Isolated Agents SDK

This is a submission for the GitHub Finish-Up-A-Thon Challenge

What I Built
The Isolated Agents SDK is a production-ready Python framework designed to run any AI agent securely inside rootless Podman, Docker, or Kubernetes containers.

As AI agents become more autonomous—capable of executing code, scraping the web, or modifying files—running them directly on a host machine becomes a massive security risk. This SDK provides a simple, Pythonic API (using decorators) to wrap existing agents from frameworks like LangChain, CrewAI, and AutoGPT in a secure sandbox. It enforces strict network policies, resource limits, and read-only filesystems.

To me, this project is about bridging the gap between rapid AI innovation and enterprise-grade security. Developers shouldn't have to choose between building powerful autonomous agents and maintaining a secure infrastructure.

Demo
Repository: Tech-Vexy/Isolated-Agents PyPI: isolated-agents-sdk

The Comeback Story
The project started with a solid core concept: sandboxing agents using basic Podman containers. However, it was missing the extensibility and flexibility required for true production usage across different environments. It sat as a really cool proof-of-concept while I tried to figure out how to scale its architecture.

For the Finish-Up-A-Thon, I dove back in and pushed it across the finish line! Here is what I added, fixed, and polished to get it to v0.2.1:

Robust Adapter Architecture: I implemented a fully pluggable adapter pattern, adding native support for Docker and Kubernetes container runtimes.
State & Telemetry: Added database adapters (SQL, NoSQL, vector) to manage agent state, along with telemetry and audit adapters so every action an agent takes is securely logged.
Security & Concurrency Hardening: Hunted down and fixed critical race conditions in adapter configurations and fortified the concurrent runtime scheduler.
Extensive Documentation: Wrote a comprehensive catalog of 80+ working examples, architecture guides, and migration docs so others can actually understand and use the SDK.
My Experience with GitHub Copilot
GitHub Copilot was an invaluable pair programmer during this final sprint. When I was building out the new adapter patterns for Docker and Kubernetes, Copilot helped scaffold the boilerplate classes and quickly suggested the correct API bindings for the container engines.

It truly shined when writing the comprehensive documentation and the examples catalog. Copilot understood the context of my custom decorators (@isolated_agent, @network, @resources) and seamlessly auto-completed realistic agent scenarios, like LangChain integrations and isolated Pandas data analysis. It turned what would have been days of writing boilerplate tests and docs into a smooth, focused development flow.

Team: @tech-vexy (Veldrine Evelia Kaharwa)