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TypeScript first class memory primitive for AI agents. Embed → store → retrieve → inject, backed by Postgres + pgvector. Swappable embedders, consumer owns the connection, zero framework coupling. MIT.
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TypeScript first class memory primitive for AI agents.
extract → embed → store → retrieve over any Postgres + pgvector backend.
Early development. Published on npm as langmem-ts@0.1.0. API surface is stable for the core primitive; expect additions, not breaking changes.
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.
npm install langmem-ts
Peer dependencies (you bring your own):
npm install openai pg
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();
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75/100 healthlangmem-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.
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 with dimensions: 1536 parameter for matryoshka truncation). 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.
docker compose up -d
# wait ~5 seconds for Postgres to accept connections
export OPENAI_API_KEY=sk-...
export DATABASE_URL=postgres://langmem:langmem@localhost:5432/langmem
npx tsx examples/basic.ts
langmem-ts ships with a minimal, stable core today: extract → embed → store → retrieve. The Python-only LangMem covers more memory concepts and operations, and porting them to TypeScript is an explicit goal. Each row below is a tracked GitHub issue — contributions welcome.
| Concept | LangMem (Python) | langmem-ts | Contributor notes |
|---|---|---|---|
| Semantic memory (facts, preferences) | ✅ | ✅ | Core extract → embed → store pipeline. |
| Episodic memory (past interactions as examples) | ✅ | ❌ | New EpisodicExtractor interface. Captures observation + thoughts + action + result rather than flat facts. See LangMem's episode schema as a reference. |
| Procedural memory (agent-authored system prompts) | ✅ | ❌ | New PromptOptimizer interface that takes trajectories + feedback and returns an updated system prompt. LangMem's signature feature. |
| Operation | LangMem (Python) | langmem-ts | Contributor notes |
|---|---|---|---|
| Extraction | ✅ | ✅ | LLMExtractor distills turns into facts. |
| Deduplication / upsert | ✅ | ❌ | Before store.write, compare against top-k neighbors above a similarity threshold; update instead of duplicate. |
| Conflict resolution | ✅ | ❌ | When a new fact contradicts an existing one ("user lives in Berlin" vs. "user lives in Lisbon"), mark the older memory stale. Design question: hard delete vs. soft supersede. |
| Thread summarization | ✅ | ❌ | summarize_messages equivalent — compress long threads into short-term working memory. |
| Background reflection | ✅ | ❌ | A ReflectionExecutor equivalent that processes extraction + consolidation off the hot path. The architectural question: Node worker? BullMQ? Leave the scheduling to the consumer? |
| Feature | LangMem (Python) | langmem-ts | Contributor notes |
|---|---|---|---|
| Namespacing (user/org/app) | ✅ | ❌ | Multi-tenancy primitive. Add a namespace: string[] field on Memory and a required filter on Retriever.search. Prevents cross-user memory bleed. |
| Metadata filtering in search | ✅ | Partial | metadata is stored but not yet a first-class filter on retriever.search. Extend the JSONB query. |
| Time-aware retrieval | Partial | ❌ | Boost recent memories via a recency decay factor in the scoring step. |
| Concern | LangMem (Python) | langmem-ts | Contributor notes |
|---|---|---|---|
| Storage backends | LangGraph BaseStore (primarily Postgres) | pgvector only | Alternative Store + Retriever implementations for SQLite/sqlite-vec, Pinecone, Qdrant, or pgvector-over-HTTP. The interface is already designed for this — these are self-contained PRs. |
| LangGraph integration | ✅ (native) | ❌ (by design) | Deliberately out of scope. Framework-agnostic is the positioning. |
If you want to help and don't know where to start:
retriever.search — extend one method, add tests, done.Memory type, Store.write, Retriever.search. Isolated but cross-cutting.SqliteVecStore + SqliteVecRetriever) — full parallel implementation of the existing interfaces. High-signal PR that proves the abstractions work.Open an issue before starting large work so we can align on interface shape.
Contributions are welcome — especially for roadmap items. Start with CONTRIBUTING.md for setup, PR expectations, and what's in vs. out of scope.
Quick summary:
src/. No framework coupling.MIT — see LICENSE.
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