Mem0

We partnered with AWS to show how developers can build persistent memory built for real-world agent workloads. The tutorial combines three components to create a robust GitHub Research Agent: - Mem0 Open Source for long-term memory orchestration. - Amazon ElastiCache for Valkey for high-speed semantic recall - Amazon Neptune Analytics for graph-based reasoning With these pieces working in sync, the agent doesn’t just answer questions, it retains what it learns, avoids repeated tool calls, and builds a connected understanding of the codebase over time. Link to the full guide in comments

AI agents work best when their memory aligns with your application’s exact needs. When you’re building agents with memory, a few things matter: - Only relevant details should turn into long-term memory. - Sensitive information shouldn’t be captured. - And, the rules for what gets stored should stay consistent across your entire project. That’s exactly what Custom Instructions in Mem0 are designed for. You define your guidelines in plain language like what to extract, what to ignore, and how information should be handled and Mem0 applies those rules across your entire project without any extra setup. This gives you far more control over what gets stored, allowing the memory layer to adapt to your specific use case. Your agent ends up working with cleaner, more reliable context that aligns with the outcomes you expect.

Not every AI agent query needs the same type of memory. Some questions are about a single fact, others depend on how different facts connect and Mem0 gives you both a vector store and graph memory to handle this. A vector store works best when the answer lives in a single memory. It’s built for semantic search like skills, notes, descriptions, FAQs, and anything that can be matched by meaning. It’s fast, lightweight, and exactly what you want for most semantic lookups. Graph memory comes in when the question is relational. Agents often need to understand how people, tasks, or concepts are linked like who works with who, who reports to who, what depends on what, or how multiple pieces fit together. Graph memory extracts entities and relationships so the agent can follow that chain directly. Here’s the trade-off in simple terms: • vector store → cheaper + low latency • graph memory → deeper reasoning when structure matters And switching between the two in Mem0 is straightforward. When adding a memory, you can enable graph extraction with one flag: enable_graph=True You can use it per-call, or turn it on project-wide if most of your data has relationships. If you are just looking for semantic lookups, stick with the vector store but for anything involving connections, turn on graph memory. Mem0 lets you use both in the same project, depending on what each query actually needs.

Congratulations to Throwback, winners of the Mem0 Track at the Cursor Hackathon! They integrated Mem0 to power the memory feature, enabling it to recall past conversations between generations and make each new interaction richer and more personal. Throwback is a storytelling app that connects the young and elderly through relaxed, guided dialogues, preserving Singapore’s cultural and historical heritage in a digital format🇸🇬 Kudos to Claudia Wijaya, Emily Choi , Luke Chen and Zhenming Lin for bringing such a thoughtful idea to life. And special thanks to 🃏 Sherry Jiang & 🥃 Agrim Singh for hosting an incredible hackathon!🙌

We’re hiring a Data Analyst at Mem0🔥 And, it’s one of the most high-impact roles we’ve ever opened. We’ve built the memory layer for AI agents, now used across thousands of AI apps. But to scale, we need to understand what truly drives impact and that’s where you come in. As our first data hire, you’ll: - Build our analytics stack from 0→1 - Define metrics that matter across product and business - Turn raw data from Postgres, PostHog, and LLM outputs into insights that guide every decision You’ll work closely with Product, Engineering, and Leadership, owning everything from data pipelines to experimentation frameworks. 📍 Bangalore, India If you’ve been waiting for a role where you can build, not just analyze, this is it. 🔗 Link in comments