# How to integrate Ai ml api MCP with LlamaIndex

```json
{
  "title": "How to integrate Ai ml api MCP with LlamaIndex",
  "toolkit": "Ai ml api",
  "toolkit_slug": "ai_ml_api",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/ai_ml_api/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/ai_ml_api/framework/llama-index.md",
  "updated_at": "2026-05-12T10:00:39.089Z"
}
```

## Introduction

This guide walks you through connecting Ai ml api to LlamaIndex using the Composio tool router. By the end, you'll have a working Ai ml api agent that can check if this image contains unsafe content, summarize this customer chat conversation, generate a polite reply to this message through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Ai ml api account through Composio's Ai ml api MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Ai ml api with

- [OpenAI Agents SDK](https://composio.dev/toolkits/ai_ml_api/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/ai_ml_api/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/ai_ml_api/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/ai_ml_api/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/ai_ml_api/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/ai_ml_api/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/ai_ml_api/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/ai_ml_api/framework/cli)
- [Google ADK](https://composio.dev/toolkits/ai_ml_api/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/ai_ml_api/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/ai_ml_api/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/ai_ml_api/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/ai_ml_api/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 Ai ml api
- Connect LlamaIndex to the Ai ml api MCP server
- Build a Ai ml api-powered agent using LlamaIndex
- Interact with Ai ml api 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 Ai ml api MCP server, and what's possible with it?

The Ai ml api MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ai ml api account. It provides structured and secure access to powerful AI/ML models, so your agent can generate text, moderate user content, and automate intelligent workflows on your behalf.
- Automated content moderation: Instantly classify and filter user-generated text or images using advanced moderation models to keep your platform safe and compliant.
- Dynamic text generation: Have your agent generate chat responses, write creative copy, or complete conversations using state-of-the-art language models.
- Context-aware conversation handling: Let your agent analyze conversation history and produce coherent, relevant replies for chatbots or digital assistants.
- Seamless integration of AI workflows: Combine moderation and text generation tools to build smart, automated pipelines tailored to your product’s needs.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `AI_ML_API_CANCEL_RUN` | Cancel Run | Tool to cancel a run that is currently in progress. Use when you need to stop an assistant run before it completes. |
| `AI_ML_API_CREATE_ASSISTANT` | Create Assistant | Tool to create an AI assistant with configurable model, instructions, and tools. Use when you need to set up a new assistant that can maintain conversation context and use tools like code_interpreter or file_search. |
| `AI_ML_API_CREATE_MESSAGE` | Create Message | Tool to create a new message in a thread. Use when you need to add a user or assistant message to an existing conversation thread. |
| `AI_ML_API_CREATE_RUN` | Create Run | Tool to create a run that executes an assistant on a thread. The assistant processes messages in the thread and generates responses based on its instructions and available tools. |
| `AI_ML_API_CREATE_THREAD` | Create Thread | Tool to create a new thread for conversation with an assistant. Threads store messages and maintain context across interactions. Use when starting a new conversation or when you need a fresh context for assistant interactions. |
| `AI_ML_API_DELETE_ASSISTANT` | Delete Assistant | Tool to delete an assistant by ID. Use when you need to remove an assistant that is no longer needed. |
| `AI_ML_API_DELETE_MESSAGE` | Delete Message | Tool to delete a specific message from a thread. Use when you need to remove an unwanted or erroneous message from a conversation thread. |
| `AI_ML_API_DELETE_THREAD` | Delete Thread | Tool to delete a thread by its ID. Use when you need to remove an existing thread from the system. |
| `AI_ML_API_GET_ASSISTANT` | Get Assistant | Tool to retrieve details of a specific assistant by ID. Use when you need to fetch configuration, model settings, instructions, or available tools for an existing assistant. |
| `AI_ML_API_GET_BILLING_BALANCE` | Get Billing Balance | Tool to retrieve the current billing balance for the account. Use when you need to check available credits, balance status, or auto-debit configuration. |
| `AI_ML_API_GET_LUMA_GENERATION` | Get Luma Generation | Tool to fetch Luma AI video generation results by generation IDs. Use after creating a generation to check its status and retrieve the generated video URL when completed. |
| `AI_ML_API_GET_MESSAGE` | Get Message | Tool to retrieve information about a specific message by its ID. Use when you need to fetch details of a particular message in a thread. |
| `AI_ML_API_GET_RESPONSE` | Get Response by ID | Tool to retrieve a previously generated model response by its unique ID. Use when you need to access details of a specific response, including its output, status, and usage statistics. |
| `AI_ML_API_GET_RUN` | Get Run | Tool to retrieve a specific run by ID from a thread. Use when you need to check the status, results, or details of a previously created run. |
| `AI_ML_API_GET_RUN_STEP` | Get Run Step | Tool to retrieve a specific run step by its ID within a thread and run. Use when you need detailed information about a particular step's execution status and results. |
| `AI_ML_API_GET_THREAD` | Get Thread | Tool to retrieve information about a specific thread by ID. Use when you need to fetch thread details, metadata, or available tool resources for an existing conversation thread. |
| `AI_ML_API_LIST_ASSISTANTS` | List Assistants | Tool to list all assistants associated with the account. Use when you need to retrieve available assistants with pagination support. |
| `AI_ML_API_LIST_BATCHES` | List Batches | Tool to get the status or results of a batch processing job. Use when you need to check the progress or retrieve results of a previously submitted batch. |
| `AI_ML_API_LIST_LUMA_GENERATIONS` | List Luma AI Generations | Tool to fetch user's Luma AI video generations. Use when you need to retrieve a list of all Luma AI video generation tasks for the authenticated user. |
| `AI_ML_API_LIST_MESSAGES` | List Thread Messages | Tool to retrieve a list of messages from a specific thread. Use when you need to fetch conversation history or message content from an AI assistant thread. |
| `AI_ML_API_LIST_MODELS` | List Models | Tool to list all available AI models from the AI/ML API. Use when you need to retrieve the complete catalog of 400+ models including chat, image, video, voice, and other model types. |
| `AI_ML_API_LIST_MODELS_WITH_DETAILS` | List Models With Details | Tool to list all available AI/ML models with detailed information including pricing, features, and capabilities. Use when you need to discover available models or get comprehensive model metadata. |
| `AI_ML_API_LIST_RUNS` | List Runs | Tool to list all runs for a specific thread. Use when you need to retrieve runs with pagination support. |
| `AI_ML_API_LIST_RUN_STEPS` | List Run Steps | Tool to list the steps in a run. Use when you need to retrieve and examine the execution steps of a specific run within a thread. |
| `AI_ML_API_SUBMIT_TOOL_OUTPUTS` | Submit Tool Outputs | Tool to submit tool outputs for a run that requires action. Use when a run has status 'requires_action' and needs tool call results to continue execution. |
| `AI_ML_API_TEXT_CHAT_COMPLETION` | Text Chat Completion | Tool to generate text completions or chat responses using a specified LLM model. Use after assembling the conversation history to produce the next response. |
| `AI_ML_API_UPDATE_ASSISTANT` | Update Assistant | Tool to modify an existing assistant's properties including name, instructions, model, and tools. Use when you need to update an assistant's configuration or behavior after it has been created. |
| `AI_ML_API_UPDATE_MESSAGE` | Update Message | Tool to modify metadata for a specific message in a thread. Use when you need to update message metadata such as tags or custom fields. |
| `AI_ML_API_UPDATE_RUN` | Update Run | Tool to update a run's metadata with key-value pairs. Use when you need to attach or modify additional information for a specific run. |
| `AI_ML_API_UPDATE_THREAD` | Update Thread | Tool to update thread metadata and tool resources in the AI/ML API. Use when you need to modify existing thread properties or attach resources. |

## Supported Triggers

None listed.

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

The Ai ml api MCP server is an implementation of the Model Context Protocol that connects your AI agent to Ai ml api. It provides structured and secure access so your agent can perform Ai ml api 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 Ai ml api account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Ai ml api

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 Ai ml api 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, ai ml api)
- 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 Ai ml api 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=["ai_ml_api"],
    )

    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 Ai ml api actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Ai ml api 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: ["ai_ml_api"],
    },
  );

  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 Ai ml api 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 Ai ml api
```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 Ai ml api, 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=["ai_ml_api"],
    )

    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 Ai ml api actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Ai ml api 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: ["ai_ml_api"],
    },
  );

  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 Ai ml api 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 Ai ml api to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Ai ml api 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 Ai ml api MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/ai_ml_api/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/ai_ml_api/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/ai_ml_api/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/ai_ml_api/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/ai_ml_api/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/ai_ml_api/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/ai_ml_api/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/ai_ml_api/framework/cli)
- [Google ADK](https://composio.dev/toolkits/ai_ml_api/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/ai_ml_api/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/ai_ml_api/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/ai_ml_api/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/ai_ml_api/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.
- [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.
- [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Ai ml api MCP?

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

### Can I manage the permissions and scopes for Ai ml api while using Tool Router?

Yes, absolutely. You can configure which Ai ml api 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 Ai ml api data and credentials are handled as safely as possible.

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