memory-search

memory-search

A hybrid search memory system for coding agents (70% vector similarity + 30% BM25 keyword).

Based on the memory system from OpenClaw (originally Clawdbot).

Features

• Hybrid search combining vector similarity and keyword matching
• Local embeddings included (no API key needed)
• Optional OpenAI embeddings for faster queries
• bun:sqlite storage (no native extensions needed)
• Support for MEMORY.md + memory/*.md files
• Embedding cache for efficiency
• Works with Claude Code, Cursor, Codex, and 30+ other agents

Quick Start

1. Install the Skill

bunx skills add rjyo/memory-search

This installs the /memory skill to your coding agent (Claude Code, Cursor, Codex, etc.).

2. Use It

Save information:

/memory remember that I prefer TypeScript over JavaScript

Search memories:

/memory what did we decide about authentication?

Or just ask naturally - the skill triggers on phrases like "remember this" or "what did we decide about X".

Automatic Memory Injection (Optional)

Want Claude to search memory automatically? Add to your CLAUDE.md:

## Memory
When questions relate to past decisions or preferences, use /memory to search first.

Or use a hook in .claude/hooks.json:

{
  "hooks": {
    "SessionStart": [{
      "command": "bunx memory-search \"project context preferences decisions\"",
      "timeout": 30000
    }]
  }
}

Programmatic Usage

import { MemoryIndex } from "memory-search";

// Create index (uses local embeddings by default)
const memory = await MemoryIndex.create({
  workspaceDir: "./my-project",
});

// Index files
await memory.sync();

// Search
const results = await memory.search("authentication");
// Returns: [{ path, startLine, endLine, score, snippet }]

// Read a file
const file = await memory.readFile({ path: "MEMORY.md" });

// Get status
const status = memory.status();

// Clean up
await memory.close();

Memory File Structure

your-project/
├── MEMORY.md           # Long-term: preferences, patterns, decisions
└── memory/
    ├── 2024-01-15.md   # Daily notes
    ├── 2024-01-16.md
    └── architecture.md # Topic-specific memory

MEMORY.md (Permanent)

• User preferences
• Project decisions
• Coding patterns
• Architecture choices

memory/*.md (Contextual)

• Daily session notes
• Work in progress
• Ideas to explore
• Meeting notes

Configuration

Local embeddings work out of the box.

First Run: The first query downloads a ~300MB embedding model. Run bunx memory-search --warmup after installing to pre-download it.

For faster embeddings, you can optionally use OpenAI:

export OPENAI_API_KEY=sk-...

Full Options

interface MemoryConfig {
  workspaceDir: string;       // Required: directory with MEMORY.md

  // Database
  dbPath?: string;            // Default: {workspaceDir}/.memory.sqlite

  // Embeddings
  embeddingProvider?: 'local' | 'openai';  // Default: local
  openaiApiKey?: string;      // Required for 'openai' provider
  openaiModel?: string;       // Default: text-embedding-3-small
  localModelPath?: string;    // Default: hf:ggml-org/embeddinggemma-300M-GGUF/...
  modelCacheDir?: string;     // Default: ~/.cache/memory-search

  // Chunking
  chunkTokens?: number;       // Default: 400
  chunkOverlap?: number;      // Default: 80

  // Search
  maxResults?: number;        // Default: 6
  minScore?: number;          // Default: 0.35
  vectorWeight?: number;      // Default: 0.7
  textWeight?: number;        // Default: 0.3
}

Development

# Install dependencies
bun install

# Run tests
bun test

# Type check
bun run typecheck

# Build
bun run build

How It Works

• Indexing: Scans MEMORY.md and memory/*.md, chunks into ~400 token pieces
• Embedding: Converts chunks to vectors via local model (or OpenAI)
• Storage: SQLite database with FTS5 for keyword search
• Search: Hybrid (70% vector similarity + 30% BM25 keyword)
• Caching: Embeddings cached to avoid re-computation

License

MIT