We built this framework to solve the problem of Agent brittleness—where standard ReAct agents often get stuck or fail when prompts aren't perfectly hand-tuned.
Instead of manual prompt engineering, LoongFlow brings Evolutionary Algorithms (EA) into the agent workflow. It treats prompts and logic as "populations" that evolve over generations (via selection, crossover, and mutation) to maximize performance.
Key features:
General-Evolve: Auto-optimizes prompts and code logic.
ML-Evolve: Specialized for machine learning tasks (AutoML agent).
We built this framework to solve the problem of Agent brittleness—where standard ReAct agents often get stuck or fail when prompts aren't perfectly hand-tuned.
Instead of manual prompt engineering, LoongFlow brings Evolutionary Algorithms (EA) into the agent workflow. It treats prompts and logic as "populations" that evolve over generations (via selection, crossover, and mutation) to maximize performance.
Key features:
General-Evolve: Auto-optimizes prompts and code logic.
ML-Evolve: Specialized for machine learning tasks (AutoML agent).
Paper: We recently released our paper on arXiv: https://arxiv.org/abs/2512.24077
The repo is fully open source (Python). We'd love to hear your feedback on the architecture and the idea of "breeding" better agents!