---
title: "GitHub - THUDM/AgentBench: A Comprehensive Benchmark"
url: https://stacklist.com/card/6aa80af3-cff3-41d8-8f2e-6f69fa1dcc2d
source_url: "https://github.com/THUDM/AgentBench"
stack: https://stacklist.com/c/technology/stack/29e01ac8-6202-4abf-ab14-a0613026186b
summary: "AgentBench is a comprehensive benchmark framework for evaluating LLMs as autonomous agents across diverse environments including OS interaction, databases, knowledge graphs, web shopping, and more. The latest version (AgentBench FC) introduces function-calling style prompts integrated with AgentRL, featuring fully-containerized Docker deployment for five task environments."
tags: "agentbench, llm-agents, benchmark, reinforcement-learning, function-calling, docker, evaluation"
key_entities: "AgentBench (technology), AgentRL (technology), VisualAgentBench (technology), Docker Compose (technology), LLM-as-Agent (concept), function-calling (concept), ALFWorld (technology), WebShop (technology), Mind2Web (technology), Redis (technology)"
classification: "framework"
content_hash: "sha256:83e72a6bc7ecf099156fcbc237ac6b859afa663b139b1311cd8558132c1d863b"
acp_version: "0.2"
token_counts_approximate: 2274
visibility: public
agent_accessible: true
status: "final"
---

# GitHub - THUDM/AgentBench: A Comprehensive Benchmark

AgentBench 🌐 Leaderboard (new) | 🐦 Twitter | ✉️ Google Group | 📃 Paper 👋 Join our Slack for Q & A or collaboration on next version of AgentBench ! 🔥[2025.10.10] Introducing AgentBench FC (Function Calling) based on AgentRL The current repository contains the function-calling version of AgentBench, integrated with AgentRL , an end-to-end multitask and mutliturn LLM Agent RL framework. If you wish to use the older version, you can revert to v0.1 and v0.2 . Comparing to the original AgentBench, this version uses a function-calling style prompt, and adds fully-containerized deployment support for the following tasks: alfworld (AF) dbbench (DB) knowledgegraph (KG) os_interaction (OS) webshop (WS) Quick Start We support a quick one-command setup for all the above tasks using Docker Compose. Before starting, please download or build the following Docker images required by the tasks: # dbbench docker pull mysql:8 # os_interaction docker build -t local-os/default -f ./data/os_interaction/res/dockerfiles/default data/os_interaction/res/dockerfiles docker build -t local-os/packages -f ./data/os_interaction/res/dockerfiles/packages data/os_interaction/res/dockerfiles docker build -t local-os/ubuntu -f ./data/os_interaction/res/dockerfiles/ubuntu data/os_interaction/res/dockerfiles To run the KG freebase server, you will also need a copy of the data found here . Download, extract and place the data at ./virtuoso_db/virtuoso.db (or modify extra/docker-compose.yml and set the mount point to your data location). Then, you can bring up the stack with: docker compose -f extra/docker-compose.yml up This command will download or build the necessary Docker images and start the following services in Docker: AgentRL Controller alfworld task worker (x1, increase as needed) dbbench task worker (x1, increase as needed) knowledgegraph task worker (x1, increase as needed) os_interaction task worker (x1, increase as needed) webshop task worker (x1, increase as needed) freebase server (for knowledgegraph task) Redis server (for container allocation) If your machine already has Redis (version 7+) running, you can omit the Redis service from the docker-compose.yml . Warning Please note that the webshop environment requires ~16GB of RAM to start, and the current implementation of alfworld leaks memory and disk space until the task worker is restarted. Make sure your machine has sufficient resources before running. Benchmarking Results We report the results of various models on the test set of AgentBench FC. Please see our Leaderboard for full results. Please contact agentbench_fc@googlegroups.com if you have any questions or would like to contribute your results. 🔥[2024.08.13] Introducing VisualAgentBench VisualAgentBench is designed for evaluating and training visual foundation agents based on large multimodel models (LMMs). We introduce 5 distinct environments spanning Embodied: VAB-OmniGibson, VAB-Minecraft GUI: VAB-Mobile, VAB-WebArena-Lite Visual Design: VAB-CSS to systematically benchmark 17 LMMs (proprietary & open LMMs). We also provide the trajectory dataset for behavior cloning training on open LMMs for you to develop your own visual foundation agents! The following is the introduction to the original AgentBench (v0.2). AgentBench: Evaluating LLMs as Agents agentbench-cover.mp4 AgentBench is the first benchmark designed to evaluate LLM-as-Agent across a diverse spectrum of different environments. It encompasses 8 distinct environments to provide a more comprehensive evaluation of the LLMs' ability to operate as autonomous agents in various scenarios. These environments include 5 freshly created domains, namely Operating System (OS) Database (DB) Knowledge Graph (KG) Digital Card Game (DCG) Lateral Thinking Puzzles (LTP) as well as 3 recompiled from published datasets: House-Holding (HH) ( ALFWorld ) Web Shopping (WS) ( WebShop ) Web Browsing (WB) ( Mind2Web ) Table of Contents Dataset Summary Leaderboard Quick Start Next Steps Citation Dataset Summary We offer two splits for each dataset: Dev and Test. The multi-turn interaction requires an LLMs to generate around 4k and 13k times respectively. Leaderboard Here is the scores on test set (standard) results of AgentBench. While LLMs begin to manifest their proficiency in LLM-as-Agent, gaps between models and the distance towards practical usability are significant. Quick Start This section will guide you on how to quickly use gpt-3.5-turbo-0613 as an agent to launch the dbbench-std and os-std tasks. For the specific framework structure, please refer to Framework Introduction . For more detailed configuration and launch methods, please check Configuration Guide and Program Entrance Guide . Step 1. Prerequisites Clone this repo and install the dependencies. Python version note: AgentBench pins older scientific Python deps (e.g. numpy~=1.23.x ). Using the recommended Python 3.9 (via conda) is the most reliable way to install dependencies. cd AgentBench conda create -n agent-bench python=3.9 conda activate agent-bench pip install -r requirements.txt Ensure that Docker is properly installed. docker ps Build required images for dbbench-std and os-std . docker pull mysql docker pull ubuntu docker build -f data/os_interaction/res/dockerfiles/default data/os_interaction/res/dockerfiles --tag local-os/default docker build -f data/os_interaction/res/dockerfiles/packages data/os_interaction/res/dockerfiles --tag local-os/packages docker build -f data/os_interaction/res/dockerfiles/ubuntu data/os_interaction/res/dockerfiles --tag local-os/ubuntu Step 2. Configure the Agent Fill in your OpenAI API Key at the correct location in configs/agents/openai-chat.yaml . (e.g. gpt-3.5-turbo-0613 ) You can try using python -m src.client.agent_test to check if your agent is configured correctly. By default, gpt-3.5-turbo-0613 will be started. You can replace it with other agents by modifying the parameters: python -m src.client.agent_test --config configs/agents/api_agents.yaml --agent gpt-3.5-turbo-0613 Step 3. Start the task server Starting the task worker involves specific tasks. Manual starting might be cumbersome; hence, we provide an automated script. The assumption for this step is that ports from 5000 to 5015 are available. For Mac OS system, you may want to follow here to free port 5000 to use. python -m src.start_task -a This will launch five task_workers each for dbbench-std and os-std tasks and automatically connect them to the controller on port 5000. After executing this command, please allow approximately 1 minute for the task setup to complete. If the terminal shows ".... 200 OK", you can open another terminal and follow step 4. Lite preset (laptops / limited RAM) If you want to start with minimal concurrency (1 worker per task), use the lite preset: python -m src.start_task -a --config configs/start_task_lite.yaml Step 4. Start the assigner This step is to actually start the tasks. If everything is correctly configured so far, you can now initiate the task tests. python -m src.assigner If you started the task server with the lite preset, you can also run the lite evaluation preset: python -m src.assigner --config configs/assignments/lite.yaml Next Steps If you wish to launch more tasks or use other models, you can refer to the content in Configuration Guide and Program Entrance Guide . For the environment of the remaining five tasks, you will need to download the Docker images we provide. longinyu/agentbench-ltp longinyu/agentbench-webshop longinyu/agentbench-mind2web longinyu/agentbench-card_game longinyu/agentbench-alfworld The resource consumption of a single task_worker for the eight tasks is roughly as follows; consider this when launching: Task Name Start-up Speed Memory Consumption webshop ~3min ~15G mind2web ~5min ~1G db ~20s < 500M alfworld ~10s < 500M card_game ~5s < 500M ltp ~5s < 500M os ~5s < 500M kg ~5s < 500M Deploy the KnowledgeGraph service loacally the KnowledgeGraph task depends on an online service which now is not stable, if you want to deploy the service locally, you can follow steps below: step1. download the database and setup the service freebase-setup . step2. change this line sparql_url: "http://164.107.116.56:3093/sparql" to sparql_url: "<your service api of sparql>" in /configs/tasks/kg.yaml . P.S. you should start your KG service before you start the agent tasks services. Extending AgentBench If you wish to add new tasks to AgentBench, you may refer to Extension Guide . References Avalon task is merged from AvalonBench , which implements a multi-agent framework. Citation @article{liu2023agentbench, title = {AgentBench: Evaluating LLMs as Agents}, author = {Xiao Liu and Hao Yu and Hanchen Zhang and Yifan Xu and Xuanyu Lei and Hanyu Lai and Yu Gu and Hangliang Ding and Kaiwen Men and Kejuan Yang and Shudan Zhang and Xiang Deng and Aohan Zeng and Zhengxiao Du and Chenhui Zhang and Sheng Shen and Tianjun Zhang and Yu Su and Huan Sun and Minlie Huang and Yuxiao Dong and Jie Tang}, year = {2023}, journal = {arXiv preprint arXiv: 2308.03688} }