tomascupr

tomascupr / sandstorm

Public

Run Claude agents in secure cloud sandboxes — via API, CLI, or Slack. One call. Full agent. Zero infrastructure.

duvo.ai agent-sdk ai ai-agents anthropic claude
419
39
100% credibility
Found Feb 17, 2026 at 298 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Sandstorm enables running AI agents for complex tasks in secure, disposable cloud workspaces via a simple command-line tool or web API, with real-time streaming of progress.

How It Works

1
🔍 Discover Sandstorm

You hear about Sandstorm, a simple way to run smart AI helpers for tough tasks like analyzing websites or writing code, without worrying about setup.

2
📦 Set it up quickly

You add it to your computer with one easy command, like grabbing a new app.

3
🔗 Connect your AI services

You link it to your favorite AI thinkers so they can do the heavy lifting securely.

4
💬 Give a task

You type a plain English instruction, like 'analyze my files and suggest improvements'.

5
👀 Watch it work live

You see every step stream by in real-time, as the AI thinks, grabs data, and builds results right before your eyes.

✅ Get your results safely

Everything finishes perfectly, you grab the polished output, and the safe workspace vanishes, leaving no mess behind.

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Star Growth

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AI-Generated Review

What is sandstorm?

Sandstorm lets you spin up full Claude AI agents with one CLI command or API call, running them in isolated E2B cloud sandboxes that auto-destroy after use. Built in Python, it wraps the Claude Agent SDK for tasks like code execution, web scraping, file analysis, and structured outputs—no servers or infra to manage. Developers get real-time SSE streaming of agent thoughts, tool calls, and results, plus file uploads and configs for subagents or custom models via OpenRouter.

Why is it gaining traction?

It cuts through agent complexity: bash tools, web fetch, and package installs happen securely inside fresh VMs, with zero state leakage between runs. Standouts include JSON schema outputs, slash-command skills for domain tasks, and easy swaps to 300+ models—perfect for GitHub repo analysis or sandstorm customs workflows without vendor lock-in. The Vercel one-click deploy and CLI (`ds "prompt"`) hook devs tired of stitching SDKs, sandboxes, and streams manually.

Who should use this?

AI prototyping teams needing secure, parallel agents for SEO audits, competitive analysis, or code reviews on GitHub one repository multiple projects. Backend devs building agent APIs for content generation or data processing, especially with one-time code login flows. Indie hackers automating one-page website optimizations or insurgency sandstorm github trend summaries.

Verdict

Grab it for quick agent experiments—solid docs, examples, and PyPI install make the 279 stars believable despite the 1.0% credibility score signaling early days. Battle-test costs and timeouts before prod; it's beta-ready for non-critical tasks.

(198 words)

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