OmniAgent

Why we included this project

Most agent frameworks ship with a fixed skill set and a static prompt. OmniAgent treats those as live: it generates new skills from repeated action patterns, repairs ones that fail, rebuilds context from interaction feedback, and updates its reasoning model through an online reinforcement loop. A dual-layer reflection system intercepts mistakes mid-task and turns them into corrective context, while the Hyper-Harness execution scaffold adds a safety layer around complex actions. The result is a Python agent that keeps improving as you use it, without you having to rewrite its prompts. That self-improvement loop is the reason we included it.