{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/agentbench-evaluating-llms-as-agents","title":"AgentBench: Evaluating LLMs as Agents","arxiv_id":"2308.03688","date":"2023-08-07","proceeding":null,"authors":["Xiao Liu","Hao Yu","Hanchen Zhang","Yifan Xu","Xuanyu Lei","Hanyu Lai","Yu Gu","Hangliang Ding","Kaiwen Men","Kejuan Yang","Shudan Zhang","Xiang Deng","Aohan Zeng","Zhengxiao Du","Chenhui Zhang","Sheng Shen","Tianjun Zhang","Yu Su","Huan Sun","Minlie Huang","Yuxiao Dong","Jie Tang"],"abstract":"Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has been an urgent need to evaluate LLMs as agents on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting. Our extensive test over 27 API-based and open-sourced (OSS) LLMs shows that, while top commercial LLMs present a strong ability of acting as agents in complex environments, there is a significant disparity in performance between them and OSS competitors. We identify the typical reasons of failures in environments and LLMs, showing that poor long-term reasoning, decision-making, and instruction following abilities are the main obstacles for developing usable LLM agents. Training on code and high quality multi-turn alignment data could improve agent performance. Datasets, environments, and an integrated evaluation package for AgentBench are released at \\url{https://github.com/THUDM/AgentBench}.","url_abs":"https://arxiv.org/abs/2308.03688v2","url_pdf":"https://arxiv.org/pdf/2308.03688v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"agentbench-evaluating-llms-as-agents","repo_url":"https://github.com/thudm/agentbench","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"instruction-following","task_name":"Instruction Following"}],"methods":[],"datasets_introduced":[{"slug":"agentbench","name":"AgentBench","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2308.03688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03688"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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