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@agentskit/memory
Advanced tools
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
fileVectorMemory (pure JS, no native deps) or Redis vector search for scaleVectorStore interface is 3 methods; bring LanceDB, Pinecone, or any custom store in minutesinMemory to sqlite to redis without touching agent codenpm install @agentskit/memory better-sqlite3
# For production: npm install redis
# For vectors: npm install vectra
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)
Use a vector backend with @agentskit/rag createRAG({ embed, store }) — fileVectorMemory and redisVectorMemory implement VectorMemory for chunk storage and search.
inMemoryChatMemory, sqliteChatMemory, redisChatMemory, fileChatMemoryfileVectorMemory (pure JS), redisVectorMemoryVectorMemory interface — 3 methods, bring any custom storeruntime, useChat, and @agentskit/ink| 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 |
MIT — see LICENSE.
FAQs
Persistent and vector memory backends for AgentsKit.
The npm package @agentskit/memory receives a total of 885 weekly downloads. As such, @agentskit/memory popularity was classified as not popular.
We found that @agentskit/memory demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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