python-v0.4.9

What's New

Anthropic Model Client

Native support for Anthropic models. Get your update:
 

pip install -U "autogen-ext[anthropic]"

The new client follows the same interface as OpenAIChatCompletionClient so you can use it directly in your agents and teams.

import asyncio
from autogen_ext.models.anthropic import AnthropicChatCompletionClient
from autogen_core.models import UserMessage


async def main():
    anthropic_client = AnthropicChatCompletionClient(
        model="claude-3-sonnet-20240229",
        api_key="your-api-key",  # Optional if ANTHROPIC_API_KEY is set in environment
    )

    result = await anthropic_client.create([UserMessage(content="What is the capital of France?", source="user")])  # type: ignore
    print(result)


if __name__ == "__main__":
    asyncio.run(main())

You can also load the model client directly from a configuration dictionary:

from autogen_core.models import ChatCompletionClient

config = {
    "provider": "AnthropicChatCompletionClient",
    "config": {"model": "claude-3-sonnet-20240229"},
}

client = ChatCompletionClient.load_component(config)

To use with AssistantAgent and run the agent in a loop to match the behavior of Claude agents, you can use Single-Agent Team.

• Add anthropic docs by @victordibia in #5882

LlamaCpp Model Client

LlamaCpp is a great project for working with local models. Now we have native support via its official SDK.

pip install -U "autogen-ext[llama-cpp]"

To use a local model file:

import asyncio

from autogen_core.models import UserMessage
from autogen_ext.models.llama_cpp import LlamaCppChatCompletionClient


async def main():
    llama_client = LlamaCppChatCompletionClient(model_path="/path/to/your/model.gguf")
    result = await llama_client.create([UserMessage(content="What is the capital of France?", source="user")])
    print(result)


asyncio.run(main())

To use it with a Hugging Face model:

import asyncio

from autogen_core.models import UserMessage
from autogen_ext.models.llama_cpp import LlamaCppChatCompletionClient


async def main():
    llama_client = LlamaCppChatCompletionClient(
        repo_id="unsloth/phi-4-GGUF", filename="phi-4-Q2_K_L.gguf", n_gpu_layers=-1, seed=1337, n_ctx=5000
    )
    result = await llama_client.create([UserMessage(content="What is the capital of France?", source="user")])
    print(result)


asyncio.run(main())

• Feature add Add LlamaCppChatCompletionClient and llama-cpp by @aribornstein in #5326

Task-Centric Memory (Experimental)

Task-Centric memory is an experimental module that can give agents the ability to:

• Accomplish general tasks more effectively by learning quickly and continually beyond context-window limitations.
• Remember guidance, corrections, plans, and demonstrations provided by users (teachability)
• Learn through the agent's own experience and adapt quickly to changing circumstances (self-improvement)
• Avoid repeating mistakes on tasks that are similar to those previously encountered.

For example, you can use Teachability as a memory for AssistantAgent so your agent can learn from user teaching.

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.experimental.task_centric_memory import MemoryController
from autogen_ext.experimental.task_centric_memory.utils import Teachability


async def main():
    # Create a client
    client = OpenAIChatCompletionClient(model="gpt-4o-2024-08-06", )

    # Create an instance of Task-Centric Memory, passing minimal parameters for this simple example
    memory_controller = MemoryController(reset=False, client=client)

    # Wrap the memory controller in a Teachability instance
    teachability = Teachability(memory_controller=memory_controller)

    # Create an AssistantAgent, and attach teachability as its memory
    assistant_agent = AssistantAgent(
        name="teachable_agent",
        system_message = "You are a helpful AI assistant, with the special ability to remember user teachings from prior conversations.",
        model_client=client,
        memory=[teachability],
    )

    # Enter a loop to chat with the teachable agent
    print("Now chatting with a teachable agent. Please enter your first message. Type 'exit' or 'quit' to quit.")
    while True:
        user_input = input("\nYou: ")
        if user_input.lower() in ["exit", "quit"]:
            break
        await Console(assistant_agent.run_stream(task=user_input))

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

Head over to its README for details, and the samples for runnable examples.

• Task-Centric Memory by @rickyloynd-microsoft in #5227

New Sample: Gitty (Experimental)

Gitty is an experimental application built to help easing the burden on open-source project maintainers. Currently, it can generate auto reply to issues.

To use:

gitty --repo microsoft/autogen issue 5212

Head over to Gitty to see details.

• Add new sample: Gitty by @gagb in #5842

Improved Tracing and Logging

In this version, we made a number of improvements on tracing and logging.

• add LLMStreamStartEvent and LLMStreamEndEvent by @EItanya in #5890
• Allow for tracing via context provider by @EItanya in #5889
• Fix span structure for tracing by @ekzhu in #5853
• Add ToolCallEvent and log it from all builtin tools by @ekzhu in #5859

Powershell Support for LocalCommandLineCodeExecutor

• feat: update local code executor to support powershell by @lspinheiro in #5884

Website Accessibility Improvements

@peterychang has made huge improvements to the accessibility of our documentation website. Thank you @peterychang!

• word wrap prev/next links on autodocs by @peterychang in #5867
• Allow Voice Access to find clickable cards by @peterychang in #5857
• copy tooltip on focus. Upgrade PDT version by @peterychang in #5848
• highlight focused code output boxes in jupyter notebook pages by @peterychang in #5819
• Fix high contrast mode focus by @peterychang in #5796
• Keyboard copy event and search bar cancellation by @peterychang in #5820

Bug Fixes

• fix: save_state should not require the team to be stopped. by @ekzhu in #5885
• fix: remove max_tokens from az ai client create call when stream=True by @ekzhu in #5860
• fix: add plugin to kernel by @lspinheiro in #5830
• fix: warn when using reflection on tool use with Claude models by @ekzhu in #5829

Other Python Related Changes

• doc: update termination tutorial to include FunctionCallTermination condition and fix formatting by @ekzhu in #5813
• docs: Add note recommending PythonCodeExecutionTool as an alternative to CodeExecutorAgent by @ekzhu in #5809
• Update quickstart.ipynb by @taswar in #5815
• Fix warning in selector gorup chat guide by @ekzhu in #5849
• Support for external agent runtime in AgentChat by @ekzhu in #5843
• update ollama usage docs by @ekzhu in #5854
• Update markitdown requirements to >= 0.0.1, while still in the 0.0.x range by @afourney in #5864
• Add client close by @afourney in #5871
• Update README to clarify Web Browsing Agent Team usage, and use animated Chromium browser by @ekzhu in #5861
• Add author name before their message in Chainlit team sample by @DavidYu00 in #5878
• Bump axios from 1.7.9 to 1.8.2 in /python/packages/autogen-studio/frontend by @dependabot in #5874
• Add an optional base path to FileSurfer by @husseinmozannar in #5886
• feat: Pause and Resume for AgentChat Teams and Agents by @ekzhu in #5887
• update version to v0.4.9 by @ekzhu in #5903

New Contributors

• @taswar made their first contribution in #5815
• @DavidYu00 made their first contribution in #5878
• @aribornstein made their first contribution in #5326

Full Changelog: python-v0.4.8...python-v0.4.9