CAMEL - Build Multi-Agent AI Systems

You can also open this on Google Colab In this cookbook, I want to show how Multi-Step environments work in CAMEL. Our RL modules were built to mimic OpenAI Gym, so if you’re familiar with Gym’s interface, you’ll feel right at home. We will use the Tic-Tac-Toe environment as an example to show the lifecycle of an environment. The Tic-Tac-Toe environment can be used to evaluate agents, generate synthetic data for distillation, or train an agent to play the game. First, we need to initialize our environment and set it up. Then we can call reset to get our initial observation. Let’s install the CAMEL package with all its dependencies:

%pip install camel-ai[all]==0.2.46

import asyncio
from camel.environments.models import Action
from camel.environments.tic_tac_toe import TicTacToeEnv, Opponent

# we can choose the playstyle of our opponent to be either 'random' or 'optimal' (computed using minimax)
opp = Opponent(play_style="random")
env = TicTacToeEnv(opponent=opp)
await env.setup()

obs = await env.reset()
print("Initial Observation:\n")
print(obs.question)

We will use GPT-4o-mini, so let’s enter our API key.

import os
from getpass import getpass

openai_api_key = getpass('Enter your API key: ')
os.environ["OPENAI_API_KEY"] = openai_api_key

Alternatively, if running on Colab, you could save your API keys and tokens as Colab Secrets, and use them across notebooks. To do so, comment out the above manual API key prompt code block(s), and uncomment the following codeblock. ⚠️ Don’t forget granting access to the API key you would be using to the current notebook.

# import os
# from google.colab import userdata

# os.environ["OPENAI_API_KEY"] = userdata.get("OPENAI_API_KEY")

Let’s next define the model-backend and the agent. You can also add a system prompt or equip your agent with tools, but for the sake of simplicity we just create a bare agent with GPT-4o-mini.

from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType
from camel.configs import ChatGPTConfig
from camel.agents import ChatAgent

model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
    model_config_dict=ChatGPTConfig().as_dict(),
)

agent = ChatAgent(model=model)

Next, we will simulate one episode.

while not env.is_done():

    llm_response = agent.step(obs.question).msgs[0].content
    agent.reset() # clear context window

    action = Action(llm_response=llm_response)
    result = await env.step(action)

    next_obs, reward, done, info = result

    obs = next_obs

    print("\nAgent Move:", action.llm_response)
    print("Observation:")
    print(next_obs.question)
    print("Reward:", reward)
    print("Done:", done)
    print("Info:", info)

Eval

We can also use this to eval a model on tic tac toe. Let’s run it for 10 episodes and see how often we win, draw and lose.

wins = 0
losses = 0
draws = 0

for episode in range(10):
    obs = await env.reset()  # Start fresh
    done = False

    while not done:
        llm_response = agent.step(obs.question).msgs[0].content
        agent.reset()  # Nuke the context

        action = Action(llm_response=llm_response)
        next_obs, reward, done, info = await env.step(action)

        obs = next_obs

    # Tally result based on final reward
    if reward == 1:
        wins += 1
    elif reward == 0.5:
        draws += 1
    else:
        losses += 1

# Final report
print("\n=== Summary after 10 Episodes ===")
print(f"Wins:   {wins}")
print(f"Draws:  {draws}")
print(f"Losses: {losses}")

As you can see, GPT-4o-mini is quite bad! Finally, we close the environment.

await env.close()

​Eval

Eval