Add octoverse (#433)

Author: yingfeng

website/blog/2025-10-28-ragflow-named-among-github-fastest-growing-open-source-projects/index.md

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+slug: ragflow-named-among-github-fastest-growing-open-source-projects
+title:  RAGFlow Named Among GitHub’s Fastest-Growing Open Source Projects, Reflecting Surging Demand for Production-Ready AI
+tags: [GitHub, RAG, opensource, landscape]
+---
+
+The release of GitHub’s 2025 Octoverse report marks a pivotal moment for the open source ecosystem—and for projects like **RAGFlow**, which has emerged as one of the **fastest-growing open source projects by contributors** this year. With a remarkable **2,596% year-over-year growth** in contributor engagement, RAGFlow isn’t just gaining traction—it’s defining the next wave of AI-powered development.
+
+### The Rise of Retrieval-Augmented Generation in Production
+
+As the Octoverse report highlights, AI is no longer experimental—it’s foundational. More than **4.3 million AI-related repositories** now exist on GitHub, and over **1.1 million public repos import LLM SDKs**, a 178% YoY increase. In this context, RAGFlow’s rapid adoption signals a clear shift: developers are moving beyond prototyping and into **production-grade AI workflows**.
+
+RAGFlow—an end-to-end retrieval-augmented generation engine with built-in agent capabilities—is perfectly positioned to meet this demand. It enables developers to build scalable, context-aware AI applications that are both powerful and practical. As the report notes, *“AI infrastructure is emerging as a major magnet”* for open source contributions, and RAGFlow sits squarely at the intersection of AI infrastructure and real-world usability.
+
+### Why RAGFlow Resonates in the AI Era
+
+Several trends highlighted in the Octoverse report align closely with RAGFlow’s design and mission:
+
+- **From Notebooks to Production**: The report notes a shift from Jupyter Notebooks (+75% YoY) to Python codebases, signaling that AI projects are maturing. RAGFlow supports this transition by offering a structured, reproducible framework for deploying RAG systems in production.
+- **Agentic Workflows Are Going Mainstream**: With the launch of GitHub Copilot coding agent and the rise of AI-assisted development, developers are increasingly relying on tools that automate complex tasks. RAGFlow’s built-in agent capabilities allow teams to automate retrieval, reasoning, and response generation—key components of modern AI apps.
+- **Security and Scalability Are Top of Mind**: The report also highlights a 172% YoY increase in Broken Access Control vulnerabilities, underscoring the need for secure-by-design AI systems. RAGFlow’s focus on enterprise-ready deployment helps teams address these challenges head-on.
+
+### A Project in Active Development
+
+RAGFlow's evolution mirrors a deliberate journey—from solving foundational RAG challenges to shaping the next generation of enterprise AI infrastructure.
+
+The project first made its mark by systematically addressing core RAG limitations through integrated technological innovation. With features such as **deep document understanding** for parsing complex formats, **hybrid retrieval** that blends multiple search strategies, and built-in advanced tools like **GraphRAG** and **RAPTOR**, RAGFlow established itself as an end-to-end solution that dramatically enhances retrieval accuracy and reasoning performance.
+
+Now, building on this robust technical foundation, RAGFlow is steering toward a bolder vision: **to become the superior context engine for enterprise-grade Agents**. Evolving from a specialized RAG engine into a unified, resilient context layer, RAGFlow is positioning itself as the essential **data foundation for LLMs** in the enterprise—enabling Agents of any kind to access rich, precise, and secure context, ensuring reliable and effective operation across all tasks.
+
+------
+
+*RAGFlow is an open source retrieval-augmented generation engine designed for building production-ready AI applications. To learn more or contribute, visit the [RAGFlow GitHub repository](https://github.com/infiniflow/ragflow).*
+
+*This post was inspired by insights from the [GitHub Octoverse 2025 Report](https://gh.io/octoverse). Special thanks to the GitHub team for amplifying the voices of open source builders everywhere.*
+
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