I had been using Openclaw for a while, but the bug rate was just too high. Flows would randomly break, and debugging cost more time than it should.

On top of that, token cost was not great. Compared with options like DeepSeek and Kimi K2.5, the price-performance ratio felt weak.

I then tried nanobot, hoping to migrate. But support for newer models was still limited, and K2.5 kept failing with “tool execution failed.” After several rounds of trial and error, I stopped waiting for a perfect off-the-shelf solution and decided to build one myself.

After hundreds of back-and-forths with Opus and Codex on architecture choices, I built a new bot in Golang: Nagobot. At least now I fully control the provider layer.

Project link: https://github.com/linanwx/nagobot

I learned a lot during implementation, especially around:

• Threads and concurrent task execution
• Agent and Skill abstractions
• Cron-based asynchronous tasks
• Session history and context compression

To be honest, a lot of the code still needs deeper review. Most of the work was built in rapid loops with AI-assisted architecture discussions. Sometimes frustrating, sometimes genuinely impressive, and overall very interesting.

So far, testing results are better than expected. The bot can already create async tasks, investigate news, and set reminders for itself, which is immediately useful in day-to-day workflows.

Most core features are now in place. My next focus is long-term memory design.

If this sounds useful to you, feel free to open an issue or reach out. I will keep iterating on this project.