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.
Hey Product Hunt! 👋
What inspired us & the problem we're solving:
With the explosion of autonomous AI agents (like those built with LangChain or AutoGen), LLMs are increasingly writing and executing Python scripts, shell commands, and data pipelines on the fly. But running untrusted, model-generated code directly on your host server or standard Docker containers is a massive security risk. Ad-hoc isolation (like monkey-patching stdlib calls) is brittle and easily bypassed. We realized the ecosystem was missing a fast, auditable, and truly secure execution guard designed specifically for the AI era.
Our approach & how it evolved:
Initially, we looked at heavy container orchestration, but it was too slow for real-time agent tool calls. We pivoted to a local-first, Rust-based daemon (Aegis) backed by Firecracker micro-VMs. This gave us hardware-level isolation with near-instant boot times.
We then evolved the policy engine to use HMAC capability tokens. Instead of a blanket 'allow/deny', developers can now issue scoped, time-bound tokens per request (e.g., 'allow read/write to /tmp, block all network access, max 64MB RAM'). Finally, we built Python wrappers and LangChain adapters so agents can route dangerous tool calls into the sandbox transparently, without rewriting their core logic.
We’ve open-sourced Vela under the MIT license because we believe secure AI execution should be a standard, accessible primitive for every developer.
I’d love to hear your thoughts, feedback, and how you are currently handling code execution in your AI workflows! Let’s discuss below. 👇
About VELA on Product Hunt
“Securely execute AI-generated & untrusted code”
VELA launched on Product Hunt on June 18th, 2026 and earned 71 upvotes and 7 comments, placing #33 on the daily leaderboard. 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.
On the analytics side, VELA competes within Developer Tools, Artificial Intelligence, GitHub and Security — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how VELA performed against the three products that launched closest to it on the same day.
Who hunted VELA?
VELA was hunted by Praveen. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of VELA including community comment highlights and product details, visit the product overview.
Praveen