Skip to content

v0.4.4

Latest
Latest
Compare
Choose a tag to compare
@ekzhu ekzhu released this 29 Jan 03:32
· 19 commits to main since this release
v0.4.4
bd5a24b
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

What's New

Serializable Configuration for AgentChat

This new feature allows you to serialize an agent or a team to a JSON string, and deserialize them back into objects. Make sure to also read about save_state and load_state: https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/state.html.

You now can serialize and deserialize both the configurations and the state of agents and teams.

For example, create a RoundRobinGroupChat, and serialize its configuration and state.

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.base import Team
from autogen_agentchat.ui import Console
from autogen_agentchat.conditions import TextMentionTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient


async def dump_team_config() -> None:
    model_client = OpenAIChatCompletionClient(model="gpt-4o")
    assistant = AssistantAgent(
        "assistant",
        model_client=model_client,
        system_message="You are a helpful assistant.",
    )
    critic = AssistantAgent(
        "critic",
        model_client=model_client,
        system_message="Provide feedback. Reply with 'APPROVE' if the feedback has been addressed.",
    )
    termination = TextMentionTermination("APPROVE", sources=["critic"])
    group_chat = RoundRobinGroupChat(
        [assistant, critic], termination_condition=termination
    )
    # Run the group chat.
    await Console(group_chat.run_stream(task="Write a short poem about winter."))
    # Dump the team configuration to a JSON file.
    config = group_chat.dump_component()
    with open("team_config.json", "w") as f:
        f.write(config.model_dump_json(indent=4))
    # Dump the team state to a JSON file.
    state = await group_chat.save_state()
    with open("team_state.json", "w") as f:
        f.write(json.dumps(state, indent=4))

asyncio.run(dump_team_config())

Produces serialized team configuration and state. Truncated for illustration purpose.

{
    "provider": "autogen_agentchat.teams.RoundRobinGroupChat",
    "component_type": "team",
    "version": 1,
    "component_version": 1,
    "description": "A team that runs a group chat with participants taking turns in a round-robin fashion\n    to publish a message to all.",
    "label": "RoundRobinGroupChat",
    "config": {
        "participants": [
            {
                "provider": "autogen_agentchat.agents.AssistantAgent",
                "component_type": "agent",
                "version": 1,
                "component_version": 1,
                "description": "An agent that provides assistance with tool use.",
                "label": "AssistantAgent",
                "config": {
                    "name": "assistant",
                    "model_client": {
                        "provider": "autogen_ext.models.openai.OpenAIChatCompletionClient",
                        "component_type": "model",
                        "version": 1,
                        "component_version": 1,
                        "description": "Chat completion client for OpenAI hosted models.",
                        "label": "OpenAIChatCompletionClient",
                        "config": {
                            "model": "gpt-4o"
                        }
{
    "type": "TeamState",
    "version": "1.0.0",
    "agent_states": {
        "group_chat_manager/25763eb1-78b2-4509-8607-7224ae383575": {
            "type": "RoundRobinManagerState",
            "version": "1.0.0",
            "message_thread": [
                {
                    "source": "user",
                    "models_usage": null,
                    "content": "Write a short poem about winter.",
                    "type": "TextMessage"
                },
                {
                    "source": "assistant",
                    "models_usage": {
                        "prompt_tokens": 25,
                        "completion_tokens": 150
                    },
                    "content": "Amidst the still and silent air,  \nWhere frost adorns the branches bare,  \nThe world transforms in shades of white,  \nA wondrous, shimmering, quiet sight.\n\nThe whisper of the wind is low,  \nAs snowflakes drift and dance and glow.  \nEach crystal, delicate and bright,  \nFalls gently through the silver night.\n\nThe earth is hushed in pure embrace,  \nA tranquil, glistening, untouched space.  \nYet warmth resides in hearts that roam,  \nFinding solace in the hearth of home.\n\nIn winter\u2019s breath, a promise lies,  \nBeneath the veil of cold, clear skies:  \nThat spring will wake the sleeping land,  \nAnd life will bloom where now we stand.",
                    "type": "TextMessage"

Load the configuration and state back into objects.

import asyncio
import json
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.base import Team

async def load_team_config() -> None:
    # Load the team configuration from a JSON file.
    with open("team_config.json", "r") as f:
        config = json.load(f)
    group_chat = Team.load_component(config)
    # Load the team state from a JSON file.
    with open("team_state.json", "r") as f:
        state = json.load(f)
    await group_chat.load_state(state)
    assert isinstance(group_chat, RoundRobinGroupChat)

asyncio.run(load_team_config())

This new feature allows you to manage persistent sessions across server-client based user interaction.

Azure AI Client for Azure-Hosted Models

This allows you to use Azure and GitHub-hosted models, including Phi-4, Mistral models, and Cohere models.

import asyncio
import os

from autogen_core.models import UserMessage
from autogen_ext.models.azure import AzureAIChatCompletionClient
from azure.core.credentials import AzureKeyCredential


async def main() -> None:
    client = AzureAIChatCompletionClient(
        model="Phi-4",
        endpoint="https://models.inference.ai.azure.com",
        # To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings.
        # Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens
        credential=AzureKeyCredential(os.environ["GITHUB_TOKEN"]),
        model_info={
            "json_output": False,
            "function_calling": False,
            "vision": False,
            "family": "unknown",
        },
    )
    result = await client.create(
        [UserMessage(content="What is the capital of France?", source="user")]
    )
    print(result)


asyncio.run(main())

Rich Console UI for Magentic One CLI

You can now enable pretty printed output for m1 command line tool by adding --rich argument.

m1 --rich "Find information about AutoGen"
Screenshot 2025-01-28 191752

Default In-Memory Cache for ChatCompletionCache

This allows you to cache model client calls without specifying an external cache service.

import asyncio

from autogen_core.models import UserMessage
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.models.cache import ChatCompletionCache


async def main() -> None:
    # Create a model client.
    client = OpenAIChatCompletionClient(model="gpt-4o")

    # Create a cached wrapper around the model client.
    cached_client = ChatCompletionCache(client)

    # Call the cached client.
    result = await cached_client.create(
        [UserMessage(content="What is the capital of France?", source="user")]
    )
    print(result.content, result.cached)

    # Call the cached client again.
    result = await cached_client.create(
        [UserMessage(content="What is the capital of France?", source="user")]
    )
    print(result.content, result.cached)


asyncio.run(main())
The capital of France is Paris. False
The capital of France is Paris. True

Docs Update

  • Update model client documentation add Ollama, Gemini, Azure AI models by @ekzhu in #5196
  • Add Model Client Cache section to migration guide by @ekzhu in #5197
  • docs: Enhance documentation for SingleThreadedAgentRuntime with usage examples and clarifications; undeprecate process_next by @ekzhu in #5230
  • docs: Update user guide notebooks to enhance clarity and add structured output by @ekzhu in #5224
  • docs: Core API doc update: split out model context from model clients; separate framework and components by @ekzhu in #5171
  • docs: Add a helpful comment to swarm.ipynb by @withsmilo in #5145
  • docs: Enhance documentation for SingleThreadedAgentRuntime with usage examples and clarifications; undeprecate process_next by @ekzhu in #5230

Bug Fixes

  • fix: update SK model adapter constructor by @lspinheiro in #5150. This allows the SK Model Client to be used inside an AssistantAgent.
  • Fix function tool naming to avoid overriding the name input by @Pierrolo in #5165
  • fix: Enhance OpenAI client to handle additional stop reasons and improve tool call validation in tests to address empty tool_calls list. by @ekzhu in #5223

Other Changes

New Contributors

Full Changelog: v0.4.3...v0.4.4

Contributors

ekzhu, bih, and 15 other contributors
Assets 2
Loading