Blog#

StateFlow - Build State-Driven Workflows with Customized Speaker Selection in GroupChat

TL;DR: Introduce Stateflow, a task-solving paradigm that conceptualizes complex task-solving processes backed by LLMs as state machines. Introduce how to use GroupChat to realize such an idea with a customized speaker selection function.

Introduction

It is a notable trend to use Large Language Models (LLMs) to tackle complex tasks, e.g., tasks that require a sequence of actions and dynamic interaction with tools and external environments. In this paper, we propose StateFlow, a novel LLM-based task-solving paradigm that conceptualizes complex task-solving processes as state machines. In StateFlow, we distinguish between "process grounding” (via state and state transitions) and "sub-task solving” (through actions within a state), enhancing control and interpretability of the task-solving procedure. A state represents the status of a running process. The transitions between states are controlled by heuristic rules or decisions made by the LLM, allowing for a dynamic and adaptive progression. Upon entering a state, a series of actions is executed, involving not only calling LLMs guided by different prompts, but also the utilization of external tools as needed.

FSM Group Chat -- User-specified agent transitions

Finite State Machine (FSM) Group Chat allows the user to constrain agent transitions.

TL;DR

Recently, FSM Group Chat is released that allows the user to input a transition graph to constrain agent transitions. This is useful as the number of agents increases because the number of transition pairs (N choose 2 combinations) increases exponentially increasing the risk of sub-optimal transitions, which leads to wastage of tokens and/or poor outcomes.

Anny: Assisting AutoGen Devs Via AutoGen

TL;DR

We are adding a new sample app called Anny-- a simple Discord bot powered by AutoGen that's intended to assist AutoGen Devs. See samples/apps/auto-anny for details.

AutoGen with Custom Models: Empowering Users to Use Their Own Inference Mechanism

TL;DR

AutoGen now supports custom models! This feature empowers users to define and load their own models, allowing for a more flexible and personalized inference mechanism. By adhering to a specific protocol, you can integrate your custom model for use with AutoGen and respond to prompts any way needed by using any model/API call/hardcoded response you want.

NOTE: Depending on what model you use, you may need to play with the default prompts of the Agent's

AutoGenBench -- A Tool for Measuring and Evaluating AutoGen Agents

AutoGenBench is a standalone tool for evaluating AutoGen agents and workflows on common benchmarks.

TL;DR

Today we are releasing AutoGenBench - a tool for evaluating AutoGen agents and workflows on established LLM and agentic benchmarks.

AutoGenBench is a standalone command line tool, installable from PyPI, which handles downloading, configuring, running, and reporting supported benchmarks. AutoGenBench works best when run alongside Docker, since it uses Docker to isolate tests from one another.

• See the AutoGenBench README for information on installation and running benchmarks.
• See the AutoGenBench CONTRIBUTING guide for information on developing or contributing benchmark datasets.

Code execution is now by default inside docker container

TL;DR

AutoGen 0.2.8 enhances operational safety by making 'code execution inside a Docker container' the default setting, focusing on informing users about its operations and empowering them to make informed decisions regarding code execution.

The new release introduces a breaking change where the use_docker argument is set to True by default in code executing agents. This change underscores our commitment to prioritizing security and safety in AutoGen.

All About Agent Descriptions

TL;DR

AutoGen 0.2.2 introduces a description field to ConversableAgent (and all subclasses), and changes GroupChat so that it uses agent descriptions rather than system_messages when choosing which agents should speak next.

This is expected to simplify GroupChat’s job, improve orchestration, and make it easier to implement new GroupChat or GroupChat-like alternatives.

If you are a developer, and things were already working well for you, no action is needed -- backward compatibility is ensured because the description field defaults to the system_message when no description is provided.

However, if you were struggling with getting GroupChat to work, you can now try updating the description field.

AgentOptimizer - An Agentic Way to Train Your LLM Agent

TL;DR: Introducing AgentOptimizer, a new class for training LLM agents in the era of LLMs as a service. AgentOptimizer is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.

More information could be found in:

Paper: https://arxiv.org/abs/2402.11359.

Notebook: https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_agentoptimizer.ipynb.

Introduction

In the traditional ML pipeline, we train a model by updating its weights according to the loss on the training set, while in the era of LLM agents, how should we train an agent? Here, we take an initial step towards the agent training. Inspired by the function calling capabilities provided by OpenAI, we draw an analogy between model weights and agent functions/skills, and update an agent’s functions/skills based on its historical performance on a training set. Specifically, we propose to use the function calling capabilities to formulate the actions that optimize the agents’ functions as a set of function calls, to support iteratively adding, revising, and removing existing functions. We also include two strategies, roll-back, and early-stop, to streamline the training process to overcome the performance-decreasing problem when training. As an agentic way of training an agent, our approach helps enhance the agents’ abilities without requiring access to the LLM's weights.

AutoGen Studio: Interactively Explore Multi-Agent Workflows

AutoGen Studio: Solving a task with multiple agents that generate a pdf document with images.

TL;DR

To help you rapidly prototype multi-agent solutions for your tasks, we are introducing AutoGen Studio, an interface powered by AutoGen. It allows you to:

• Declaratively define and modify agents and multi-agent workflows through a point and click, drag and drop interface (e.g., you can select the parameters of two agents that will communicate to solve your task).
• Use our UI to create chat sessions with the specified agents and view results (e.g., view chat history, generated files, and time taken).
• Explicitly add skills to your agents and accomplish more tasks.
• Publish your sessions to a local gallery.

See the official AutoGen Studio documentation for more details.

AutoGen Studio is open source code here, and can be installed via pip. Give it a try!

pip install autogenstudio

Agent AutoBuild - Automatically Building Multi-agent Systems

TL;DR: Introducing AutoBuild, building multi-agent system automatically, fast, and easily for complex tasks with minimal user prompt required, powered by a new designed class AgentBuilder. AgentBuilder also supports open-source LLMs by leveraging vLLM and FastChat. Checkout example notebooks and source code for reference:

• AutoBuild Examples
• AgentBuilder

Introduction

In this blog, we introduce AutoBuild, a pipeline that can automatically build multi-agent systems for complex tasks. Specifically, we design a new class called AgentBuilder, which will complete the generation of participant expert agents and the construction of group chat automatically after the user provides descriptions of a building task and an execution task.

AgentBuilder supports open-source models on Hugging Face powered by vLLM and FastChat. Once the user chooses to use open-source LLM, AgentBuilder will set up an endpoint server automatically without any user participation.