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@Tony7466 Tony7466 released this 12 Apr 14:26
python-v0.5.1
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What's New

AgentChat Message Types (Type Hint Changes)

Important

TL;DR: If you are not using custom agents or custom termination conditions, you don't need to change anything.
Otherwise, update AgentEvent to BaseAgentEvent and ChatMessage to BaseChatMessage in your type hints.

This is a breaking change on type hinting only, not on usage.

We updated the message types in AgentChat in this new release.
The purpose of this change is to support custom message types defined by applications.

Previously, message types are fixed and we use the union types ChatMessage and AgentEvent to refer to all the concrete built-in message types.

Now, in the main branch, the message types are organized into hierarchy: existing built-in concrete message types are subclassing either BaseChatMessage and BaseAgentEvent, depending it was part of the ChatMessage or AgentEvent union. We refactored all message handlers on_messages, on_messages_stream, run, run_stream and TerminationCondition to use the base classes in their type hints.

If you are subclassing BaseChatAgent to create your custom agents, or subclassing TerminationCondition to create your custom termination conditions, then you need to rebase the method signatures to use BaseChatMessage and BaseAgentEvent.

If you are using the union types in your existing data structures for serialization and deserialization, then you can keep using those union types to ensure the messages are being handled as concrete types. However, this will not work with custom message types.

Otherwise, your code should just work, as the refactor only makes type hint changes.

This change allows us to support custom message types. For example, we introduced a new message type StructureMessage[T] generic, that can be used to create new message types with a BaseModel content. On-going work is to get AssistantAgent to respond with StructuredMessage[T] where T is the structured output type for the model.

See the API doc on AgentChat message types: https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.messages.html

  • Use class hierarchy to organize AgentChat message types and introduce StructuredMessage type by @ekzhu in microsoft#5998
  • Rename to use BaseChatMessage and BaseAgentEvent. Bring back union types. by @ekzhu in microsoft#6144

Structured Output

We enhanced support for structured output in model clients and agents.

For model clients, use json_output parameter to specify the structured output type
as a Pydantic model. The model client will then return a JSON string
that can be deserialized into the specified Pydantic model.

import asyncio
from typing import Literal

from autogen_core import CancellationToken
from autogen_ext.models.openai import OpenAIChatCompletionClient
from pydantic import BaseModel

# Define the structured output format.
class AgentResponse(BaseModel):
    thoughts: str
    response: Literal["happy", "sad", "neutral"]

 model_client = OpenAIChatCompletionClient(model="gpt-4o-mini")

 # Generate a response using the tool.
response = await model_client.create(
    messages=[
        SystemMessage(content="Analyze input text sentiment using the tool provided."),
        UserMessage(content="I am happy.", source="user"),
    ],
    json_ouput=AgentResponse,
)

print(response.content)
# Should be a structured output.
# {"thoughts": "The user is happy.", "response": "happy"}

For AssistantAgent, you can set output_content_type to the structured output type. The agent will automatically reflect on the tool call result and generate a StructuredMessage with the output content type.

import asyncio
from typing import Literal

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.messages import TextMessage
from autogen_agentchat.ui import Console
from autogen_core import CancellationToken
from autogen_core.tools import FunctionTool
from autogen_ext.models.openai import OpenAIChatCompletionClient
from pydantic import BaseModel

# Define the structured output format.
class AgentResponse(BaseModel):
    thoughts: str
    response: Literal["happy", "sad", "neutral"]


# Define the function to be called as a tool.
def sentiment_analysis(text: str) -> str:
    """Given a text, return the sentiment."""
    return "happy" if "happy" in text else "sad" if "sad" in text else "neutral"


# Create a FunctionTool instance with `strict=True`,
# which is required for structured output mode.
tool = FunctionTool(sentiment_analysis, description="Sentiment Analysis", strict=True)

# Create an OpenAIChatCompletionClient instance that supports structured output.
model_client = OpenAIChatCompletionClient(
    model="gpt-4o-mini",
)

# Create an AssistantAgent instance that uses the tool and model client.
agent = AssistantAgent(
    name="assistant",
    model_client=model_client,
    tools=[tool],
    system_message="Use the tool to analyze sentiment.",
    output_content_type=AgentResponse,
)

stream = agent.on_messages_stream([TextMessage(content="I am happy today!", source="user")], CancellationToken())
await Console(stream)
---------- assistant ----------
[FunctionCall(id='call_tIZjAVyKEDuijbBwLY6RHV2p', arguments='{"text":"I am happy today!"}', name='sentiment_analysis')]
---------- assistant ----------
[FunctionExecutionResult(content='happy', call_id='call_tIZjAVyKEDuijbBwLY6RHV2p', is_error=False)]
---------- assistant ----------
{"thoughts":"The user expresses a clear positive emotion by stating they are happy today, suggesting an upbeat mood.","response":"happy"}

You can also pass a StructuredMessage to the run and run_stream methods of agents and teams as task messages. Agents will automatically deserialize the message to string and place them in their model context. StructuredMessage generated by an agent will also be passed to other agents in the team, and emitted as messages in the output stream.

Azure AI Search Tool

Added a new tool for agents to perform search using Azure AI Search.

See the documentation for more details.

SelectorGroupChat Improvements

Code Executors Improvements

Model Client Improvements

TokenLimitedChatCompletionContext

Introduce TokenLimitedChatCompletionContext to limit the number of tokens in the context
sent to the model.
This is useful for long-running agents that need to keep a long history of messages in the context.

Bug Fixes

Other Python Related Changes

New Contributors

Full Changelog: microsoft/autogen@python-v0.4.9.3...python-v0.5.1

Contributors

ekzhu, yusufk, and 28 other contributors
Assets 2
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