🚀 I just open-sourced chatstore — a lightweight, framework-agnostic persistent chat library for LLM...
If you've ever built an AI assistant or agent, you know the pain:
→ Where do I store conversation history?
→ How do I feed a sliding window to the LLM without blowing the context limit?
→ How do I retrieve relevant past context without spinning up a server?
Most solutions either lock you into a framework (LangChain), require Docker + a running server (Zep), or need an LLM call just to store a memory (Mem0).
If you have ever built a production-grade LLM agent for customer support, you know the exact moment...
Every LLM app has the same problem — the model forgets everything between conversations. Cloud...
One thing cloud chatbots do badly is remember you. Each session starts from zero. When I built...
Introduction Before I dug into how an LLM works, I assumed each chat stored its memory or...
Every new chat with your LLM starts the same way. Hi, here's the context. Here's the stack. Here's...
Every LLM app I've built has the same broken pattern. Request comes in - reconstruct context from...
