README.md

Llama Stack

Quick Start | Documentation | OpenAI API Compatibility | Discord

Open-source agentic API server for building AI applications. OpenAI-compatible. Any model, any infrastructure.

Llama Stack is a drop-in replacement for the OpenAI API that you can run anywhere — your laptop, your datacenter, or the cloud. Use any OpenAI-compatible client or agentic framework. Swap between Llama, GPT, Gemini, Mistral, or any model without changing your application code.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8321/v1", api_key="fake")
response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Hello"}],
)

What you get

• Chat Completions & Embeddings — standard /v1/chat/completions, /v1/completions, and /v1/embeddings endpoints, compatible with any OpenAI client
• Responses API — server-side agentic orchestration with tool calling, MCP server integration, and built-in file search (RAG) in a single API call (learn more)
• Vector Stores & Files — /v1/vector_stores and /v1/files for managed document storage and search
• Batches — /v1/batches for offline batch processing
• Open Responses conformant — the Responses API implementation passes the Open Responses conformance test suite

Use any model, use any infrastructure

Llama Stack has a pluggable provider architecture. Develop locally with Ollama, deploy to production with vLLM, or connect to a managed service — the API stays the same.

┌─────────────────────────────────────────────────────────────────────────┐
│                          Llama Stack Server                             │
│               (same API, same code, any environment)                    │
│                                                                         │
│  /v1/chat/completions  /v1/responses  /v1/vector_stores  /v1/files      │
│  /v1/embeddings        /v1/batches    /v1/models         /v1/connectors │
├───────────────────┬──────────────────┬──────────────────────────────────┤
│  Inference        │  Vector stores   │  Tools & connectors              │
│    Ollama         │    FAISS         │    MCP servers                   │
│    vLLM, TGI      │    Milvus        │    Brave, Tavily (web search)    │
│    AWS Bedrock    │    Qdrant        │    File search (built-in RAG)    │
│    Azure OpenAI   │    PGVector      │                                  │
│    Fireworks      │    ChromaDB      │  File storage & processing       │
│    Together       │    Weaviate      │    Local filesystem, S3          │
│    ...15+ more    │    Elasticsearch │    PDF, HTML (file processors)   │
│                   │    SQLite-vec    │                                  │
└───────────────────┴──────────────────┴──────────────────────────────────┘

See the provider documentation for the full list.

Get started

Install and run a Llama Stack server:

# One-line install
curl -LsSf https://github.com/llamastack/llama-stack/raw/main/scripts/install.sh | bash

# Or install via uv
uv pip install llama-stack

# Start the server (uses the starter distribution with Ollama)
llama stack run

Then connect with any OpenAI client — Python, TypeScript, curl, or any framework that speaks the OpenAI API.

See the Quick Start guide for detailed setup.

Resources

• Documentation — full reference
• OpenAI API Compatibility — endpoint coverage and provider matrix
• Getting Started Notebook — text and vision inference walkthrough
• Contributing — how to contribute

Client SDKs:

Language	SDK	Package
Python	llama-stack-client-python
TypeScript	llama-stack-client-typescript

Community

We hold regular community calls every Thursday at 09:00 AM PST — see the Community Event on Discord for details.

Thanks to all our amazing contributors!