Meta

llama-stack is Meta’s open-source framework that provides a standardized, provider-agnostic API layer for deploying Llama models across local machines, on-premises servers, and cloud environments. It abstracts inference, retrieval-augmented generation, agentic workflows, and safety into a single unified stack — so the same application code runs against Ollama on a laptop or vLLM on an H100 cluster by changing only the configuration file. What Is Llama Stack? Meta’s Unified AI Deployment Framework llama-stack is a composable deployment framework that standardizes how applications interact with Llama models regardless of where or how those models run. Llama models have been downloaded over 1.2 billion times by April 2025, making them the most widely adopted open-weight AI model family in the world — yet deployment has historically required building separate integration layers for each inference backend. llama-stack solves this by defining a set of provider-agnostic APIs (Inference, Safety, Memory, Agents, Tools) that map to interchangeable backends called providers. Switch from Ollama to vLLM to AWS Bedrock by changing a single field in a YAML configuration file, with zero application code changes. The framework ships with an OpenAI-compatible REST API, which means existing applications built against the OpenAI Python SDK can switch to llama-stack with a one-line endpoint change. Projected enterprise spending on Llama solutions reached $2.5 billion by 2026, with over 50% of Fortune 500 companies having piloted Llama solutions by March 2025. llama-stack is the deployment layer that makes that enterprise adoption operationally manageable. ...