chatarena 0.1.3

Multi-Agent Language Game Environments for LLMs

🏟 Chat Arena

Multi-Agent Language Game Environments for LLMs

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Chat Arena is a Python library designed to facilitate communication, collaboration and competition between multiple LLMs in a language-driven environment. It provides the following features:

• Language-driven Game Environment: it provides a framework for creating a language-driven environment.
• Infrastructure for Multi-LLM Interaction: it enables rapid definition and creation of LLM-based agents, and seamlessly communication, collaboration, and competition between them.
• Playground & Testbed for C3 Capabilities: it provides a set of environments for evaluating and developing the communication, collaboration, and competition (C3) capabilities of LLMs.

Getting Started

Installation

Requirements:

• Python >= 3. 7
• OpenAI API key (optional, for using GPT-3.5-turbo or GPT-4 as an LLM agent)

Install with pip:

pip install chatarena

or install from this repository:

git clone https://github.com/chatarena/chatarena
cd chatarena
pip install -e .

To use GPT-3 as an LLM agent, set your OpenAI API key:

export OPENAI_API_KEY="your_api_key_here"

Launch the Demo

To launch the demo, run the following command in the root directory of the repository:

gradio app.py

This will launch a demo UI of the Chat Arena in your browser.

Basic Usage

Key Concepts

• Player: a player is an agent that can interact with other players in a game environment. A player can be a human or a large language model (LLM). A player is defined by its name, its backend, and its role.
  • Backend: a backend is a Python class that defines how a player interacts with other players. A backend can be a human, a LLM, or a combination of them. A backend is defined by its name, its type, and its parameters.
• Environment: an environment is a Python class that defines the rules of a game. An environment is defined by its name, its type, and its parameters.
  • Moderator: a moderator is a Python class that defines how the game is played. A moderator is defined by its name, its type, and its parameters.
• Arena: an arena is a Python class that defines the overall game. An arena is defined by its name, its type, and its parameters.

Step 1: Defining Multiple Players with LLM Backend

from chatarena.agent import Player
from chatarena.backends import OpenAIChat

# Describe the environment (which is shared by all players)
environment_description = "It is in a university classroom ..."

# A "Professor" player
player1 = Player(name="Professor", backend=OpenAIChat(),
                 role_desc="You are a professor in ...",
                 global_prompt=environment_description)
# A "Student" player
player2 = Player(name="Student", backend=OpenAIChat(),
                 role_desc="You are a student who is interested in ...",
                 global_prompt=environment_description)
# A "Teaching Assistant" player
player3 = Player(name="Teaching assistant", backend=OpenAIChat(),
                 role_desc="You are a teaching assistant of module ...",
                 global_prompt=environment_description)

Step 2: Create a Language Game Environment

You can also create a language model-driven environment and add it to the Chat Arena:

from chatarena.environments.conversation import Conversation

env = Conversation(player_names=[p.name for p in [player1, player2, player3]])

Step 3: Run the Language Games using Arena

Arena is a utility class to help you run language games.

from chatarena.arena import Arena

arena = Arena(players=[player1, player2, player3],
              environment=env, global_prompt=environment_description)
# Run the game for 10 steps
arena.run(num_steps=10)

# Alternatively, you can run your own main loop
for _ in range(10):
    arena.step()
    # Your code goes here ...

You can easily save your game play history to file

arena.save_history(path=...)

and save your game config to file

arena.save_config(path=...)

Other Utilities

Load Arena from config file (here we use examples/nlp-classroom-3players.json in this repository as an example)

arena = Arena.from_config("examples/nlp-classroom-3players.json")
arena.run(num_steps=10)

Run the game in an interactive CLI interface

arena.launch_cli()

Advanced Usage

ModeratedConversation

We support a more advanced environment called ModeratedConversation that allows you to control the game dynamics using an LLM. The moderator is a special player that controls the game state transition and determines when the game ends. For example, you can define a moderator that track the board status of a board game, and end the game when a player wins. You can try out our Tic-tac-toe and Rock-paper-scissors games to get a sense of how it works:

# Tic-tac-toe game
Arena.from_config("examples/tic-tac-toe.json").launch_cli()

# Rock-paper-scissors game
Arena.from_config("examples/rock-paper-scissors.json").launch_cli()

Defining your Custom Environment

You can define your own environment by extending the Environment class. We provide an example to demonstrate how to define a custom environment. In this example, we develop a language game based on The Chameleon.

Steps to Develop a Custom Class

1. Define the class: Start by defining the class and inherit from a suitable base class (e.g., Environment). In this case, the custom class Chameleon inherits from the Environment base class.

class Chameleon(Environment):
    type_name = "chameleon"

The type_name is required and it is used by the ENV_REGISTRY to identify the class when loading the class from a config file.

Make sure you add the class to ALL_ENVIRONMENTS in environments/__init__.py so that it can be detected.

2. Initialize the class: Define the __init__ method to initialize the class attributes, such as player names, game state, and any other necessary variables.

def __init__(self, player_names: List[str], topic_codes: Dict[str, List[str]] = None, **kwargs):
    super().__init__(player_names=player_names, **kwargs)

    if topic_codes is None:
        topic_codes = DEFAULT_TOPIC_CODES
    self.topic_codes = topic_codes

    # The "state" of the environment is maintained by the message pool
    self.message_pool = MessagePool()
    ...

3. Implement game mechanics: Write methods that define the game mechanics, such as giving clues, voting, and guessing the secret word. In the Chameleon class, these mechanics are implemented in the step method.

def step(self, player_name: str, action: str) -> TimeStep:
    ...

You may create helper methods to perform common operations for game mechanics such as

def _text2vote(self, text) -> str:
    ...


def _is_true_code(self, text) -> bool:
    ...

4. Handle game states and rewards: Implement methods to manage game states, such as resetting the environment, getting observations, checking if the game has reached a terminal state, and giving rewards to players.

def reset(self):
    ...


def get_observation(self, player_name=None) -> List[Message]:
    ...


def is_terminal(self) -> bool:
    ...


def get_rewards(self, ...) -> Dict[str, float]:
    ...

5. Develop your role description prompts for the players: Now that you have defined the game mechanics, you can develop the role description prompts for the players. These prompts are used to guide the LLM-powered players to play the game correctly. You can use the CLI for this purpose. For example, you can run the following code to launch the CLI:

alice = Player(name="Alice", backend=OpenAIChat(), role_desc="Write your prompt here")
bob = Player(name="Bob", backend=OpenAIChat(), role_desc="Write your prompt here")
env = Chameleon(player_names=["Alice", "Bob"], topic_codes=...)
arena = Arena(players=[alice, bob], environment=env).launch_cli()

Once you are happy with you prompts, you can save them to a config file for future use or sharing.

arena.save_config(path=...)

Another option is using the Web UI. You can run the following code to launch the Web UI:

gradio app.py

and select your custom environment from the dropdown menu.

9. For example, in the Chameleon class, the role description prompts are defined in the role_descs attribute.

Test your custom class by simulating a game or by integrating it into an existing framework, such as an OpenAI Gym environment. Ensure that the class works as expected and correctly implements the desired game mechanics.

Contributing

We welcome contributions to improve and extend Chat Arena. Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Commit your changes to the new branch.
4. Create a pull request describing your changes.
5. We will review your pull request and provide feedback or merge your changes.

Please ensure your code follows the existing style and structure.

License

Chat Arena is released under the Apache License.

Contact

If you have any questions or suggestions, feel free to open an issue or submit a pull request. You can also reach out to the maintainer at [e-mail].

Happy chatting!