The RIGHT WAY To Build AI Agents with CrewAI (BONUS: 100% Local)
TLDRIn this video, the presenter outlines the optimal setup for a Crew AI team, as demonstrated by the founder of Crew AI. The process involves using Lightning AI, a cloud-based code editor that facilitates collaboration and open-source model integration. The video guides viewers through building a financial analyst Crew AI code framework, utilizing YAML for defining agents and tasks, and structuring the codebase for clarity and modularity. The presenter also showcases how to swap out GP4 with Mixol or Mistol for powering the AI. The video concludes with the successful creation of a financial analysis team, demonstrating the efficiency and speed of Lightning AI's cloud environment and GPU capabilities.
Takeaways
- 🚀 **Optimal AI Team Setup**: The video demonstrates the best practices for setting up a Crew AI team, using insights from the founder of Crew AI.
- 🌟 **Lightning AI Sponsorship**: The video is sponsored by Lightning AI, which provides a cloud-based code editor for collaboration and open-source model powering.
- 💻 **Cloud-Based Development**: The use of Lightning AI's cloud environment simplifies Python environment management, offering a fresh environment every time.
- 📁 **Modular Framework**: The Crew AI code framework is structured to be modular, with separate areas for tools, YAML for defining agents and tasks, and a streamlined main.py file.
- 🔍 **Research and Analysis Tasks**: Two main tasks are defined: researching a company's stock information and analyzing the financial metrics for a thorough financial analysis.
- 🤖 **Agent Definitions**: Agents are defined with specific roles and goals, such as 'company researcher' and 'company analyst', each with a backstory and settings for delegation and verbosity.
- 📝 **YAML for Structure**: YAML files are used to define the structure of agents and tasks, which may soon allow for API endpoint exposure for controlling the crew.
- 🔗 **Plugin Integration**: The process of integrating tools from the Crew AI examples library, such as SEC tools, is shown to streamline the development process.
- 🔑 **Gro API Key**: A Gro API key is used to power the AI models, with the presenter noting the importance of revoking the key before publishing for security.
- 🛠️ **Poetry for Dependency Management**: Poetry is used to manage project dependencies, lock them, and install them, which is becoming a standard practice in the development process.
- 🔋 **Leverage Lightning AI's GPU Power**: The video shows how to use Lightning AI's GPUs to power open-source models, highlighting the option to choose from various GPU types based on model needs.
Q & A
What is the main topic of the video?
-The main topic of the video is demonstrating the optimal way to set up a Crew AI team using Lightning AI, a cloud-based code editor, and open-source models.
What is Lightning AI and how does it help in the process?
-Lightning AI is a cloud-based code editor that allows for collaboration on code in the cloud and the ability to power open-source models. It simplifies the process by providing a fresh environment every time, eliminating the need for manual Python environment management.
How does the video presenter plan to structure the Crew AI code framework?
-The presenter plans to structure the Crew AI code framework in a modular way, with separate areas for tools, using YAML to define agents and tasks, and having everything pipe into a concise main.py file.
What are the two main tasks that the presenter defines in the script?
-The two main tasks defined are 'research company task', which involves looking up a company's stock information, and 'analyze company task', which involves providing a financial analysis including various financial metrics.
What is the role of the 'config' folder in the setup?
-The 'config' folder is used to store the definitions of tasks and agents in YAML files, which are essential for structuring the Crew AI code framework.
How does the presenter plan to use the Grock model in the process?
-The presenter plans to use the Grock model as the LLM (Large Language Model) to power the agents within the Crew AI team, allowing for natural language processing capabilities.
What is the advantage of using an open-source model powered by Lightning AI?
-The advantage of using an open-source model powered by Lightning AI is the ability to leverage their GPUs for faster processing, and the flexibility to plug in any model of choice into the Crew AI setup.
How does the video demonstrate the use of Lightning Studio?
-The video demonstrates the use of Lightning Studio by showing how to create a new studio, set up the environment, and start building the Crew AI code framework directly in the cloud-based interface.
What is the significance of the 'financial analyst crew' folder created in the source directory?
-The 'financial analyst crew' folder is significant as it represents the specific type of Crew AI team being built in the video, focusing on financial analysis tasks.
How does the presenter handle the creation of agents and their association with tasks?
-The presenter creates separate agents for each task, defining their roles, goals, and backstory. Each agent is then associated with a specific task in the code, ensuring a clear mapping between agent capabilities and task requirements.
What is the purpose of the 'main.py' file in the script?
-The 'main.py' file serves as the entry point of the application, where the crew base is defined, and the agents and tasks are initialized and run sequentially to perform the desired analysis.
How does the presenter ensure that the environment is ready to use without manual setup?
-The presenter ensures this by using Lightning AI's cloud-based environment, which automatically saves files and maintains a consistent setup, eliminating the need for manual environment configuration.
Outlines
🚀 Introduction to Building a Crew AI Team with Lightning AI
The video begins with the host expressing excitement about demonstrating the optimal setup for a Crew AI team, as advised by the Crew AI founder. The process involves using Lightning AI, a cloud-based code editor that facilitates collaboration and open-source model integration. The host outlines the steps to create a new studio on Lightning AI, emphasizing the ease of environment management and the modular structure of the Crew AI codebase. The video promises to guide viewers through building a Crew AI team, swapping out GP4, and using either Mixol or Mistol to power the team.
📚 Structuring the Crew AI Code Framework and Tasks
The host delves into the specifics of setting up the Crew AI code framework, starting with the creation of a 'source' folder and a 'financial analyst crew' subfolder. Within this structure, a 'config' folder is established to house YAML files defining agents and tasks. The video demonstrates how to define tasks using variables and expected outputs, with examples including researching a company's stock information and analyzing financial metrics. The host also discusses the potential for an API to control the Crew based on its structure.
🤖 Defining Agents and Combining Tasks in the Main File
The video continues with the creation of agent definitions, detailing the structure and attributes of a 'company researcher' and a 'company analyst'. The host explains the process of setting roles, goals, and preferences for delegation and verbosity. The agents are then implemented in a 'crew.py' file, which also includes the integration of the Gradio library for model interaction. The main file, 'main.py', is introduced as a simple entry point that will eventually invoke the Crew AI operations.
🔌 Running the Crew AI with Gro and Exploring Open-Source Model Integration
The host demonstrates running the Crew AI with Gro, showcasing the quick execution and output of financial analysis for Tesla. The video then explores integrating an open-source model powered by Lightning AI's GPUs. The process includes setting up a template, exposing an API endpoint, and configuring the Crew AI to use this endpoint. The host successfully tests the integration, noting the slower performance due to the large model size and non-premium GPU. The video concludes with a call to like and subscribe for more content.
Mindmap
Keywords
💡Crew AI
💡Lightning AI
💡YAML
💡Main.py
💡Financial Analyst Crew
💡Grok
💡Mixture of Experts (MoE)
💡API Endpoint
💡Poetry
💡Plugin
💡GPUs
Highlights
Demonstrates the optimal way to set up a Crew AI team using Lightning AI, a cloud-based code editor that facilitates collaboration and open-source model integration.
Introduction of a modular structure for the Crew AI codebase, emphasizing the use of YAML for defining agents and tasks.
Explanation of how to create a new studio in Lightning AI and the benefits of its cloud environment for Python environment management.
Creation of a 'financial analyst crew' within the source folder to house the new Crew AI code framework.
Utilization of the 'agents.yml' and 'tasks.yml' files to define the roles, goals, and expected outputs for the AI agents and tasks.
Discussion on the potential for Crew AI to expose an endpoint for controlling the crew based on the defined structure, enabling API creation.
Description of the two main tasks: 'research company task' and 'analyze company task', including the parameters and expected financial outcomes.
Introduction of two AI agents, 'company researcher' and 'company analyst', each with specific roles and goals aligned with the tasks.
Mention of the convenience of Lightning AI's automatic saving feature, eliminating the need for manual file saving.
Overview of creating the main file 'crew.py' that integrates all agents and tasks within the financial analyst crew.
Importance of setting up the Gradio information for the crew, including the model name and temperature settings.
Instructions on creating a 'main.py' file that serves as the entry point for running the financial analyst crew with a specific company name as input.
Use of the 'poetry' tool to manage dependencies and run the project, showcasing the setup process for a modern Crew AI project.
Solution to a 'module not found' issue by installing necessary packages and successfully running the financial analyst crew.
Real-time demonstration of the crew's functionality, providing fast and accurate financial analysis of a company like Tesla.
Showcasing how to run the crew with an open-source model powered by Lightning AI's GPUs, providing flexibility in model choice.
Explanation of exposing an API endpoint with LLM (Large Language Model) compatibility and integrating it with the Crew AI project.
Successful execution of the Crew AI project with an open-source model, confirming its practical application and effectiveness.
Encouragement for viewers to like, subscribe, and follow for more content on building AI agents and utilizing modern AI tools.