Alaya Memory Engine

alaya-mcp

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.

Stores conversation episodes, consolidates knowledge through a cognitive lifecycle, and builds a personal knowledge graph — all in a local SQLite database.

Install

Claude Code

Add to ~/.claude/claude_code_config.json:

{
  "mcpServers": {
    "alaya": {
      "command": "npx",
      "args": ["-y", "alaya-mcp"]
    }
  }
}

Claude Desktop

Add to your Claude Desktop MCP config:

{
  "mcpServers": {
    "alaya": {
      "command": "npx",
      "args": ["-y", "alaya-mcp"],
      "env": {
        "ALAYA_LLM_API_KEY": "sk-...",
        "ALAYA_LLM_API_URL": "https://api.openai.com/v1/chat/completions",
        "ALAYA_LLM_MODEL": "gpt-4o-mini"
      }
    }
  }
}

The ALAYA_LLM_* env vars are optional — they enable automatic knowledge extraction. Without them, the agent extracts knowledge from its own context.

MCP Tools

Tool	Description
remember	Store a conversation episode
recall	Semantic search across memories
status	Memory system health and stats
knowledge	Browse extracted knowledge
learn	Store pre-extracted facts and relationships
categories	View emergent category taxonomy
preferences	Track crystallized user preferences
neighbors	Explore the knowledge graph
lifecycle	Trigger maintenance (strengthen, transform, forget)
configure	Set embedding provider and other options

How It Works

1. Remember — store conversation episodes as they happen
2. Consolidate — extract facts, relationships, and concepts from episodes
3. Strengthen — co-retrieved memories strengthen their connections (Hebbian LTP); retrieving memory A actively suppresses competing memories B and C (retrieval-induced forgetting)
4. Categorize — emergent categories form automatically from your knowledge
5. Forget — Bjork dual-strength decay separates storage strength (how well-encoded) from retrieval strength (how easily found); weak memories fade, strong ones persist
6. Crystallize — implicit preferences emerge from accumulated impressions (vasana), no LLM required

All data stays on your machine in ~/.alaya/memory.db.

Why Alaya

Problem	File-based memory	Alaya
Token waste	Full-context injection (~35K tokens/msg)	Ranked retrieval — only top-k relevant memories
No structure	Everything in one file	Three typed stores: episodes, knowledge, preferences
No forgetting	Files grow until manually curated	Bjork dual-strength decay + retrieval-induced forgetting
No associations	Flat files, no links	Hebbian co-retrieval graph (LTP/LTD)
Brittle preferences	Agent-authored summary, drifts	Implicit preferences emerge from impressions (vasana)
LLM required	Can't function without one	Graceful degradation: no embeddings → BM25-only

Links

• GitHub
• Documentation
• crates.io (Rust library)
• PyPI (Python bindings)

License

MIT