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Building Scalable and Observable AI Agents
A Tutorial with Mastra(TypeScript), Langfuse, and Tigris Data
The world of AI agents, powered by Large Language Models (LLMs), is brimming with potential. But building and maintaining these complex systems can feel like navigating uncharted territory. How do you understand their behavior? How do you ensure they scale effectively?
Enter the “dream team”: Mastra, the intuitive AI agent framework; Langfuse, the essential observability platform for LLMs; and Tigris Data, the robust and scalable backend storage. Together, they provide a powerful foundation for building sophisticated and reliable AI applications.
In this tutorial, we’ll guide you through the process of creating a basic AI agent using Mastra, integrating Langfuse for deep insights, and leveraging Tigris Data for scalable data storage. Get ready to build smarter and more transparent AI!
Understanding the Core Components
Let’s get acquainted with our toolkit:
A. Mastra: The AI Agent Framework
Mastra is a TypeScript framework designed to streamline the creation of AI agents. It provides a structured way to define agent behavior, manage memory, utilize tools, and even implement Retrieval Augmented…
