Alaya MCP Server

io.github.SecurityRonin/alaya-mcp

Documentation

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

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

License

MIT