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## π₯[2024.08.13] Introducing [VisualAgentBench](https://github.com/THUDM/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! ## πIntroducing AgentBench v0.2π You are now browsing AgentBench v0.2. If you wish to use the older version, you can revert to [v0.1](https://github.com/THUDM/AgentBench/tree/v0.1). Based on [v0.1](https://github.com/THUDM/AgentBench/tree/v0.1), we: - Updated the framework architecture for easier use and extension - Adjusted some task settings - Added test results for more models - Released the full data for the Dev and Test sets # AgentBench: Evaluating LLMs as Agents https://github.com/THUDM/AgentBench/assets/129033897/656eed6e-d9d9-4d07-b568-f43f5a451f04 **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](https://github.com/alfworld/alfworld)) - Web Shopping (WS) ([WebShop](https://github.com/princeton-nlp/webshop)) - Web Browsing (WB) ([Mind2Web](https://github.com/OSU-NLP-Group/Mind2Web))  ## Table of Contents - [Dataset Summary](#dataset-summary) - [Leaderboard](#leaderboard) - [Quick Start](#quick-start) - [Next Steps](#next-steps) - [Citation](#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](docs/Introduction_en.md). For more detailed configuration and launch methods, please check [Configuration Guide](docs/Config_en.md) and [Program Entrance Guide](docs/Entrance_en.md). ### Step 1. Prerequisites Clone this repo and install the dependencies. ```bash cd AgentBench conda create -n agent-bench python=3.9 conda activate agent-bench pip install -r requirements.txt ``` Ensure that [Docker](https://www.docker.com/) is properly installed. ```bash docker ps ``` Build required images for `dbbench-std` and `os-std`. ```bash 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: ```bash 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](https://stackoverflow.com/questions/69955686/why-cant-i-run-the-project-on-port-5000) to free port 5000 to use. ```bash 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. ### 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. ```bash python -m src.assigner ``` ## Next Steps If you wish to launch more tasks or use other models, you can refer to the content in [Configuration Guide](docs/Config_en.md) and [Program Entrance Guide](docs/Entrance_en.md). 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.**