Permanent memory (after OptMem)

An agent whose memory outlives its sessions, after VictorTaelin's OptMem, built as an instance tree.

• memory.tsx is the root. It owns the memory: an append-only log of one-line memories (@log) and a binary tree of one-line summaries over it (@tree, where block lo-hi summarizes its two halves). It mounts one session at a time and moves to the next when a session stops.
• session.tsx (Session) is one session. It wakes with the memory's cover and works on its task with four tools:
  • note records a memory (through the root's onNote callback);
  • recall is a regex search over every memory, word for word;
  • zoom opens a summary into its two halves;
  • answer ends the session.

The cover is cover in views/strategies/context.tsx, OptMem's tiling. Recent memories stay verbatim and older ones collapse into tree nodes, in at most wake_lines lines. A block is compressed by asking for one line of at most N bytes that keeps what will still matter later and adds nothing.

Where it differs from OptMem:

• No naps. When a note completes a block of the tree, the root compresses it at once with a model call (model(<Compress/>)), smallest block first. A session never stops its work to summarize.
• Write access is structural. A session writes memory only through the onNote callback its parent passes it. A helper the session mounts gets no onNote, so it can read what it is given but cannot write.
• It is durable. The memory is the root's journaled state, so a crash resumes the whole tree (root and current session) from the journals.
• A session wakes once. wake is fixed when the session mounts (useState(wake)). Notes made during the session change the root's memory, not the session's first message.

Props: sessions (one task per session), template (messages for a hosted chat API, glm53), wake_lines (default 96), chars (the maximum length of a memory or summary, default 280).

Run it on a hosted model. Four sessions on DeepSeek V4.1 Flash took 27 turns and about 40K prompt tokens, under $0.01. The root needs a model binding (root.model) for its compressions.

Its deployment is affordance.yaml: set the provider's url and model, export its key as API_KEY, and run affordance run views/demo/memory.

The memory ends in .affordance/root/snapshot.json (state.__ui), and each session's wake is in .affordance/root/session-<n>/snapshot.json. The offline test is a_memory_outlives_its_sessions_compresses_old_notes_and_recalls_them in tests/runtime.rs.

Source: views/demo/memory/README.md, about 2 minutes to read.