Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI (github.com)

LlamaFarm (YC W22) is an open-source, local-first framework for building retrieval-augmented and agentic AI applications. It provides a single CLI (lf) to bootstrap projects, manage datasets, run an interactive chat TUI, and call a production-like REST API (OpenAI-compatible) at localhost:8000. Ship defaults include Ollama for local models and Chroma for vector storage, but everything is pluggable—runtimes (vLLM, Together, OpenAI-compatible endpoints), embedders, parsers, extractors, and stores can be swapped by editing a YAML schema (llamafarm.yaml). Projects are defined by validated YAML schemas, letting you configure RAG pipelines, embedding and retrieval strategies, dataset ingestion (PDF parsers, extractors, chunking), and deployment behavior without changing orchestration code. The repo contains examples, tests, docs, and an extendability guide; it runs via Docker orchestration or manual Nx commands. Significance: LlamaFarm bridges local experimentation and production-ready RAG services with a config-over-code approach that speeds iteration and enforces reproducibility. Technical implications include easy portability between small local models and hosted LLMs by changing runtime.provider/base_url/api_key, OpenAI-compatible chat and RAG endpoints for straightforward integration, and a schema-driven plugin model that simplifies adding new vector stores or parsers. For teams and researchers, it reduces boilerplate around ingestion, retrieval strategies, and orchestration—making it faster to prototype, audit, and scale RAG/agentic systems while keeping full ownership of the stack (Apache 2.0).