How to integrate Openai MCP with Mastra AI

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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 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 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

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.

Supported Tools & Triggers

Tools
Add Upload PartTool to add a part (chunk of bytes) to an Upload object.
Cancel batchTool to cancel an in-progress batch.
Cancel evaluation runTool to cancel an ongoing evaluation run.
Cancel ResponseTool to cancel a background model response by its ID.
Cancel RunTool to cancel a run that is currently in progress.
Cancel uploadTool to cancel an upload.
Compact ResponseTool to compact a conversation or response to reduce token usage.
Create Audio TranscriptionTool to transcribe audio files to text via OpenAI Audio Transcriptions API.
Create Audio TranslationTool to translate audio files to English text via OpenAI Audio Translations API.
Create BatchTool to create and execute a batch from an uploaded file of requests.
Create Chat CompletionTool to create a chat completion response from OpenAI models.
Create Completion (Legacy)Tool to generate text completions using OpenAI's legacy Completions API.
Create ContainerTool to create a new container with configurable memory, expiration, file access, and network policies.
Create Container FileTool to create a file in a container.
Create ConversationTool to create a new conversation for multi-turn interactions.
Create Conversation ItemsTool to create items in a conversation with the given ID.
Create EmbeddingsTool to generate text embeddings via the OpenAI embeddings endpoint.
Create EvalTool to create an evaluation structure for testing a model's performance.
Create Evaluation RunTool to create a new evaluation run for testing model configurations.
Create fine-tuning jobTool to create a fine-tuning job which begins the process of creating a new model from a given dataset.
Generate ImageTool to generate an image via the OpenAI Images API and return hosted image asset URL and metadata.
Edit ImageTool to create edited or extended images via OpenAI Images Edit API.
Create Image VariationTool to create a variation of a given image using the OpenAI Images API.
Create MessageTool to create a new message in a specific thread.
Create ModerationTool to classify text and/or image inputs for potentially harmful content via the OpenAI Moderation API.
Create Realtime CallTool to create a Realtime API call over WebRTC and receive the SDP answer needed to complete the peer connection.
Create Realtime Client SecretTool to create an ephemeral client secret for authenticating Realtime API connections.
Create Realtime SessionTool to create an ephemeral API token for client-side Realtime API applications.
Create Realtime Transcription SessionTool to create an ephemeral API token for realtime transcriptions via the Realtime API.
Create ResponseTool to generate a one-shot model response via the Responses API.
Create RunTool to create a run on a thread with an assistant.
Create SkillTool to create a skill from uploaded files.
Create Speech (TTS)Tool to generate text-to-speech audio using OpenAI's Audio API.
Create ThreadTool to create a new thread.
Create Thread And RunTool to create a thread and run it in one request.
Create UploadTool to create an intermediate Upload object for large file uploads.
Create Vector StoreTool to create a new vector store.
Create Vector Store FileTool to create a vector store file by attaching a File to a vector store.
Create vector store file batchTool to create a vector store file batch.
Create VideoTool to create a video using Sora models via the OpenAI Videos API.
Create Video RemixTool to create a video remix from an existing generated video using OpenAI's Video API.
Delete assistantTool to delete a specific assistant by its ID.
Delete chat completionTool to delete a stored chat completion by its ID.
Delete containerTool to delete a specific container by its ID.
Delete container fileTool to delete a file from a container.
Delete conversationTool to delete a conversation by its ID.
Delete conversation itemTool to delete an item from a conversation with the given IDs.
Delete evaluationTool to delete a specific evaluation by its ID.
Delete evaluation runTool to delete an evaluation run.
Delete fileTool to delete a file by its ID after confirming the target.
Delete messageTool to delete a message from a thread.
Delete responseTool to delete a model response with the given ID.
Delete skillTool to delete a specific skill by its ID.
Delete threadTool to delete a thread by its ID.
Delete Vector StoreTool to delete a vector store.
Delete Vector Store FileTool to delete a vector store file.
Delete videoTool to delete a video by its ID.
Download fileTool to download the contents of a specified file by its ID.
Download Video ContentTool to download video content (MP4) or preview assets from OpenAI Videos API.
Get Chat CompletionTool to retrieve a stored chat completion.
Get Chat Completion MessagesTool to retrieve messages from a stored chat completion.
Get ChatKit threadTool to retrieve a ChatKit thread by its ID.
Get Conversation ItemTool to retrieve a single item from a conversation.
Get EvalTool to retrieve an evaluation by ID.
Get Evaluation RunTool to retrieve an evaluation run by ID to check status and results.
Get Eval Run Output ItemTool to retrieve a specific output item from an evaluation run by its ID.
Get eval run output itemsTool to get a list of output items for an evaluation run.
Get Evaluation RunsTool to get a paginated list of runs for an evaluation.
Get Input Token CountsTool to calculate input token counts for OpenAI API requests.
Get MessageTool to retrieve a specific message from a thread by its ID.
Get ResponseTool to retrieve a model response by ID.
Get Run StepTool 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 StoreTool to retrieve a vector store by its ID.
Get Vector Store FileTool to retrieve a file from a vector store.
Get Vector Store File BatchTool to retrieve a vector store file batch.
Get VideoTool to retrieve a video generation job by its unique identifier.
List AssistantsTool to list assistants to discover the correct assistant_id by name or metadata.
List BatchesTool to list your organization's batches.
List Chat CompletionsTool to list stored chat completions that were created with the `store` parameter set to true.
List ChatKit thread itemsTool to list ChatKit thread items.
List container filesTool to list files in a container.
List ContainersTool to list containers.
List Conversation ItemsTool to list all items for a conversation with the given ID.
List enginesTool to list available engines and their basic information.
List EvalsTool to list evaluations for a project.
List filesTool to retrieve a list of files uploaded to your organization/project context.
List Files in Vector Store BatchTool to list vector store files in a batch.
List fine-tunesTool to list your organization's fine-tuning jobs.
List fine-tuning job eventsTool to get status updates for a fine-tuning job.
List fine-tuning job checkpointsTool to list checkpoints for a fine-tuning job.
List Input ItemsTool to retrieve input items for a given response from the OpenAI Responses API.
List MessagesTool to list messages in an Assistants thread to fetch the assistant's generated outputs after a run completes.
List modelsTool to list available models scoped to the current account/organization — some public models may be absent due to permissions.
List RunsTool to list runs belonging to a thread.
List Run StepsTool to list run steps for an Assistants API run to track detailed execution progress, inspect tool calls, and view message creation events.
List SkillsTool to list skills.
List ChatKit ThreadsTool to list ChatKit threads with pagination and filtering.
List Vector Store FilesTool to list files in a vector store.
List Vector StoresTool to list vector stores to discover available vector stores by name or metadata.
List VideosTool to list all video generation jobs.
Modify AssistantTool to modify an existing assistant.
Modify MessageTool to modify an existing message's metadata in a thread.
Modify RunTool to modify a run's metadata.
Modify threadTool to modify an existing thread's metadata.
Modify Vector StoreTool to modify an existing vector store.
Retrieve assistantTool to retrieve details of a specific assistant.
Retrieve BatchTool to retrieve a batch by ID to check its status, progress, and results.
Retrieve containerTool to retrieve details of a specific container by its ID.
Retrieve container fileTool to retrieve metadata for a specific file in a container.
Retrieve container file contentTool to retrieve the content of a file within a container.
Retrieve engineTool to retrieve details of a specific engine.
Retrieve fileTool to retrieve information about a specific file.
Retrieve fine-tuning jobTool to retrieve information about a fine-tuning job.
Retrieve modelTool 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 runTool to retrieve an Assistants run by ID to check status, errors, and usage.
Retrieve threadTool 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 ContentTool to retrieve the parsed contents of a vector store file.
Run graderTool to run a grader to evaluate model performance on a given sample.
Search Vector StoreTool to search a vector store for relevant chunks based on a query and file attributes filter.
Submit Tool Outputs to RunTool to submit tool call outputs to continue a run that requires action.
Update Chat CompletionTool to update metadata for a stored chat completion.
Update ConversationTool to update a conversation's metadata.
Update EvalTool to update certain properties of an evaluation (name and metadata).
Update Vector Store File AttributesTool to update custom attributes on a vector store file.
Upload fileTool to upload a file for use across OpenAI endpoints.
Validate grader configurationTool to validate a grader configuration for fine-tuning jobs.

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

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

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.

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

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

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

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

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

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

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

How to build Openai MCP Agent with another framework

FAQ

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

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.

Can I use Tool Router MCP with Mastra AI?

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.

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

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.

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 Openai data and credentials are handled as safely as possible.

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