hermes agent by nous research does something i havent seen any other agent framework do after completing a complex task it synthesizes the experience into a permanent reusable skill document. next time a similar task comes up it loads the skill instead of solving from scratch. the skills also self improve during use, the agent updates them when it finds a better approach one user reported 40% speedup on repeated research tasks after the agent created three skill documents over two hours. the longer it runs the more capable it becomes the team behind it (nous research, $65M funded) also built the hermes model family so the integration between agent and model is tighter than third party wrappers. they have an RL framework called atropos built in for batch trajectory generation so you can fine tune smaller models on the agents own successful task completions 22k github stars, MIT license, 7 major releases in 5 weeks, 187 hackathon submissions. theres even an autonomous novel writing pipeline that completed a 79,000 word manuscript across iterative sessions the self improving loop is real but the learning engine (honcho) is off by default which is the biggest gotcha for new users. once enabled the difference compounds noticeably over sessions the memory wars in the agent space are just getting started submitted by /u/virtualunc
Originally posted by u/virtualunc on r/ArtificialInteligence