Installation
npx skills add cloudflare/skills --skill sandbox-sdk 3.7K
Installs
Cloudflare Sandbox SDK
Build secure, isolated code execution environments on Cloudflare Workers.
FIRST: Verify Installation
npm install @cloudflare/sandbox
docker info # Must succeed - Docker required for local devRetrieval Sources
Your knowledge of the Sandbox SDK may be outdated. Prefer retrieval over pre-training for any Sandbox SDK task.
| Resource | URL |
|---|---|
| Docs | https://developers.cloudflare.com/sandbox/ |
| API Reference | https://developers.cloudflare.com/sandbox/api/ |
| Examples | https://github.com/cloudflare/sandbox-sdk/tree/main/examples |
| Get Started | https://developers.cloudflare.com/sandbox/get-started/ |
When implementing features, fetch the relevant doc page or example first.
Required Configuration
wrangler.jsonc (exact - do not modify structure):
{
"containers": [{
"class_name": "Sandbox",
"image": "./Dockerfile",
"instance_type": "lite",
"max_instances": 1
}],
"durable_objects": {
"bindings": [{ "class_name": "Sandbox", "name": "Sandbox" }]
},
"migrations": [{ "new_sqlite_classes": ["Sandbox"], "tag": "v1" }]
}Worker entry - must re-export Sandbox class:
import { getSandbox } from '@cloudflare/sandbox';
export { Sandbox } from '@cloudflare/sandbox'; // Required exportQuick Reference
| Task | Method |
|---|---|
| Get sandbox | getSandbox(env.Sandbox, 'user-123') |
| Run command | await sandbox.exec('python script.py') |
| Run code (interpreter) | await sandbox.runCode(code, { language: 'python' }) |
| Write file | await sandbox.writeFile('/workspace/app.py', content) |
| Read file | await sandbox.readFile('/workspace/app.py') |
| Create directory | await sandbox.mkdir('/workspace/src', { recursive: true }) |
| List files | await sandbox.listFiles('/workspace') |
| Expose port | await sandbox.exposePort(8080) |
| Destroy | await sandbox.destroy() |
Core Patterns
Execute Commands
const sandbox = getSandbox(env.Sandbox, 'user-123');
const result = await sandbox.exec('python --version');
// result: { stdout, stderr, exitCode, success }Code Interpreter (Recommended for AI)
Use runCode() for executing LLM-generated code with rich outputs:
const ctx = await sandbox.createCodeContext({ language: 'python' });
await sandbox.runCode('import pandas as pd; data = [1,2,3]', { context: ctx });
const result = await sandbox.runCode('sum(data)', { context: ctx });
// result.results[0].text = "6"Languages: python, javascript, typescript
State persists within context. Create explicit contexts for production.
File Operations
await sandbox.mkdir('/workspace/project', { recursive: true });
await sandbox.writeFile('/workspace/project/main.py', code);
const file = await sandbox.readFile('/workspace/project/main.py');
const files = await sandbox.listFiles('/workspace/project');When to Use What
| Need | Use | Why |
|---|---|---|
| Shell commands, scripts | exec() |
Direct control, streaming |
| LLM-generated code | runCode() |
Rich outputs, state persistence |
| Build/test pipelines | exec() |
Exit codes, stderr capture |
| Data analysis | runCode() |
Charts, tables, pandas |
Extending the Dockerfile
Base image (docker.io/cloudflare/sandbox:0.7.0) includes Python 3.11, Node.js 20, and common tools.
Add dependencies by extending the Dockerfile:
FROM docker.io/cloudflare/sandbox:0.7.0
# Python packages
RUN pip install requests beautifulsoup4
# Node packages (global)
RUN npm install -g typescript
# System packages
RUN apt-get update && apt-get install -y ffmpeg && rm -rf /var/lib/apt/lists/*
EXPOSE 8080 # Required for local dev port exposureKeep images lean - affects cold start time.
Preview URLs (Port Exposure)
Expose HTTP services running in sandboxes:
const { url } = await sandbox.exposePort(8080);
// Returns preview URL for the serviceProduction requirement: Preview URLs need a custom domain with wildcard DNS (*.yourdomain.com). The .workers.dev domain does not support preview URL subdomains.
See: https://developers.cloudflare.com/sandbox/guides/expose-services/
OpenAI Agents SDK Integration
The SDK provides helpers for OpenAI Agents at @cloudflare/sandbox/openai:
import { Shell, Editor } from '@cloudflare/sandbox/openai';See examples/openai-agents for complete integration pattern.
Sandbox Lifecycle
getSandbox()returns immediately - container starts lazily on first operation- Containers sleep after 10 minutes of inactivity (configurable via
sleepAfter) - Use
destroy()to immediately free resources - Same
sandboxIdalways returns same sandbox instance
Anti-Patterns
- Don't use internal clients (
CommandClient,FileClient) - usesandbox.*methods - Don't skip the Sandbox export - Worker won't deploy without
export { Sandbox } - Don't hardcode sandbox IDs for multi-user - use user/session identifiers
- Don't forget cleanup - call
destroy()for temporary sandboxes
Detailed References
- references/api-quick-ref.md - Full API with options and return types
- references/examples.md - Example index with use cases
Installs
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cloudflare/skills
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How to use this skill
Install sandbox-sdk by running npx skills add cloudflare/skills --skill sandbox-sdk in your project directory. Run the install command above in your project directory. The skill file will be downloaded from GitHub and placed in your project.
No configuration needed. Your AI agent (Claude Code, Cursor, Windsurf, etc.) automatically detects installed skills and uses them as context when generating code.
The skill enhances your agent's understanding of sandbox-sdk, helping it follow established patterns, avoid common mistakes, and produce production-ready output.
What you get
Skills are plain-text instruction files — not executable code. They encode expert knowledge about frameworks, languages, or tools that your AI agent reads to improve its output. This means zero runtime overhead, no dependency conflicts, and full transparency: you can read and review every instruction before installing.
Compatibility
This skill works with any AI coding agent that supports the skills.sh format, including Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider, and other tools that read project-level context files. Skills are framework-agnostic at the transport level — the content inside determines which language or framework it applies to.
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