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How to find benchmarks for evaluating LLM agents on games with Awesome AI for Games

Locate benchmark papers such as Orak and VideoGameBench to compare agent evaluations.

  • Research
  • Beginner
  • 15 minutes
  • From the README
  • Any agent

Who it is forA QA lead or AI evaluation engineer who needs a benchmark to measure how well an LLM agent plays real video games.

The problemChoosing a benchmark means comparing many papers that each define their own games, tasks, and metrics.

The outcomeA shortlist of game-agent benchmarks with their source links, ready for a side-by-side review.

What you need first

  • A web browser to open the README and the linked survey map and literature search pages
  • Basic familiarity with the game topic you want to research

Steps

  1. Open the README and go to the Play and Act section.
  2. Look for entries whose titles name a benchmark, such as Orak, VideoGameBench, FlashAdventure, and EMemBench.
  3. Open each benchmark's paper link and note the games it covers and how it evaluates agents.
  4. Use the Literature search link to find newer benchmark papers that are not yet in the list.

Prompt for your agent

Using the Eurekaleo/awesome-ai-for-games README, list every benchmark paper in the Play and Act section that evaluates LLM or VLM agents on video games. For each one, give the title and paper link, and state whether the README links code for it.

Caveat. The README describes these entries only by title and link, so the games and metrics in each benchmark must be confirmed from the linked paper.

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