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AgentSims: An Open-Source Sandbox for Large Language Model Evaluation

How to evaluate the ability of large language models (LLM) is an open question after ChatGPT-like LLMs prevailing the community. Existing evaluation methods suffer from following shortcomings: (1) constrained evaluation abilities, (2) vulnerable benchmarks, (3) unobjective metrics. We suggest that task-based evaluation, where LLM agents complete tasks in a simulated environment, is a one-for-all solution to solve above problems.

We present AgentSims, an easy-to-use infrastructure for researchers from all disciplines to test the specific capacities they are interested in. Researchers can build their evaluation tasks by adding agents and buildings on an interactive GUI or deploy and test new support mechanisms, i.e. memory system and planning system, by a few lines of codes. The demonstration is on https://agentsims.com/.

Our system has better customization capabilities compared to other similar systems, as it is designed for open source custom task building. See our arXiv paper.

Image text

Dependency

Python: 3.9.x
MySQL: 8.0.31

We recommend deploying on MacOS or Linux for better stability.

API Key

For the security of your API Key, we have not included the parameter file in git. Please create the following file

config/api_key.json

yourself and remember not to push it to git.

The file parameter example is:

{"gpt-4": "xxxx", "gpt-3.5": "xxxx"}

If you want to deploy your own model, see DOCS.

Folder Creation

Before running, please

mkdir snapshot
mkdir logs

In addition, we recommend modifying the count_limit (number of loops per run) and cooldown (cooldown time between runs) in config/app.json to suitable values before running, in order to balance protection of your API key and experiment runtime efficiency.

If you encounter any issues during runtime, please first refer to our DOCS in the wiki. If not resolved, please raise an issue or contact us directly.

To use our system, please follow these steps:

1.MySQL Init

MySQL is used for data storage on the server. After installing the appropriate version of MySQL, start the SQL service and initialize it as follows:

use mysql
ALTER USER 'root'@'localhost' IDENTIFIED WITH mysql_native_password BY '';
flush privileges;

create database `llm_account` default character set utf8mb4 collate utf8mb4_unicode_ci;
create database `llm_game` default character set utf8mb4 collate utf8mb4_unicode_ci;
create database `llm_game0001` default character set utf8mb4 collate utf8mb4_unicode_ci;
create database `llm_game0002` default character set utf8mb4 collate utf8mb4_unicode_ci;

2.Install

pip install tornado
pip install mysql-connector-python
pip install websockets
pip install openai_async

or

pip install -r requirements.txt

3.Design Tasks

You can build tasks at this point. If you just want to try out the system first, you can skip this step. For task building, please refer to the DOCS in the wiki or Section 4.2 Developer Mode in our arXiv paper.

4.Run Server

Start server:

./restart.sh

When you see

--------Server Started--------

in Server Terminal, the server has been started successfully.

5.Run Client

Once the server has started successfully, please launch the client. In the current version, we provide a web-based client. Please open client/index.html in your browser.

Note: Sometimes the web client fails to open correctly. We recommend right-clicking the index.html in your python IDE and select open in browser. If you are familiar with nginx, that is also a great choice.

When you see

somebody linked.

in Server Terminal, the client has been started successfully.

6.Create agents and buildings

You can create agents and buildings at this point. For creation, please refer to the DOCS in the wiki or Section 4.1 User Mode in our arXiv paper.

7.Set Evaluation Target and Measurements

In AgentSims, evaluation are made by QA forms. Every k ticks, system would ask the subject agent an evaluation question. You can customize your subject agent, evaluation question, measurement of response in config/eval.json The example in config/eval.json shows an experiment called 'know pH'. The experiment will ask agent Alan 'Are you acquainted with pH' every 1 tick and if 'Yes' in response, the eval function will return True.

{
  "id": "know pH", # the human-readable name of evaluation, 
  "target_nickname": "Alan", # name of the subject agent
  "query": "Are you acquainted with pH ?", # evaluation qustion 
  "measurement": " 'Yes' in response" # measurement, 
  "interval": 1 # Evaluate every 1 tick
}

8.Run Simulation

You can start tick or mayor with the buttons on the web client. You can also start with:

python -u tick.py
python -u mayor.py

For the difference with tick and mayor, refer to our arXiv paper.

9.Restart

The following reset steps need to be performed each time upon restarting:

  rm -rf snapshot/app.json

  sudo mysql
  drop database llm_account;
  drop database llm_game0001;
  create database `llm_game0001` default character set utf8mb4 collate utf8mb4_unicode_ci;
  create database `llm_account` default character set utf8mb4 collate utf8mb4_unicode_ci;

 ./restart.sh

Authors and Citation

Authors: Jiaju Lin,Haoran Zhao*,Aochi Zhang,Yiting Wu, Huqiuyue Ping,Qin Chen

About Us: PTA Studio is a startup company dedicated to providing a better open source architecture for the NLP community and more interesting AI games for players.

Contact Us: [email protected]

Please cite our paper if you use the code or data in this repository.

@misc{lin2023agentsims,
      title={AgentSims: An Open-Source Sandbox for Large Language Model Evaluation}, 
      author={Jiaju Lin and Haoran Zhao and Aochi Zhang and Yiting Wu and Huqiuyue Ping and Qin Chen},
      year={2023},
      eprint={2308.04026},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}