@agentskit/memory

@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

How this fits the ecosystem

@agentskit/memory gives agents continuity: chat history, vector memory, graph memory, encrypted stores, and local or hosted backends.

• AgentsKit: compose it with the other packages in this repo to build agents from small, swappable parts.
• Registry: look for ready agents and templates that already use this layer at registry.agentskit.io.
• Playbook: learn the production patterns behind this layer at playbook.agentskit.io.
• AKOS: run the same concepts with enterprise deployment, governance, and observability at akos.agentskit.io.

Docs: package guide · agent handoff

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 (3)

• fileChatMemory({ path }) — JSON on disk; zero infra.
• sqliteChatMemory({ path }) — WAL-mode SQLite; indexed by session.
• redisChatMemory({ client, keyPrefix }) — distributed, serverless-friendly.

All on top of createInMemoryMemory / createLocalStorageMemory from @agentskit/core.

Vector memory (7)

• fileVectorMemory — pure-JS, file-persisted (good to ~10k vectors).
• redisVectorMemory — Redis Stack / Redis 8+ HNSW.
• pgvector — BYO SQL runner (postgres.js, pg, Drizzle, Prisma, Neon).
• pinecone — managed; namespaces + metadata filters.
• qdrant — self-hosted or cloud via HTTP.
• chroma — HTTP collection client.
• upstashVector — serverless HTTP.

Same 3-method VectorStore contract — swap without touching agent code.

Higher-order wrappers (6)

• createHierarchicalMemory — MemGPT-style tiers: working / recall / archival. Recipe.
• createVirtualizedMemory — hot window + cold retriever for long sessions. Recipe.
• createAutoSummarizingMemory (via @agentskit/core/auto-summarize) — fold oldest turns into a running summary. Recipe.
• createEncryptedMemory — AES-GCM-256 envelope over any ChatMemory; keys never leave the caller. Recipe.
• createInMemoryGraph — knowledge graph (nodes + edges + BFS). Recipe.
• createInMemoryPersonalization + renderProfileContext — per-user trait profile. Recipe.

Memory contract v1 (ADR 0003) — substitutable across runtime, useChat, and every framework binding.

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

Contributors

License

MIT — see LICENSE.

Docs

Full documentation · GitHub