As Large Language Models (LLMs) evolve into autonomous agents, the need for a unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To address this, we introduce AgentCompass, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based agents.
AgentCompass organizes the evaluation process around three independent components: Benchmark, Harness, and Environment, enabling flexible configurations without requiring the reimplementation of complex execution logic. Furthermore, it features a fault-tolerant asynchronous runtime and comprehensive trajectory analysis tools to transparently diagnose nuanced failure modes like reward-hacking. Natively supporting over 20 benchmarks across five capability dimensions, AgentCompass provides the community with a scalable and reproducible infrastructure for advancing agent research.
Blogger's Review: The launch of AgentCompass is a strong response to the fragmented evaluation methods in agent research. It not only addresses the issues of disjointed evaluation processes but also offers flexible configuration options, significantly enhancing reproducibility and efficiency in research.