Llama Stack and the case for an open “run-anywhere” contract for agents

Llama Stack and the case for an open “run-anywhere” contract for agents The 4 layers of Llama Stack 1. Build layer (Client SDK/Toolkit) 2. Agent artifacts and dependencies 3. Platform / API layer 4. Provider model The Kubernetes analogy The standards question: OpenAI APIs vs. MCP The governance question Why this matters The adaptable enterprise: Why AI readiness is disruption readiness About the authors Tushar Katarki Adel Zaalouk More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Why do we really need Llama Stack when popular frameworks like LangChain, LangFlow, and CrewAI already exist? This is the question we get asked most often. It’s a fair one—after all, those frameworks already give developers rich tooling for retrieval-augmented generation (RAG) and agents. But we see Llama Stack as more than “another agent framework. ” It’s better understood as four distinct layers: A familiar surface for building agents. Here it overlaps with LangChain, LangFlow, and CrewAI. Developers can author agents using common abstractions. Example : An agent built with CrewAI looks like a small project folder with config files and environment variables.