RAGFlow

OpenRAG is an open-source agentic Retrieval-Augmented Generation platform for ingesting, processing, filtering, and searching timestamped Markdown, documents, and other knowledge through an AI-backed conversational interface. It uses Langflow for document ingestion and retrieval workflows, Docling for parsing messy real-world data, and OpenSearch for semantic and enterprise search; its agentic workflows support re-ranking and multi-agent coordination. The platform is built with FastAPI and Next.js, runs with Docker or Podman, provides Python and TypeScript/JavaScript SDKs for chat integration, and includes a built-in Model Context Protocol server at the /mcp endpoint exposing RAG chat, semantic search, document ingestion, knowledge filters, and settings-management tools to compatible AI assistants.

LangFlow is an open-source, low-code visual builder for creating and running language-model pipelines, agent workflows, and retrieval-augmented generation (RAG) applications. The project is developed and maintained by the open-source community led by the GitHub user logaretm and integrates with language-model providers and tools such as LangChain.

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.

Code-Graph-RAG is an open-source retrieval-augmented generation system for querying, understanding, and editing multilingual codebases. Its Tree-sitter-based parser extracts functions, classes, methods, modules, and relationships, then stores them in Memgraph under a unified graph schema. An interactive CLI converts natural-language questions into Cypher queries, retrieves source code by name or intent, and supports AI-assisted editing and optimization. It provides AST-based surgical patches with diff previews, structural search and rewriting through ast-grep, dead-code discovery by traversing call and reference edges, and runtime-call overlays from test traces or production eBPF profiles. The project also supports MCP integration and publishes the `cgr` command-line tool to PyPI.