@agentskit/memory

@agentskit/memory

Persist conversations and add vector search to your agents — swap backends without changing agent code.

Why

• Conversations that survive restarts — SQLite for local development, Redis for production; your agent remembers context across sessions with zero code changes
• RAG-ready vector search — store and retrieve embeddings with fileVectorMemory (pure JS, no native deps) or Redis vector search for scale
• Plug any backend — the VectorStore interface is 3 methods; bring LanceDB, Pinecone, or any custom store in minutes

Install

npm install @agentskit/memory better-sqlite3
# For production:  npm install redis
# For vectors:     npm install vectra

Quick example

import { createRuntime } from '@agentskit/runtime'
import { anthropic } from '@agentskit/adapters'
import { sqliteChatMemory, fileVectorMemory } from '@agentskit/memory'

const runtime = createRuntime({
  adapter: anthropic({ apiKey: process.env.ANTHROPIC_API_KEY, model: 'claude-sonnet-4-6' }),
  memory: sqliteChatMemory({ path: './chat.db' }),
})

// Agent now remembers previous conversations across process restarts
const result = await runtime.run('What did we discuss yesterday?')
console.log(result.content)

With RAG

Use a vector backend with @agentskit/rag createRAG({ embed, store }) — fileVectorMemory and redisVectorMemory implement VectorMemory for chunk storage and search.

Next steps

• Swap sqliteChatMemory for Redis or in-memory variants from the same package for different deployment targets
• Pair embedders from @agentskit/adapters with RAG — see @agentskit/rag

Ecosystem

Package	Role
@agentskit/core	Memory, VectorMemory types
@agentskit/rag	Chunking + retrieval on top of vector memory
@agentskit/runtime	memory / retriever options
@agentskit/adapters	Embeddings for RAG

Docs

Full documentation