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AgentOS
Rust runtime for AI agents with time-travel replay
AgentOS is a Rust runtime layer that sits underneath agent frameworks instead of replacing them. One command gives you a supervised agent process, a health endpoint, a gRPC message bus, a live SSE event stream and a journaled trace. Every LLM exchange and tool result is recorded at the provider boundary, so any run replays deterministically with no API cost. Open source, local-first, currently alpha.
Hey Product Hunt, I'm Wahib, the maker.
Most agent tooling helps you build a workflow. My problem started after that: the workflow had to run as a long-lived process, fail clearly, restart carefully, and stay inspectable afterwards. Debugging step 7 of a run meant paying for real API calls again, and never getting the same behaviour twice.
So AgentOS journals every LLM exchange and tool result at the provider boundary. `agentOS replay` re-runs a session offline with the recorded responses and reports drift.
One process also gives you supervision, a health endpoint, a gRPC bus, an SSE event stream and secret isolation via a vault crate. It is built in Rust and designed to sit under LangGraph, AutoGen, CrewAI or your own agents rather than replace them.
Being honest about the stage: the run / ps / logs / trace / replay CLI flows are stable locally. `agentOS fork` (branching a run from a checkpoint) is scaffolded but not implemented yet — that's what I'm building next, along with the dashboard, WASM plugin runtime and stronger restart guarantees.
One question I would love answers to: if you run agents today, what breaks first for you — supervision, observability, or reproducing failures?
About AgentOS on Product Hunt
“Rust runtime for AI agents with time-travel replay”
AgentOS was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #51 on the daily leaderboard. AgentOS is a Rust runtime layer that sits underneath agent frameworks instead of replacing them. One command gives you a supervised agent process, a health endpoint, a gRPC message bus, a live SSE event stream and a journaled trace. Every LLM exchange and tool result is recorded at the provider boundary, so any run replays deterministically with no API cost. Open source, local-first, currently alpha.
On the analytics side, AgentOS competes within Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how AgentOS performed against the three products that launched closest to it on the same day.
Who hunted AgentOS?
AgentOS was hunted by Wahib | Software Developer. 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 AgentOS including community comment highlights and product details, visit the product overview.
Wahib | Software Developer