PAI Memory Server

Semantic and keyword search across your AI work sessions, learnings, reflections, failures, research, and relationships. Built for PAI (Personal AI Infrastructure).

What It Does

Indexes your PAI MEMORY/ directory into a searchable SQLite database with:

• Keyword search via FTS5 (full-text search) — works offline, no dependencies
• Semantic search via LM Studio embeddings — meaning-based retrieval using nomic-embed-text-v1.5
• MCP server exposing 7 tools for cross-tool access via Model Context Protocol

Supported Memory Types

Type	Source	Description
work	MEMORY/WORK/	Work sessions with META.yaml, tasks, and markdown notes
learning	MEMORY/LEARNING/ALGORITHM/, SYSTEM/	Algorithm execution and system learnings
reflection	MEMORY/LEARNING/REFLECTIONS/	JSONL self-assessment after each task
rating	MEMORY/LEARNING/SIGNALS/	JSONL session ratings with sentiment
failure	MEMORY/LEARNING/FAILURES/	Context dumps from low-rated sessions
research	MEMORY/RESEARCH/	Research output files
relationship	MEMORY/RELATIONSHIP/	Relationship memory notes

Installation

cd ~/.claude/MCPs/pai-memory
bun install

CLI Usage

# Index all MEMORY/ content into SQLite
bun cli.ts index

# Search (semantic if LM Studio running, keyword fallback)
bun cli.ts search "hook performance"

# Generate embeddings via LM Studio
bun cli.ts embed

# Show database statistics
bun cli.ts stats

MCP Tools

When registered as an MCP server, exposes these tools:

Tool	Description
memory_search	Semantic/keyword search across all memory types
memory_recent_work	List recent work sessions with status filter
memory_recent_learnings	List recent learnings with category filter
memory_get_work	Get full details of a specific work entry
memory_stats	Database statistics — entry counts, size, embedding coverage
memory_failures	List recent failure analyses
memory_reflections	List algorithm performance reflections

MCP Registration

Add to your .mcp.json:

{
  "mcpServers": {
    "pai-memory": {
      "command": "bun",
      "args": ["run", "mcp-server.ts"],
      "cwd": "/path/to/pai-memory"
    }
  }
}

Architecture

cli.ts              CLI entry point (index, search, embed, stats)
mcp-server.ts       MCP server (7 tools via StdioServerTransport)
src/
  types.ts          Shared types (MemoryEntry, SearchResult, MemoryStats)
  db.ts             SQLite layer (FTS5 + cosine similarity + embeddings)
  memory-reader.ts  Filesystem parser (YAML, JSON, JSONL, Markdown)
  search.ts         Unified search (semantic first, keyword fallback)
  embedder.ts       LM Studio embedding client (nomic-embed-text-v1.5)
data/
  pai-memory.db     SQLite database (generated, not committed)

Requirements

• Bun runtime
• PAI with populated MEMORY/ directory
• Optional: LM Studio with nomic-embed-text-v1.5 for semantic search

License

MIT