AG2

pip install agentops pyautogen

import agentops
import autogen

# Initialize AgentOps
agentops.init(<INSERT YOUR API KEY HERE>)

# Configure your AG2 agents
config_list = [
    {
        "model": "gpt-4",
        "api_key": "<YOUR_OPENAI_API_KEY>"
    }
]

llm_config = {
    "config_list": config_list,
    "timeout": 60,
}

# Create AG2 agents
assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config=llm_config,
    system_message="You are a helpful AI assistant."
)

user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="TERMINATE",
    max_consecutive_auto_reply=10,
    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
    code_execution_config={"last_n_messages": 3, "work_dir": "coding"},
)

# Initiate a conversation
user_proxy.initiate_chat(
    assistant,
    message="How can I implement a basic web scraper in Python?"
)

# All agent interactions are automatically tracked by AgentOps

import agentops
import autogen

# Initialize AgentOps
agentops.init(<INSERT YOUR API KEY HERE>)

# Configure LLM
config_list = [
    {
        "model": "gpt-4",
        "api_key": "<YOUR_OPENAI_API_KEY>"
    }
]

llm_config = {
    "config_list": config_list,
    "timeout": A 60,
}

# Create a team of agents
researcher = autogen.AssistantAgent(
    name="researcher",
    llm_config=llm_config,
    system_message="You are a researcher who specializes in finding accurate information."
)

coder = autogen.AssistantAgent(
    name="coder",
    llm_config=llm_config,
    system_message="You are an expert programmer who writes clean, efficient code."
)

critic = autogen.AssistantAgent(
    name="critic",
    llm_config=llm_config,
    system_message="You review solutions and provide constructive feedback."
)

user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="TERMINATE",
    max_consecutive_auto_reply=10,
    is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
    code_execution_config={"last_n_messages": 3, "work_dir": "coding"},
)

# Create a group chat
groupchat = autogen.GroupChat(
    agents=[user_proxy, researcher, coder, critic],
    messages=[],
    max_round=12
)

manager = autogen.GroupChatManager(
    groupchat=groupchat,
    llm_config=llm_config
)

# Initiate the group chat
user_proxy.initiate_chat(
    manager,
    message="Create a Python program to analyze sentiment from Twitter data."
)

# All agent interactions across the group chat are automatically tracked by AgentOps

AGENTOPS_API_KEY=<YOUR API KEY>
OPENAI_API_KEY=<YOUR OPENAI API KEY>