Autogen - Microsoft's best AI Agent framework that is controllable?
Welcome to our blog post on Autogen - Microsoft's best AI Agent framework. If you're interested in multi-agent frameworks, you've come to the right place! Autogen is designed to solve the challenges faced by users when giving feedback to agents and when working with multiple agents in a conversation. In this post, we'll explore the unique features of Autogen, such as user proxy agents and group chat managers, and how they enable users to have more control and collaboration in complex tasks.
Introducing Autogen
Autogen is a new multi-agent framework developed by Microsoft. It addresses two common challenges faced by users when working with AI agents. The first challenge is the difficulty in giving feedback to agents during the process. Often, users realize that the agents have delivered only 50% of what they actually wanted after the whole process is completed. Autogen solves this problem by introducing user proxy agents who can interact with other assistant agents on behalf of users and ask for human input when necessary. This gives users more control and allows them to iterate on the output delivered by the agents.
The second challenge is the limitation of existing frameworks in terms of the number of agents that can be added to a conversation. While some frameworks allow two agents to work together, Autogen allows users to add as many agents as they want through its group chat manager feature. This enables collaboration and brings in different perspectives from various agents. For example, in a strategy planning task, users can involve CEOs, product managers, data analysts, and engineers, each providing their insights and expertise.
Getting Started with Autogen
Let's dive into using Autogen by installing it on your computer and creating a basic example. First, create a folder and open it in Visual Studio Code. Install the Autogen package and create a special file called "oai_config_list". This file will be used to import the list of OpenAI API keys. Once you've completed these steps, create a new file called "basic.py". In this file, we'll create a simple conversation between an assistant agent and a user proxy agent.
``` import autogen assistant = autogen.Agent(name="assistant") user_proxy = autogen.Agent(name="user proxy", code_execution_config="coding") conversation = user_proxy.new_chat().send_message("Show me a chart of Nvidia and Tesla stock prices") print(conversation.messages) ```In this example, we create two agents - an assistant agent and a user proxy agent. The user proxy agent interacts with the assistant agent and runs the Python code generated by the assistant. You can then trigger a conversation using the user proxy agent, providing instructions or queries. The assistant agent will generate Python code and provide detailed instructions on how to complete the task. You can provide feedback during the conversation to iterate on the output.
Creating a Collaborative Coding Agent
Autogen allows you to create complex multi-agent applications. Let's take a look at how you can create a coding agent involving three different agents - a user proxy agent, a coder agent, and a product manager agent. In a file called "code_agent.py", import the necessary libraries and define the configuration for the Autogen API. Create the three agents and define their roles within the coding process. Finally, create a group chat using the group chat manager to coordinate the conversation between the agents.
``` import autogen user_proxy = autogen.Agent(name="user proxy", code_execution_config="coding") coder = autogen.Agent(name="coder") product_manager = autogen.Agent(name="product manager") group_chat_manager = autogen.GroupChatManager() group_chat_manager.add_agents(user_proxy, coder, product_manager) group_chat_manager.create_group_chat() conversation = user_proxy.new_chat().send_message("Build a classic Pong game with two players in Python") print(conversation.messages) ```In this example, we create three agents - a user proxy agent, a coder agent, and a product manager agent. By using the group chat manager, we can coordinate their conversation and tasks. The user proxy agent triggers the conversation by providing the initial instructions, and the agents work together to complete the coding task. The conversation can involve iterations and feedback from the user, resulting in a better end product.
Building a Content Generation Pipeline
Autogen is not only useful for coding tasks but can also be used for content generation. Let's explore how you can create a content generation pipeline using Autogen. In a file called "content_agent.py", import the required libraries and define the functions for research and content writing. Create a research function that collects information based on a given topic and a writing function that creates a structure for the content and generates a well-written article.
``` import autogen researcher = autogen.Agent(name="researcher") editor = autogen.Agent(name="editor") writer = autogen.Agent(name="writer") reviewer = autogen.Agent(name="reviewer") user_proxy = autogen.Agent(name="user proxy", function_map={"research": researcher, "write_content": writer}) group_chat_manager = autogen.GroupChatManager() group_chat_manager.add_agents(user_proxy, editor, writer, reviewer) group_chat_manager.create_group_chat() feedback = user_proxy.new_chat().send_message("Write a blog about Autogen or AI agent frameworks") print(feedback.messages) ```In this example, we create four agents - a researcher, an editor, a writer, and a reviewer. The researcher collects information based on the given topic, and the writer generates a well-written article based on the research material and the structure provided by the editor. The reviewer provides feedback, and the writer iterates on the content accordingly. The conversation allows users to give feedback and iterate on the content until it meets their requirements.
Conclusion
Autogen is Microsoft's powerful multi-agent framework that revolutionizes AI agent interactions. Through its user proxy agents and group chat managers, Autogen enables users to have more control and collaboration in complex tasks. Whether you're working on coding projects, content generation, or other use cases, Autogen's features and capabilities make it a fantastic tool for developers, researchers, and content creators.
Ready to get started with Autogen? Install it today and unleash the power of multi-agent frameworks in your projects. Have an interesting use case in mind? Comment below and let us know! We're excited to see what incredible multi-agent applications you'll create. Stay tuned for more interesting AI tutorials and updates by subscribing to our channel. Thank you for reading, and we'll see you next time!
Frequently Asked Questions
1. What is Autogen?
Autogen is Microsoft's multi-agent framework that allows users to create groups of AI agents to work together on complex tasks.
2. How does Autogen solve the feedback challenge?
Autogen introduces user proxy agents that can interact with other assistant agents on behalf of users and ask for human input when necessary, enabling users to provide feedback and iterate on the output.
3. Can Autogen handle conversations with more than two agents?
Yes, Autogen's group chat manager allows users to create conversations with as many agents as they want, enabling collaboration and different perspectives from various agents.
4. How can Autogen be used in content generation?
Autogen can be used to create a content generation pipeline by involving agents such as researchers, writers, editors, and reviewers. This allows for collaboration and iteration on the content until it meets the desired requirements.
5. Where can I find more information about Autogen?
You can find more information about Autogen on the official Autogen GitHub repository. Check it out for detailed documentation and examples.
