How to integrate Openai MCP with LlamaIndex

This guide walks you through connecting Openai to LlamaIndex using the Composio tool router. By the end, you'll have a working Openai agent that can list all available openai models, upload a file for fine-tuning, create a new assistant with gpt-4 through natural language commands. This guide will help you understand how to give your LlamaIndex agent real control over a Openai account through Composio's Openai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Introduction

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

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

Also integrate Openai with

ChatGPT OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenCode Cursor VS Code OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI CrewAI

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 Openai
Connect LlamaIndex to the Openai MCP server
Build a Openai-powered agent using LlamaIndex
Interact with Openai 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 Openai MCP server, and what's possible with it?

The Openai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your OpenAI account. It provides structured and secure access to your models, assistants, files, threads, and fine-tuning jobs, so your agent can perform actions like managing assistants, handling conversations, uploading or organizing files, and working with OpenAI models on your behalf.

Assistant and conversation management: Quickly create, update, or delete OpenAI assistants and manage threads or messages for seamless conversational flows.
File uploads and organization: Let your agent upload new files, list all uploaded documents, or delete unnecessary files to keep your workspace tidy.
Model discovery and utilization: Effortlessly list all available OpenAI models—including vision and multimodal—and retrieve their details to choose the best fit for your tasks.
Fine-tuning job insights: View a complete list of your organization's fine-tune jobs and track their progress or review results as needed.
Thread and run management: Create, modify, or inspect threads and run steps to fully control and monitor interactive agent conversations.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS

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 Openai account and project
Basic familiarity with async Python/Typescript

Getting API Keys for OpenAI, Composio, and Openai

OpenAI API key (OPENAI_API_KEY)

Go to the OpenAI dashboard
Create an API key if you don't have one
Assign it to OPENAI_API_KEY in .env

Composio API key and user ID

Log into the Composio dashboard
Copy your API key from Settings
- Use this as COMPOSIO_API_KEY
Pick a stable user identifier (email or ID)
- Use this as COMPOSIO_USER_ID

Installing dependencies

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

Create a new Typescript project and install the necessary dependencies:

@composio/llamaindex: Composio's LlamaIndex integration
@llamaindex/openai: OpenAI LLM integration
@llamaindex/tools: MCP client for LlamaIndex
@llamaindex/workflow: Workflow framework for LlamaIndex
dotenv: Environment variable management

Set environment variables

bash

OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id

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 Openai access

Import modules

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();

Create a new file called openai_llamaindex_agent.ts and import the required modules:

Key imports:

dotenv.config loads .env at runtime
readline gives us a simple CLI chat loop
Composio is the main Composio SDK client
mcp connects to an MCP endpoint
createAgent builds a LlamaIndex agent
openai configures the LLM backend

Load environment variables and initialize Composio

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");

What's happening:

This ensures missing credentials cause early, clear errors before the agent attempts to initialise.

Create a Tool Router session and build the agent function

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: ["openai"],
    },
  );

  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 Openai actions." ,
    llm,
    tools,
  });

  return agent;
}

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, openai)
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 Openai tools.
The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.

Create an interactive chat loop

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();
}

What's happening:

We're creating a direct terminal interface to chat with Openai
The LLM's responses are streamed to the CLI for faster interaction.
The agent uses context to maintain conversation history
The agent processes the request, selects appropriate Openai tools, and returns a result
We extract the answer from the result data structure and display it to the user
You can type 'quit' or 'exit' to stop the chat loop gracefully
Agent responses and any errors are streamed in a clear, readable format

Define the main entry point

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

main();

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 Openai

Run the agent

npx ts-node llamaindex-agent.ts

When prompted, authenticate and authorise your agent with Openai, then start asking questions.

Complete Code

Here's the complete code to get you started with Openai and LlamaIndex:

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: ["openai"],
    },
  );

  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 Openai 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 Openai to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways:

Tool Router dynamically exposes Openai 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.

TOOLS

Supported Tools

Every Openai action and event your agent gets out of the box.

Add Upload Part

Tool to add a part (chunk of bytes) to an Upload object.

Cancel batch

Tool to cancel an in-progress batch.

Cancel evaluation run

Tool to cancel an ongoing evaluation run.

Cancel Response

Tool to cancel a background model response by its ID.

Cancel Run

Tool to cancel a run that is currently in progress.

Cancel upload

Tool to cancel an upload.

Compact Response

Tool to compact a conversation or response to reduce token usage.

Create Audio Transcription

Tool to transcribe audio files to text via OpenAI Audio Transcriptions API.

Create Audio Translation

Tool to translate audio files to English text via OpenAI Audio Translations API.

Create Batch

Tool to create and execute a batch from an uploaded file of requests.

Create Chat Completion

Tool to create a chat completion response from OpenAI models.

Create Completion (Legacy)

Tool to generate text completions using OpenAI's legacy Completions API.

Create Container

Tool to create a new container with configurable memory, expiration, file access, and network policies.

Create Container File

Tool to create a file in a container.

Create Conversation

Tool to create a new conversation for multi-turn interactions.

Create Conversation Items

Tool to create items in a conversation with the given ID.

Create Embeddings

Tool to generate text embeddings via the OpenAI embeddings endpoint.

Create Eval

Tool to create an evaluation structure for testing a model's performance.

Create Evaluation Run

Tool to create a new evaluation run for testing model configurations.

Create fine-tuning job

Tool to create a fine-tuning job which begins the process of creating a new model from a given dataset.

Generate Image

Tool to generate an image via the OpenAI Images API and return hosted image asset URL and metadata.

Edit Image

Tool to create edited or extended images via OpenAI Images Edit API.

Create Image Variation

Tool to create a variation of a given image using the OpenAI Images API.

Create Message

Tool to create a new message in a specific thread.

Create Moderation

Tool to classify text and/or image inputs for potentially harmful content via the OpenAI Moderation API.

Create Realtime Call

Tool to create a Realtime API call over WebRTC and receive the SDP answer needed to complete the peer connection.

Create Realtime Client Secret

Tool to create an ephemeral client secret for authenticating Realtime API connections.

Create Realtime Session

Tool to create an ephemeral API token for client-side Realtime API applications.

Create Realtime Transcription Session

Tool to create an ephemeral API token for realtime transcriptions via the Realtime API.

Create Response

Tool to generate a one-shot model response via the Responses API.

Create Run

Tool to create a run on a thread with an assistant.

Create Skill

Tool to create a skill from uploaded files.

Create Speech (TTS)

Tool to generate text-to-speech audio using OpenAI's Audio API.

Create Thread

Tool to create a new thread.

Create Thread And Run

Tool to create a thread and run it in one request.

Create Upload

Tool to create an intermediate Upload object for large file uploads.

Create Vector Store

Tool to create a new vector store.

Create Vector Store File

Tool to create a vector store file by attaching a File to a vector store.

Create vector store file batch

Tool to create a vector store file batch.

Create Video

Tool to create a video using Sora models via the OpenAI Videos API.

Create Video Remix

Tool to create a video remix from an existing generated video using OpenAI's Video API.

Delete assistant

Tool to delete a specific assistant by its ID.

Delete chat completion

Tool to delete a stored chat completion by its ID.

Delete container

Tool to delete a specific container by its ID.

Delete container file

Tool to delete a file from a container.

Delete conversation

Tool to delete a conversation by its ID.

Delete conversation item

Tool to delete an item from a conversation with the given IDs.

Delete evaluation

Tool to delete a specific evaluation by its ID.

Delete evaluation run

Tool to delete an evaluation run.

Delete file

Tool to delete a file by its ID after confirming the target.

Delete message

Tool to delete a message from a thread.

Delete response

Tool to delete a model response with the given ID.

Delete skill

Tool to delete a specific skill by its ID.

Delete thread

Tool to delete a thread by its ID.

Delete Vector Store

Tool to delete a vector store.

Delete Vector Store File

Tool to delete a vector store file.

Delete video

Tool to delete a video by its ID.

Download file

Tool to download the contents of a specified file by its ID.

Download Video Content

Tool to download video content (MP4) or preview assets from OpenAI Videos API.

Get Chat Completion

Tool to retrieve a stored chat completion.

Get Chat Completion Messages

Tool to retrieve messages from a stored chat completion.

Get ChatKit thread

Tool to retrieve a ChatKit thread by its ID.

Get Conversation Item

Tool to retrieve a single item from a conversation.

Get Eval

Tool to retrieve an evaluation by ID.

Get Evaluation Run

Tool to retrieve an evaluation run by ID to check status and results.

Get Eval Run Output Item

Tool to retrieve a specific output item from an evaluation run by its ID.

Get eval run output items

Tool to get a list of output items for an evaluation run.

Get Evaluation Runs

Tool to get a paginated list of runs for an evaluation.

Get Input Token Counts

Tool to calculate input token counts for OpenAI API requests.

Get Message

Tool to retrieve a specific message from a thread by its ID.

Get Response

Tool to retrieve a model response by ID.

Get Run Step

Tool to retrieve a specific run step from an Assistants API run to inspect detailed execution progress, view tool calls, or check message creation.

Get Vector Store

Tool to retrieve a vector store by its ID.

Get Vector Store File

Tool to retrieve a file from a vector store.

Get Vector Store File Batch

Tool to retrieve a vector store file batch.

Get Video

Tool to retrieve a video generation job by its unique identifier.

List Assistants

Tool to list assistants to discover the correct assistant_id by name or metadata.

List Batches

Tool to list your organization's batches.

List Chat Completions

Tool to list stored chat completions that were created with the `store` parameter set to true.

List ChatKit thread items

Tool to list ChatKit thread items.

List container files

Tool to list files in a container.

List Containers

Tool to list containers.

List Conversation Items

Tool to list all items for a conversation with the given ID.

List engines

Tool to list available engines and their basic information.

List Evals

Tool to list evaluations for a project.

List files

Tool to retrieve a list of files uploaded to your organization/project context.

List Files in Vector Store Batch

Tool to list vector store files in a batch.

List fine-tunes

Tool to list your organization's fine-tuning jobs.

List fine-tuning job events

Tool to get status updates for a fine-tuning job.

List fine-tuning job checkpoints

Tool to list checkpoints for a fine-tuning job.

List Input Items

Tool to retrieve input items for a given response from the OpenAI Responses API.

List Messages

Tool to list messages in an Assistants thread to fetch the assistant's generated outputs after a run completes.

List models

Tool to list available models scoped to the current account/organization — some public models may be absent due to permissions.

List Runs

Tool to list runs belonging to a thread.

List Run Steps

Tool to list run steps for an Assistants API run to track detailed execution progress, inspect tool calls, and view message creation events.

List Skills

Tool to list skills.

List ChatKit Threads

Tool to list ChatKit threads with pagination and filtering.

List Vector Store Files

Tool to list files in a vector store.

List Vector Stores

Tool to list vector stores to discover available vector stores by name or metadata.

List Videos

Tool to list all video generation jobs.

Modify Assistant

Tool to modify an existing assistant.

Modify Message

Tool to modify an existing message's metadata in a thread.

Modify Run

Tool to modify a run's metadata.

Modify thread

Tool to modify an existing thread's metadata.

Modify Vector Store

Tool to modify an existing vector store.

Retrieve assistant

Tool to retrieve details of a specific assistant.

Retrieve Batch

Tool to retrieve a batch by ID to check its status, progress, and results.

Retrieve container

Tool to retrieve details of a specific container by its ID.

Retrieve container file

Tool to retrieve metadata for a specific file in a container.

Retrieve container file content

Tool to retrieve the content of a file within a container.

Retrieve engine

Tool to retrieve details of a specific engine.

Retrieve file

Tool to retrieve information about a specific file.

Retrieve fine-tuning job

Tool to retrieve information about a fine-tuning job.

Retrieve model

Tool to retrieve details of a specific model, confirming its metadata (ownership, created date) and verifying access under your org — a model appearing in OPENAI_LIST_MODELS does not guarantee access.

Retrieve run

Tool to retrieve an Assistants run by ID to check status, errors, and usage.

Retrieve thread

Tool to retrieve metadata of a specific thread by its ID — does not include message bodies or assistant replies (those require a completed run and separate message listing).

Retrieve Vector Store File Content

Tool to retrieve the parsed contents of a vector store file.

Run grader

Tool to run a grader to evaluate model performance on a given sample.

Search Vector Store

Tool to search a vector store for relevant chunks based on a query and file attributes filter.

Submit Tool Outputs to Run

Tool to submit tool call outputs to continue a run that requires action.

Update Chat Completion

Tool to update metadata for a stored chat completion.

Update Conversation

Tool to update a conversation's metadata.

Update Eval

Tool to update certain properties of an evaluation (name and metadata).

Update Vector Store File Attributes

Tool to update custom attributes on a vector store file.

Upload file

Tool to upload a file for use across OpenAI endpoints.

Validate grader configuration

Tool to validate a grader configuration for fine-tuning jobs.

FRAMEWORKS

How to build Openai MCP Agent with another framework

ChatGPT

Use Openai MCP with ChatGPT

OpenAI Agents SDK

Use Openai MCP with OpenAI Agents SDK

Claude Agent SDK

Use Openai MCP with Claude Agent SDK

Claude Code

Use Openai MCP with Claude Code

Claude Cowork

Use Openai MCP with Claude Cowork

Codex

Use Openai MCP with Codex

Cursor

Use Openai MCP with Cursor

OpenClaw

Use Openai MCP with OpenClaw

Hermes

Use Openai MCP with Hermes

CLI

Use Openai MCP with CLI

Google ADK

Use Openai MCP with Google ADK

LangChain

Use Openai MCP with LangChain

Vercel AI SDK

Use Openai MCP with Vercel AI SDK

Mastra AI

Use Openai MCP with Mastra AI

CrewAI

Use Openai MCP with CrewAI

OpenCode

Use Openai MCP with OpenCode

VS Code

Use Openai MCP with VS Code

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Composio

No Auth

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

No Auth

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

Api Key

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

No Auth

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.

FAQ

Frequently asked questions

With a standalone Openai MCP server, the agents and LLMs can only access a fixed set of Openai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Openai and many other apps based on the task at hand, all through a single MCP endpoint.

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 Openai tools.

Yes, absolutely. You can configure which Openai scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Openai data and credentials are handled as safely as possible.

Start with Openai.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Openai tool your agent needs.Free to start.

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Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Openai MCP with LlamaIndex

Connect Openai without auth hassles

Introduction

Also integrate Openai with

TL;DR

What is LlamaIndex?

What is the Openai MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI, Composio, and Openai

Installing dependencies

Set environment variables

Import modules

Load environment variables and initialize Composio

Create a Tool Router session and build the agent function

Create an interactive chat loop

Define the main entry point

Run the agent

Complete Code

Conclusion

Supported Tools

How to build Openai MCP Agent with another framework

ChatGPT

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

Cursor

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

CrewAI

OpenCode

VS Code

Explore Other Toolkits

Composio

Composio search

Perplexityai

Browser tool

Frequently asked questions

What are the differences in Tool Router MCP and Openai MCP?+

Can I use Tool Router MCP with LlamaIndex?+

Can I manage the permissions and scopes for Openai while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Openai.It takes 30 seconds.

Teams ship real agents on Composio