Building AI Applications with OGX, the Open-Source Agentic API Server

Here's the fact worth knowing before anything else about this topic: OGX is the rebrand of Meta's Llama Stack. Its own GitHub page states it directly, Llama Stack is now OGX, with the name change accompanying a real shift in mission toward being model-agnostic, multi-SDK, and production-grade — not a new, unrelated project with a confusingly similar architecture. That single fact resolves a lot of confusion for anyone researching agentic API servers, OpenAI-compatible APIs, or local AI application development right now, since a good amount of what's written about Llama Stack still applies directly to OGX under its new name.

What Is OGX (and Why It Used to Be Called Llama Stack)

OGX is plainly described as: an open-source agentic API server for building AI applications, OpenAI-compatible, any model, any infrastructure. Released under the MIT license, it's a genuinely established project — over 8,400 GitHub stars and 1,400 forks as of this writing — with regular community calls and active development.

The core pitch is a drop-in replacement for the OpenAI API that you run yourself, on a laptop, in a datacenter, or in the cloud, and point any OpenAI-compatible client or agentic framework at it. Behind that single API sits a pluggable provider architecture: develop locally against Ollama, deploy to production against vLLM, or connect to a managed cloud service — all through the exact same API surface, so swapping the backend later doesn't mean rewriting the application on top of it. It goes further than just chat completions too: the Responses API handles server-side agentic orchestration, tool calling, MCP server integration, and built-in file search for retrieval-augmented generation (RAG) in a single API call, and OGX natively supports the OpenAI, Anthropic, and Google GenAI SDKs against the same server, not just one vendor's client library.

Prerequisites and Installing OGX

Starting the Server and Verifying It Works

uv run ogx go