The RIGHT WAY To Build AI Agents with CrewAI (BONUS: 100% Local)
TLDRThe video presents a comprehensive guide on setting up a Crew AI team using Lightning AI, a cloud-based code editor that facilitates collaboration and supports open-source models. The host, with guidance from Crew AI's founder, demonstrates how to build a modular code framework for a financial analyst team. The process involves creating a new studio, structuring the code with a main.py file, defining agents and tasks in YAML files, and utilizing Lightning AI's environment for seamless Python environment management. The video also covers the integration of various tools and models, such as gp4, Mixol, and Mistol, to perform tasks like researching and analyzing a company's stock performance. Additionally, the host shows how to expose an API endpoint from an open-source model and use it to power the Crew AI team, highlighting the flexibility and speed of Lightning AI's platform.
Takeaways
- 🌟 The video demonstrates the optimal way to set up a Crew AI team using Lightning AI, a cloud-based code editor that facilitates collaboration and supports open-source models.
- 🛠️ The presenter introduces a modular structure for the Crew AI code framework, utilizing YAML files to define agents and tasks, which simplifies the integration into a main.py file.
- 📁 A new Lightning studio is created to house the project, emphasizing the ease of environment setup and the avoidance of common Python environment management issues.
- 🔍 The 'research company task' is defined to gather stock information using a search tool, aiming to prepare data for an informed analysis of a company's stock performance.
- 📊 The 'analyze company task' is outlined to include various financial metrics such as profitability, liquidity, and solvency ratios for a comprehensive financial analysis.
- 👥 Two agents are created: 'company researcher' and 'company analyst', each with specific roles and goals aligned with the tasks, and both set to not delegate and to provide verbose output.
- 🔧 The video showcases the integration of the Lang chain and Gradio libraries for building and running the Crew AI project, highlighting the flexibility to use different models for each agent.
- 🔗 The process of creating a main.py file is simplified, with the script automatically handling file saves and environment persistence in the cloud.
- 🔄 The video guides through fixing a 'module not found' issue by reinstalling necessary packages, demonstrating troubleshooting steps in the development process.
- 🚀 The final demonstration runs the Crew AI team using Gro, an AI model, and then transitions to using an open-source model powered by Lightning AI's GPUs, showcasing the flexibility and power of the setup.
- 🔗 The presenter also explains how to expose an API endpoint using the olama mixture of experts model and integrate it with the Crew AI team, emphasizing the modern approach to AI development.
Q & A
What is the main topic of the video?
-The main topic of the video is to demonstrate the optimal way to set up a Crew AI team using Lightning AI, a cloud-based code editor, and how to integrate it with open-source models like Mixol or Mistol.
What is Lightning AI and how does it help in building AI agents?
-Lightning AI is a cloud-based code editor that facilitates collaboration on code in the cloud. It also enables the powering of open-source models, which is showcased in the video for building AI agents.
What is the purpose of the 'source' folder created in the video?
-The 'source' folder is created to structure the codebase for the Crew AI project, providing a modular and organized environment for the code and related files.
What is the role of YAML files in defining agents and tasks in the Crew AI framework?
-YAML files are used to define the structure and properties of agents and tasks within the Crew AI framework. They help in organizing the code and making it easier to manage and understand.
How does the video demonstrate the creation of tasks in the Crew AI codebase?
-The video demonstrates creating tasks by defining them in a YAML file named 'task.yml'. It includes descriptions of the tasks, expected inputs, and expected outputs, such as researching a company's stock information and analyzing company financials.
What is the significance of the 'agents.yml' file in the Crew AI setup?
-The 'agents.yml' file is crucial as it contains the definitions of the agents, including their names, roles, goals, and other attributes. It helps in structuring how agents will operate within the Crew AI framework.
How does the video show the integration of the Gradio model with the Crew AI team?
-The video does not specifically mention Gradio, but it does show the integration of the Gro model (an AI model) with the Crew AI team by defining tasks and agents that utilize this model for financial analysis.
What is the purpose of the 'main.py' file in the Crew AI project?
-The 'main.py' file serves as the entry point of the Crew AI project. It imports necessary libraries, defines the crew, and orchestrates the execution of agents and tasks within the framework.
How does the video guide the process of setting up the environment for running the Crew AI project?
-The video guides the process by showing the creation of a new Lightning studio, setting up the code structure, and using Poetry to manage dependencies and run the project with the necessary commands.
What is the final outcome demonstrated in the video after setting up and running the Crew AI project?
-The final outcome is a functioning Crew AI team that performs financial analysis tasks. It successfully researches a company's stock information and analyzes financial metrics, demonstrating the effectiveness of the setup.
Outlines
🚀 Introduction to Setting Up a Crew AI Team with Lightning AI
The speaker introduces the video's goal to demonstrate the optimal setup for a Crew AI team, based on insights from the Crew AI founder. The process will utilize Lightning AI, a cloud-based code editor that facilitates collaboration and open-source model integration. The video will guide viewers through building a Crew AI team, transitioning from using GP4 to powering it with either Mixol or Mistol. The speaker expresses gratitude to Lightning AI for sponsoring the video and invites viewers to access Lightning Studio through a link in the description. The initial steps involve creating a new studio on Lightning AI, emphasizing the benefits of its cloud-based environment for streamlined Python environment management. The high-level structure of the Crew AI will be modular, with separate areas for tools, YAML files for defining agents and tasks, and a streamlined main.py file.
📚 Building the Crew AI Code Framework and Task Definition
The speaker proceeds to build the Crew AI code framework in a new Lightning studio, detailing the process of creating a source folder and a financial analyst crew subfolder. Inside the crew folder, a config subfolder is created to house the agent and task definitions in YAML files. The speaker outlines the first task, 'research company task', which involves using a search tool to gather stock information on a specified company, with the aim of preparing for an informed analysis of the company's stock performance. The expected output is defined as all relevant financial information about the company's stock performance. A second task, 'analyze company task', is then introduced, which will use the company's financial information to perform a financial analysis, including various financial ratios. The speaker also mentions the potential future integration of an API based on the structured YAML definitions.
👷♂️ Defining Agents and Setting Up the Main File for Crew AI
The speaker moves on to defining agents in the Crew AI framework, starting with 'company researcher' and 'company analyst' agents, each with specific roles and goals aligned with the tasks. The agents are designed not to delegate tasks and to provide verbose output for transparency. The main file, crew.py, is then created to integrate all agents and tasks. The speaker explains the process of importing necessary libraries and setting up the crew base class, 'financial analyst crew', which includes loading agents and tasks, and integrating with Gradio for model interaction. The speaker also demonstrates how to define tasks and agents within the main file, using decorators and referencing the YAML configurations.
🔧 Finalizing the Crew AI Setup and Testing with Lightning AI
The video concludes with the finalization of the Crew AI setup, including the creation of a .env file for the Gradio API key and the incorporation of existing tools from the Crew AI examples library, specifically the SEC tools. The speaker uses Poetry to manage dependencies and run the project, detailing the steps to install Poetry, set up the project configuration, lock dependencies, and install the necessary packages. The main.py file is updated to import the financial analyst crew and run the crew with a sample input, 'Tesla'. The speaker encounters a module not found issue, which is resolved by reinstalling the required packages. The final output demonstrates the successful execution of the Crew AI, providing financial analysis and metrics for Tesla. The speaker also explores the option of running the Crew AI with an open-source model powered by Lightning AI's GPUs, showing the process of setting up and exposing an API endpoint for integration.
Mindmap
Keywords
💡Crew AI
💡Lightning AI
💡YAML
💡Main.py
💡Financial Analyst Crew
💡Grok
💡Lang Chain
💡MiXOL
💡API Endpoint
💡Poetry
Highlights
Introduction to the optimal way to set up a Crew AI team using Lightning AI, a cloud-based code editor.
Lightning AI enables collaboration on code in the cloud and powering open-source models.
Building a Crew AI team using modular structure with separate areas for tools and YAML for defining agents and tasks.
Creating a new studio in Lightning and setting up a fresh environment for Python without environment management issues.
Explanation of the main.py file and its role in the overall structure of the Crew AI codebase.
Creating a source folder and a financial analyst crew within it for structuring the project.
Details on creating a config folder and defining tasks and agents in YAML files.
Description of the 'research company task' for analyzing a company's stock information.
Creation of a 'company analysis task' including financial metrics for thorough financial analysis.
Agent definitions with roles, goals, and backstories for the 'company researcher' and 'company analyst'.
Setting up the main file 'crew.py' to import libraries and integrate agents and tasks.
Using Lang chain and Gradio to power the AI with different models like Mixol or Mistol.
Automatic saving feature of Lightning AI that eliminates the need to manually save files.
Running the project using Poetry to manage dependencies and execute the financial analyst crew.
Troubleshooting a module not found issue by reinstalling necessary packages.
Demonstration of the crew's functionality with Tesla's stock information and financial analysis.
Switching to an open-source model powered by Lightning AI to showcase flexibility in model usage.
Using the studio templates page in Lightning to set up a preconfigured environment for running models like Mixol.
Exposing an API endpoint with Olama and integrating it with the Crew AI project.
Final confirmation of the successful integration of the open-source model with the Crew AI framework.
