---
title: Code interpreter with Workers AI
description: Build a code interpreter using Workers AI GPT-OSS model with the official workers-ai-provider package.
image: https://edgetunnel-b2h.pages.dev/dev-products-preview.png
---

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# Code interpreter with Workers AI

Build a powerful code interpreter that gives the [gpt-oss model](https://edgetunnel-b2h.pages.dev/workers-ai/models/gpt-oss-120b/) on Workers AI the ability to execute Python code using the Cloudflare Sandbox SDK.

**Time to complete:** 15 minutes

## What you'll build

A Cloudflare Worker that accepts natural language prompts, uses GPT-OSS to decide when Python code execution is needed, runs the code in isolated sandboxes, and returns results with AI-powered explanations.

## Prerequisites

1. Sign up for a [Cloudflare account ↗](https://dash.cloudflare.com/sign-up/workers-and-pages).
2. Install [Node.js ↗](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).

Node.js version manager

Use a Node version manager like [Volta ↗](https://volta.sh/) or [nvm ↗](https://github.com/nvm-sh/nvm) to avoid permission issues and change Node.js versions. [Wrangler](https://edgetunnel-b2h.pages.dev/workers/wrangler/install-and-update/), discussed later in this guide, requires a Node version of `16.17.0` or later.

You'll also need:

* [Docker ↗](https://www.docker.com/) running locally

## 1\. Create your project

Create a new Sandbox SDK project:

 npm  yarn  pnpm 

```
npm create cloudflare@latest -- workers-ai-interpreter --template=cloudflare/sandbox-sdk/examples/code-interpreter
```

```
yarn create cloudflare workers-ai-interpreter --template=cloudflare/sandbox-sdk/examples/code-interpreter
```

```
pnpm create cloudflare@latest workers-ai-interpreter --template=cloudflare/sandbox-sdk/examples/code-interpreter
```

```sh
cd workers-ai-interpreter
```

## 2\. Review the implementation

The template includes a complete implementation using the latest best practices. Let's examine the key components:

**TypeScript**

```typescript
// src/index.ts
import { getSandbox } from "@cloudflare/sandbox";
import { generateText, stepCountIs, tool } from "ai";
import { createWorkersAI } from "workers-ai-provider";
import { z } from "zod";


const MODEL = "@cf/openai/gpt-oss-120b" as const;


async function handleAIRequest(input: string, env: Env): Promise<string> {
  const workersai = createWorkersAI({ binding: env.AI });


  const result = await generateText({
    model: workersai(MODEL),
    messages: [{ role: "user", content: input }],
    tools: {
      execute_python: tool({
        description: "Execute Python code and return the output",
        inputSchema: z.object({
          code: z.string().describe("The Python code to execute"),
        }),
        execute: async ({ code }) => {
          return executePythonCode(env, code);
        },
      }),
    },
    stopWhen: stepCountIs(5),
  });


  return result.text || "No response generated";
}
```

**Key improvements over direct REST API calls:**

* **Official packages**: Uses `workers-ai-provider` instead of manual API calls
* **Vercel AI SDK**: Leverages `generateText()` and `tool()` for clean function calling
* **No API keys**: Uses native AI binding instead of environment variables
* **Type safety**: Full TypeScript support with proper typing

## 3\. Check your configuration

The template includes the proper Wrangler configuration:

* [  wrangler.jsonc ](#tab-panel-11389)
* [  wrangler.toml ](#tab-panel-11390)

**JSONC**

```jsonc
{
  "name": "sandbox-code-interpreter-example",
  "main": "src/index.ts",
  // Set this to today's date
  "compatibility_date": "2026-07-20",
  "ai": {
    "binding": "AI"
  },
  "containers": [
    {
      "class_name": "Sandbox",
      "image": "./Dockerfile",
      "name": "sandbox",
      "max_instances": 1,
      "instance_type": "basic"
    }
  ],
  "durable_objects": {
    "bindings": [
      {
        "class_name": "Sandbox",
        "name": "Sandbox"
      }
    ]
  }
}
```

**TOML**

```toml
name = "sandbox-code-interpreter-example"
main = "src/index.ts"
# Set this to today's date
compatibility_date = "2026-07-20"


[ai]
binding = "AI"


[[containers]]
class_name = "Sandbox"
image = "./Dockerfile"
name = "sandbox"
max_instances = 1
instance_type = "basic"


[[durable_objects.bindings]]
class_name = "Sandbox"
name = "Sandbox"
```

**Configuration highlights:**

* **AI binding**: Enables direct access to Workers AI models
* **Container setup**: Configures sandbox container with Dockerfile
* **Durable Objects**: Provides persistent sandboxes with state management

## 4\. Test locally

Start the development server:

```sh
npm run dev
```

Note

First run builds the Docker container (2-3 minutes). Subsequent runs are much faster.

Test with curl:

```sh
# Simple calculation
curl -X POST http://localhost:8787/run \
  -H "Content-Type: application/json" \
  -d '{"input": "Calculate 5 factorial using Python"}'


# Complex operations
curl -X POST http://localhost:8787/run \
  -H "Content-Type: application/json" \
  -d '{"input": "Use Python to find all prime numbers under 20"}'


# Data analysis
curl -X POST http://localhost:8787/run \
  -H "Content-Type: application/json" \
  -d '{"input": "Create a list of the first 10 squares and calculate their sum"}'
```

## 5\. Deploy

Deploy your Worker:

```sh
npx wrangler deploy
```

Warning

After first deployment, wait 2-3 minutes for container provisioning before making requests.

## 6\. Test your deployment

Try more complex queries:

```sh
# Data visualization preparation
curl -X POST https://workers-ai-interpreter.YOUR_SUBDOMAIN.workers.dev/run \
  -H "Content-Type: application/json" \
  -d '{"input": "Generate sample sales data for 12 months and calculate quarterly totals"}'


# Algorithm implementation
curl -X POST https://workers-ai-interpreter.YOUR_SUBDOMAIN.workers.dev/run \
  -H "Content-Type: application/json" \
  -d '{"input": "Implement a binary search function and test it with a sorted array"}'


# Mathematical computation
curl -X POST https://workers-ai-interpreter.YOUR_SUBDOMAIN.workers.dev/run \
  -H "Content-Type: application/json" \
  -d '{"input": "Calculate the standard deviation of [2, 4, 4, 4, 5, 5, 7, 9]"}'
```

## How it works

1. **User input**: Send natural language prompts to the `/run` endpoint
2. **AI decision**: GPT-OSS receives the prompt with an `execute_python` tool available
3. **Smart execution**: Model decides whether Python code execution is needed
4. **Sandbox isolation**: Code runs in isolated Cloudflare Sandbox containers
5. **AI explanation**: Results are integrated back into the AI's response for final output

## What you built

You deployed a sophisticated code interpreter that:

* **Native Workers AI integration**: Uses the official `workers-ai-provider` package for seamless integration
* **Function calling**: Leverages Vercel AI SDK for clean tool definitions and execution
* **Secure execution**: Runs Python code in isolated sandbox containers
* **Intelligent responses**: Combines AI reasoning with code execution results

## Next steps

* [Analyze data with AI](https://edgetunnel-b2h.pages.dev/sandbox/tutorials/analyze-data-with-ai/) \- Add pandas and matplotlib for advanced data analysis
* [Code Interpreter API](https://edgetunnel-b2h.pages.dev/sandbox/api/interpreter/) \- Use the built-in code interpreter with structured outputs
* [Streaming output](https://edgetunnel-b2h.pages.dev/sandbox/guides/streaming-output/) \- Show real-time execution progress
* [API reference](https://edgetunnel-b2h.pages.dev/sandbox/api/) \- Explore all available sandbox methods

## Related resources

* [Workers AI](https://edgetunnel-b2h.pages.dev/workers-ai/) \- Learn about Cloudflare's AI platform
* [workers-ai-provider package ↗](https://github.com/cloudflare/ai/tree/main/packages/workers-ai-provider) \- Official Workers AI integration
* [Vercel AI SDK ↗](https://sdk.vercel.ai/) \- Universal toolkit for AI applications
* [GPT-OSS model documentation](https://edgetunnel-b2h.pages.dev/workers-ai/models/gpt-oss-120b/) \- Model details and capabilities

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