Byterover is a self-improving memory layer for your AI coding agentsβcreate, retrieve, manage vibe-coding best practices across projects and teams. You can start now by installing Byterover's extension via your AI IDE like Cursor, Windsurf, and more.
Byterover
MindPal
@andy_byterover congrats for the launch
Byterover
@maiquangtuan thank tuan; it would be grate if mindpal team can try and leave us your feedback
@andy_byterover Nice products bro, congrats for the launch!
Byterover
@tonyhothu Thanks so much, Tony β your support really means a lot to us!
Byterover
Hi builders everywhere, we canβt wait to see what you all do with our memory layer!
Weβve launched an earlier version. From Solana trading bots to automated Meta Ads tools, weβre seeing builders use Byterover for a variety of use casesβnot just to store coding practices, but increasingly to capture vertical business logic of the application as well. Some use us to switch seamlessly between Cursor and Windsurf, and others without losing context.
Looking forward to seeing what you can build with our memory.
Byterover
@minh_phan6 it is exactly what byterover is buit for, this is the way we can fuse our knowlege with the agent's capability, kind of "context" engineering
Auralix
Congrats on the launch! I'm trying it for myself but curious about how does Byterover handle conflicting coding practices when sharing memory across teams?
Byterover
@chanitypham Hey, thanks a lot for the awesome question! So, ByteRover is what we call an βagentic memoryβ β basically, thereβs an internal agent that handles all the memory stuff for you. Hereβs how it works: when it notices a new coding experience, it creates a memory for it. If it sees something needs to be updated with new concepts, it updates it. And if it finds a concept that clashes with an old one, itβll delete the outdated memory and replace it with a fresh one. Hope that makes things a bit clearer!
Byterover
@aadarshkt Thanks for your question! To be honest, we donβt know the exact internal mechanism of ChatGPTβs memory, and whether they use dedicated agents for memory management. However, weβre continuously working to improve our own memory management process. Some areas weβre focusing on:
- Enriching memory with more context, for example by integrating with your MCP server.
- Adding a reflection process, where the agent regularly re-evaluates and updates stored memories.
- Continuously improving memory quality and relevance.
Weβll keep updating our approach and are open to exploring strategies used in other systems like ChatGPT as we learn more.