run-llama

run-llama / sandboxed-lit

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Sandboxed shell agent with access to LiteParse

19
1
100% credibility
Found May 12, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

This project runs a smart AI assistant in a secure, isolated space where it can browse files, read and parse documents including PDFs images and office files, and perform basic operations on a chosen folder.

How It Works

1
📦 Discover the safe AI helper

You come across sandboxed-lit, a tool that lets a smart AI safely look at your files, read documents like PDFs or images, and do simple tasks without any risk to your computer.

2
🔧 Get your computer ready

You install a couple of free helper programs on your computer so everything can run smoothly.

3
🧠 Link to smart AI

You connect a thinking AI service, like one from OpenAI, so the helper can understand your words and make decisions.

4
📁 Pick your files folder

You choose a folder on your computer full of documents or files you want the AI to work with.

5
💬 Tell it what to do

You type a simple instruction, like 'Summarize every PDF in here' or 'Find key points in the report', and start it up.

6
👀 Watch the magic happen

You see the AI listing files, reading contents, pulling out info from tricky formats, and running safe tasks in its own protected bubble, all shown in colorful updates on screen.

🎉 Enjoy your results

The AI finishes and gives you exactly what you asked for, like summaries or extracted insights, keeping everything secure and tidy.

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Star Growth

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AI-Generated Review

What is sandboxed-lit?

sandboxed-lit is a Rust CLI tool that spins up an LLM agent (powered by OpenAI GPT models) inside a microsandbox VM, giving it safe access to list files, parse PDFs/images/Office docs via LiteParse, and run shell commands—all confined to an isolated environment. Mount any host directory to the sandbox's `/app/data` path, feed it a prompt like "Summarize every PDF here," and watch it stream reasoning and results to your terminal. It solves the pain of letting agents touch real files or bash without nuking your machine.

Why is it gaining traction?

Unlike raw agent frameworks, it bundles isolation out-of-the-box via microsandbox, plus seamless LiteParse integration for structured doc extraction—no extra setup for common file types. The dead-simple CLI and colored event streaming make prototyping agent workflows feel instant, standing out for devs wanting sandboxed shell access without Docker hacks or security headaches. Early buzz on GitHub stems from its tight focus on secure, file-aware agents.

Who should use this?

AI engineers building proof-of-concept agents that need file reading or light scripting, like analyzing doc folders or automating reports. Rust fans experimenting with agent-sdk for OpenAI tools, or teams prototyping sandboxed LLM pipelines before scaling. Avoid if you're not running a microsandbox host already.

Verdict

Worth a quick spin for agent tinkerers—solid README, easy build, and clever LiteParse tie-in—but with 19 stars and 1.0% credibility, it's an early prototype lacking tests or broad validation. Fork and harden it if isolation is your jam.

(178 words)

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