--- title: Build an AI coding agent with OpenAI Agents SDK description: Use the OpenAI Agents SDK with Cloudflare Sandbox to build a Python agent that writes, tests, and delivers code in an isolated environment. image: https://edgetunnel-b2h.pages.dev/dev-products-preview.png --- > Documentation Index > Fetch the complete documentation index at: https://edgetunnel-b2h.pages.dev/sandbox/llms.txt > Use this file to discover all available pages before exploring further. [Skip to content](#%5Ftop) # Build an AI coding agent with OpenAI Agents SDK The [OpenAI Agents SDK ↗](https://openai.github.io/openai-agents-python/) is a lightweight Python framework for building multi-agent workflows. A Cloudflare Sandbox integration is provided out of the box and ensures that the SDK includes a first-class Cloudflare backend that gives your agents isolated containers for running code, installing packages, and managing files. In this tutorial, you will deploy a sandbox bridge Worker and build a Python agent that accepts a coding task, executes it inside a Cloudflare Sandbox, and copies the output files to your local machine. **Time to complete**: 20 minutes ## Prerequisites 1. Sign up for a [Cloudflare account ↗](https://dash.cloudflare.com/sign-up/workers-and-pages) with the Containers / Sandbox beta enabled. 2. Install [Python 3.12+ ↗](https://www.python.org/) and [uv ↗](https://docs.astral.sh/uv/). 3. Obtain an [OpenAI API key ↗](https://platform.openai.com/api-keys). ## 1\. Deploy the sandbox bridge The [sandbox bridge](https://edgetunnel-b2h.pages.dev/sandbox/bridge/) is a Cloudflare Worker that exposes the Sandbox API over HTTP so non-Worker clients — such as a Python script using the OpenAI Agents SDK — can create and control sandboxes. The Sandbox environment comes pre-configured for Node.js and Python development, so your agents can start writing and running code immediately. Deploy the bridge to your Cloudflare account: [![Deploy to Cloudflare](https://deploy.workers.cloudflare.com/button)](https://deploy.workers.cloudflare.com/?url=https://github.com/cloudflare/sandbox-sdk/tree/main/bridge/worker) The button deploys the Worker and generates a `SANDBOX_API_KEY` secret for authentication. When deployment finishes, note your Worker URL and API key — you will need them in the next step. Manual deployment If you prefer to deploy step by step: 1. Install [Node.js ↗](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) and [Docker ↗](https://www.docker.com/). 2. Scaffold the bridge project: ```sh npm create cloudflare sandbox-bridge --template=cloudflare/sandbox-sdk/bridge/worker cd sandbox-bridge ``` 3. Authenticate with Cloudflare: ```sh npx wrangler login ``` 4. Set the API key secret: ```sh openssl rand -hex 32 | tee /dev/stderr | npx wrangler secret put SANDBOX_API_KEY ``` The key is printed to your terminal and piped to Wrangler. Save it — you will need it to authenticate API requests. 5. Deploy the Worker: ```sh npx wrangler deploy ``` 6. Verify the deployment: ```sh curl https://cloudflare-sandbox-bridge..workers.dev/health ``` You should see `{"ok":true}`. ## 2\. Set up your Python project Create a new directory for the agent: ```sh mkdir openai-sandbox-agent && cd openai-sandbox-agent ``` Create a `.env` file with your credentials: **.env** ```sh OPENAI_API_KEY=sk-your-openai-key CLOUDFLARE_SANDBOX_API_KEY=your-bridge-token CLOUDFLARE_SANDBOX_WORKER_URL=https://cloudflare-sandbox-bridge.your-subdomain.workers.dev ``` ## 3\. Build the agent Create `main.py` with the following content. The inline script metadata tells `uv` which dependencies to install, so everything is contained in a single file: **main.py** ```python # /// script # requires-python = ">=3.12" # dependencies = ["openai-agents[cloudflare]"] # /// """One-shot coding agent backed by a Cloudflare Sandbox.""" from __future__ import annotations import asyncio import os import sys from pathlib import Path from agents import Runner from agents.extensions.sandbox.cloudflare import ( CloudflareSandboxClient, CloudflareSandboxClientOptions, ) from agents.run import RunConfig from agents.sandbox import SandboxAgent, SandboxRunConfig from agents.sandbox.capabilities import Shell MODEL = "gpt-5.4" INSTRUCTIONS = """\ You are an expert developer working inside a sandbox. The sandbox has bun, node, npm, and python available on the PATH. Implement the user's task in /workspace, test it, then copy deliverable files to /workspace/output/. """.strip() async def copy_output(session, dest: Path) -> list[Path]: """Download files from /workspace/output/ in the sandbox to a local directory.""" dest.mkdir(parents=True, exist_ok=True) ls = await session.exec("find", "/workspace/output", "-maxdepth", "1", "-type", "f", shell=False) if not ls.ok(): return [] copied: list[Path] = [] for name in (l.strip() for l in ls.stdout.decode().splitlines() if l.strip()): handle = await session.read(Path(name)) local = dest / Path(name).name payload = handle.read(); handle.close() local.write_bytes(payload if isinstance(payload, bytes) else payload.encode()) copied.append(local) return copied async def run(prompt: str, output_dir: Path) -> None: worker_url = os.environ.get("CLOUDFLARE_SANDBOX_WORKER_URL", "") if not worker_url: sys.exit("Error: CLOUDFLARE_SANDBOX_WORKER_URL is not set.") agent = SandboxAgent( name="Developer", model=MODEL, instructions=INSTRUCTIONS, capabilities=[Shell()], ) client = CloudflareSandboxClient() options = CloudflareSandboxClientOptions(worker_url=worker_url) session = await client.create(manifest=agent.default_manifest, options=options) try: async with session: run_config = RunConfig( sandbox=SandboxRunConfig(session=session), tracing_disabled=True, ) # Stream tool calls so the user can follow progress. result = Runner.run_streamed(agent, prompt, run_config=run_config) async for ev in result.stream_events(): if ev.type == "run_item_stream_event" and ev.name == "tool_called": print(f" [tool] {getattr(ev.item.raw_item, 'name', '')}") elif ev.type == "run_item_stream_event" and ev.name == "tool_output": print(f" [output] {str(getattr(ev.item, 'output', ''))[:200]}") # Copy output files from the sandbox to the local machine. copied = await copy_output(session, output_dir) if copied: print(f"\nCopied {len(copied)} file(s) to {output_dir}:") for p in copied: print(f" {p}") else: print("\nAgent did not produce any output files.") finally: await client.delete(session) if __name__ == "__main__": prompt = sys.argv[1] if len(sys.argv) > 1 else "Create a hello world HTTP server using Bun.serve" asyncio.run(run(prompt, Path("output"))) ``` Here is what the key pieces do: | Component | Purpose | | ------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------- | | SandboxAgent | An Agent subclass that accepts sandbox-specific configuration, including capabilities. | | Shell() | A capability that exposes a shell tool to the LLM, allowing it to run commands inside the sandbox. | | CloudflareSandboxClient | Creates and manages sandbox sessions through the bridge Worker. Reads CLOUDFLARE\_SANDBOX\_API\_KEY from the environment for authentication. | | CloudflareSandboxClientOptions | Points the client at your bridge Worker URL. | | Runner.run\_streamed() | Executes the agent and yields streaming events for tool calls and text output. | | SandboxRunConfig | Attaches a live sandbox session to the run so the agent's tools execute inside the container. | ## 4\. Run the agent ```sh uv run --env-file .env main.py "Create a hello world HTTP server using Bun.serve" ``` You should see tool calls and output streaming to the console: ```txt Sending task to sandbox agent (gpt-5.4)... [tool] exec_command [output] exit_code=0 stdout: mkdir: created directory '/workspace/output' [tool] exec_command [output] exit_code=0 stdout: Listening on http://localhost:3000 Copied 1 file(s) to output: output/server.ts ``` The agent wrote the code, tested it inside the sandbox, and copied the deliverable to your local machine. ## What you built You built a Python coding agent that: * Accepts a natural-language coding task * Executes code in an isolated Cloudflare Sandbox container * Installs packages, runs tests, and iterates until the task is complete * Copies deliverable files back to your local machine The bridge Worker's `Dockerfile` can be fully customized to suit your needs — install additional languages, system packages, or tools to match your use case. The Cloudflare Sandbox provides more capabilities you can integrate into your agents: * **PTY sessions** — Open interactive terminal sessions to sandboxes via WebSocket for real-time I/O. * **Bucket mounts** — Mount R2 or S3-compatible buckets as local directories inside the sandbox for persistent data. * **Workspace backup and restore** — Persist workspace state with `persist_workspace()` and `hydrate_workspace()` to resume work across sandbox lifecycles. * **File operations** — Read, write, and manage files programmatically within the sandbox. ## Next steps * [Workspace chat example ↗](https://github.com/cloudflare/sandbox-sdk/tree/main/bridge/examples/workspace-chat) — A full-stack chat application with a file browser sidebar, built with the OpenAI Agents SDK and Cloudflare Sandbox. * [OpenAI Agents SDK documentation ↗](https://openai.github.io/openai-agents-python/) — Learn about multi-agent handoffs, guardrails, tracing, and more. * [Sandbox bridge](https://edgetunnel-b2h.pages.dev/sandbox/bridge/) — Overview of the bridge Worker, usage examples, and configuration. * [HTTP API reference](https://edgetunnel-b2h.pages.dev/sandbox/bridge/http-api/) — Complete route reference for the bridge API. * [Sandbox tutorials](https://edgetunnel-b2h.pages.dev/sandbox/tutorials/) — More tutorials covering code execution, data analysis, and CI/CD pipelines. ```json {"@context":"https://schema.org","@type":"TechArticle","@id":"https://edgetunnel-b2h.pages.dev/sandbox/tutorials/openai-agents/#page","headline":"Build an AI coding agent with OpenAI Agents SDK · Cloudflare Sandbox SDK docs","description":"Use the OpenAI Agents SDK with Cloudflare Sandbox to build a Python agent that writes, tests, and delivers code in an isolated environment.","url":"https://edgetunnel-b2h.pages.dev/sandbox/tutorials/openai-agents/","inLanguage":"en","image":"https://edgetunnel-b2h.pages.dev/dev-products-preview.png","dateModified":"2026-05-05","publisher":{"@type":"Organization","name":"Cloudflare","url":"https://www.cloudflare.com/"},"isPartOf":{"@type":"WebSite","@id":"https://edgetunnel-b2h.pages.dev/#website","name":"Cloudflare Docs","url":"https://edgetunnel-b2h.pages.dev/"},"keywords":["Python","OpenAI"]} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"/directory/","name":"Directory"}},{"@type":"ListItem","position":2,"item":{"@id":"/sandbox/","name":"Sandbox SDK"}},{"@type":"ListItem","position":3,"item":{"@id":"/sandbox/tutorials/","name":"Tutorials"}},{"@type":"ListItem","position":4,"item":{"@id":"/sandbox/tutorials/openai-agents/","name":"Build an AI coding agent with OpenAI Agents SDK"}}]} ```