llama-stack-mirror/docs/source/providers/index.md
2025-07-23 10:26:13 -04:00

2.5 KiB

API 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., Meta Reference, Ollama, Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, OpenAI, Anthropic, Gemini, WatsonX, etc.),
  • Vector databases (e.g., FAISS, SQLite-Vec, ChromaDB, Weaviate, Qdrant, Milvus, PGVector, etc.),
  • Safety providers (e.g., Meta's Llama Guard, Prompt Guard, Code Scanner, AWS Bedrock Guardrails, etc.),
  • Tool Runtime providers (e.g., RAG Runtime, Brave Search, 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.

:maxdepth: 1

external.md
:start-after: ## OpenAI API Compatibility

Inference

Runs inference with an LLM.

:maxdepth: 1

inference/index

Agents

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

:maxdepth: 1

agents/index

DatasetIO

Interfaces with datasets and data loaders.

:maxdepth: 1

datasetio/index

Safety

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

:maxdepth: 1

safety/index

Telemetry

Collects telemetry data from the system.

:maxdepth: 1

telemetry/index

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.

:maxdepth: 1

vector_io/index

Tool Runtime

Is associated with the ToolGroup resources.

:maxdepth: 1

tool_runtime/index

Files Providers

Is associated with the files APIs

:maxdepth: 1

files/index