Jev evaluates agent runs to save reusable cross-harness memory
Beacon records a Claude Code session and Jev judges whether the run is worth saving as memory; after a correction Jev approves it and a separate Codex session recalls it.
Another insane Jev use case! Decision models like Jev are making it dramatically cheaper to evaluate what actually happens inside an agent run. And now there’s an open-source memory layer that puts those evaluations to work, making useful lessons reusable across 20+ agent harnesses: - Claude Code - Codex - Cursor - OpenCode - and more. The project is Beacon, from @asymptotelabs. GitHub: https://github.com/Asymptote-Labs/agent-beacon (don't forget to star 🌟) Here’s what I did in the video below. 1) I gave Claude Code a coding task. It made changes and ran tests while Beacon automatically recorded the session. 2) I had Jev evaluate the run. The first evaluation returned review because I hadn’t provided a correction, so my app saved nothing. 3) I corrected the agent, and it updated its work and reran the tests. Jev evaluated the updated session, and this time the results met my app’s rules for saving a memory. 4) I saved the correction and its supporting evidence as shared memory. Then I opened a separate Codex session and explicitly asked it to read that memory. Codex recalled the correction from the earlier Claude Code session without me having to explain it again. Beacon captured the session history. Jev evaluated what was worth keeping, while my app applied the storage rules and saved the lesson. That correction was now available as context outside the conversation where I first made it. I cover this build in more detail in the video with Justin, co-founder of Asymptote Labs, alongside moving sessions between agents, creating reusable skills, and detecting security risks across agent activity. Watch the full video below.
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