Architecture

  ┌───────────────────────────────────────────────┐
  │           causal-memory (Rust, MCP)            │
  │                                                │
  │  17 tools ← Agent (stdio / HTTP)                │
  │    ↓                                           │
  │  Write-time gatekeeping                        │
  │    raw turns → session_logs (audit only)       │
  │    distill → facts + causal edges (searchable) │
  │    ↓                                           │
  │  Unified retrieval (RRF fusion)                │
  │    BM25 + semantic cosine → RRF merge          │
  │    Fact layer (BM25 + embeddings)              │
  │    ↓                                           │
  │  ┌──── Hippocampus engine ──────────────────┐  │
  │  │ CSR graph + spreading activation          │  │
  │  │  caused (+1.0)   enabled (+0.5)           │  │
  │  │  prevented (−0.3) ← GABA inhibitory       │  │
  │  │  fact (+0.8)     meta (+0.6)              │  │
  │  │  co_occurrence (Hebbian, dynamic)         │  │
  │  │                                            │  │
  │  │ DG: SimHash pattern separation             │  │
  │  │ CA3: K-hop spreading (forward + reverse)   │  │
  │  │ CA1: Novelty entropy trigger               │  │
  │  │ SWR: LTP/LTD/GC (immutable delta + clone)  │  │
  │  │ Q-value: Bellman dynamics (MemRL-style)    │  │
  │  └────────────────────────────────────────────┘  │
  │    ↓                                           │
  │  SQLite (causal.db) — never compacted          │
  └───────────────────────────────────────────────┘

The causal_edges table is never compacted — it lives outside the agent's context window. That's the entire point.

Edge types

Edge type	Spread coeff	Biological analogue	Meaning
`caused`	+1.0	Glutamate (strong excitatory)	"Doing X caused Y"
`fact`	+0.8	Semantic association	"User is/has Z"
`meta`	+0.6	Cortical top-down	Cross-task pattern link
`enabled`	+0.5	Weak excitatory	"Doing X enabled Y"
`co_occurrence`	dynamic	Hebbian LTP	"X and Y frequently co-occur"
`prevented`	−0.3	GABA (inhibitory)	"Doing X prevented Y"
`no_effect`	0.0	—	No causal relationship

The excitatory/inhibitory duality

HeLa-Mem (ACL 2026) builds the excitatory side (Hebbian co-activation, positive spread). causal-memory adds the inhibitory side (prevented edges spread negative activation — a GABA analogue). A complete memory needs both: "what caused this" and "what prevents this from happening again."

Interactive version: Architecture explorer.