Core Concepts of Llama Stack
Describe the key architectural components of Llama Stack, including APIs, Providers, Resources, and Distributions, and their interrelationships.
After running our first Llama Stack application in the previous lesson, you’ve already experienced the surface of the stack: you started a server, called an inference API, and got back a model-generated response. But what exactly happened under the hood? What does Llama Stack consist of, and how do its parts work together?
Understanding the core concepts of Llama Stack will help us prepare you for what’s ahead: building agents, using tools, storing documents, enabling retrieval, and more. This is the architectural backbone that supports every future application you’ll build.
Why abstraction matters in GenAI development
Before diving into the technical details, let’s step back for a moment.
As generative AI applications become more complex, the need for structure increases. Building robust apps requires more than just prompting a model; it’s about managing tools, memory, safety, evaluation, and provider infrastructure. Without a framework, developers end up building brittle, one-off pipelines.
Llama Stack provides a systemized way to manage these concerns. Its design philosophy emphasizes clean interfaces, modular components, and provider independence.
APIs: the foundation of Llama Stack
At the heart of Llama Stack is its API first architecture. Each functional capability, whether it’s inference, document retrieval, tool execution, or safety checks, is exposed via a REST API.
Here’s a quick overview of the supported APIs:
Inference: Executes a forward pass through an LLM to generate completions.
Agents: Manages multi-turn workflows that combine inference with memory and tool usage.
Tools: ...