Agents#

Warning

AgentChat is Work in Progress. APIs may change in future releases.

AutoGen AgentChat provides a set of preset Agents, each with variations in how an agent might respond to messages. All agents share the following attributes and methods:

See autogen_agentchat.messages for more information on AgentChat message types.

Assistant Agent#

AssistantAgent is a built-in agent that uses a language model with ability to use tools.

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.messages import TextMessage
from autogen_core.base import CancellationToken
from autogen_ext.models import OpenAIChatCompletionClient


# Define a tool that searches the web for information.
async def web_search(query: str) -> str:
    """Find information on the web"""
    return "AutoGen is a programming framework for building multi-agent applications."


# Create an agent that uses the OpenAI GPT-4o model.
model_client = OpenAIChatCompletionClient(
    model="gpt-4o",
    # api_key="YOUR_API_KEY",
)
agent = AssistantAgent(
    name="assistant",
    model_client=model_client,
    tools=[web_search],
    system_message="Use tools to solve tasks.",
)

We can call the on_messages() method to get the agent to respond to a message.

async def assistant_run() -> None:
    response = await agent.on_messages(
        [TextMessage(content="Find information on AutoGen", source="user")],
        cancellation_token=CancellationToken(),
    )
    print(response.inner_messages)
    print(response.chat_message)


# Use asyncio.run(assistant_run()) when running in a script.
await assistant_run()

[ToolCallMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=61, completion_tokens=15), content=[FunctionCall(id='call_hqVC7UJUPhKaiJwgVKkg66ak', arguments='{"query":"AutoGen"}', name='web_search')]), ToolCallResultMessage(source='assistant', models_usage=None, content=[FunctionExecutionResult(content='AutoGen is a programming framework for building multi-agent applications.', call_id='call_hqVC7UJUPhKaiJwgVKkg66ak')])]
source='assistant' models_usage=RequestUsage(prompt_tokens=92, completion_tokens=14) content='AutoGen is a programming framework designed for building multi-agent applications.'

The call to the on_messages() method returns a Response that contains the agent’s final response in the chat_message attribute, as well as a list of inner messages in the inner_messages attribute, which stores the agent’s “thought process” that led to the final response.

Stream Messages#

We can also stream each message as it is generated by the agent by using the on_messages_stream() method, and use Console to print the messages as they appear to the console.

from autogen_agentchat.task import Console


async def assistant_run_stream() -> None:
    # Option 1: read each message from the stream.
    # async for message in agent.on_messages_stream(
    #     [TextMessage(content="Find information on AutoGen", source="user")],
    #     cancellation_token=CancellationToken(),
    # ):
    #     print(message)

    # Option 2: use Console to print all messages as they appear.
    await Console(
        agent.on_messages_stream(
            [TextMessage(content="Find information on AutoGen", source="user")],
            cancellation_token=CancellationToken(),
        )
    )


# Use asyncio.run(assistant_run_stream()) when running in a script.
await assistant_run_stream()

---------- assistant ----------
[FunctionCall(id='call_fSp5iTGVm2FKw5NIvfECSqNd', arguments='{"query":"AutoGen information"}', name='web_search')]
[Prompt tokens: 61, Completion tokens: 16]
---------- assistant ----------
[FunctionExecutionResult(content='AutoGen is a programming framework for building multi-agent applications.', call_id='call_fSp5iTGVm2FKw5NIvfECSqNd')]
---------- assistant ----------
AutoGen is a programming framework designed for building multi-agent applications. If you need more detailed information or specific aspects about AutoGen, feel free to ask!
[Prompt tokens: 93, Completion tokens: 32]
---------- Summary ----------
Number of inner messages: 2
Total prompt tokens: 154
Total completion tokens: 48
Duration: 4.30 seconds

The on_messages_stream() method returns an asynchronous generator that yields each inner message generated by the agent, and the last item is the final response message in the chat_message attribute.

From the messages, you can see the assistant agent used the web_search tool to search for information and responded using the search results.

Understanding Tool Calling#

Large Language Models (LLMs) are typically limited to generating text or code responses. However, many complex tasks benefit from the ability to use external tools that perform specific actions, such as fetching data from APIs or databases.

To address this limitation, modern LLMs can now accept a list of available tool schemas (descriptions of tools and their arguments) and generate a tool call message. This capability is known as Tool Calling or Function Calling and is becoming a popular pattern in building intelligent agent-based applications.

For more information on tool calling, refer to the documentation from OpenAI and Anthropic.

Other Preset Agents#

The following preset agents are available:

Next Step#

Now we have discussed how to use the AssistantAgent, we can move on to the next section to learn how to use the teams feature of AgentChat.

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