infiniflow/ragflow

RAGFlow is a Retrieval-Augmented Generation platform that ingests, structures, and retrieves data from diverse sources including documents, web pages, and cloud storage. The system provides a modular architecture for document parsing, chunking, and embedding, supporting both Python and Go implementations for high-performance processing. It features a comprehensive agent framework with pluggable tools, sandboxed code execution, and workflow orchestration for complex reasoning tasks. Additionally, it offers a unified API and CLI for managing datasets, chat sessions, and administrative configurations.

This period focused on restructuring the RAG pipeline into a modular, flow-based execution engine while introducing a comprehensive Python SDK and CLI for programmatic interaction. The work also expanded data source connectors for GitHub, Bitbucket, and Google Drive, and introduced deep document parsing and memory management capabilities.

75 changes

This period was dominated by the migration of the RAGFlow backend to Go, covering the CLI, server, and core service layers. Concurrently, the team significantly expanded test coverage across API endpoints, RAG components, and the new Go modules to ensure functional parity and reliability.

138 changes

infiniflow/ragflow was measured the same way every project in this corpus was: the same rubric, at a pinned commit, with the result published in full. Point the surveyor at a repository you know and see whether you agree with it.