Terminal-Bench provides a suite of benchmarks for evaluating the capabilities of AI agents in terminal environments. It offers standardized tasks, leaderboards, and performance metrics to help researchers and developers assess and compare agent performance. The platform is open source and designed for the AI research community.
In the LLM eval & observability space, Terminal-Bench takes a focused approach. It focuses on measuring and comparing the performance of AI agents in terminal-based tasks. Terminal-Bench is an open-source project aimed at AI researchers and agent developers. The project is open source (Apache-2.0). It ships for the web and the command line.
Behind Terminal-Bench is Nicholas Carlini, and it first shipped in 2025. Development happens publicly on GitHub with 2.7k stars and 496 commits in the last 90 days. Key capabilities include agent benchmarking, leaderboard, and task examples.
Summary written by a language model from the project’s public pages.
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