CozoDB-backed semantic memory with embeddings for SkillKit
npm install @skillkit/memory
Semantic memory with embeddings for SkillKit - CozoDB-backed persistent memory with vector search for AI agents.
``bash`
npm install @skillkit/memory
- CozoDB Backend: Embedded graph database with HNSW vector index
- Semantic Search: Cosine similarity search over embeddings
- Xenova Transformers: Local embedding generation (no API keys)
- Observations & Learnings: Store raw observations and compressed learnings
- Memory Compression: Extract patterns from observations
- Persistent Storage: SQLite-backed durability
- Memory Reinforcement: Boost memory relevance through usage
`typescript
import { MemoryStore, EmbeddingEncoder } from '@skillkit/memory';
// Initialize encoder
const encoder = new EmbeddingEncoder();
await encoder.init();
// Create memory store
const store = new MemoryStore('./my-project/.skillkit/memory');
await store.init();
// Store an observation
await store.addObservation({
content: 'User prefers TypeScript strict mode',
tags: ['typescript', 'preferences'],
source: 'conversation',
});
// Search memories
const results = await store.search('typescript configuration', { limit: 5 });
`
`typescript
import { MemoryCompressor } from '@skillkit/memory';
// Compress observations into learnings
const compressor = new MemoryCompressor(store);
const learnings = await compressor.compress({
minObservations: 3,
maxAge: 7 24 60 60 1000, // 7 days
});
`
`typescript
// Get embedding for a query
const embedding = await encoder.encode('React best practices');
// Search by vector
const results = await store.searchByVector(embedding, {
limit: 10,
threshold: 0.7,
});
`
`typescript
import { MemoryExporter } from '@skillkit/memory';
// Export memories to a skill file
const exporter = new MemoryExporter(store);
const skill = await exporter.toSkill({
name: 'project-patterns',
tags: ['patterns', 'best-practices'],
});
`
`typescript`
interface MemoryStore {
init(): Promise
addObservation(obs: Observation): Promise
addLearning(learning: Learning): Promise
search(query: string, options?: SearchOptions): Promise
searchByVector(embedding: number[], options?: SearchOptions): Promise
reinforce(id: string): Promise
close(): Promise
}
`typescript`
interface EmbeddingEncoder {
init(): Promise
encode(text: string): Promise
encodeBatch(texts: string[]): Promise
dispose(): Promise
}
`typescript
interface Observation {
content: string;
tags?: string[];
source?: string;
metadata?: Record
}
interface Learning {
title: string;
content: string;
tags?: string[];
confidence?: number;
}
interface Memory {
id: string;
content: string;
embedding: number[];
score?: number;
createdAt: Date;
}
``
Full documentation: https://github.com/rohitg00/skillkit
Apache-2.0