A continuity engine for AI agents: a relevance-router that re-lights the working thread every time a fresh instance wakes — the difference between a model that answers to your name and one that picks up your sentence mid-stride.
Everyone treats agent memory as a storage problem: can the system retrieve the fact? That measures the store, not the agent. The harder, more honest test:
Drop a fresh model cold into the chair — no resume document, no hints, only its auto-loaded substrate. Does it wake up as itself? And does it know what it was mid-doing?
We ran that test on our own working setup, cold, and scored it. The protocol is public and reproducible — a synthetic demo corpus ships with the repo so you can run the same probes on your own agent.
A session-start hook that runs the router before any user input; an active-thread pointer — a tiny, auto-updated marker of what the work was mid-doing; and a budget — the relevant slice, relevance-ranked and bounded, never the whole log. What crosses each session boundary is the pointer and the recall ability. Not the history.
The engine is deterministic, stdlib-only Python — no network, no dependencies, pluggable relevance backend — and ships wrapped as an MCP server so any MCP-speaking agent can plug in. MIT licensed.
Two successful proof runs (2026-07-04), one model, one harness: a cold abstract probe that fails without wake-recall and passes with it, and a working-session handoff that continued mid-stride across a fresh window. Replication on a second agent — different model, different failure mode — is in progress. No quantitative benchmarks yet, and this page cites none.
That's the same discipline AURA runs on: claim what the runs show, ship the protocol so anyone can check. The repo's cold-probe experiment exists so the n grows in public.
The value lives in the substrate, not the model that happens to be running. Give your agent a thread that survives its restarts.