The RIGHT WAY To Build AI Agents with CrewAI (BONUS: 100% Local)

The video showcases the process of setting up a Crew AI team using Lightning AI, from structuring agents and tasks to integrating tools and powering the crew with open-source models. The demonstration focuses on creating a financial analyst crew, utilizing Grok for information retrieval, and leveraging Lightning AI’s GPU capabilities for improved model performance.

In the video, the speaker demonstrates the optimal way to set up a Crew AI team, as advised by the Crew AI founder. The process involves using Lightning AI, a cloud-based code editor that allows for collaboration on code and powering open-source models. The speaker shows how to build a team, swap out GPT-4 for Mixol or Mistol, and thanks Lightning AI for sponsoring the video. A new Lightning Studio is created to start building the Crew AI code framework. The structure involves separate areas for tools, YAML files for defining agents and tasks, and a file to bring everything together.

The process begins by creating a source folder in the new Lightning Studio, followed by creating a specific crew folder, in this case, a financial analyst crew. Within this folder, a config folder is created to house definitions of agents and tasks. YAML files for agents and tasks are then created, detailing the tasks of researching a specific company and analyzing company data. The agents’ roles and goals are defined in the agents.yaml file, matching each agent to an individual task.

Moving on to the file, the speaker imports relevant libraries and defines the financial analyst crew class, loading agents and tasks previously created. The Grok information is set up, and agents and tasks are pulled into the file. The crew is then defined, bringing together all agents and tasks in a sequential process. The file is created to print a message, and an API key for Grok is provided in a separate file.

The process continues with the creation of tools, where existing SEC tools are utilized from the Crew AI examples library. Poetry is used to manage dependencies, and the code is run to check if the crew setup works correctly. The process is successful, showcasing the crew in action, providing stock information, financial analysis, and relevant metrics. Furthermore, the speaker demonstrates how to power the crew with an open-source model using Lightning AI’s GPU support, showing the process of setting up an API endpoint and integrating it into the crew for enhanced functionality.

In conclusion, the video provides a detailed walkthrough of setting up a Crew AI team using Lightning AI, structuring agents and tasks, integrating tools, and powering the crew with open-source models. This comprehensive guide covers the setup of a financial analyst crew, the use of Grok for information retrieval, and the utilization of Lightning AI’s GPU capabilities to enhance model performance. The step-by-step instructions, along with practical demonstrations, offer valuable insights into building efficient AI agents with Crew AI, empowering viewers to create their AI projects effectively.