Welcome to LlamaFarm
LlamaFarm helps you ship retrieval-augmented and agentic AI apps from your laptop to production. It is fully open-source and intentionally extendable—swap model providers, vector stores, parsers, and CLI workflows without rewriting your project.
📺 Video Demo
Quick Overview (90 seconds): https://youtu.be/W7MHGyN0MdQ
Get a fast introduction to LlamaFarm's core features and see it in action.
What You Can Do Today
- Prototype locally with Ollama or any OpenAI-compatible runtime (vLLM, Together, custom gateways).
- Ingest and query documents using configurable RAG pipelines defined entirely in YAML.
- Automate workflows with a single CLI (
lf) that manages projects, datasets, and chat interactions. - Extend everything from model handlers to data processors by updating schemas and wiring your own implementations.
Choose Your Own Adventure
| Get Started | Go Deeper | Build Your Own |
|---|---|---|
| Quickstart – install, init, chat, ingest your first dataset. | Core Concepts – architecture, sessions, and components. | Extending LlamaFarm – add runtimes, stores, parsers, and CLI commands. |
| CLI Reference – command matrix and examples. | Configuration Guide – schema-driven project settings. | RAG Guide – strategies, processing pipelines, and monitoring. |
Philosophy
- Local-first, cloud-aware – everything works offline, yet you can point at remote runtimes when needed.
- Configuration over code – projects are reproducible because behaviour lives in
llamafarm.yaml. - Composable modules – RAG, prompts, and runtime selection work independently but integrate cleanly.
- Open for extension – documentation includes patterns for registering new providers, stores, and utilities.
🎥 In-Depth Tutorial
Complete Walkthrough (7 minutes): https://youtu.be/HNnZ4iaOSJ4
Watch a comprehensive demonstration of LlamaFarm's features including project setup, dataset ingestion, RAG queries, and configuration options.
Ready to build? Start with the Quickstart and keep the CLI open in another terminal.