langmem-ts

TypeScript-native memory primitive for AI agents.

extract → embed → store → retrieve over any Postgres + pgvector backend.

Status

Early development. Not yet published to npm. API surface is stable for the core primitive; expect additions, not breaking changes.

Why

LangMem (Python) is excellent but Python-only. Mem0 is TypeScript-capable but VC-backed with the usual bifurcation risks. There is no credible MIT-licensed, framework-agnostic, TypeScript-native memory library for AI agents. langmem-ts fills that gap.

Install

npm install langmem-ts

Peer dependencies (you bring your own):

npm install openai pg

Quick start

import {
  OpenAIEmbedder,
  PgVectorStore,
  PgVectorRetriever,
  LLMExtractor,
} from "langmem-ts";

// Compose the four primitives
const extractor = new LLMExtractor({ apiKey: process.env.OPENAI_API_KEY! });
const embedder = new OpenAIEmbedder({ apiKey: process.env.OPENAI_API_KEY! });
const store = new PgVectorStore({ connectionString: process.env.DATABASE_URL! });
const retriever = new PgVectorRetriever({ connectionString: process.env.DATABASE_URL! });

// Validate that the column dimension matches your embedder output
await store.init(1536);

// Extract facts from a conversation turn
const facts = await extractor.extract({
  role: "user",
  content: "I decided to use pgvector for my memory system",
});

// Embed and store each fact
for (const fact of facts) {
  const vector = await embedder.embed(fact);
  await store.write({ content: fact, vector, metadata: { tags: ["tech"] } });
}

// Retrieve by semantic query later
const queryVector = await embedder.embed("what did I decide about vector databases");
const results = await retriever.search(queryVector, { topK: 5 });

for (const { memory, score } of results) {
  console.log(`[${score.toFixed(2)}] ${memory.content}`);
}

// Clean up when done
await store.close();
await retriever.close();

Core concepts

langmem-ts exposes four interfaces, each with a default OpenAI + Postgres implementation. Consumers swap any piece without touching library internals.

| Interface | Default | Purpose | |---|---|---| | Embedder | OpenAIEmbedder | Turns text into a vector | | Store | PgVectorStore | Writes memories to Postgres + pgvector | | Retriever | PgVectorRetriever | Searches memories by vector similarity | | Extractor | LLMExtractor | Distills conversation turns into facts worth remembering |

Every default accepts config via its constructor. The library never reads process.env on your behalf.

Requirements

• Node.js 18 or newer
• Postgres with the pgvector extension enabled
• OpenAI API key (for default embedder + extractor — swap these out if you want)

Database setup

Run the reference migration against your Postgres database:

-- langmem-ts reference migration
-- Creates the pgvector extension and the default `memories` table.
--
-- IMPORTANT: The `embedding` column's dimension (1536 below) must match
-- the dimensions your Embedder produces. The library validates this at
-- startup and will refuse to run if they disagree.
--
-- If you use a different embedding model or dimension, change vector(1536)
-- to your model's output dimension BEFORE running this migration.

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE IF NOT EXISTS memories (
  id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  content     TEXT NOT NULL,
  embedding   vector(1536) NOT NULL,
  metadata    JSONB DEFAULT '{}',
  created_at  TIMESTAMPTZ DEFAULT now()
);

CREATE INDEX IF NOT EXISTS memories_embedding_idx
  ON memories USING ivfflat (embedding vector_cosine_ops);

The 1536 dimension matches the default embedder (OpenAI text-embedding-3-large truncated via matryoshka). Changing the embedding model or dimensions after memories exist will corrupt retrieval — PgVectorStore.init() validates this at startup and throws with an educational error on mismatch.

Design principles

• Interface-first. Every component is an interface with a default implementation. Swap any piece.
• Database-agnostic. Postgres + pgvector is the reference backend, not the only option.
• Framework-agnostic. No LangGraph coupling. Works inside any TypeScript agent or plain script.
• Zero env reads. Every config value is injected at construction.
• Extraction precedes embedding. Raw conversation text retrieves poorly; extracted facts retrieve well.

License

MIT — see LICENSE.