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. 👇
Congrats on the launch! Host-level agent code execution is a risk people underestimate, so great to see a policy-driven guard here. Will give it a spin on our end.
The interesting problem here isn't running untrusted code, it's what the sandbox actually contains. A lot of solutions stop at process isolation but still share a network namespace, which means a malicious payload can exfiltrate data or reach internal services even if it can't touch the host filesystem. Curious whether VELA does full network isolation per execution, and how you handle cases where the code legitimately needs outbound access, like an AI-generated script that has to hit an external API to do anything useful.
The Firecracker micro-VM approach makes sense here, the ~150ms cold start seems totally workable for tool call use cases. I run a lot of AI-generated code via Claude Code and this is exactly the kind of safety layer I'd want underneath it. Does it handle MCP tool call contexts or is it mainly focused on raw script execution right now? Congrats on shipping!
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.
VELA was featured in Developer Tools (515.4k followers), Artificial Intelligence (473.1k followers), GitHub (41.3k followers) and Security (2.7k followers) on Product Hunt. Together, these topics include over 209.8k products, making this a competitive space to launch in.
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.
Want to see how VELA stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Praveen