AG2: Open-Source AgentOS for AI Agents

from autogen import AssistantAgent, UserProxyAgent, LLMConfig

llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")

assistant = AssistantAgent("assistant", llm_config=llm_config)

user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding", "use_docker": False})

user_proxy.run(assistant, message="Summarize the main differences between Python lists and tuples.").process()

import logging
from autogen import ConversableAgent, LLMConfig

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load LLM configuration
llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")

# Define agents
coder = ConversableAgent(
    name="coder",
    system_message="You are a Python developer. Write short Python scripts.",
    llm_config=llm_config,
)

reviewer = ConversableAgent(
    name="reviewer",
    system_message="You are a code reviewer. Analyze provided code and suggest improvements. "
                   "Do not generate code, only suggest improvements.",
    llm_config=llm_config,
)

# Start a conversation
response = reviewer.run(
            recipient=coder,
            message="Write a Python function that computes Fibonacci numbers.",
            max_turns=10
        )

response.process()

logger.info("Final output:\n%s", response.summary)

import logging
from autogen import ConversableAgent, LLMConfig
from autogen.agentchat import run_group_chat
from autogen.agentchat.group.patterns import AutoPattern

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")

# Define lesson planner and reviewer
planner_message = "You are a classroom lesson planner. Given a topic, write a lesson plan for a fourth grade class."
reviewer_message = "You are a classroom lesson reviewer. Compare the plan to the curriculum and suggest up to 3 improvements."

lesson_planner = ConversableAgent(
    name="planner_agent",
    system_message=planner_message,
    description="Creates or revises lesson plans.",
    llm_config=llm_config,
)

lesson_reviewer = ConversableAgent(
    name="reviewer_agent",
    system_message=reviewer_message,
    description="Provides one round of feedback to lesson plans.",
    llm_config=llm_config,
)

teacher_message = "You are a classroom teacher. You decide topics and collaborate with planner and reviewer to finalize lesson plans. When satisfied, output DONE!"

teacher = ConversableAgent(
    name="teacher_agent",
    system_message=teacher_message,
    is_termination_msg=lambda x: "DONE!" in (x.get("content", "") or "").upper(),
    llm_config=llm_config,
)

auto_selection = AutoPattern(
    agents=[teacher, lesson_planner, lesson_reviewer],
    initial_agent=lesson_planner,
    group_manager_args={"name": "group_manager", "llm_config": llm_config},
)

response = run_group_chat(
    pattern=auto_selection,
    messages="Let's introduce our kids to the solar system.",
    max_rounds=20,
)

response.process()

logger.info("Final output:\n%s", response.summary)

import logging
from autogen import ConversableAgent, LLMConfig, UserProxyAgent
from autogen.agentchat import run_group_chat
from autogen.agentchat.group.patterns import AutoPattern

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")

# Same agents as before, but now the human validator will pass to the planner who will check for "APPROVED" and terminate
planner_message = "You are a classroom lesson planner. Given a topic, write a lesson plan for a fourth grade class."
reviewer_message = "You are a classroom lesson reviewer. Compare the plan to the curriculum and suggest up to 3 improvements."
teacher_message = "You are an experienced classroom teacher. You don't prepare plans, you provide simple guidance to the planner to prepare a lesson plan on the key topic."

lesson_planner = ConversableAgent(
    name="planner_agent",
    system_message=planner_message,
    description="Creates or revises lesson plans before having them reviewed.",
    is_termination_msg=lambda x: "APPROVED" in (x.get("content", "") or "").upper(),
    human_input_mode="NEVER",
    llm_config=llm_config,
)

lesson_reviewer = ConversableAgent(
    name="reviewer_agent",
    system_message=reviewer_message,
    description="Provides one round of feedback to lesson plans back to the lesson planner before requiring the human validator.",
    llm_config=llm_config,
)

teacher = ConversableAgent(
    name="teacher_agent",
    system_message=teacher_message,
    description="Provides guidance on the topic and content, if required.",
    llm_config=llm_config,
)

human_validator = UserProxyAgent(
    name="human_validator",
    system_message="You are a human educator who provides final approval for lesson plans.",
    description="Evaluates the proposed lesson plan and either approves it or requests revisions, before returning to the planner.",
)

auto_selection = AutoPattern(
    agents=[teacher, lesson_planner, lesson_reviewer],
    initial_agent=teacher,
    user_agent=human_validator,
    group_manager_args={"name": "group_manager", "llm_config": llm_config},
)

response = run_group_chat(
    pattern=auto_selection,
    messages="Let's introduce our kids to the solar system.",
    max_rounds=20,
)

response.process()

logger.info("Final output:\n%s", response.summary)

import logging
from datetime import datetime
from typing import Annotated
from autogen import ConversableAgent, register_function, LLMConfig

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")

# Tool: returns weekday for a given date
def get_weekday(date_string: Annotated[str, "Format: YYYY-MM-DD"]) -> str:
    date = datetime.strptime(date_string, "%Y-%m-%d")
    return date.strftime("%A")

date_agent = ConversableAgent(
    name="date_agent",
    system_message="You find the day of the week for a given date.",
    llm_config=llm_config,
)

executor_agent = ConversableAgent(
    name="executor_agent",
    human_input_mode="NEVER",
    llm_config=llm_config,
)

# Register tool
register_function(
    get_weekday,
    caller=date_agent,
    executor=executor_agent,
    description="Get the day of the week for a given date",
)

# Use tool in chat
chat_result = executor_agent.initiate_chat(
    recipient=date_agent,
    message="I was born on 1995-03-25, what day was it?",
    max_turns=2,
)

logger.info("Final output:\n%s", chat_result.chat_history[-1]["content"])