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π Building Production-Ready AI Agents with Scalable Long-Term Memory β
## New Memory Algorithm (April 2026) | Benchmark | Old | New | Tokens | Latency p50 | | --- | --- | --- | --- | --- | | **LoCoMo** | 71.4 | **91.6** | 7.0K | 0.88s | | **LongMemEval** | 67.8 | **93.4** | 6.8K | 1.09s | | **BEAM (1M)** | β | **64.1** | 6.7K | 1.00s | | **BEAM (10M)** | β | **48.6** | 6.9K | 1.05s | All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops). **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. 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 - **91.6 on LoCoMo** -- +20 points over the previous algorithm - **93.4 on LongMemEval** -- +26 points, with +53.6 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") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. It remembers user preferences, adapts to individual needs, and continuously learns over timeβideal for customer support chatbots, AI assistants, and autonomous systems. ### Key Features & Use Cases **Core Capabilities:** - **Multi-Level Memory**: Seamlessly retains User, Session, and Agent state with adaptive personalization - **Developer-Friendly**: Intuitive API, cross-platform SDKs, and a fully managed service option **Applications:** - **AI Assistants**: Consistent, context-rich conversations - **Customer Support**: Recall past tickets and user history for tailored help - **Healthcare**: Track patient preferences and history for personalized care - **Productivity & Gaming**: Adaptive workflows and environments based on user behavior ## π Quickstart Guide Choose between our hosted platform or self-hosted package: ### Hosted Platform Get up and running in minutes with automatic updates, analytics, and enterprise security. 1. Sign up on [Mem0 Platform](https://app.mem0.ai) 2. Embed the memory layer via SDK or API keys ### Self-Hosted (Open Source) Install the sdk via pip: ```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 ``` ### 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. ### Basic Usage Mem0 requires an LLM to function, with `gpt-5-mini` from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our [Supported LLMs documentation](https://docs.mem0.ai/components/llms/overview). Mem0 uses `text-embedding-3-small` from OpenAI as the default embedding model. For best results with hybrid search (semantic + keyword + entity boosting), we recommend using 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. First step is to instantiate the memory: ```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: # Retrieve relevant memories 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"]) # Generate Assistant response 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 # Create new memories from the conversation messages.append({"role": "assistant", "content": assistant_response}) memory.add(messages, user_id=user_id) return assistant_response def main(): print("Chat with AI (type 'exit' to quit)") while True: user_input = input("You: ").strip() if user_input.lower() == 'exit': print("Goodbye!") break print(f"AI: {chat_with_memories(user_input)}") if __name__ == "__main__": main() ``` For detailed integration steps, see the [Quickstart](https://docs.mem0.ai/quickstart) and [API Reference](https://docs.mem0.ai/api-reference). ## π 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)) ## π Documentation & Support - Full docs: https://docs.mem0.ai - Community: [Discord](https://mem0.dev/DiG) Β· [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} } ``` ## βοΈ License Apache 2.0 β see the [LICENSE](https://github.com/mem0ai/mem0/blob/main/LICENSE) file for details.