AG2 (AutoGen) Integration Guide

pip install ag2[openai]
# Or use the alias if you prefer
pip install autogen[openai]

import os
from autogen import LLMConfig

# Option 1: Using environment variables
os.environ["OPENAI_API_KEY"] = "your-apipie-api-key"
os.environ["OPENAI_API_BASE"] = "https://apipie.ai/v1"

# Option 2: Using configuration file (recommended)
config_list = [
    {
        "model": "gpt-4o-mini",  # You can use any model available on APIpie
        "api_key": "your-apipie-api-key",
        "base_url": "https://apipie.ai/v1",
    }
]

# Create an LLMConfig object
llm_config = LLMConfig.from_config_dict(config_list[0])

import json
import os
from autogen import LLMConfig

# Create a configuration file
config = [
    {
        "model": "gpt-4o-mini",
        "api_key": "your-apipie-api-key",
        "base_url": "https://apipie.ai/v1",
    }
]

# Save to a file (make sure to add it to .gitignore)
with open("oai_config.json", "w") as f:
    json.dump(config, f)

# Load from file
llm_config = LLMConfig.from_json(path="oai_config.json")

import os
from autogen import AssistantAgent, UserProxyAgent, LLMConfig

# Configure APIpie
os.environ["OPENAI_API_KEY"] = "your-apipie-api-key"
os.environ["OPENAI_API_BASE"] = "https://apipie.ai/v1"

# Or load from configuration
llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")

# Create an assistant that uses APIpie
with llm_config:
    assistant = AssistantAgent(
        name="assistant",
        system_message="You are a helpful AI assistant specializing in data analysis."
    )

# Create a user proxy agent that can execute code
user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={"work_dir": "coding", "use_docker": False}
)

# Start a conversation
user_proxy.initiate_chat(
    assistant,
    message="Analyze the following data and create a visualization: [1, 5, 3, 8, 2, 7, 4]"
)

import os
from autogen import AssistantAgent, UserProxyAgent, LLMConfig, GroupChat, GroupChatManager

# Configure APIpie
llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")

with llm_config:
    # Create specialized agents
    planner = AssistantAgent(
        name="planner",
        system_message="You break down complex tasks into manageable steps. Be concise and clear.",
    )

    researcher = AssistantAgent(
        name="researcher",
        system_message="You find and provide information needed to complete tasks. Focus on reliable sources.",
    )

    coder = AssistantAgent(
        name="coder",
        system_message="You write code to solve problems. Explain your code clearly.",
    )

    critic = AssistantAgent(
        name="critic",
        system_message="You review solutions and suggest improvements. Be constructive.",
    )

# User proxy for human input and code execution
user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={"work_dir": "coding", "use_docker": False},
    is_termination_msg=lambda msg: "TASK COMPLETE" in msg.get("content", ""),
)

# Create a group chat
groupchat = GroupChat(
    agents=[user_proxy, planner, researcher, coder, critic],
    messages=[],
    max_round=15,
)

# Create a manager to orchestrate the conversation
manager = GroupChatManager(
    groupchat=groupchat,
    llm_config=llm_config,
)

# Start the conversation
user_proxy.initiate_chat(
    manager,
    message="Create a Python script that pulls weather data for New York City and displays it as a chart."
)

import os
from typing import List, Dict, Any
from autogen import register_function, AssistantAgent, UserProxyAgent, LLMConfig

# Configure APIpie
llm_config = LLMConfig(api_type="openai", model="gpt-4o-mini")

# Define a custom tool
def search_products(query: str, max_results: int = 5) -> List[Dict[str, Any]]:
    """
    Search for products matching the query.

    Args:
        query: The search query string.
        max_results: Maximum number of results to return.

    Returns:
        A list of product dictionaries with name, price, and rating.
    """
    # In a real application, this would call your API
    # This is just a mock example
    mock_data = [
        {"name": "Smartphone X", "price": 899.99, "rating": 4.5},
        {"name": "Wireless Earbuds", "price": 149.99, "rating": 4.3},
        {"name": "Laptop Pro", "price": 1299.99, "rating": 4.7},
        {"name": "Smart Watch", "price": 249.99, "rating": 4.2},
        {"name": "Bluetooth Speaker", "price": 79.99, "rating": 4.4},
    ]

    filtered = [p for p in mock_data if query.lower() in p["name"].lower()]
    return filtered[:max_results]

# Create an assistant with tool use capability
with llm_config:
    assistant = AssistantAgent(
        name="shopping_assistant",
        system_message="You help users find products. Use the search_products tool when needed.",
    )

# Create a user proxy
user_proxy = UserProxyAgent(
    name="user",
    human_input_mode="TERMINATE",
    code_execution_config={"work_dir": "shopping", "use_docker": False},
)

# Register the custom tool
register_function(
    search_products,
    caller=assistant,
    executor=user_proxy,
    description="Search for products matching a query string",
)

# Start the conversation
user_proxy.initiate_chat(
    assistant,
    message="I'm looking for wireless audio devices. Can you help me find some options?"
)