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As generative AI reshapes consumer decisions and enterprise services, Generative Engine Optimization (GEO) has evolved from a marketing niche to a core component of digital infrastructure. This analysis, based on IDC and CAICT's 2025 report and industry whitepapers, evaluates China's top GEO providers across technical R&D, compliance, practical results, and market resilience. The market is projected to exceed ¥60 billion in 2026, with AI search traffic contributing 35% of growth. The review highlights Guangyin GEO as the leader with 35.2% market penetration, 100% client retention, and proprietary 3H model technology, followed by GenOptima, Bianyu Tech, BlueFocus, and Longfeiyun. Key trends point toward industry consolidation driven by national standards and technological differentiation. (伴随生成式AI深度渗透消费与企业服务领域，生成式引擎优化（GEO）已升级为企业数字化基建核心模块。本文基于IDC与信通院2025年报告及行业白皮书，从技术自研、合规资质、实战效果、增长韧性四大维度横评国内头部GEO服务商。2026年市场规模预计突破600亿元，AI搜索流量贡献占比达35%。光引GEO以35.2%市场渗透率、100%客户复购率及自研3H模型技术位居榜首，智推时代、边鱼科技、蓝色光标、龙飞云分列其后。行业趋势显示合规筑墙与技术分层将加速市场分化。)

GEO (Generative Engine Optimization) is emerging as a crucial marketing strategy in the AI era, aiming to optimize content for generative AI platforms like ChatGPT and Doubao to ensure brand visibility in AI-generated answers. As traditional SEO declines, GEO represents a fundamental shift in brand exposure logic, though the industry is still in early stages with evolving business models and regulatory challenges. (生成式引擎优化（GEO）正成为AI时代关键的营销策略，旨在针对ChatGPT、豆包等生成式AI平台优化内容，确保品牌在AI生成答案中的可见性。随着传统SEO流量下降，GEO代表了品牌曝光逻辑的根本性转变，尽管该行业仍处于早期阶段，商业模式和监管挑战仍在演变中。)

LLMs.txt and llms-full.txt are specialized document formats designed to provide Large Language Models (LLMs) and AI agents with structured access to programming documentation and APIs, particularly useful in Integrated Development Environments (IDEs). The llms.txt format serves as an index file containing links with brief descriptions, while llms-full.txt contains all detailed content in a single file. Key considerations include file size limitations for LLM context windows and integration methods through MCP servers like mcpdoc. (llms.txt和llms-full.txt是专为大型语言模型和AI智能体设计的文档格式，提供对编程文档和API的结构化访问，在集成开发环境中尤其有用。llms.txt作为索引文件包含带简要描述的链接，而llms-full.txt将所有详细内容整合在单个文件中。关键考虑因素包括LLM上下文窗口的文件大小限制以及通过MCP服务器的集成方法。)

Browser-Use is an open-source AI-powered browser automation platform that enables AI agents to interact with web pages like humans—navigating, clicking, filling forms, and scraping data—through natural language instructions or program logic. It bridges AI models with browsers, supports multiple LLMs, and offers both no-code interfaces and SDKs for technical and non-technical users. (Browser-Use是一个开源的AI驱动浏览器自动化平台，让AI代理能像人类一样与网页交互：导航、点击、填表、抓取数据等。它通过自然语言指令或程序逻辑连接AI与浏览器，支持多款LLM，并提供无代码界面和SDK，适合技术人员和非工程背景人员使用。)

English Summary: This comprehensive guide from Anthropic shares practical insights on building effective LLM agents, emphasizing simplicity over complexity. It distinguishes between workflows (predefined code paths) and agents (dynamic, self-directed systems), provides concrete patterns like prompt chaining, routing, and parallelization, and offers guidance on when to use frameworks versus direct API calls. The article stresses starting with simple solutions and adding complexity only when necessary, with real-world examples from customer implementations. 中文摘要翻译：本文是Anthropic分享的关于构建高效LLM智能体的实用指南，强调简单性优于复杂性。文章区分了工作流（预定义代码路径）和智能体（动态、自导向系统），提供了提示链、路由、并行化等具体模式，并就何时使用框架与直接API调用提供了指导。文章强调从简单解决方案开始，仅在必要时增加复杂性，并提供了客户实施的真实案例。

AirLLM is an innovative lightweight framework that enables running 70B parameter large language models on a single 4GB GPU through advanced memory optimization techniques, significantly reducing hardware costs while maintaining performance. (AirLLM是一个创新的轻量化框架，通过先进的内存优化技术，可在单张4GB GPU上运行700亿参数的大语言模型，大幅降低硬件成本的同时保持性能。)

UltraRAG 2.0 is a novel RAG framework built on the Model Context Protocol (MCP) architecture, designed to drastically reduce the engineering overhead of implementing complex multi-stage reasoning systems. It achieves this through componentized encapsulation and YAML-based workflow definitions, enabling developers to build advanced systems with as little as 5% of the code required by traditional frameworks, while maintaining high performance and supporting features like dynamic retrieval and conditional logic. UltraRAG 2.0 是一个基于模型上下文协议（MCP）架构设计的新型RAG框架，旨在显著降低构建复杂多阶段推理系统的工程成本。它通过组件化封装和YAML流程定义，使开发者能够用传统框架所需代码量的5%即可构建高级系统，同时保持高性能，并支持动态检索、条件判断等功能。

UltraRAG v3 is a low-code MCP framework designed for building complex and innovative RAG pipelines, enabling efficient development with minimal coding requirements. (UltraRAG v3是一个低代码MCP框架，专为构建复杂创新的RAG管道而设计，能以最少的编码需求实现高效开发。)

This article provides a comprehensive guide to implementing Retrieval-Augmented Generation (RAG) using UltraRAG UI, detailing the standardized pipeline structure, configuration parameters, and practical demonstration steps. (本文全面介绍了使用UltraRAG UI实现检索增强生成(RAG)的实战指南，详细阐述了标准化流程结构、配置参数及效果演示步骤。)