What Is Llama Stack?
Learn about Llama Stack, a modular, API-first framework that unifies the core infrastructure and workflows needed to build, run, and scale generative AI applications in real-world environments.
Llama Stack was created to address a growing pain in AI: while models have rapidly advanced, building actual applications with them remains frustratingly complex. Developers often must patch together multiple libraries, services, and configurations to make a simple chatbot reliable and safe. Llama Stack streamlines this by offering an architecture with sensible defaults that allows you to customize and extend components as needed.
Despite its name, Llama Stack isn’t limited to Meta’s Llama models; it’s a flexible framework that can support almost any model through its provider abstraction layer.
The purpose of this lesson is to lay the groundwork for everything to come. Before we explore APIs, build agents, or connect retrieval systems, we need to understand what Llama Stack is fundamentally solving, how it’s designed, and what kind of applications it enables. We won’t be revisiting the basics of LLMs here. You’re expected to already be familiar with core generative AI concepts. Here, we’re tackling how to turn that model knowledge into application-level development without reinventing the wheel at every layer.
AI development today
If you’ve tried to build anything more than a one-off demo with an LLM, you’ve probably encountered the same core frustrations:
You start with a model API, maybe OpenAI, maybe Hugging Face. Great. But now you want to add document retrieval. You reach for a vector store like FAISS or Pinecone. To make this robust, you must chunk and embed documents, manage query routing, and cache results. Now, you’re juggling several SDKs and hand-coding glue logic between them.
Then comes safety. Your users may input offensive prompts, and your models may respond unpredictably. You add moderation, profanity filters, or a learned safety model. These safety checks live in a separate part of your codebase, disconnected from your app’s main logic, making them harder to maintain and reason about.
You want to add tools, maybe search the web or call an internal API. More glue. Then you want multi-turn conversations. Now you’re managing memory. Add telemetry, model evaluation, provider switching… and suddenly, you’re maintaining a dozen subsystems to keep your app functional.
This is not just your struggle but a common pattern across teams and companies attempting to build robust applications powered by large language models. Llama Stack addresses this challenge by offering a unified infrastructure layer that abstracts the glue code, integrates safety and memory, and allows you to focus on what your application should do, not on how to connect its components.
The Llama Stack perspective: unify, abstract, and empower
Llama Stack is built around a single core idea: software engineers should be able to focus on what their AI application does, not how it’s wired together across providers, SDKs, and orchestration layers.
It does this through three primary strategies:
Service-oriented APIs: Every capability, model inference, tool execution, safety filtering, and retrieval is exposed as a clean, RESTful API. These APIs have consistent request/response shapes and can be used locally, remotely, or in hybrid setups.
Pluggable providers: Each API is backed by interchangeable providers. Run inference locally with Ollama, or switch to Together AI or Fireworks for cloud-based GPU acceleration. Llama Stack handles the provider switching behind the scenes, so your app logic stays the same.
Composed workflows: Instead of stitching together isolated prompts or writing brittle pipelines, you define dynamic, multi-step logic using Llama Stack’s agent framework. An agent is the central orchestrator, managing tools, memory, safety checks, and reasoning over multiple interactions.
As a result, you get a consistent developer experience without sacrificing control over runtime behavior.
What makes up the Llama Stack?
Conceptually, Llama Stack is a layered system, though the boundaries are fluid and modular in practice.
At the bottom are the providers, the engines that perform the work: large language models (LLMs), vector databases, safety checkers, and tool runtimes. These providers may be local (Python modules running in process) or remote (such, hosted APIs and databases).
Above that are APIs, the formal interfaces that abstract over those providers. This includes APIs like
inference,agents,tools,safety,eval, and more.Sitting above the APIs are resources, entities like models, databases, tool groups, and scoring functions that can be registered and managed via configuration or code.
Finally, clients interact with the stack using SDKs or HTTP calls, allowing developers to define, execute, and monitor application workflows.
This architecture decouples functionality from implementation. You’re no longer tied to a single vendor’s way of doing things. Want to swap from FAISS to Weaviate? Replace a single line. Want to change from CPU inference to GPU? Swap providers without reworking your logic.
Llama Stack is deliberately designed to support different stages of application development:
Prototype locally: Start with Ollama and SQLite-Vec. Use a basic Llama model and run your entire stack locally on the CPU. Everything, including the model, vector store, and telemetry, can run in-process.
Test integrations: Register a remote model via Fireworks or Together AI. Replace the vector DB with Chroma or Qdrant. Hook in a shield like Llama Guard. The client code stays the same.
Deploy to production: Containerize your application and deploy it using Docker. Choose either cloud-optimized providers or self-hosted infrastructure. Enable telemetry output to OpenTelemetry collectors or dashboards.
This transition is seamless because the APIs don’t change across environments. There are no rewrites, no tangled migration scripts, and just consistent behavior from dev to prod.
A stack designed for learning and iteration
Unlike many LLM frameworks, which focus on inference or fine-tuning out of the box, Llama Stack supports interactive, iterative development as a built-in pattern. This is especially useful for applications that evolve through trial and error.
For example:
You try a prompt and notice that the model is hallucinating. You attach a RAG tool to the domain documents.
Now the outputs are more grounded, but the user queries are inconsistent. You attach a safety shield.
The results are accurate, but the model continues to over-explain. You tweak the system instructions.
You want to measure improvement. You run evaluations on a benchmark dataset.
Each of these steps doesn’t require a rewrite or a new script. It’s just a config or API tweak. The core design goal is to keep the dev loop fast, observable, and composable.
Fun fact: The term “tech stack” comes from the layered OSI protocol stacks of the 1980s, but it took off in 1998 with the rise of LAMP (Linux, Apache, MySQL, PHP), which made bundling and naming your tech layers cool.
What you will learn in this course
Throughout this course, you’ll develop a document-aware chatbot using Llama Stack. But more importantly, you’ll learn how to think about stack components and workflows.
This includes:
Configuring Llama Stack in a local dev environment.
Connecting to an inference backend and testing basic prompts.
Ingesting and querying documents using vector IO.
Using agents to combine tools, memory, and safety policies.
Performing evaluations and tracking performance.
By the end, you’ll be comfortable with the conceptual architecture and the practical skills needed to build real-world GenAI apps using Llama Stack.