mem0/mem0-ts

Mem0 TypeScript SDK

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Mem0 gives AI assistants and agents persistent memory. It extracts useful facts from conversations, scopes them to a user, agent, or run, and retrieves the relevant facts for later interactions. The TypeScript package, mem0ai on npm, includes MemoryClient for the hosted Mem0 Platform, imported from mem0ai, and Memory for open-source, in-process memory, imported from mem0ai/oss.

Requirements

• Node.js 20 or later
• Hosted Platform: MEM0_API_KEY from the Mem0 dashboard
• Open source with the default providers: OPENAI_API_KEY

Install

npm install mem0ai

Platform or open source

	Platform (MemoryClient)	Open source (Memory)
Import	import { MemoryClient } from "mem0ai"	import { Memory } from "mem0ai/oss"
Where memories live	Mem0's hosted API	Your configured vector store
Required key	MEM0_API_KEY	OPENAI_API_KEY with the defaults, or keys for your chosen providers
Extraction	Managed and asynchronous	Runs against your configured LLM
Best for	Zero-ops production use	Local development and custom infrastructure

Platform quickstart

Set MEM0_API_KEY, then add a conversation:

import { MemoryClient, type Message } from "mem0ai";

const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });

const messages: Message[] = [
  { role: "user", content: "I am vegetarian and allergic to nuts." },
  { role: "assistant", content: "I will remember that." },
];
await client.add(messages, { userId: "alex" });

Hosted add() queues extraction and usually returns an eventId with status: "PENDING". Do not search immediately after add(). Wait for processing to finish in the dashboard, or use a memory_add webhook, then search:

import { MemoryClient } from "mem0ai";

const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! });
const results = await client.search("What does Alex eat?", {
  filters: { user_id: "alex" },
  topK: 5,
});
console.log(results.results);

search() and getAll() take entity IDs inside filters with snake_case keys. add() and deleteAll() take userId, agentId, or runId as top-level camelCase options.

Open-source quickstart

Set OPENAI_API_KEY before using the default OpenAI LLM and embedder:

import { Memory } from "mem0ai/oss";

const memory = new Memory();

const messages = [
  { role: "user", content: "I am vegetarian and allergic to nuts." },
  { role: "assistant", content: "I will remember that." },
];
await memory.add(messages, { userId: "alex" });

const results = await memory.search("What does Alex eat?", {
  filters: { user_id: "alex" },
  topK: 5,
});
console.log(results.results);

The default Memory configuration uses OpenAI gpt-5-mini, OpenAI text-embedding-3-small, a SQLite-backed vector store at ~/.mem0/vector_store.db, and a SQLite history database at memory.db. Pass a config object to Memory to change the LLM, embedder, vector store, history path, or reranker.

Configuration and features

Feature	Documentation
Memory operations: add, search, get, getAll, update, delete, deleteAll, history, all async and Promise-based	Node quickstart
Entity scoping with userId, agentId, and runId	Entity-scoped memory
Metadata and filters	Metadata filtering
LLMs, embedders, vector stores, and rerankers	Components
Graph memory	Graph memory
Custom instructions	Custom instructions
Multimodal input	Multimodal support
Platform webhooks, export, feedback, expiration, and custom categories	Platform features

Benchmarks

Benchmarking Mem0's token-efficient memory algorithm

Benchmark	Old	New	Tokens	Latency p50
LoCoMo	71.4	92.5	7.0K	0.88s
LongMemEval	67.8	94.4	6.8K	1.09s
BEAM (1M)	n/a	64.1	6.7K	1.00s
BEAM (10M)	n/a	48.6	6.9K	1.05s

All benchmarks use the same production-representative model stack, single-pass retrieval, and a top-200 retrieval budget. Scores reflect the managed Platform, which includes proprietary optimizations not available in the open-source SDK. Open-source results should show similar directional gains, but may not match these scores.

The current algorithm uses single-pass ADD-only extraction, first-class agent facts, entity linking, multi-signal retrieval, and temporal reasoning. Read the research paper, the migration guide, or the open-source evaluation framework.

Self-hosted server

Run Mem0 as a FastAPI service with PostgreSQL, pgvector, and Neo4j:

# Recommended: start the stack, create an admin, and issue the first API key.
cd server && make bootstrap

# Manual: start the stack, then finish setup in the browser wizard.
cd server && docker compose up -d

Self-hosted authentication is enabled by default. See the self-hosted documentation and upgrade notes.

CLI

Manage hosted memories from your terminal:

npm install -g @mem0/cli

mem0 init
mem0 add "Prefers dark mode and vim keybindings" --user-id alice
mem0 search "What does Alice prefer?" --user-id alice

AI agents can create an account without email or a dashboard:

mem0 init --agent --agent-caller claude-code

The human owner can claim the account later with mem0 init --email <their-email>. The API key and memories remain unchanged. See the CLI documentation and agent signup guide.

Agent skills

Install reference skills to give compatible coding assistants Mem0 context:

npx skills add https://github.com/mem0ai/mem0 --skill mem0
npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli

Install pipeline skills for end-to-end workflows:

npx skills add https://github.com/mem0ai/mem0 --skill mem0-integrate
npx skills add https://github.com/mem0ai/mem0 --skill mem0-test-integration
npx skills add https://github.com/mem0ai/mem0 --skill mem0-oss-to-platform

See the skills catalog or Vibecoding with Mem0.

Integrations and demos

• ChatGPT with Memory demo
• Browser extension
• LangGraph integration
• CrewAI integration

Documentation and help

• Node quickstart
• Platform quickstart
• API reference
• Discord
• GitHub issues
• Email: founders@mem0.ai

Contributing

Read CONTRIBUTING.md before opening an issue or pull request.

Citation

@article{mem0,
  title={Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory},
  author={Chhikara, Prateek and Khant, Dev and Aryan, Saket and Singh, Taranjeet and Yadav, Deshraj},
  journal={arXiv preprint arXiv:2504.19413},
  year={2025}
}

License

Apache 2.0. See LICENSE.