turbomem

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Getting started ​

turbomem gives your LLM agents persistent, semantically-searchable memory that runs entirely inside your Node/Bun process. Type-safe, adapter-based, and dependency-light. No separate memory server required.

Install ​

bash
npm install turbomem

The default stack (OpenAI embeddings + PGlite storage) works out of the box. PGlite ships as a dependency, no extra setup.

Environment ​

Set your OpenAI API key for embeddings and fact extraction:

bash
export OPENAI_API_KEY=sk-...

Basic usage ​

ts
import { TurboMemory } from "turbomem";

const memory = new TurboMemory({
  embeddings: "openai",
  storage: "pglite",
  extraction: { provider: "openai", model: "gpt-4o-mini" },
  openai: { apiKey: process.env.OPENAI_API_KEY },
  pglite: { dataDir: ".turbomem" },
});

await memory.init();

await memory.add(
  [
    { role: "user", content: "Hey, I love hiking and I'm training for a half marathon this fall." },
    { role: "assistant", content: "Nice — I'll remember your fitness goals." },
  ],
  { userId: "user_123" },
);

const results = await memory.search("What outdoor activities is the user into?", {
  userId: "user_123",
  limit: 5,
});

for (const { memory: m, score } of results) {
  console.log(`[${score.toFixed(3)}] ${m.content}`);
}

await memory.close();

init() is required before any other method. It runs storage migrations and loads embedding models (for local embeddings). Calling other methods before init() throws NotInitialisedError.

Why embedded memory? ​

Many agent-memory setups rely on a separate service. A Python server, a hosted platform, or a vector database you operate yourself. For a TypeScript app that often means running another process, managing extra infrastructure, and crossing a network boundary for every memory operation.

turbomem is fully embedded:

turbomem (embedded)Server-based memory
RuntimeTypeScript, in-processSeparate server / hosted API
Deploymentnpm installRun or host a service
Network hopNone (local)HTTP per call
StoragePGlite (WASM Postgres)External vector store
Best forTS apps, edge, embedding into a productMulti-language or managed infra

If you need a cross-language managed platform, a dedicated memory service may fit better. If you're shipping a TypeScript app and want memory as a library, that's turbomem.

Next steps ​