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How to integrate Openai MCP with Mastra AI

Connect Mastra AI to Openai MCP. List all available openai models, upload a file for fine-tuning, and more using natural language, with authentication handled for you.

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Openai is a powerful AI platform offering advanced language, vision, and multimodal APIs. It's your gateway to building, managing, and scaling intelligent assistants and workflows.

128 Tools

Introduction

This guide walks you through connecting Openai to Mastra AI using the Composio tool router. By the end, you'll have a working Openai agent that can generate chat completions and responses, create and edit images, transcribe audio, create embeddings, and run fine-tuning jobs through natural language commands.

This guide will help you understand how to give your Mastra AI 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

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TL;DR

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

What is Mastra AI?

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

Key features include:

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

What is the 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:

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

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

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

Getting API Keys for OpenAI and Composio

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

Install dependencies

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

Install the required packages.

What's happening:

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

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models
5

Import libraries and validate environment

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

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

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

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

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

Create a Tool Router session for Openai

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

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

Configure Mastra MCP client and fetch tools

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

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

Create the Mastra agent

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

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

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

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

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

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

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

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

    const { text } = response;

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

    rl.prompt();
  });

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

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

Complete Code

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

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

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

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

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

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

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

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

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

  let messages: AiMessageType[] = [];

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

  rl.prompt();

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

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

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

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

    rl.prompt();
  });

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

main();

Conclusion

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

Supported Tools

Every 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

Request cancellation of an in-progress assistant run by thread and run ID, for example when the run is no longer needed.

Cancel upload

Tool to cancel an upload.

Compact Response

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

Complete Upload

Complete a multipart Upload and create its usable File object.

Create Audio Transcription

Transcribes audio files to text via the OpenAI Audio Transcriptions API, optionally returning timestamps.

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

Creates a chat completion from OpenAI models for conversational AI, text generation, function calling, vision or audio tasks, and structured JSON outputs.

Create Completion (Legacy)

Generate single-turn text completions using OpenAI's legacy Completions API with an available compatible model.

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 Decision

Evaluate evidence with typed predicate, choice, and score questions.

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

Creates an evaluation run for testing with a specified data source and model configuration.

Create fine-tuning job

Creates a fine-tuning job from an uploaded training dataset and returns job details and status.

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

Create alternative versions of an existing square PNG image using the legacy image variations operation, with options for count, size, and response format.

Create Message

Create a message in an Assistants conversation thread.

Create Moderation

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

Create Realtime Call

Creates a Realtime API call over WebRTC and returns 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

Creates a model response via the Responses API for text or image analysis and structured JSON output.

Create Run

Create an assistant run on an existing thread to execute the assistant and generate responses, optionally adding messages and setting run options.

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

Create an Assistants API conversation thread with optional starter messages and metadata.

Create Thread And Run

Create an Assistants thread and run in one request to start a conversation and execute an assistant.

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

Create an asynchronous video generation job from a text prompt using the OpenAI Videos API; the job ID and status were used to track generation.

Create Video Remix

Submit a remix job for an existing generated video using a new prompt.

Delete assistant

Delete a specific Assistants API assistant by 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

Deletes a specific evaluation run.

Delete file

Deletes a file by its ID.

Delete message

Delete a specific message from an Assistants conversation thread using the thread and message IDs.

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

Delete an Assistants API conversation 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

Delete a video by ID from OpenAI's Videos API, returning a deletion result.

Download file

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

Download Video Content

Download a generated video's MP4 content or its thumbnail or spritesheet by video ID.

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

Retrieve a specific message from an Assistants thread by thread and message IDs to inspect its content and details.

Get Response

Tool to retrieve a model response by ID.

Get Run Step

Retrieve a specific Assistants run step by thread, run, and step ID to inspect execution progress, tool calls, or 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

Retrieve a file batch by its vector store and batch IDs to check the status and file counts of a batch processing operation.

Get Video

Retrieve a video generation job by ID to inspect its status, progress, and details.

List Assistants

List Assistants API assistants to discover an assistant ID from returned names 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

List items from a ChatKit thread, including messages, tool calls, and tasks.

List container files

List files in a specific container.

List Containers

List a page of containers, optionally filtering by name.

List Conversation Items

List a page of items in a conversation by ID for review or processing.

List engines

Tool to list available engines and their basic information.

List Evals

Tool to list evaluations for a project.

List files

Lists files in the authenticated organization/project context.

List Files in Vector Store Batch

Tool to list vector store files in a batch.

List fine-tunes

Lists fine-tuning jobs for the authenticated context, with cursor pagination.

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

List messages in an Assistants thread, including outputs generated by a run.

List models

Lists models available to the current account or organization, with model identifiers and ownership metadata.

List Runs

List a page of Assistants runs belonging to a thread to inspect their status or execution history.

List Run Steps

List steps in an Assistants API run to inspect execution progress, tool calls, and message creation events.

List Skills

List skills with pagination and timestamp ordering, returning their IDs, names, and descriptions.

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

List video generation jobs and their statuses, with pagination and timestamp ordering, to review multiple tasks.

Modify Assistant

Modify an existing Assistants API assistant's configuration, such as its model, instructions, tools, or metadata.

Modify Message

Modify metadata on a specific message in an Assistants conversation thread, identified by thread and message IDs.

Modify Run

Update metadata on a specific Assistants run identified by its thread and run IDs.

Modify thread

Update metadata on an existing Assistants API thread identified by its thread ID.

Modify Vector Store

Tool to modify an existing vector store.

Retrieve assistant

Retrieve a specific assistant by ID to inspect its configuration and metadata.

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

Retrieve a legacy OpenAI engine by ID to inspect its availability and metadata.

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

Retrieve an Assistants run by thread and run ID to check its status, errors, and usage, such as when polling for completion before reading thread messages.

Retrieve thread

Retrieve an Assistants API thread by ID to inspect its metadata; message bodies are obtained separately.

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

Submit all tool call outputs together to continue an Assistants API run whose required action is submit_tool_outputs.

Update Chat Completion

Tool to update metadata for a stored chat completion.

Update Conversation

Tool to update a conversation's metadata.

Update Eval

Updates an evaluation's name or metadata without changing its data source configuration or testing criteria.

Update Vector Store File Attributes

Tool to update custom attributes on a vector store file.

Upload file

Upload file bytes to OpenAI for a specified purpose, such as fine-tuning, and return the uploaded file's details.

Validate grader configuration

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

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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. Mastra AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right 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.

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