Tastebud Memory: Reversible Agent Memory via Hyperdimensional Computing Vectors

Tastebud Memory is an open-source (GitHub) agent memory index that uses hyperdimensional computing (HDC) to reversibly encode daily work logs into 4096-dimensional ±1 vectors. Each project slug seeds a random near-orthogonal vector; a day's weighted sum is decomposed back into project memberships via dot products — effectively un-mixing the paint.

Key details

• No embedding search limitations. HDC enables queries that standard vector search can't: "List ALL days that touched project X" (complete set), "When did X start, including under its old name?" (recency bury issue), "What was active in March but dead by June?" (set-difference), "Which workstreams never got documentation?" (absence detection).
• Unknown ingredient detection ("chef's palate"): if a day's vector residue has high unexplained energy, the system flags an unnamed project — backtested to detect projects on day 0-2 that ran 13+ days undocumented.
• Backtest protocol: 31 of 92 days blind-re-derived by adversarial verifier agents, 93.5% faithfulness, caught 2 real tagging errors.
• Technology independent: pure Node (600 lines), two JSON files, no dependencies. Ships with an MCP server so any agent platform can use it, plus fictional sample data to demo commands immediately.
• Model gate: Gemma 26B failed tagging quality (0.74 agreement vs 0.80 bar) — recommended primary is a large cloud model, with smaller models as fallback alerts.

Honest limitations

The plain composition table handles most routine queries. The vector layer adds value for lossless decode (recover exact project list from a day), day-similarity drift tracking, and fixed-size encoding — not for basic lookups. This is an index, not a summarizer; it recovers ingredients, not the recipe.