Ragflow — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Ragflow. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Jun 28, 2026 • Sentiment updated: Jun 28, 2026

GitHub Statistics

Community Sentiment

Community Buzz: The AI open-source ecosystem today is dominated by a massive surge in **agent skill frameworks and security** as seen on GitHub

Pros & Cons

What People Love

Reddit users praise the flexibility of RAGFlow, Dev.to users appreciate the community support for AI agent development

Common Complaints

bug reports on GitHub, difficulty in setting up RAGFlow

Biggest Positive: community support

Biggest Negative: bug reports

Why Ragflow Stands Out

RAGFlow stands out from alternatives by fusing cutting-edge Retrieval-Augmented Generation with Agent capabilities, creating a superior context layer for LLMs. Its converged context engine and pre-built agent templates enable developers to transform complex data into high-fidelity AI systems with exceptional efficiency and precision. The project's support for features like data synchronization from various sources and orchestrable ingestion pipelines further enhances its value. By leveraging RAGFlow, developers can build more sophisticated AI systems that can handle complex tasks like document parsing and multi-modal analysis.

Built With

Build a custom document parser to extract insights from PDFs — RAGFlow enables this through its support for MinerU and Docling as document parsing methods, Build an AI-powered research assistant that can read and summarize long documents — RAGFlow's context engine and pre-built agent templates make this possible, Build a multi-modal model to analyze images within PDF or DOCX files — RAGFlow supports using a multi-modal model for this purpose, Build a cross-language query system to search for information across languages — RAGFlow's support for cross-language query makes this feasible, Build an orchestrable ingestion pipeline to streamline data processing — RAGFlow's latest updates include support for this feature

Getting Started

  1. Install RAGFlow using the command `docker pull infiniflow/ragflow:v0.24.0`
  2. Configure the environment by setting up the necessary dependencies and parameters
  3. Build a Docker image using the command `docker build -t ragflow .`
  4. Launch the service from source for development using the command `docker run -p 8080:8080 ragflow`
  5. Try the demo at https://cloud.ragflow.io to verify that RAGFlow is working as expected

About

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs

Official site: https://ragflow.io

Category & Tags

Category: memory

Tags: agentic-ai, agentic-retrieval, agentic-search, ai, ai-agents, context-engine, context-management, llm-apps, rag, retrieval-augmented-generation

Market Context

Competitive market with various AI agent frameworks