JackHopkins/factorio-learning-environment

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1156 stars · 95 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An open-source framework for developing and evaluating LLM agents in the game of Factorio, providing an open-ended, non-saturating benchmark environment. Agents interact via Python code synthesis through a REPL pattern, with OpenAI Gym compatibility, headless scaling, and MCP support.

Use cases

• evaluate LLM agents on long-horizon planning tasks
• benchmark frontier models in an open-ended game environment
• run multi-turn interactive agent experiments with a gym interface
• research multimodal agents with pixel observations
• test agent recovery and adaptation in dynamic environments
• connect Claude Code or other agents to Factorio via MCP

When to choose

• you need a challenging, non-saturating eval for frontier LLM agents
• you want a gym-compatible interactive environment for agent research
• you need scalable headless game-based evaluation with Docker

When to avoid

• you need a simple static benchmark rather than an interactive game environment
• you cannot run Docker or manage Factorio server clusters
• your focus is not agent evaluation or long-horizon reasoning research

Facets

framework · maturity active

agent-framework machine-learning benchmarking simulation sdk cli mcp artificial-intelligence large-language-models reinforcement-learning developer-tools python windows cli llm-evaluation factorio agent-benchmark open-ended-eval gym-environment code-synthesis research ai-agents game-development docker linux macos

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markdown · JSON · MCP: product_card(name="JackHopkins/factorio-learning-environment")