FLE is an open-source framework for developing and evaluating LLM agents in Factorio. It provides a controlled environment where AI models can attempt complex automation, resource management, and optimisation tasks in a grounded world with meaningful constraints.
A critical advantage of Factorio as a benchmark is its unbounded nature. Unlike many evals that are quickly saturated by newer models, Factorio's geometric complexity scaling means it won't be "solved" in the next 6 months (or possibly even years). This allows us to meaningfully compare models by the order-of-magnitude of resources they can produce - creating a benchmark with longevity.
The project began 18 months ago after years of playing Factorio, recognising its potential as an AI research testbed. A few months ago, our team (myself, Akbir, and Mart) came together to create a benchmark that tests agent capabilities in spatial reasoning and long-term planning.
Two technical innovations drove this project forward: First, we discovered that piping Lua into the Factorio console over TCP enables running (almost) arbitrary code without directly modding the game. Second, we developed a first-class Python API that wraps these Lua programs to provide a clean, type-hinted interface for AI agents to interact with Factorio through familiar programming paradigms.
Agents interact with FLE through a REPL pattern: 1. They observe the world (seeing the output of their last action) 2. Generate Python code to perform their next action 3. Receive detailed feedback (including exceptions and stdout)
We provide two main evaluation settings: - Lab-play: 24 structured tasks with fixed resources - Open-play: An unbounded task of building the largest possible factory on a procedurally generated map
We found that while LLMs show promising short-horizon skills, they struggle with spatial reasoning in constrained environments. They can discover basic automation strategies (like electric-powered drilling) but fail to achieve more complex automation (like electronic circuit manufacturing). Claude Sonnet 3.5 is currently the best model (by a significant margin).
The code is available at https://github.com/JackHopkins/factorio-learning-environment.
You'll need: - Factorio (version 1.1.110) - Docker - Python 3.10+
The README contains detailed installation instructions and examples of how to run evaluations with different LLM agents.
We would love to hear your thoughts and see what others can do with this framework!
It makes sense why LLMs are bad with spatial reasoning. Not a lot of training data for it. I wonder what additional reasoning abilities will emerge when spatial reasoning is solved.
Isnt it literally infinite via even the simplest simulator?
You could generate an unlimited training set just by implementing tik tac toe on an unbound grid, for example, in like 10 lines of code.
In this particular case with Factorio, I suspect generating the synthetic data would be easier, since the rules of the environment are relatively simple and well defined, with quantifiable outcomes.