Mem0

Our co-founder & CTO Deshraj Yadav, presented the Mem0 research paper this week at ECAI 2025 in Bologna🇮🇹 ECAI (European Conference on Artificial Intelligence) is Europe’s flagship AI research conference that brings together leading researchers and labs shaping the future of intelligent systems. The paper explores how Mem0 enables persistent, production-ready memory for AI, built to retain context across interactions, scale as memories grow, and adapt across use cases. The talk sparked thoughtful discussions around: - Multimodal memory support - How Mem0 compares with RAG-based approaches - Managing large and evolving memory graphs - What to store vs. forget, since memory is always task-specific Proud to have presented our work on the memory layer for AI and to see it spark meaningful discussions at one of the leading global AI research venues.

Excited to announce Mem0's $24M (Seed + Series A)🔥 Led by Basis Set, with participation from Peak XV Partners, Kindred Ventures, GitHub Fund, and Y Combinator. AI today can reason, code, and plan, yet forgets everything as soon as the session ends. That’s the bottleneck holding back truly intelligent systems. Mem0 fixes that. With just three lines of code, developers can give their agents long-term memory, enabling them to adapt, personalize, and improve over time. In just a year, Mem0 has become the memory layer for the AI ecosystem: - 41K+ GitHub stars - 14M+ downloads - 186M API calls in just Q3 Used in production by thousands of developers and companies We’re creating a new foundation for the agentic economy where intelligence doesn’t just think, but remembers, learns, and evolves across time and context. Grateful to our team, community, and investors for believing in this mission. Intelligence needs memory & we're building it for everyone.

What does it actually take to give an LLM memory? Avishek Biswas explored that question by recreating the architecture described in the Mem0 paper using DSPy, showing how extraction, indexing, retrieval, and updates come together inside an agentic memory system. The video distills these complex ideas into a clear, hands-on walkthrough and also demonstrating how the Mem0 API brings those concepts to life in real applications. It’s a great introduction to how memory systems work and why they become complex to build and maintain as they scale.

Made my first open source contribution. Added support for custom Hugging Face models Mem0. Looking forward for more such contributions.

Mem0 v1.0.0 is live 🚀 Bringing faster memory retrieval, new integrations, and major performance upgrades across SDKs. Here's what's new: - Assistant memory retrieval upgrade -Default async mode for better performance - Azure MySQL & Azure AI Search support - Tool Calls for LangChainLLM - Custom Hugging Face model support - New rerankers: Cohere, ZeroEntropy, Hugging Face - Improved Databricks, Milvus & Weaviate stability - Refreshed docs, examples & playground - OpenAI 2.x compatibility Big shoutout to all our new contributors joining the release🚀

The hardest part about giving AI agents memory? Making them understand time. Most agents know what happened but not when it happened or when it stopped being true. That’s why Mem0 makes time-awareness native with with two powerful features👇 1. Timestamp - Record when something actually occurred not when it was added. - keeps chronological order intact - supports backfilled or imported data - enables time-based reasoning & analytics 2. Expiration - Give your memories a shelf life. Once expired, they’re automatically ignored in retrievals. - prevents stale or irrelevant data - ensures cleaner, more context-aware responses With these, Mem0 helps your agents reason across time, not just across data.

Prompt Engineering vs Context Engineering Prompt engineering is the art of crafting precise instructions, shaping what you say and how you say it so the model produces the right output. It’s about optimizing the input text itself through roles, examples, and structure. A few years ago, the craft of prompt engineering dominated how we built with language models. And it worked, until we started building agents. Because prompts alone can’t manage state. Each turn adds new data like messages, tool outputs, retrieved docs and only a fraction fits into the model’s limited context window. So the question shifted from “What should I tell the model?” to “What configuration of information helps it reason best right now?” That’s where context engineering comes in. Context engineering is the discipline of designing the environment around the prompt, deciding what the model should remember, retrieve, or focus on at any moment. It optimizes the information flow, not just the words. It’s about managing the model’s attention budget which is indeed a finite resource. -Too little context and the model loses awareness. -Too much, and precision decays, a phenomenon known as context rot. Good context engineering curates the smallest, highest-signal set of tokens that drive useful behavior. It involves: - Keeping system prompts clear and structured. - Using tools to fetch data just-in-time. - Managing memory like what to persist, summarize, or forget. - Orchestrating retrieval and state to keep information relevant. This evolution makes LLMs more than responders. It makes them capable agents that think and adapt over time. That’s where Mem0 fits in - the memory layer inside this context pipeline, deciding what’s worth keeping, compacting, or recalling so your agents always operate with the right context. Because the future of AI isn’t about better prompts - it’s about smarter context. #AI #ContextEngineering #PromptEngineering

Implement long-term memory for AI agents 🧠 Learn how to architect persistent memory using Azure AI Foundry, Azure AI Search, and Mem0. This tutorial by Eitan Sela explains how Mem0 fits into the Azure ecosystem - managing what to remember, summarize, and forget. While AI Search handles retrieval and Foundry orchestrates the agent. 🔗Blog link in the comments. #mem0 #microsoft #aimemory