As a solo maker, I've been diving deep into the world of autonomous AI agents (LangChain, LlamaIndex, etc.). One of the biggest bottlenecks I kept hitting was code execution.
When an agent needs to analyze data, scrape a site, or run a simulation, it writes code. But where do you safely run it?
exec() or subprocess on the host machine? Terrifying. One bad prompt injection and the LLM accesses your .env files or exfiltrates data.
Standard Docker containers? Too slow and heavy for rapid-fire agent tool calls.
Cloud sandboxes? Great, but I didn't want to send my local data or proprietary agent logic to a 3rd party API just to run a simple pandas script.
Autonomous AI agents are writing and executing code, but running it on your host server is a massive security risk. Vela (powered by the Aegis runtime) solves this. It’s a policy-driven execution guard that uses Firecracker micro-VMs and HMAC capability tokens to safely run untrusted code. Get structured results, fine-grained filesystem/network restrictions, and a full JSONL audit trail. Open-source, MIT licensed, and built for LangChain/LlamaIndex.
