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.
| 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
- Remember — store conversation episodes as they happen
- Consolidate — extract facts, relationships, and concepts from episodes
- Strengthen — co-retrieved memories strengthen their connections (Hebbian LTP); retrieving memory A actively suppresses competing memories B and C (retrieval-induced forgetting)
- Categorize — emergent categories form automatically from your knowledge
- Forget — Bjork dual-strength decay separates storage strength (how well-encoded) from retrieval strength (how easily found); weak memories fade, strong ones persist
- 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
License
MIT
