The problem that OpenSpace wants to handle is that today’s AI agents are powerful, but they have a critical weakness: they never learn, adapt and evolve fom real-world experiences, let alone share with each other. This caused some serious issues:
- massive token waste. -> How to reuse successful task patterns instead of reasoning from scratch and burning tokens every time?
- repeated costly failures. -> How to share solutions across agents instead of repeating the same costly exploration and mistakes?
- poor and unreliable skills. -> How to maintain skill reliability as tools and APIs evolve, while ensuring community-contributed skills neet rigorous quality standards?