Building the Dream Team of LLMs

Microsoft has entered the arena of Large Language Model (LLM) application frameworks with its newly introduced open-source Python library named AutoGen. According to Microsoft, AutoGen is tailored to streamline the management, augmentation, and automation of LLM processes. At its core, AutoGen operates on the principle of creating “agents” — modular code components powered by LLMs such as GPT-4. These agents interact with each other through natural language messages, enabling a diverse range of tasks to be accomplished seamlessly.

Developers can customize and enrich agents through prompt engineering techniques and supplementary tools. These enhancements empower agents to efficiently retrieve information or execute code. Leveraging the AutoGen platform, developers are empowered to construct a network of agents specialized in different tasks, collaborating seamlessly to achieve collective objectives.

LLM Agents

In the world of language models, the basic ones are really good at things like translating and answering questions. But sometimes, they feel like skilled workers without their essential tools. However, there’s something interesting we’ve noticed: with the right tools, these models can think and act surprisingly well. Even though they might not fully understand everything, there’s a way to help them learn more using special data and prompts.

There are two main ways we can do this: one is by giving models extra information through special pipelines, and the other is by letting models use tools on their own. For example, imagine a model like GPT-4 using a tool to search the internet for answers or to check how well stocks are doing. When we combine models with tools like this, it’s like creating an AI assistant that can think and make decisions.

The world of tools and platforms for making these AI assistants is always changing, and Autogen is one of the newest ones. So, let’s take a closer look at Autogen and see how we can use it to make really smart AI assistants using language models.

What is Autogen?

AutoGen is a framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

To complete tasks, Autogen provides various types of Agents, such as

Assistant Agent: This agent is in charge of performing tasks such as coding, reviewing, etc.

User Proxy Agent: These agents, as the name signifies, operate for the users in their place. They carry out a duty to include humans in interaction with agent systems by steering discussions.

Agent That Can Be Taught: This agent is set up to be taught very well. We have the capability of giving this agent clear information that does not exist in LLMs.

Main Features​

• AutoGen enables building next-gen LLM applications based on multi-agent conversations with minimal effort. It simplifies the orchestration, automation, and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcomes their weaknesses.
• It supports diverse conversation patterns for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy, the number of agents, and agent conversation topology.
• It provides a collection of working systems with different complexities. These systems span a wide range of applications from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.

Use Cases of Autogen Multi-Agent Framework

AutoGen can be used to build applications such as: Hierarchical chat, Dynamic group chat, and Next-gen LLM applications.

AutoGen supports the following conversation patterns: Fully autonomous conversations, Human-in-the-loop problem-solving, Static conversations, and Dynamic conversations.

AutoGen can streamline complex tasks and automate workflows by enabling agents to communicate and work together autonomously. AutoGen agents can be based on LLMs, tools, humans, and even a combination of them.

• Code execution: The UserProxyAgent triggers code execution automatically when it detects an executable code block in a received message and no human user input is provided
• Automated task solving: AutoGen can use code generation, execution, and debugging to solve tasks
• Automated code generation and question answering: AutoGen can use retrieval augmented agents to generate code and answer questions

Importance of Autogen

AutoGen is addressing the need for efficient and adaptable communication among multiple AI agents. Its significance is evident in its ability to:

1. Streamline, automate, and enhance complex workflows involving Large Language Models (LLMs) more effectively.
2. Boost the efficiency and effectiveness of LLM models while mitigating their inherent limitations.
3. Facilitate the seamless creation of next-generation LLM applications centered around multi-agent dialogues.

Getting Started with Autogen

To get started with AutoGen, you need to follow a few steps:

Install AutoGen:

pip install pyautogen

Establish API Endpoint:

Organize the API endpoint for AutoGen by importing the autogen library and arranging the LLM configuration.

import autogen
from autogen import AssistantAgent, UserProxyAgent

llm_config = {"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}

Description of Agents:

Describe the agents and their positions within the AutoGen framework, stating their abnnilities and ways they interact.

there are two basics agents in autogen

1. UserProxy Agent : This agent acts like a go-between for people. It can ask humans for help or execute code when needed. It can even use LLM to generate responses when it’s not executing code. You can control code execution and LLM usage with settings like code_execution_config and llm_config.
2. Assistant Agent :This agent is like a helpful Strategic AI Team Building assistant. It can write Python code for you to run when you give it a task. It uses a smart program called LLM (like GPT-4) to write the code. It can also check the results and suggest fixes. You can change how it behaves by giving it new instructions. You can also tweak how LLM works with it using the llm_config.

assistant = AssistantAgent("assistant", llm_config=llm_config)

user_proxy = UserProxyAgent(
    "user_proxy", code_execution_config={"executor": autogen.coding.LocalCommandLineCodeExecutor(work_dir="coding")}
)

Execute Tasks:

Use the defined agents to execute tasks and observe the collaborative output.

# Start the chat
user_proxy.initiate_chat(
    assistant,
    message="Plot a chart of NVDA and TESLA stock price change YTD.",
)

Multi-Agent Conversation Framework:

Autogen enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools, and humans. By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code.

The figure below shows an example conversation flow with AutoGen.

Why Autogen

While there are many definitions of agents, in AutoGen, an agent is an entity that can send messages, receive messages and generate a reply using models, tools, human inputs or a mixture of them. This abstraction not only allows agents to model real-world and abstract entities, such as people and algorithms, but it also simplifies implementation of complex workflows as collaboration among agents.

Further, AutoGen is extensible and composable: you can extend a simple agent with customizable components and create workflows that can combine these agents and power a more sophisticated agent, resulting in implementations that are modular and easy to maintain.

Most importantly, AutoGen is developed by a vibrant community of researchers and engineers. It incorporates the latest research in multi-agent systems and has been used in many real-world applications, including agent platform, advertising, AI employees, blog/article writing, blockchain, calculate burned areas by wildfires, customer support, cybersecurity, data analytics, debate, education, finance, gaming, legal consultation, research, robotics, sales/marketing, social simulation, software engineering, software security, supply chain, t-shirt design, training data generation, Youtube service…

Multiple Applications of Autogen:

• Code Generation, Execution, and Debugging
• Multi-Agent Collaboration (>3 Agents)
• Applications
• Tool Use
• Agent Teaching and Learning

Key Takeaways

• AutoGen introduces a new era where you can create your personalized AI dream team, composed of conversable agents skilled in various domains, working seamlessly together.
• AutoGen streamlines complex tasks and automates workflows, making it a powerful tool for orchestrating and optimizing tasks involving Large Language Models (LLMs).
• Managing API keys and sensitive data securely is paramount when working with AutoGen. It’s essential to follow best practices to protect your information.
• The config_list is a crucial component, enabling agents to adapt and excel in various tasks by efficiently handling multiple configurations and interactions with the OpenAI API.

Conclusion:

AutoGen stands out as a robust and versatile tool with applications across diverse domains. From complex configurations to real-world implementations in fields like finance and healthcare, its capabilities are extensive. As we delve deeper into the potential of Large Language Models, platforms like AutoGen illuminate the path forward, ensuring that our journey is not only viable but also efficient and intuitive.

Ref doc— https://microsoft.github.io/autogen/docs/Getting-Started

autogen github — https://github.com/microsoft/autogen

autogen notebook examples — https://github.com/microsoft/autogen/tree/main/notebook