For anyone building LLM apps, agents, or autonomous workflows, we kept running into the same limitation: ➡️ context disappears ➡️ models forget everything after a few messages ➡️ vector search ≠ real memory So we built OpenMemory — an open-source, drop-in memory framework that turns any LLM into a stateful, context-aware, and self-improving system. 🧠 What OpenMemory does * Persistent long-term memory across conversations * Automatic memory extraction (no manual prompting) * Embeddings-free recall for fast, accurate retrieval * Supports any LLM – OpenAI, local models, cloud inference, Ollama, etc. * Composable architecture for agent frameworks, chatbots, copilots, and custom AI apps * Lightweight, fast, and production-ready 💡 Why we built it LLMs feel magical… until they forget everything. OpenMemory makes them feel alive — able to learn, adapt, and reason with accumulated experience. 🎯 Who it’s for * AI engineers * Agent framework developers * Founders building AI-driven apps * Researchers exploring memory architectures * Anyone who wants an LLM that actually remembers 🔧 Open Source. Free. MIT License. We believe memory is foundational to AGI research — so OpenMemory will always remain fully open-source. GitHub: https://github.com/CaviraOSS/Ope... If this is something you’ve been waiting for, we’d love your support on Product Hunt today — your feedback helps shape the roadmap and upcoming features. ❤️

OpenMemory

Give AI agents long-term memory. Not vector search or RAG

Give AI agents long-term memory. Not vector search or RAG