Large Language Models (LLMs) are rapidly saturating existing benchmarks, necessitating new open-ended evaluations. We introduce the Factorio Learning Environment (FLE), based on the game of Factorio, that tests agents in long-term planning, program synthesis, and resource optimization.

FLE provides open-ended and exponentially scaling challenges - from basic automation to complex factories processing millions of resource units per second. We provide two settings:

1. Lab-play consisting of 24 structured tasks with fixed resources.
2. Open-play with the unbounded task of building the largest factory from scratch on a procedurally generated map.

We demonstrate across both settings that models still lack strong spatial reasoning. In lab-play, we find that LLMs exhibit promising short-horizon skills, yet are unable to operate effectively in constrained environments, reflecting limitations in error analysis. In open-play, while LLMs discover automation strategies that improve growth (e.g electric-powered drilling), they fail to achieve complex automation (e.g electronic-circuit manufacturing).