llama-stack/docs/source/providers/index.md

Providers Overview

The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include:

• LLM inference providers (e.g., Ollama, Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, etc.),
• Vector databases (e.g., ChromaDB, Weaviate, Qdrant, Milvus, FAISS, PGVector, SQLite-Vec, etc.),
• Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)

Providers come in two flavors:

• Remote: the provider runs as a separate service external to the Llama Stack codebase. Llama Stack contains a small amount of adapter code.
• Inline: the provider is fully specified and implemented within the Llama Stack codebase. It may be a simple wrapper around an existing library, or a full fledged implementation within Llama Stack.

Importantly, Llama Stack always strives to provide at least one fully inline provider for each API so you can iterate on a fully featured environment locally.

External Providers

Llama Stack supports external providers that live outside of the main codebase. This allows you to create and maintain your own providers independently. See the External Providers Guide for details.

Agents

Run multi-step agentic workflows with LLMs with tool usage, memory (RAG), etc.

DatasetIO

Interfaces with datasets and data loaders.

Eval

Generates outputs (via Inference or Agents) and perform scoring.

Inference

Runs inference with an LLM.

Post Training

Fine-tunes a model.

Post Training Providers

The following providers are available for Post Training:

:maxdepth: 1

external
post_training/huggingface
post_training/torchtune
post_training/nvidia_nemo

Safety

Applies safety policies to the output at a Systems (not only model) level.

Scoring

Evaluates the outputs of the system.

Telemetry

Collects telemetry data from the system.

Tool Runtime

Is associated with the ToolGroup resouces.

Vector IO

Vector IO refers to operations on vector databases, such as adding documents, searching, and deleting documents. Vector IO plays a crucial role in Retreival Augmented Generation (RAG), where the vector io and database are used to store and retrieve documents for retrieval.

Vector IO Providers

The following providers (i.e., databases) are available for Vector IO:

:maxdepth: 1

external
vector_io/faiss
vector_io/sqlite-vec
vector_io/chromadb
vector_io/pgvector
vector_io/qdrant
vector_io/milvus
vector_io/weaviate