LlamaIndex 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.

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