OpenRAG

RAGFlow is an open-source retrieval-augmented generation (RAG) engine from infiniflow that provides a context layer between documents and large language models, combining document understanding with agent capabilities. It ingests heterogeneous sources including Word, Slides, Excel, text files, images, scanned documents, structured data, and web pages; extracts knowledge from complex formats; and uses configurable template-based chunking, multiple retrieval methods, and fused re-ranking to assemble context for language-model applications. Its workflows provide traceable citations and visualized text chunks for human review, and it supports configurable language and embedding models, agent templates, orchestration, APIs, and self-hosting. A hosted cloud service is also available.

Production Agentic RAG Course is a GitHub-hosted course on building production Retrieval-Augmented Generation systems through a seven-week, hands-on arXiv Paper Curator project. Its learning path covers Docker-based infrastructure, FastAPI, PostgreSQL, OpenSearch, Airflow, academic-paper ingestion with the arXiv API and Docling, BM25 keyword search, section-aware chunking, Jina embeddings, hybrid retrieval with reciprocal-rank fusion, local LLM serving with Ollama, streaming responses, Gradio, Langfuse tracing, Redis caching, and LangGraph-based agentic RAG. The agentic RAG stage uses guardrail, retrieval, document-grading, query-rewriting, and generation nodes coordinated in a state-based LangGraph workflow. It includes adaptive retrieval, semantic relevance evaluation, domain-boundary detection, reasoning-step tracking, and an asynchronous Telegram bot interface. The course is organized through weekly notebooks, blog posts, and tagged code releases in the repository. The project is distributed under the MIT License and is designed to run locally with Docker Compose, Python 3.12 or later, and the UV package manager; optional external services include Jina embeddings, Langfuse, and Telegram.

OpenSearch is a community-driven, Apache 2.0-licensed open-source search and analytics suite for ingesting, searching, visualizing, and analyzing data. Its capabilities include vector search, enterprise and document search, observability and log analysis, security analytics, anomaly detection, and machine-learning and AI tools. In the cited OpenRAG pipeline, OpenSearch serves as the vector-search and vector-store component.

OpenHuman is a local-first personal AI system from tinyhumansai that combines persistent memory, agent orchestration, and research tools. It stores a user's data as scored Markdown trees in SQLite on the local machine and mirrors the result to an editable Obsidian vault; its TokenJuice component compresses tool output before it reaches the language model. Its orchestration layer runs checkpointed, durable agent workflows and worker fleets on graphs, with triggers, approvals, steering, halting, and replay support. The system also provides web search, scraping, coding tools, a browser, native voice through in-process Whisper, model routing, messaging integrations, and support for provider keys or fully local Ollama models. The repository describes it as an early beta under active development.