The video provides a step-by-step tutorial on setting up Crew AI and integrating Lang Trace to monitor the performance of AI agent applications, emphasizing the importance of visibility into token usage and quality insights. The presenter demonstrates the installation process, project configuration, and how to track performance metrics, ultimately showcasing the benefits of using Lang Trace for optimizing AI applications.
In the video, the presenter demonstrates how to set up Crew AI from scratch and install Lang Trace to monitor the performance of AI agent applications. The tutorial emphasizes the importance of having visibility into the operations of AI applications, particularly regarding token usage and quality insights provided by Lang Trace. The presenter uses Visual Studio Code (VS Code) for the setup process, starting with the installation of Crew AI tools via pip commands.
After ensuring Crew AI is installed, the presenter creates a new project named “demo” and selects OpenAI as the provider, specifically using the GPT-4 model. The process involves generating an OpenAI API key, which is then integrated into the project. The presenter highlights the ease of setting up the project, as it automatically creates the necessary directory structure and files. The initial configuration includes defining agents for research and reporting tasks, which are essential for the demo.
Once the basic setup is complete, the presenter runs the Crew AI application, which generates a report based on the research conducted by the AI agents. The output is a detailed report on recent AI developments, confirming that the initial setup works as intended. The presenter then transitions to Lang Trace, explaining how to sign up and create a project to track the application’s performance. The Lang Trace SDK is installed similarly to Crew AI, and the presenter integrates it into the main application code.
The video showcases the capabilities of Lang Trace, allowing users to monitor various metrics, including token usage and execution times for different tasks within the AI application. The presenter demonstrates how to view detailed traces of each task, providing insights into the performance of the AI agents and the models used. The metrics tab in Lang Trace displays the total tokens used, costs incurred, and other relevant statistics, making it easier for developers to analyze their applications.
Finally, the presenter runs the application again to generate a second report, demonstrating how Lang Trace updates automatically with new data. The video concludes with a discussion on the benefits of using Lang Trace for optimizing AI applications, including the ability to compare different models and their performance metrics. The presenter encourages viewers to explore Lang Trace, highlighting its open-source nature and the advantages it offers for building production-ready AI applications. The video ends with a call to action for viewers to like and subscribe for more tutorial content.