""" This example shows how to use direct messaging to implement a simple chat completion agent. The agent receives a message from the main function, sends it to the chat completion model, and returns the response to the main function. """ import asyncio from dataclasses import dataclass from agnext.application import SingleThreadedAgentRuntime from agnext.components import TypeRoutedAgent, message_handler from agnext.components.models import ( ChatCompletionClient, OpenAIChatCompletionClient, SystemMessage, UserMessage, ) from agnext.core import CancellationToken @dataclass class Message: content: str class ChatCompletionAgent(TypeRoutedAgent): def __init__(self, description: str, model_client: ChatCompletionClient) -> None: super().__init__(description) self._system_messages = [SystemMessage("You are a helpful AI assistant.")] self._model_client = model_client @message_handler async def handle_user_message(self, message: Message, cancellation_token: CancellationToken) -> Message: user_message = UserMessage(content=message.content, source="User") response = await self._model_client.create(self._system_messages + [user_message]) assert isinstance(response.content, str) return Message(content=response.content) async def main() -> None: runtime = SingleThreadedAgentRuntime() agent = runtime.register_and_get( "chat_agent", lambda: ChatCompletionAgent("Chat agent", OpenAIChatCompletionClient(model="gpt-3.5-turbo")) ) # Send a message to the agent. message = Message(content="Can you tell me something fun about SF?") result = await runtime.send_message(message, agent) # Process messages until the agent responds. while result.done() is False: await runtime.process_next() # Get the response from the agent. response = await result assert isinstance(response, Message) print(response.content) if __name__ == "__main__": import logging logging.basicConfig(level=logging.WARNING) logging.getLogger("agnext").setLevel(logging.DEBUG) asyncio.run(main())