chatstore – persistent chat history service for LLM apps, zero infrastructure

A developer open-sourced chatstore, a lightweight, framework-agnostic persistent chat library for LLM applications. The library requires zero infrastructure, uses SQLite for storage, supports sliding window context, and offers optional semantic search with local embeddings. It works with any LLM and can be installed via pip with minimal code.

🚀 I just open-sourced chatstore — a lightweight, framework-agnostic persistent chat library for LLM applications. 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 . chatstore does none of that. ✅ One class. Zero infrastructure. ✅ Works with any LLM — OpenAI, Gemini, Anthropic, Ollama, anything ✅ Persistent history backed by SQLite swappable to Postgres ✅ Sliding window context — configurable, token-aware ✅ Optional semantic search with local embeddings no API key needed 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝟭 — drop in and go: pip install chatstore 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝟮 — add vector memory with one flag: pip install chatstore semantic Start using it in 3 lines: from chatstore import ChatService chat = ChatService project id="my app" chat.save message "user", "Hello " That's it. No config files. No environment setup. No servers. 🔗 GitHub → https://github.com/namankr/chatstore https://github.com/namankr/chatstore If this saves you even an hour of boilerplate work, drop a ⭐ on the repo — it genuinely helps more developers discover it. And if you're building something with LLMs, I'd love to hear what you're working on. Drop a comment or DM me 👇