Exploring 2 Multi-Agent LLM Libraries: Camel & Langroid

pip install git+https://github.com/camel-ai/camel.git@v0.1.0

# https://github.com/camel-ai/camel/tree/7f0315f91a6891d6d7bcf78be0a7815f6a49f96c/examples
import camel

from colorama import Fore

# Note, in the example ai_society/role_playing/ in the link above,
# this was: 
# `from camel.societies import RolePlaying`.
from camel.agents import RolePlaying
from camel.utils import print_text_animated

import openai

openai.api_key = "" INSERT YOUR OPENAI API KEY!

task_prompt = "Develop a trading bot for the stock market"
role_play_session = RolePlaying(
    assistant_role_name="Python Programmer",
    user_role_name="Stock Trader",
    task_prompt=task_prompt,
    with_task_specify=True,
)

print(
    Fore.GREEN +
    f"AI Assistant sys message:\n{role_play_session.assistant_sys_msg}\n")
print(Fore.BLUE +
      f"AI User sys message:\n{role_play_session.user_sys_msg}\n")

print(Fore.YELLOW + f"Original task prompt:\n{task_prompt}\n")
print(
    Fore.CYAN +
    f"Specified task prompt:\n{role_play_session.specified_task_prompt}\n")
print(Fore.RED + f"Final task prompt:\n{role_play_session.task_prompt}\n")

chat_turn_limit, n = 10, 0  # Up the chat_turn_limit for longer conversations between the agents. Note: this gets expensive!
input_assistant_msg, _ = role_play_session.init_chat()
while n < chat_turn_limit:
    n += 1
    assistant_response, user_response = role_play_session.step(
        input_assistant_msg)

    if assistant_response[1]:  # Tuple of length 3. The example in the repo is outdated. No worries.
        print(Fore.GREEN +
              ("AI Assistant terminated. Reason: "
                f"{assistant_response.info['termination_reasons']}."))
        break
    if user_response[1]:  # Tuple of length 3. Same deal as assistant_response.
        print(Fore.GREEN +
              ("AI User terminated. "
                f"Reason: {user_response.info['termination_reasons']}."))
        break

    print_text_animated(Fore.BLUE +
                        f"AI User:\n\n{user_response[0].content}\n")  # A small edit here from the original example to get it working.
    print_text_animated(Fore.GREEN + "AI Assistant:\n\n"
                        f"{assistant_response[0].content}\n")  # Same here.

    if "CAMEL_TASK_DONE" in user_response[0].content:  # And here.
        break

    input_assistant_msg = assistant_response[0]

pip install langroid

# Langroid checks for a OPENAI_API_KEY in your .env folder. 

openai_api_key = ""  # INSERT YOUR OPENAI_API_KEY HERE!

with open(".env", "w") as f:
  f.write("OPENAI_API_KEY="+openai_api_key)

import langroid

from langroid.utils.constants import NO_ANSWER
from langroid.agent.chat_agent import ChatAgent, ChatAgentConfig
from langroid.agent.task import Task
from langroid.language_models.openai_gpt import OpenAIChatModel, OpenAIGPTConfig

config = ChatAgentConfig(
    llm = OpenAIGPTConfig(
        chat_model=OpenAIChatModel.GPT4,
    ),
    vecdb = None,
)

repeater_agent = ChatAgent(config)
repeater_task = Task(
    repeater_agent,
    name = "Repeater",
    system_message="""
    Your job is to repeat whatever number you receive.
    """,
    llm_delegate=True, # LLM takes charge of task
    single_round=False, 
)

even_agent = ChatAgent(config)
even_task = Task(
    even_agent,
    name = "EvenHandler",
    system_message=f"""
    You will be given a number. 
    If it is even, divide by 2 and say the result, nothing else.
    If it is odd, say {NO_ANSWER}
    """,
    single_round=True,  # task done after 1 step() with valid response
)

odd_agent = ChatAgent(config)
odd_task = Task(
    odd_agent,
    name = "OddHandler",
    system_message=f"""
    You will be given a number n. 
    If it is odd, return (n*3+1), say nothing else. 
    If it is even, say {NO_ANSWER}
    """,
    single_round=True,  # task done after 1 step() with valid response
)

repeater_task.add_sub_task([even_task, odd_task])
repeater_task.run("3")