Byterover is a self-improving memory layer for your AI coding agents—create, 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 is a self-improving memory layer for your AI coding agents—create, 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.
Hey Product Community,
We’re super excited to introduce Byterover - a self-improving memory layer for AI coding agents that actually remembers how you vibe-code, and bring the memory across projects and teams.
If you've used AI coding tools like Cursor, Windsurf, GitHub Copilot, or more, you've probably hit this frustration:
- Teaching your agent the same logic patterns over and over
- Coding agents that forget everything you teach as soon as you switch projects
- Losing all your custom code structuring from one project to the next
- No easy way to share learned vibe-coding practices across your dev team
As developers, we kept running into this—solo and with our teams. So we decided to build a fix. That's why we started Byterover.
✨ With Byterover, you can:
📁 Create, organize memory by workspace, and project.
🧠 Edit, retrieve, and manage memory for your coding agent.
⭐️ Star important memory so your agent prioritizes it
🧹 Delete outdated memories to keep things clean
🤝 Share memory across your team—so agents learn together
You can start simply by installing Byterover's extension on your AI IDE. Everything happens inside your IDE—no workflow changes, no vendor lock-in.
We’d love your feedback and thoughts—on the dev experience, the workflows on Byterover, to help us improve more 💬
Thanks for checking us out- and if you believe in what we are doing at Byterover, we’d love your support 🙌
Hi builders everywhere, we can’t wait to see what you all do with our memory layer!
We’ve launched an earlier version. From Solana trading bots to automated Meta Ads tools, we’re seeing builders use Byterover for a variety of use cases—not 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.
@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
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?
@chanitypham Hey, thanks a lot for the awesome question! So, ByteRover is what we call an “agentic memory” — basically, there’s an internal agent that handles all the memory stuff for you. Here’s 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, it’ll delete the outdated memory and replace it with a fresh one. Hope that makes things a bit clearer!
@aadarshkt Thanks for your question! To be honest, we don’t know the exact internal mechanism of ChatGPT’s memory, and whether they use dedicated agents for memory management. However, we’re continuously working to improve our own memory management process. Some areas we’re 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.
We’ll keep updating our approach and are open to exploring strategies used in other systems like ChatGPT as we learn 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 — your support really means a lot to us!
Byterover
Hi builders everywhere, we can’t wait to see what you all do with our memory layer!
We’ve launched an earlier version. From Solana trading bots to automated Meta Ads tools, we’re seeing builders use Byterover for a variety of use cases—not 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 “agentic memory” — basically, there’s an internal agent that handles all the memory stuff for you. Here’s 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, it’ll 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 don’t know the exact internal mechanism of ChatGPT’s memory, and whether they use dedicated agents for memory management. However, we’re continuously working to improve our own memory management process. Some areas we’re 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.
We’ll keep updating our approach and are open to exploring strategies used in other systems like ChatGPT as we learn more.