@inproceedings{zhu-etal-2026-agentcompass,
title = "{A}gent{C}ompass: A Unified Evaluation Infrastructure for Agent Capabilities",
author = "Zhu, Dongsheng and
Jiang, Shufan and
Tian, Wenhui and
Shang, Zixin and
Zhou, Peiheng and
Liang, Tianhao and
Li, Mo and
Li, Qingqiu and
Liu, Shudong and
Li, Zonglin and
Wu, Liwei and
Ding, Zichen and
Yang, Bowen and
Li, Zehao and
Wu, Zhenyu and
XU, Jun and
Wang, Zun and
Zhang, Songyang and
Yuan, Dingbo and
Zerun, Ma and
Chen, Kai",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.25/",
pages = "253--263",
ISBN = "979-8-89176-461-3",
abstract = "As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation platform 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 platform for evaluating LLM-based agents. AgentCompass organizes the evaluation process around three independent components, namely $\textit{Benchmark}$, $\textit{Harness}$, and $\textit{Environment}$, thereby 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."
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<abstract>As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation platform 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 platform for evaluating LLM-based agents. AgentCompass organizes the evaluation process around three independent components, namely Benchmark, Harness, and Environment, thereby 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.</abstract>
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%0 Conference Proceedings
%T AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
%A Zhu, Dongsheng
%A Jiang, Shufan
%A Tian, Wenhui
%A Shang, Zixin
%A Zhou, Peiheng
%A Liang, Tianhao
%A Li, Mo
%A Li, Qingqiu
%A Liu, Shudong
%A Li, Zonglin
%A Wu, Liwei
%A Ding, Zichen
%A Yang, Bowen
%A Li, Zehao
%A Wu, Zhenyu
%A XU, Jun
%A Wang, Zun
%A Zhang, Songyang
%A Yuan, Dingbo
%A Zerun, Ma
%A Chen, Kai
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F zhu-etal-2026-agentcompass
%X As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation platform 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 platform for evaluating LLM-based agents. AgentCompass organizes the evaluation process around three independent components, namely Benchmark, Harness, and Environment, thereby 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.
%U https://aclanthology.org/2026.emnlp-demos.25/
%P 253-263
Markdown (Informal)
[AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities](https://aclanthology.org/2026.emnlp-demos.25/) (Zhu et al., EMNLP 2026)
ACL
- Dongsheng Zhu, Shufan Jiang, Wenhui Tian, Zixin Shang, Peiheng Zhou, Tianhao Liang, Mo Li, Qingqiu Li, Shudong Liu, Zonglin Li, Liwei Wu, Zichen Ding, Bowen Yang, Zehao Li, Zhenyu Wu, Jun XU, Zun Wang, Songyang Zhang, Dingbo Yuan, Ma Zerun, and Kai Chen. 2026. AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 253–263, Budapest, Hungary. Association for Computational Linguistics.