Deploy a RAG pipeline as a REST API using RAGLight
Modular RAG with MCP integration, but Langchain and LlamaIndex already dominate.
RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.
RAG library with serve command, but Langchain, LlamaIndex, and Verba already dominate.
Backend developers, ML engineers building RAG applications
LangChain · LlamaIndex · Verba
raglight serve # FastAPI server (ingest, query, list collections) raglight serve --ui # same + Streamlit chat UI
Config is env vars (LLM provider, embeddings provider, collection name, port).Modular RAG with MCP integration, but Langchain and LlamaIndex already dominate.
Yet another model runner when Ollama already dominates this space.
Yet another Markdown server when markserv, docsify, and mkdocs already exist.
CUDA for phones: native runtimes, thin bridges, real demos shipping GGUF and ONNX inference.
LangChain alternative with 2 dependencies and async-native architecture from the start.
Ollama and llama.cpp server already do this with more maturity and model support.