Where the Memory god is, where it's going, and where it grows to.

The one-line trajectory:

From a live vector-recall backend → a unified, provenance-tracked memory API → a benchmarked, A/B-tested stack of multiple memory models spanning the full layered hierarchy.

① Current — where it is today

Phase 1 v1 is implemented and running. This is no longer a scaffold — a real service wraps the layer stack end to end:

What is still explicitly deferred (see Architecture for the current component/flow diagrams):

② Goals — near-term next steps

  1. Meta + enterprise layer wiring. Bring the Obsidian vault into the live escalation path as the top (meta) layer, and stand up the enterprise scope so cross-project standards/CBAs are recallable, not just project-local memory.
  2. Continuous indexing. Move indexing off explicit reindex calls onto a Multica-native schedule (event- or interval-driven, no localized cron) so vector + code-graph + vault indexes stay fresh automatically.
  3. Argus/Metis integration. Route every recall/remember decision + metric record to Argus/Metis, matching the pattern every other Pantheon god follows.
  4. Consus/Janus read model. A minimal view that lets a human browse layers, trace a recall's provenance, and spot stale scopes.
  5. First multi-layer god integration, proven. ≥1 other god (Minerva is the natural first) using the unified API across ≥3 layers, with recall shown to beat find/grep on token cost for a representative query set.

Near-term success signals: one recall/write API in use by ≥1 other god spanning ≥3 layers; provenance on every hit (already true for the layers that are wired); memory browsable in Consus/Janus with layer + freshness visibility.

③ Long-term vision — where it grows to

Multiple memory models, A/B-tested and solidified. The layer slots are pluggable by design (Qdrant default + swappable vector store; Obsidian default + swappable meta store). The long game is to run competing memory backends head-to-head — different vector stores, different embedders, different graph/store strategies behind the same recall/remember contract — measure them on the same query sets (recall quality, token cost, latency, freshness), and solidify the winners per slot. Memory stops being a single fixed engine and becomes a benchmarked marketplace of interchangeable models.

The full layered memory stack, first-class and coherent:

meta → enterprise → project → code-graph → vector → file

Every layer continuously indexed, every recall escalating narrow↔broad with merged, ranked, provenance-stamped hits — so "where is X?" has exactly one answer, and "what breaks if I change this?" is a graph query, not a guess.

Platform-wide direction (why the pluggability matters): across the whole Pantheon, everything is swappable — you can toggle any language, model, plugin, or god on/off and compare metrics at every step. Mnemosyne is memory's expression of that principle: the layer slots are ABI-swappable, Vesta owns which backend fills each slot, and Argus/Metis capture the decision + metric records so an A/B swap is a config change with a measured before/after — not a rewrite.

Where it ends up: memory browsable and traceable in Consus/Janus (layers, freshness, provenance, stale-scope detection), continuously indexed, backend-benchmarked, and the obvious default retrieval path for every agent in the swarm.

Good first contributions

New here? Read idea-brief.md first (the source-of-truth design brief), then Architecture for the current component/flow diagrams. Work follows the Pantheon SDLC via plugin-hive; the host is pantheon-v2.