Alaya

The only memory engine with neuroscience-grounded memory dynamics — Bjork dual-strength forgetting, retrieval-induced suppression, and Hebbian co-activation — in a zero-dependency embeddable Rust library.

Alaya (Sanskrit: alaya-vijnana, "storehouse consciousness") is an embeddable Rust library. One SQLite file. No external services. Your agent stores conversations, retrieves what matters, and lets the rest fade. The graph reshapes through use, like biological memory.

let alaya = Alaya::open("memory.db")?;
alaya.episodes().store(&episode)?;           // store
let results = alaya.knowledge().query(&query)?; // retrieve
alaya.lifecycle().consolidate(&provider)?;   // distill knowledge
alaya.lifecycle().transform()?;              // dedup, LTD, discover categories
alaya.lifecycle().forget()?;                 // decay what's stale
let cats = alaya.admin().categories(None)?;  // emergent ontology
alaya.admin().purge(PurgeFilter::Session("s1"))?; // cascade delete + tombstones

The Problem

Most AI agents treat memory as flat files. OpenClaw writes to MEMORY.md. Claudesidian writes to Obsidian. Hand-rolled systems write to JSON or Markdown. It works at first.

Then the files grow. Context windows fill. The agent dumps everything into the prompt and hopes the LLM finds what matters.

The cost is measurable. OpenClaw injects ~35,600 tokens of workspace files into every message, 93.5% of which is irrelevant (#9157). Heavy users report $3,600/month in token costs. Community tools like QMD and memsearch cut 70-96% of that waste by replacing full-context injection with ranked retrieval (Levine, 2026).

The structure problem compounds the cost. MEMORY.md conflates decisions, preferences, and knowledge into one unstructured blob. Users independently invent decision.md files, working-context.md snapshots, and 12-layer memory architectures to compensate. Monday you mention "Alice manages the auth team." Wednesday you ask "who handles auth permissions?" The agent retrieves both memories by text similarity but cannot connect them (Chawla, 2026).

How A

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