@agentskit/memory

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

Tags: ai · agents · llm · agentskit · ai-agents · memory · vector-db · embeddings · rag · sqlite · redis · vector-search

Why memory

• 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
• One interface, every deployment target — swap from inMemory to sqlite to redis without touching agent code

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.

Features

• Chat memory: inMemoryChatMemory, sqliteChatMemory, redisChatMemory, fileChatMemory
• Vector memory: fileVectorMemory (pure JS), redisVectorMemory
• VectorMemory interface — 3 methods, bring any custom store
• Memory contract v1 (ADR 0003) — substitutable across runtime, useChat, and @agentskit/ink

Ecosystem

Contributors

License

MIT — see LICENSE.

Docs

Full documentation · GitHub