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BrainLayer

Persistent memory for AI agents. Search, think, recall — across every conversation you've ever had.

Your AI agent forgets everything between sessions. Every architecture decision, every debugging session, every preference you've expressed — gone.

BrainLayer fixes this. It's a local-first memory layer that gives any MCP-compatible AI agent the ability to remember, think, and recall across conversations.

Key Features

  • 14 MCP tools — think, recall, search, session analysis, file history, and more
  • Local-first — SQLite + sqlite-vec, single file, no cloud, no Docker
  • Hybrid search — semantic vectors + keyword, merged with Reciprocal Rank Fusion
  • 10-field enrichment — summary, tags, importance, intent, and more via local LLM
  • Multi-source — Claude Code, WhatsApp, YouTube, Markdown, Claude Desktop, manual
  • Works everywhere — Claude Code, Cursor, Zed, VS Code, any MCP client

Quick Example

pip install brainlayer
brainlayer init              # Interactive setup wizard
brainlayer index             # Index your conversations

Add to Claude Code (~/.claude.json): 

{
  "mcpServers": {
    "brainlayer": {
      "command": "brainlayer-mcp"
    }
  }
}

Your agent now has persistent memory. Ask it:

  • "What approach did I use for auth last month?" → brainlayer_think
  • "Show me everything about this file" → brainlayer_recall
  • "What was I working on yesterday?" → brainlayer_current_context
  • "Remember this for later" → brainlayer_store

Architecture Overview

graph LR
    A["Claude Code / Cursor / Zed"] -->|MCP| B["BrainLayer MCP Server<br/>14 tools"]
    B --> C["Hybrid Search<br/>semantic + keyword (RRF)"]
    C --> D["SQLite + sqlite-vec<br/>single .db file"]

    E["Conversations<br/>JSONL / WhatsApp / YouTube"] --> F["Pipeline"]
    F -->|extract → classify → chunk → embed| D
    G["Local LLM<br/>Ollama / MLX"] -->|enrich| D

Next Steps