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

> Source: <https://dev.to/naman_kumar_29295fe9d5838/chatstore-persistent-chat-history-service-for-llm-apps-zero-infrastructure-566f>
> Published: 2026-06-19 07:02:50+00:00

🚀 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 👇