# How to integrate Codeinterpreter MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Codeinterpreter MCP with OpenAI Agents SDK",
  "toolkit": "Codeinterpreter",
  "toolkit_slug": "codeinterpreter",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/codeinterpreter/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/codeinterpreter/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-06T08:06:43.629Z"
}
```

## Introduction

This guide walks you through connecting Codeinterpreter to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Codeinterpreter agent that can run this python script and show output, generate a line chart from your csv file, upload a dataset and summarize key stats through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Codeinterpreter account through Composio's Codeinterpreter MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Codeinterpreter with

- [Claude Agent SDK](https://composio.dev/toolkits/codeinterpreter/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/codeinterpreter/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/codeinterpreter/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/codeinterpreter/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/codeinterpreter/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/codeinterpreter/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/codeinterpreter/framework/cli)
- [Google ADK](https://composio.dev/toolkits/codeinterpreter/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/codeinterpreter/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/codeinterpreter/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/codeinterpreter/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/codeinterpreter/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/codeinterpreter/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for Codeinterpreter
- Configure an AI agent that can use Codeinterpreter as a tool
- Run a live chat session where you can ask the agent to perform Codeinterpreter operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

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

The Codeinterpreter MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Codeinterpreter environment. It provides structured and secure access to interactive Python sandboxes, so your agent can run scripts, analyze data, visualize results, and manage files on your behalf.
- On-demand code execution: Instantly execute Python code snippets, scripts, or notebooks and receive real-time output, including errors and logs.
- Sandbox creation and management: Have your agent spin up isolated coding environments for running experiments, testing ideas, or working with data securely.
- File upload and retrieval: Seamlessly upload datasets, scripts, or assets to the sandbox and fetch generated files, reports, or images for further analysis.
- Terminal command automation: Direct your agent to run Linux shell commands inside the sandbox, enabling advanced automation and environment setup.
- Data visualization and reporting: Generate charts, plots, and visual reports by executing code that saves outputs as files—perfect for data-driven tasks.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CODEINTERPRETER_CREATE_SANDBOX` | Create Sandbox | Create a sandbox to execute python code in a jupyter notebook cell. this is useful for agents to communicate, execute code, see output, read files, write files, etc. it's like you own personal computer, but in the cloud. use /home/user folder to write/read files. |
| `CODEINTERPRETER_EXECUTE_CODE` | Execute Code | Execute python code in a sandbox and return any result, stdout, stderr, and error. use /home/user folder to write/read files. try to not use plt.show() as the code is executed remotely. use files for image/chart output instead. |
| `CODEINTERPRETER_GET_FILE_CMD` | Get File | Get a file from the sandbox and returns the file. the files should be read from /home/user folder. |
| `CODEINTERPRETER_RUN_TERMINAL_CMD` | Run Terminal Command | Run a command in the terminal and returns the stdout, stderr, and error code. use /home/user folder to write/read files. |
| `CODEINTERPRETER_UPLOAD_FILE_CMD` | Upload File | Upload a file to the sandbox environment. the files should be uploaded to the /home/user folder. |

## Supported Triggers

None listed.

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

The Codeinterpreter MCP server is an implementation of the Model Context Protocol that connects your AI agent to Codeinterpreter. It provides structured and secure access so your agent can perform Codeinterpreter 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 starting, make sure you have:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Codeinterpreter project
- Some knowledge of Python or Typescript

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Codeinterpreter.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only codeinterpreter.
- The router checks the user's Codeinterpreter connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Codeinterpreter.
- This approach keeps things lightweight and lets the agent request Codeinterpreter tools only when needed during the conversation.
```python
# Create a Codeinterpreter Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["codeinterpreter"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Codeinterpreter
const session = await composio.create(userId as string, {
  toolkits: ['codeinterpreter'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Codeinterpreter. "
        "Help users perform Codeinterpreter operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Codeinterpreter. Help users perform Codeinterpreter operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

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

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["codeinterpreter"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Codeinterpreter. "
        "Help users perform Codeinterpreter operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['codeinterpreter'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Codeinterpreter. Help users perform Codeinterpreter operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

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

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

main().catch((err) => {
  console.error('Fatal error:', err);
  process.exit(1);
});
```

## Conclusion

This was a starter code for integrating Codeinterpreter MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Codeinterpreter.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Codeinterpreter MCP Agent with another framework

- [Claude Agent SDK](https://composio.dev/toolkits/codeinterpreter/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/codeinterpreter/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/codeinterpreter/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/codeinterpreter/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/codeinterpreter/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/codeinterpreter/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/codeinterpreter/framework/cli)
- [Google ADK](https://composio.dev/toolkits/codeinterpreter/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/codeinterpreter/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/codeinterpreter/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/codeinterpreter/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/codeinterpreter/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/codeinterpreter/framework/crew-ai)

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- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.
- [Bolt iot](https://composio.dev/toolkits/bolt_iot) - Bolt IoT is a platform for building and managing IoT projects with cloud-based device control and monitoring. It makes connecting sensors and actuators to the internet seamless for automation and data insights.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Codeinterpreter MCP?

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

### Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 Codeinterpreter tools.

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

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

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