AutoGen Studio Tutorial - NO CODE AI Agent Builder (100% Local)
TLDRAutoGen Studio, a groundbreaking AI agent project by Microsoft's research team, is now open source and localizable. This video tutorial demonstrates how to install, set up, and utilize AutoGen Studio with both GPT-4 and local models for tasks like plotting stock charts and writing code. It covers environment creation with conda, API key generation, and the basics of skills, agents, and workflows within the AutoGen framework, showcasing the flexibility and power of this AI tool.
Takeaways
- π Autogen Studio is a new release from the Microsoft research team, allowing users to create advanced AI agent teams without coding.
- π It's an open-source project that can be run locally and can be powered by Chat GPT or local models.
- π οΈ Users can perform a variety of tasks such as plotting stock charts, planning trips, and writing code through Autogen Studio.
- π§ Before using Autogen Studio with GPT-4, install and set up the conda environment manager for Python.
- π An OpenAI API key is required, which is set in the environment for Autogen to access.
- π Autogen Studio comes with a user interface that allows for easy creation and management of skills, agents, and workflows.
- π Skills in Autogen Studio are tools written in code that agents can use, such as image generation or fetching papers from archives.
- π€ Agents are individual AI units with roles, tools, and the ability to perform tasks autonomously or with user input.
- π Workflows combine everything, including the team and task, and can be tested in the playground section of Autogen Studio.
- π¨ Autogen Studio can be set up with local models using tools like Olama and Light LLM for offline functionality.
- π§ Different models can power different agents, allowing for tailored AI solutions for specific tasks.
Q & A
What is Autogen Studio and what can it be used for?
-Autogen Studio is an open-source project developed by the Microsoft research team. It allows users to create sophisticated AI agent teams with ease and can be powered by Chat GPT or local models. It can handle tasks such as plotting stock charts, planning trips, and writing code.
How does one get started with Autogen Studio?
-To get started with Autogen Studio, one needs to install conda to manage Python environments, create a new conda environment with Python 3.11, and install Autogen Studio using pip. An OpenAI API key is also required, which is used to power the AI agents.
What is the significance of skills in Autogen Studio?
-Skills in Autogen Studio are tools that can be given to AI agents and agent teams. They are usually written in code and can be used to perform a variety of tasks, such as generating images or finding papers on archives. Skills can be customized and added to agents as needed.
What is the role of agents in Autogen Studio?
-Agents in Autogen Studio are individual AI entities that have a role, tools, and can perform tasks. They can be powered by different models and can be used in various workflows. By default, Autogen Studio comes with two primary agents: the primary assistant and the user proxy.
How are workflows structured in Autogen Studio?
-Workflows in Autogen Studio put everything together, including the team and the task to be accomplished. They define the method to summarize the conversation, the sender and receiver, and the agents involved. Workflows can be customized to include multiple models and skills to achieve the desired outcome.
How can Autogen Studio be used with local models?
-Autogen Studio can be used with local models by utilizing tools like Olama and Light LLM. Olama allows users to power models locally, and Light LLM exposes an API for the local models. Users can create agents and workflows that are powered by these local models instead of or in addition to Chat GPT.
What is the purpose of the playground in Autogen Studio?
-The playground in Autogen Studio is where users can test out different agent teams. It allows users to create sessions for agent teams to accomplish tasks, and users can see the results of these tasks in the playground interface.
How can one switch between different models for different agents in Autogen Studio?
-Users can switch between different models for different agents by setting up multiple local models using tools like Olama and Light LLM, and then creating agents that are powered by these local models. Each agent can be configured to use a specific local model by providing the base URL of the corresponding model in the agent settings.
What is the significance of the 'human input mode' in Autogen Studio?
-The 'human input mode' in Autogen Studio determines when user input is required during the execution of a workflow. It can be set to 'never', 'only on terminate', or 'always on every step', allowing users to control the level of interaction with the AI agents.
How can one customize the behavior of agents in Autogen Studio?
-The behavior of agents in Autogen Studio can be customized by defining system messages and adding skills. System messages control the agent's behavior during interactions, while skills allow agents to perform specific tasks or use specific APIs.
What does Autogen Studio offer in terms of authentication and sharing among teams?
-Autogen Studio allows for the implementation of custom authentication logic, making it possible to share the environment among teams. This feature can be particularly useful for collaborative projects where multiple users need access to the AI agents and workflows.
Outlines
π Introduction to Autogen Studio and Setup
The paragraph introduces Autogen Studio, a revolutionary AI agent project by Microsoft's research team. It highlights the release of this open-source platform that enables users to create sophisticated AI agent teams with ease. The speaker explains the process of setting up Autogen Studio, emphasizing the need for a Python environment managed by conda. They guide the user through creating a new conda environment, installing Autogen Studio, and using it with GPT-4 and local models. The importance of obtaining an OpenAI API key and setting it in the environment is also discussed. The paragraph concludes with instructions on launching Autogen Studio and accessing its user interface.
π οΈ Exploring Autogen Studio's Features and Skills
This paragraph delves into the various features of Autogen Studio, focusing on skills and agents. Skills are defined as tools that AI agents can utilize, typically written in code. The paragraph explains the default skills provided, such as generating images and finding papers on archives, and how they function by connecting to APIs. It also discusses the ability to create new skills and the potential for integrating with services like Zapier for sophisticated task automation. The concept of agents is introduced, with a distinction made between primary assistants, user proxies, and other autonomous agents. The paragraph further explains the creation of new agents and the assignment of specific models to them.
π Workflows and Agent Team Management
The paragraph discusses the concept of workflows in Autogen Studio, which integrate teams and tasks. It describes how to set up a group chat workflow, including defining summary methods, senders, and receivers. The importance of the group chat manager in complex teams is highlighted. The paragraph also covers the process of adding models and skills to workflows, and the ability to test these configurations in the playground. An example of creating a stock price plot for Nvidia and Tesla using the visualization agent workflow is provided, demonstrating the step-by-step interaction between agents and the completion of tasks.
π Local Model Integration and Authentication
This paragraph focuses on the local integration of models in Autogen Studio and the setup of authentication. It explains the process of using Olama and Light LLM to power models locally and expose an API. The steps for installing Olama, downloading a local model, and setting up a server with Light LLM are detailed. The paragraph then demonstrates how to create a local mistal assistant in Autogen Studio and integrate it into workflows. The process of testing the local model's functionality, such as telling a joke or writing code, is shown. Additionally, the paragraph touches on the ability to have different models for different agents and the customization of the authentication process within Autogen Studio.
π Conclusion and Future Possibilities
The final paragraph wraps up the discussion on Autogen Studio, expressing admiration for its capabilities. It reiterates the ease of managing tools and the flexibility of setting up different agents with various local models. The paragraph also discusses the potential for using different fine-tuned models for specific tasks. The speaker encourages viewers to share their thoughts and requests for further exploration of Autogen Studio. The video concludes with a call to action for likes, subscriptions, and comments from the audience.
Mindmap
Keywords
π‘Autogen Studio
π‘AI Agent
π‘Skills
π‘Workflows
π‘Local Models
π‘Chat GPT
π‘Environment Variables
π‘CondA
π‘Olama
π‘Light LLM
Highlights
Autogen Studio is a new tool developed by the Microsoft research team, allowing users to create sophisticated AI agent teams with ease.
It is a fully open-source project that can be run locally, offering flexibility in terms of powering it with different models, including GPT-4 and local models.
The tool can handle a variety of tasks, from plotting stock charts to planning trips and writing code, fulfilling the potential of chatbot GPTs.
To get started with GPT-4, users need to have Conda installed, which simplifies the management of Python environments.
Autogen Studio comes with a user-friendly interface, making it easy for users to install, set up, and use the tool effectively.
Users can create an API key from their OpenAI account and set it in their environment to allow Autogen to access it.
Autogen Studio allows users to define 'skills', which are tools or abilities that AI agents can utilize, often written in code and accessible by any agent.
Agents in Autogen Studio are individual AI entities with specific roles, tools, and task performance capabilities.
Workflows in Autogen Studio combine everything, including the team and the tasks to be accomplished, offering a structured approach to agent collaboration.
The platform enables users to test different agent teams in the 'playground', where tasks are assigned and results are displayed asynchronously.
Autogen Studio also supports local model usage, with tools like Olama and Light LLM, allowing for offline and customizable model deployment.
By using local models, users can have different models powering different agents, offering a tailored solution for specific tasks.
The platform provides a sign-out functionality, which can be customized for team sharing and authentication purposes.
Autogen Studio's interface is visually appealing and straightforward to navigate, making the management of AI agents more accessible.
The tool offers a high level of sophistication in task accomplishment, integrating with services like Zapier for extensive application interactions.
Users can create and save agent workflows, reusing solutions for future tasks without the need for recreation.
Autogen Studio provides a robust environment for AI agent development, with the potential for both simple and complex task automation.
The platform's ability to chain different models together offers a fallback option in case the primary model fails, ensuring continuity in task execution.