Mem0 - The Memory Layer for Personalized AI

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๐Ÿ“„ Benchmarking Mem0's token-efficient memory algorithm โ†’

## New Memory Algorithm (April 2026) | 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 run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops) at a top_200 retrieval budget. Scores reflect Mem0's managed platform, which includes proprietary optimizations not available in the open-source SDK; open-source users should expect directionally similar gains but not identical numbers. **What changed:** - **Single-pass ADD-only extraction**: one LLM call, no UPDATE/DELETE. Memories accumulate; nothing is overwritten. - **Agent-generated facts are first-class**: when an agent confirms an action, that information is now stored with equal weight. - **Entity linking**: entities are extracted, embedded, and linked across memories for retrieval boosting. - **Multi-signal retrieval**: semantic, BM25 keyword, and entity matching scored in parallel and fused. - **Temporal Reasoning**: time-aware retrieval that ranks the right dated instance for queries about current state, past events, and upcoming plans. See the [migration guide](https://docs.mem0.ai/migration/oss-v2-to-v3) for upgrade instructions. The [evaluation framework](https://github.com/mem0ai/memory-benchmarks) is open-sourced so anyone can reproduce the numbers. ## Research Highlights - **92.5 on LoCoMo**: +21 points over the previous algorithm - **94.4 on LongMemEval**: +27 points, with 98.2 on assistant memory recall - **64.1 on BEAM (1M)**: production-scale memory evaluation at 1M tokens - [Read the full paper](https://mem0.ai/research) # Introduction [Mem0](https://mem0.ai) ("mem-zero") gives AI assistants and agents persistent memory. It stores facts extracted from conversations, scopes them to a user, agent, or run, and retrieves the relevant ones on the next query. ### Key Features & Use Cases **Core capabilities:** - Memory scoped to `user_id`, `agent_id`, or `run_id`, with metadata and filters on top - The same API across the OSS library, self-hosted server, and hosted Platform, plus Python and TypeScript SDKs **Use cases:** - AI assistants and chatbots that keep context across sessions - Customer support tools that recall a user's past tickets and preferences - Coding agents that remember project conventions and prior decisions ([Agent Skills](#agent-skills)) ## ๐Ÿš€ Quickstart Guide ### Sign up as an agent AI agents can mint a working Mem0 API key in under five seconds: no email, no dashboard, no OTP. Four commands end-to-end: ```bash # 1. Install npm install -g @mem0/cli # or: pip install mem0-cli # 2. Sign up as an agent (replace `claude-code` with your name) mem0 init --agent --agent-caller claude-code # 3. Add a memory mem0 add "I am using mem0" # 4. Search mem0 search "am I using mem0" ``` The human owner can claim the account later with `mem0 init --email ` (same key, memories preserved). Full guide: [Sign up as an agent](https://docs.mem0.ai/platform/agent-signup). | | Library | Self-Hosted Server | Cloud Platform | |---|---------|-------------------|----------------| | **Best for** | Testing, prototyping | Teams running on their own infrastructure | Zero-ops production use | | **Setup** | `pip install mem0ai` | `docker compose up` | Sign up at [app.mem0.ai](https://app.mem0.ai?utm_source=oss&utm_medium=readme) | | **Dashboard** | n/a | [Yes](https://docs.mem0.ai/open-source/setup) | Yes | | **Auth & API Keys** | n/a | Yes | Yes | | **Advanced Features** | n/a | Teasers | All included | Just testing? Use the library. Building for a team? Self-hosted. Want zero ops? Cloud. ### Library (pip / npm) ```bash pip install mem0ai ``` For enhanced hybrid search with BM25 keyword matching and entity extraction, install with NLP support: ```bash pip install mem0ai[nlp] python -m spacy download en_core_web_sm ``` Install sdk via npm: ```bash npm install mem0ai ``` ### Self-Hosted Server > **Note:** Self-hosted auth is on by default. Upgrading from a pre-auth build? Set `ADMIN_API_KEY`, register an admin through the wizard, or `AUTH_DISABLED=true` for local dev only. See [upgrade notes](https://docs.mem0.ai/open-source/setup#upgrade-notes). ```bash # Recommended: one command starts the stack, creates an admin, and issues the first API key. cd server && make bootstrap # Manual: start the stack and finish setup via the browser wizard. cd server && docker compose up -d # http://localhost:3000 ``` See the [self-hosted docs](https://docs.mem0.ai/open-source/overview) for configuration. ### Cloud Platform 1. Sign up on [Mem0 Platform](https://app.mem0.ai?utm_source=oss&utm_medium=readme) 2. Embed the memory layer via SDK or API keys 3. Using hosted Qdrant vectors? See the [Platform migration guide](https://docs.mem0.ai/migration/oss-to-platform) to import them into Mem0 Platform. ### CLI Manage memories from your terminal: ```bash npm install -g @mem0/cli # or: pip install mem0-cli mem0 init mem0 add "Prefers dark mode and vim keybindings" --user-id alice mem0 search "What does Alice prefer?" --user-id alice ``` See the [CLI documentation](https://docs.mem0.ai/platform/cli) for the full command reference. ### Agent Skills Teach your AI coding assistant (Claude Code, Codex, Cursor, Windsurf, OpenCode, OpenClaw, and any tool that supports the skills standard) how to build with Mem0. Two categories: **Reference skills, always on** (SDK knowledge loaded into the assistant's context): ```bash npx skills add https://github.com/mem0ai/mem0 --skill mem0 npx skills add https://github.com/mem0ai/mem0 --skill mem0-cli npx skills add https://github.com/mem0ai/mem0 --skill mem0-vercel-ai-sdk ``` **Pipeline skills, run on demand** (execute an end-to-end workflow in an existing repo): ```bash 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 ``` Use `/mem0-integrate` to wire Mem0 into an existing repo via a test-first pipeline, then `/mem0-test-integration` to verify. Use `/mem0-oss-to-platform` to migrate an existing project from Mem0 OSS to the hosted Platform SDK. See the [skills catalog](./skills/) or [Vibecoding with Mem0](https://docs.mem0.ai/vibecoding) for the full picture. ### Basic Usage Mem0 requires an LLM to function, with `gpt-5-mini` from OpenAI as the default. It supports a variety of LLMs; see [Supported LLMs](https://docs.mem0.ai/components/llms/overview). The default embedding model is `text-embedding-3-small` from OpenAI. For best results with hybrid search (semantic + keyword + entity boosting), use at least [Qwen 600M](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) or a comparable embedding model. See [Supported Embeddings](https://docs.mem0.ai/components/embedders/overview) for configuration details. **Self-hosted (`Memory`, `pip install mem0ai`):** ```python from openai import OpenAI from mem0 import Memory openai_client = OpenAI() memory = Memory() def chat_with_memories(message: str, user_id: str = "default_user") -> str: relevant_memories = memory.search(query=message, filters={"user_id": user_id}, top_k=3) memories_str = "\n".join(f"- {entry['memory']}" for entry in relevant_memories["results"]) system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}" messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}] response = openai_client.chat.completions.create(model="gpt-5-mini", messages=messages) assistant_response = response.choices[0].message.content messages.append({"role": "assistant", "content": assistant_response}) memory.add(messages, user_id=user_id) return assistant_response print(chat_with_memories("I prefer dark mode and vim keybindings")) print(chat_with_memories("What editor settings do I like?")) ``` **Hosted Platform (`MemoryClient`, `MEM0_API_KEY` from [app.mem0.ai](https://app.mem0.ai?utm_source=oss&utm_medium=readme)):** ```python import os from mem0 import MemoryClient client = MemoryClient(api_key=os.environ["MEM0_API_KEY"]) messages = [{"role": "user", "content": "I prefer dark mode and vim keybindings"}] client.add(messages, user_id="alice") results = client.search("What does Alice prefer?", filters={"user_id": "alice"}, top_k=3) all_memories = client.get_all(filters={"user_id": "alice"}) ``` **TypeScript** (`npm install mem0ai`; see [`mem0-ts/README.md`](https://github.com/mem0ai/mem0/blob/main/mem0-ts/README.md) and the [Node quickstart](https://docs.mem0.ai/open-source/node-quickstart)): ```typescript import { MemoryClient, type Message } from "mem0ai"; import { Memory } from "mem0ai/oss"; const messages: Message[] = [{ role: "user", content: "I prefer dark mode and vim keybindings" }]; const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY! }); await client.add(messages, { userId: "alice" }); const results = await client.search("What does Alice prefer?", { filters: { user_id: "alice" } }); const memory = new Memory(); await memory.add(messages, { userId: "alice" }); const local = await memory.search("What does Alice prefer?", { filters: { user_id: "alice" } }); ``` For detailed integration steps, see the [Python Quickstart](https://docs.mem0.ai/open-source/python-quickstart), [Platform Quickstart](https://docs.mem0.ai/platform/quickstart), and [API Reference](https://docs.mem0.ai/api-reference). ### What the SDK Covers | Feature | Docs | |---|---| | Memory ops: `add`, `search`, `get`, `get_all`, `update`, `delete`, `delete_all`, `history` | [Python Quickstart](https://docs.mem0.ai/open-source/python-quickstart) | | Entity scoping (`user_id`, `agent_id`, `run_id`) | [Entity-scoped memory](https://docs.mem0.ai/platform/features/entity-scoped-memory) | | Metadata and filters | [Metadata filtering](https://docs.mem0.ai/open-source/features/metadata-filtering) | | Async clients (`AsyncMemory`, `AsyncMemoryClient`) | [Async memory](https://docs.mem0.ai/open-source/features/async-memory) | | Graph memory | [Graph memory](https://docs.mem0.ai/platform/features/graph-memory) | | Rerankers | [Reranker-enhanced search](https://docs.mem0.ai/open-source/features/reranker-search) | | Custom instructions | [Custom instructions](https://docs.mem0.ai/open-source/features/custom-instructions) | | Multimodal (images, files) | [Multimodal support](https://docs.mem0.ai/open-source/features/multimodal-support) | | Webhooks (Platform) | [Webhooks](https://docs.mem0.ai/platform/features/webhooks) | | Memory export (Platform) | [Memory export](https://docs.mem0.ai/platform/features/memory-export) | | Feedback (Platform) | [Feedback mechanism](https://docs.mem0.ai/platform/features/feedback-mechanism) | | Memory expiration (Platform) | [Memory expiration](https://docs.mem0.ai/platform/features/memory-expiration) | | Custom categories (Platform) | [Custom categories](https://docs.mem0.ai/platform/features/custom-categories) | | Dream, memory synthesis (Platform) | [Dream](https://docs.mem0.ai/platform/features/dream) | ## ๐Ÿ”— Integrations & Demos - **ChatGPT with Memory**: Personalized chat powered by Mem0 ([Live Demo](https://mem0.dev/demo)) - **Browser Extension**: Store memories across ChatGPT, Perplexity, and Claude ([Chrome Extension](https://chromewebstore.google.com/detail/onihkkbipkfeijkadecaafbgagkhglop?utm_source=item-share-cb)) - **Langgraph Support**: Build a customer bot with Langgraph + Mem0 ([Guide](https://docs.mem0.ai/integrations/langgraph)) - **CrewAI Integration**: Tailor CrewAI outputs with Mem0 ([Example](https://docs.mem0.ai/integrations/crewai)) ## ๐Ÿ“š Documentation & Support - Full docs: https://docs.mem0.ai - Community: [Discord](https://mem0.dev/DiG) ยท [X (formerly Twitter)](https://x.com/mem0ai) - Contact: founders@mem0.ai ## Citation We now have a paper you can cite: ```bibtex @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} } ``` ## โš–๏ธ License Apache 2.0. See the [LICENSE](https://github.com/mem0ai/mem0/blob/main/LICENSE) file for details.