HydraPlus — the memory and context layer for AI agents that actually knows your users. Open Source

AI agents are clueless geniuses. 🧠 They pass every benchmark. They can't remember what you told them yesterday. The real problem isn't intelligence. It's context. Your agent doesn't know who the user is, what they've built, what they've rejected, what they care about deeply. Every session starts from zero. Every response is generic. Every recommendation misses. HydraPlus fixes that. ⚡ 🤝 **One memory layer. Every agent. Fully aware.** Whether you're running one agent or an entire team of them — coding agent, research agent, support agent — they all pull from the same live knowledge graph. No duplicate context. No conflicting memory. Every agent in your system knows exactly what the others know. Build a 10-agent pipeline and every single one of them shares the same understanding of the user from day one. 🚫 **Flat embeddings are not enough.** Similarity is not relevance. Vector search finds what's close. It doesn't find what matters. An agent that retrieves the nearest chunks isn't reasoning — it's guessing. HydraPlus combines graph traversal, semantic search, and BM25 into a single retrieval layer that understands relationships, not just distances. It knows that "React" in one conversation connects to "frontend stack" in another, to "team preference" in a third. Context that's actually grounded. Responses that are actually useful. ⏳ **Your agent knows the full timeline, not just the last message.** Most RAG systems treat memory as a flat index. Everything lives at the same depth, the same weight, the same timestamp. HydraPlus versions every memory write like Git commits. Your agent knows what changed, when it changed, and what the user believed before that. That's not retrieval. That's reasoning over time. 🛡️ **Fully secure. From every angle.** Web pages, PDFs, tool responses, other agents — any of it can carry a prompt injection attempt. Most memory layers store it without question. HydraPlus blocks it at ingestion. Two layers of defense — pattern detection and LLM semantic analysis — covering 6 attack surfaces. Your agent's memory stays clean regardless of what it reads or who it talks to. 100% detection rate. Zero false positives. 🌱 **A memory that manages itself.** It doesn't grow forever and degrade. Recent facts stay hot. Aging facts compress. Irrelevant facts archive. 51% leaner without losing a single fact. The system stays sharp at session 5000 the same way it was at session one. No maintenance. No manual cleanup. No performance cliff. 🔌 **Works with your stack. Out of the box.** OpenAI, Gemini, Groq — or run it fully offline with zero API dependency. No infra headache. No weeks of integration. Drop it into your agent pipeline and your agent immediately knows who it's talking to, what they care about, and what happened last month. This is what agent memory should have been from the start. 🔥 Contributions welcome. What gaps are you hitting with agent memory right now? Drop below 👇