Using Jev to filter reusable lessons into a cross-agent memory layer
The author uses Jev to judge whether an agent trace contains a reusable engineering lesson, passing only approved lessons to the next agent, paired with Beacon across 20+ harnesses.
Another insane way to use Jev: Jev makes it dramatically cheaper to identify which corrections and trajectories from an agent run are reusable across agents. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode - Hermes, and 20+ more Beacon by @asymptotelabs continuously captures agent sessions across harnesses and stores their traces in a shared index. I used it with Jev to build a live cross-agent memory workflow. Beacon captures each run as it happens across all harnesses on my machine. Then Jev evaluates whether the trace contains a reusable engineering lesson, and the approved lesson becomes available to the next agent. GitHub repo: http://github.com/Asymptote-Labs/agent-beacon (don't forget to star it ⭐) Preserving an agent run and learning from it are two different problems. Most traces contain file reads, failed commands, routine exploration, and implementation details that only apply to one task. Passing the complete transcript to another agent adds context without identifying what matters. In this workflow, one agent never receives another agent's raw transcript. It receives only the specific engineering lesson produced from the evaluated trace. A correction made in one agent can then prevent the same mistake when another agent encounters a similar problem. The recording shows the complete workflow I built. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.
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