OpenAI Agents SDK with FastAPI

OpenAI Agents SDK FastAPI Starter with Vercel Sandbox

Minimal FastAPI app that runs the OpenAI Agents SDK with Vercel Sandbox on Vercel's Python runtime. Each request spins up an isolated microVM, gives the agent shell access to analyze data, and tears it down when done.

Prerequisites

• Python 3.12+ (Vercel default is 3.12).
• A Vercel account and Vercel CLI (npm i -g vercel).
• An OpenAI API key.

Environment variables

Setup

Copy the example env file and fill in your values:

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cp .env.example .env.local

Then edit .env.local with your keys:

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OPENAI_API_KEY=sk-...
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VERCEL_TOKEN=your_access_token
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VERCEL_TEAM_ID=team_xxx
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VERCEL_PROJECT_ID=prj_xxx

Local development

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uv sync
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uv run uvicorn app:app --reload --host 127.0.0.1 --port 8000

Open http://127.0.0.1:8000 to use the interactive demo. The agent has shell access to a sandbox with sample sales data (sales.csv).

API endpoints:

• GET /api/health returns {"status": "ok", "openai_configured": true}
• POST /api/run with {"input": "Which region grew the most?"} runs the sandbox agent

Deploy to Vercel

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vercel

Vercel detects app.py and the app ASGI instance. Dependencies come from pyproject.toml [blocked].

After deploying, make sure OPENAI_API_KEY, VERCEL_TOKEN, VERCEL_TEAM_ID, and VERCEL_PROJECT_ID are set under Project Settings > Environment Variables.

Timeouts

Sandbox creation and agent runs can take several seconds. Heavy workloads may need Fluid Compute or Vercel Workflow for durable steps.

How it works

1. Each POST /api/run creates a fresh Vercel Sandbox microVM with sample data.
2. A SandboxAgent with Shell capability receives the user's prompt.
3. The agent writes and runs shell commands inside the sandbox to answer the question.
4. The sandbox is torn down after the response is returned.

License

MIT (match your org's policy when publishing).