Hi HN, we are the team behind LoongFlow. We built this framework to use evolve thinking solve any tasks.

LoongFlow brings Evolutionary Algorithms (EA) into the agent workflow. It evolves taskss over generations (via selection, crossover, and mutation) to maximize performance.

Key features:

General-Evolve: Good at Algorithm task.

ML-Evolve: Specialized for machine learning tasks.

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!

Interesting direction. Using evolutionary pressure to improve agent reasoning feels promising, especially beyond static benchmarks. One trade-off I’m curious about is evaluation drift—when tasks co-evolve, how do you ensure you’re not just optimizing for the framework itself rather than real-world generalization?