This repo contains a growing suite of example applications for Llama Stack that demonstrate various stack features and common application patterns.
See the corresponding README for each example for more information. Here we summarize the examples.
These applications are in the apps directory.
01-chatbot: A getting-start chatbot app, which shows how to build and deploy Llama Stack applications. It includes two different UI options and inference with an ollama-hosted Llama 3 model.02-deep-research: A deep research app (under development), which illustrates an emerging, common application pattern for AI. The user asks for detailed information about a topic, for example the market performance and financials for a publicly-traded company, agents find relevant data from diverse sources, and finally an LLM digests the information retrieved and prepares a report. This example will demonstrate Llama Stack support for agent-based application development, including the use of protocols like MCP.
These examples use Jupyter notebooks to illustrate their concepts. They are located in the notebooks directory.
01-responses: This notebook demonstrates how to use the Llama Stack Responses API for simple inference, Retrieval-Augmented Generation (RAG), and Model Context Protocol (MCP) tool calling.
Note
Please join us! We welcome new examples. You can submit them or submit improvements to the current examples using PRs, make suggestions or report bugs as issues, or use the discussions forum for general questions and suggestions. For more information about joining this project or other AI Alliance projects, go here. The main AI Alliance website is here.
If you are interested in running Llama Stack on Kubernetes or OpenShift, see these examples from opendatahub.io.