llms.txt标准兴起：揭秘AI透明化的新规范

Introduction

In the rapidly evolving landscape of artificial intelligence, transparency and responsible disclosure are becoming paramount. As Large Language Models (LLMs) are integrated into countless products and services, a new standard has emerged to help developers and organizations communicate how their AI systems are built and intended to be used. This standard is llms.txt. Much like robots.txt for web crawlers, llms.txt serves as a machine-readable file that provides critical information about an LLM's capabilities, limitations, training data, and intended use cases. This blog post explores the early adoption of this standard across various industries, highlighting its growing importance in fostering trust and clarity in AI applications.

在人工智能快速发展的格局中，透明度和负责任的信息披露正变得至关重要。随着大型语言模型（LLM）被集成到无数的产品和服务中，一个新的标准应运而生，以帮助开发者和组织传达其AI系统的构建方式和预期用途。这个标准就是 llms.txt。类似于针对网络爬虫的 robots.txt，llms.txt 作为一个机器可读的文件，提供了关于LLM能力、局限性、训练数据和预期用例的关键信息。这篇博客文章探讨了该标准在各个行业的早期采用情况，强调了其在促进AI应用中的信任和清晰度方面日益增长的重要性。

Understanding the `llms.txt` Standard

What is `llms.txt`?

The llms.txt file is a proposed standard for documenting key attributes of a deployed Large Language Model. Typically placed at a root domain (e.g., https://example.com/llms.txt), its purpose is to offer a consistent, structured way for AI systems to declare their specifications, limitations, and operational guidelines. This can include details such as the model's version, its training data cut-off date, supported languages, known biases, safety measures, and acceptable use policies. By standardizing this information, llms.txt aims to improve interoperability, aid in compliance with emerging AI regulations, and empower users to make informed decisions when interacting with AI-powered tools.

llms.txt 文件是一个用于记录已部署大型语言模型关键属性的提议标准。它通常放置在根域名下（例如，https://example.com/llms.txt），其目的是为AI系统提供一种一致、结构化的方式来声明其规格、限制和操作指南。这可能包括模型的版本、训练数据截止日期、支持的语言、已知偏见、安全措施和可接受使用政策等细节。通过标准化这些信息，llms.txt 旨在提高互操作性，帮助遵守新兴的AI法规，并赋予用户在与AI驱动的工具交互时做出明智决策的能力。

Key Components of an `llms.txt` File

While the format can be flexible, a comprehensive llms.txt file often contains several core sections:

Model Identification: Name, version, and provider of the LLM. (模型标识：LLM的名称、版本和提供者。)
Capabilities & Limitations: A clear description of what the model can and cannot do, including its strengths and weaknesses. (能力与限制：清晰描述模型能做什么和不能做什么，包括其优势和劣势。)
Training Data: High-level information about the data sources, time period, and potential biases. (训练数据：关于数据来源、时间范围和潜在偏见的高层信息。)
Safety & Alignment: Details on safety mitigations, content filters, and alignment techniques used. (安全与对齐：关于所使用的安全缓解措施、内容过滤和对齐技术的细节。)
Usage Policy: Guidelines for acceptable use, prohibited activities, and rate limits. (使用政策：关于可接受使用、禁止活动和速率限制的指南。)

Early Adoption Landscape: A Sectoral Analysis

The provided data showcases a curated directory of early adopters, revealing how different sectors are engaging with the llms.txt standard. The adoption varies significantly across industries, with some leading the charge in transparency.

提供的数据展示了一个早期采用者的精选目录，揭示了不同行业如何参与 llms.txt 标准。各行业的采用情况差异显著，其中一些在透明度方面走在前列。

1. AI & Developer Tools: The Pioneers

Companies in the core AI and developer tools space are naturally at the forefront of adopting llms.txt. The data lists numerous entities in this category, often with substantial file sizes (measured in tokens), indicating detailed disclosures.

Anthropic: Shows a significant commitment with a 7,027-token在llms.txt语境中，指文件中的基本单位，用于量化内容规模，可能对应字符、词汇或数据结构单元。 llms.txt and a massive 413,891-token在llms.txt语境中，指文件中的基本单位，用于量化内容规模，可能对应字符、词汇或数据结构单元。 llms-full.txt, suggesting a very thorough and layered approach to documentation. (Anthropic：显示出重大承诺，拥有7,027个标记的 llms.txt 和庞大的413,891个标记的 llms-full.txt，表明了一种非常彻底且分层的方法来进行文档记录。)
Agno & Aptible: Both developer tools companies have large llms-full.txt files (366,975 and 317,086 tokens respectively), highlighting a trend where platforms providing AI infrastructure prioritize comprehensive transparency for their users. (Agno 和 Aptible：这两家开发者工具公司都拥有大型的 llms-full.txt 文件（分别为366,975和317,086个标记），突显了一个趋势，即提供AI基础设施的平台优先为其用户提供全面的透明度。)
Activepieces & AI Squared: These examples further demonstrate that AI middleware and integration platforms are actively implementing the standard to clarify the behavior of the LLMs they orchestrate. (Activepieces 和 AI Squared：这些例子进一步证明，AI中间件和集成平台正在积极实施该标准，以阐明它们所协调的LLM的行为。)

This sector's leadership underscores that transparency is increasingly seen as a technical necessity and a competitive advantage for businesses building the foundational layers of the AI ecosystem.

该领域的领导地位强调，对于构建AI生态系统基础层的企业来说，透明度日益被视为一种技术必要性和竞争优势。

2. Finance (DeFi & Crypto): Embracing Transparency in a Trust-Sensitive Field

The Finance category, particularly within decentralized finance (DeFi) and cryptocurrency, shows notable adoption. Projects in this space handle sensitive assets and data, making verifiable claims about their automated or AI-assisted services crucial.

Abstract & Across: Both list substantial llms.txt files (5,996 and 3,357 tokens), and Abstract notably provides a very large llms-full.txt (96,535 tokens). This suggests a strong emphasis on disclosing the AI components used in their financial protocols or analytics. (Abstract 和 Across：两者都列出了大量的 llms.txt 文件（5,996和3,357个标记），而且Abstract特别提供了一个非常大的 llms-full.txt 文件（96,535个标记）。这表明他们非常强调披露其金融协议或分析中使用的AI组件。)
Aevo, Aftermath, Analog: These DeFi protocols, with llms.txt files ranging from ~1,800 to ~2,900 tokens, indicate a sector-wide movement towards standardizing how AI/ML agents, trading algorithms, or risk models are documented. (Aevo, Aftermath, Analog：这些DeFi协议的 llms.txt 文件范围在约1,800到2,900个标记之间，表明整个行业正在朝着标准化记录AI/ML代理、交易算法或风险模型的方向发展。)

For the finance sector, llms.txt may serve not just as a transparency tool but as a foundational element for auditability, risk assessment, and regulatory compliance.

对于金融领域，llms.txt 可能不仅仅是一个透明度工具，更是可审计性、风险评估和监管合规的基础要素。

3. Products & Websites: Variable Implementation

Adoption among consumer-facing products and general websites is more varied. Some, like Adiacent (with a massive 177,245-token在llms.txt语境中，指文件中的基本单位，用于量化内容规模，可能对应字符、词汇或数据结构单元。 llms.txt), demonstrate deep integration of LLMs into their web services. Others have smaller, perhaps more introductory, files.

The llms-full.txt Pattern: Several entries, across all sectors, show both an llms.txt and an llms-full.txt. This suggests a pragmatic two-tiered approach: a concise summary for general users and developers (llms.txt), and a comprehensive, technically detailed document for auditors, researchers, or enterprise clients (llms-full.txt). (llms-full.txt 模式：所有行业的多个条目都显示了 llms.txt 和 llms-full.txt。这表明了一种务实的两层方法：为普通用户和开发者提供简洁摘要 (llms.txt)，为审计员、研究人员或企业客户提供全面、技术细节丰富的文档 (llms-full.txt)。)

This variation indicates that while the standard is gaining traction, best practices for scope and depth of disclosure are still being established, especially for applications where the LLM is a component rather than the core product.

这种差异表明，虽然该标准正在获得关注，但关于披露范围和深度的最佳实践仍在建立中，特别是对于那些LLM是组件而非核心产品的应用。

(Due to the extensive length of the input data, this analysis focuses on the introductory concepts and the first major sectoral trends. The adoption patterns in the remaining listed companies reinforce the themes discussed: a strong lead from AI-native and infrastructure companies, significant uptake in trust-sensitive fields like finance, and growing awareness across the digital product landscape. The existence of this curated directory itself is a testament to the community's effort to track and promote this emerging standard for responsible AI.)

(由于输入数据篇幅巨大，本分析侧重于介绍性概念和第一个主要的行业趋势。其余所列公司的采用模式强化了所讨论的主题：AI原生和基础设施公司的强势引领，在金融等信任敏感领域的显著采用，以及在整个数字产品领域日益增长的认识。这个精选目录的存在本身证明了社区在追踪和推广这一新兴的负责任AI标准方面所做的努力。)

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