microsoft/autogen python-v0.4.5 on GitHub

What's New

Streaming for AgentChat agents and teams

Introduce ModelClientStreamingChunkEvent for streaming model output and update handling in agents and console by @ekzhu in #5208

To enable streaming from an AssistantAgent, set model_client_stream=True when creating it. The token stream will be available when you run the agent directly, or as part of a team when you call run_stream.

If you want to see tokens streaming in your console application, you can use Console directly.

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main() -> None:
    agent = AssistantAgent("assistant", OpenAIChatCompletionClient(model="gpt-4o"), model_client_stream=True)
    await Console(agent.run_stream(task="Write a short story with a surprising ending."))

asyncio.run(main())

If you are handling the messages yourself and streaming to the frontend, you can handle
autogen_agentchat.messages.ModelClientStreamingChunkEvent message.

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main() -> None:
    agent = AssistantAgent("assistant", OpenAIChatCompletionClient(model="gpt-4o"), model_client_stream=True)
    async for message in agent.run_stream(task="Write 3 line poem."):
        print(message)

asyncio.run(main())

source='user' models_usage=None content='Write 3 line poem.' type='TextMessage'
source='assistant' models_usage=None content='Silent' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' whispers' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' glide' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=',' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content='  \n' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content='Moon' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content='lit' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' dreams' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' dance' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' through' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' the' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' night' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=',' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content='  \n' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content='Stars' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' watch' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' from' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content=' above' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=None content='.' type='ModelClientStreamingChunkEvent'
source='assistant' models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0) content='Silent whispers glide,  \nMoonlit dreams dance through the night,  \nStars watch from above.' type='TextMessage'
TaskResult(messages=[TextMessage(source='user', models_usage=None, content='Write 3 line poem.', type='TextMessage'), TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=0, completion_tokens=0), content='Silent whispers glide,  \nMoonlit dreams dance through the night,  \nStars watch from above.', type='TextMessage')], stop_reason=None)

Also, see the sample showing how to stream a team's messages to ChainLit frontend: https://github.com/microsoft/autogen/tree/python-v0.4.5/python/samples/agentchat_chainlit

R1-style reasoning output

Support R1 reasoning text in model create result; enhance API docs by @ekzhu in #5262

import asyncio
from autogen_core.models import UserMessage, ModelFamily
from autogen_ext.models.openai import OpenAIChatCompletionClient

async def main() -> None:
    model_client = OpenAIChatCompletionClient(
        model="deepseek-r1:1.5b",
        api_key="placeholder",
        base_url="http://localhost:11434/v1",
        model_info={
            "function_calling": False,
            "json_output": False,
            "vision": False,
            "family": ModelFamily.R1,
        }
    )

    # Test basic completion with the Ollama deepseek-r1:1.5b model.
    create_result = await model_client.create(
        messages=[
            UserMessage(
                content="Taking two balls from a bag of 10 green balls and 20 red balls, "
                "what is the probability of getting a green and a red balls?",
                source="user",
            ),
        ]
    )

    # CreateResult.thought field contains the thinking content.
    print(create_result.thought)
    print(create_result.content)

asyncio.run(main())

Streaming is also supported with R1-style reasoning output.

See the sample showing R1 playing chess: https://github.com/microsoft/autogen/tree/python-v0.4.5/python/samples/agentchat_chess_game

FunctionTool for partial functions

FunctionTool partial support by @nour-bouzid in #5183

Now you can define function tools from partial functions, where some parameters have been set before hand.

import json
from functools import partial
from autogen_core.tools import FunctionTool


def get_weather(country: str, city: str) -> str:
    return f"The temperature in {city}, {country} is 75°"


partial_function = partial(get_weather, "Germany")
tool = FunctionTool(partial_function, description="Partial function tool.")

print(json.dumps(tool.schema, indent=2))

{
  "name": "get_weather",
  "description": "Partial function tool.",
  "parameters": {
    "type": "object",
    "properties": {
      "city": {
        "description": "city",
        "title": "City",
        "type": "string"
      }
    },
    "required": [
      "city"
    ]
  }
}

CodeExecutorAgent update

Added an optional sources parameter to CodeExecutorAgent by @afourney in #5259

New Samples

Streamlit + AgentChat sample by @husseinkorly in #5306
ChainLit + AgentChat sample with streaming by @ekzhu in #5304
Chess sample showing R1-Style reasoning for planning and strategizing by @ekzhu in #5285

Documentation update:

Add Semantic Kernel Adapter documentation and usage examples in user guides by @ekzhu in #5256
Update human-in-the-loop tutorial with better system message to signal termination condition by @ekzhu in #5253

Moves

Remove old autogen_magentic_one package. by @afourney in #5305

Bug Fixes

fix: handle non-string function arguments in tool calls and add corresponding warnings by @ekzhu in #5260
Add default_header support by @nour-bouzid in #5249
feat: update OpenAIAssistantAgent to support AsyncAzureOpenAI client by @ekzhu in #5312

All Other Python Related Changes

Update website for v0.4.4 by @ekzhu in #5246
update dependencies to work with protobuf 5 by @MohMaz in #5195
Adjusted M1 agent system prompt to remove TERMINATE by @afourney in #5263
#5270
chore: update package versions to 0.4.5 and remove deprecated requirements by @ekzhu in #5280
Update Distributed Agent Runtime Cross-platform Sample by @linznin in #5164
fix: windows check ci failure by @bassmang in #5287
fix: type issues in streamlit sample and add streamlit to dev dependencies by @ekzhu in #5309
chore: add asyncio_atexit dependency to docker requirements by @ekzhu in #5307
feat: add o3 to model info; update chess example by @ekzhu in #5311

New Contributors

@nour-bouzid made their first contribution in #5183
@linznin made their first contribution in #5164
@husseinkorly made their first contribution in #5306

Full Changelog: v0.4.4...python-v0.4.5