# Llmstxt > In order to understand how llms.txt can be used with editors and IDEs, --- # Source: https://llmstxt.org/ed-commonmark.md # `ed`, the standard text editor In order to understand how llms.txt can be used with editors and IDEs, let’s look at how `ed`, the [standard text editor](https://www.gnu.org/fun/jokes/ed-msg.html), could work (assuming it’s updated to use this proposal). In our example we will look at how the user might then tell `ed` to retrieve the LLM docs from [fastht.ml/docs](https://fastht.ml/docs), and then use the results to write a simple [FastHTML](https://fastht.ml) web app. Even if you use a non-standard editor or IDE such as vscode, Cursor, vim, or Emacs, your software’s interaction with `/llms.txt` would look similar to this general approach. ``` sh $ ed * H ``` Our user starts `ed` and enables helpful error messages (just for the purpose of this walkthru - obviously a real `ed` user doesn’t need “helpful error messages”). ``` sh * L fastht.ml/docs Checking for /llms.txt at fastht.ml/docs... Found /llms.txt. Parsing... Fetching URLs from "Docs" section... Fetching URLs from "Examples" section... Skipping "Optional" section for brevity. Creating XML-based context for Claude... Context created and loaded. ``` The user invokes the hypothetical `L` (load) command, which in this LLM-enhanced version of `ed` retrieves and processes the `llms.txt` file. `ed` checks for the file (if it didn’t exist, it would fall back to scraping the HTML of the website the old-fashioned way), parses it, fetches the relevant URLs, and creates an XML-based context suitable for Claude (perhaps an `ed` config file could be used to choose what LLM to use, and would determine how the context is formatted). All of this happens with the characteristic silence of `ed`, broken only by these reassuring progress messages. ``` sh * x Create a simple FastHTML app which outputs 'Hello, World!', in a
. Analyzing context and prompt... Generating FastHTML app... App written to buffer. ``` Next, our user invokes the hypothetical `x` (eXecute AI) command, providing instructions for the LLM to create a simple FastHTML app. In the world of LLM-enhanced `ed`, this is understood as a request to generate code based on the given prompt and the previously loaded context. ``` sh * n 5 * p from fasthtml.common import * app,rt = fast_app() @rt def index(): return div("Hello, World!") serve() ``` The editor analyzes the loaded context along with the provided prompt, generates the FastHTML app, and writes it to the buffer. The user then views the generated app line count (`n`) and contents (`p`), marveling at how much functionality is packed into those 5 lines. ``` sh *w hello_world.py 5 *q ``` Finally, our user saves the app to a file and quits `ed`, presumably to run their new FastHTML app and reflect on the unexpected productivity boost provided by their trusty line editor. --- # Source: https://llmstxt.org/index.md # The /llms.txt file Jeremy Howard 2024-09-03 ## Background Large language models increasingly rely on website information, but face a critical limitation: context windows are too small to handle most websites in their entirety. Converting complex HTML pages with navigation, ads, and JavaScript into LLM-friendly plain text is both difficult and imprecise. While websites serve both human readers and LLMs, the latter benefit from more concise, expert-level information gathered in a single, accessible location. This is particularly important for use cases like development environments, where LLMs need quick access to programming documentation and APIs. ## Proposal
llms.txt logo
We propose adding a `/llms.txt` markdown file to websites to provide LLM-friendly content. This file offers brief background information, guidance, and links to detailed markdown files. llms.txt markdown is human and LLM readable, but is also in a precise format allowing fixed processing methods (i.e. classical programming techniques such as parsers and regex). We furthermore propose that pages on websites that have information that might be useful for LLMs to read provide a clean markdown version of those pages at the same URL as the original page, but with `.md` appended. (URLs without file names should append `index.html.md` instead.) The [FastHTML project](https://fastht.ml) follows these two proposals for its documentation. For instance, here is the [FastHTML docs llms.txt](https://www.fastht.ml/docs/llms.txt). And here is an example of a [regular HTML docs page](https://www.fastht.ml/docs/tutorials/by_example.html), along with exact same URL but with [a .md extension](https://www.fastht.ml/docs/tutorials/by_example.html.md). This proposal does not include any particular recommendation for how to process the llms.txt file, since it will depend on the application. For example, the FastHTML project opted to automatically expand the llms.txt to two markdown files with the contents of the linked URLs, using an XML-based structure suitable for use in LLMs such as Claude. The two files are: [llms-ctx.txt](https://fastht.ml/docs/llms-ctx.txt), which does not include the optional URLs, and [llms-ctx-full.txt](https://fastht.ml/docs/llms-ctx-full.txt), which does include them. They are created using the [`llms_txt2ctx`](https://llmstxt.org/intro.html#cli) command line application, and the FastHTML documentation includes information for users about how to use them. The versatility of llms.txt files means they can serve many purposes - from helping developers find their way around software documentation, to giving businesses a way to outline their structure, or even breaking down complex legislation for stakeholders. They’re just as useful for personal websites where they can help answer questions about someone’s CV, for e-commerce sites to explain products and policies, or for schools and universities to provide quick access to their course information and resources. Note that all [nbdev](https://nbdev.fast.ai/) projects now create .md versions of all pages by default. All Answer.AI and fast.ai software projects using nbdev have had their docs regenerated with this feature. For an example, see the [markdown version](https://fastcore.fast.ai/docments.html.md) of [fastcore’s docments module](https://fastcore.fast.ai/docments.html). ## Format At the moment the most widely and easily understood format for language models is Markdown. Simply showing where key Markdown files can be found is a great first step. Providing some basic structure helps a language model to find where the information it needs can come from. The `llms.txt` file is unusual in that it uses Markdown to structure the information rather than a classic structured format such as XML. The reason for this is that we expect many of these files to be read by language models and agents. Having said that, the information in llms.txt follows a specific format and can be read using standard programmatic-based tools. The llms.txt file spec is for files located in the root path `/llms.txt` of a website (or, optionally, in a subpath). A file following the spec contains the following sections as markdown, in the specific order: - An H1 with the name of the project or site. This is the only required section - A blockquote with a short summary of the project, containing key information necessary for understanding the rest of the file - Zero or more markdown sections (e.g. paragraphs, lists, etc) of any type except headings, containing more detailed information about the project and how to interpret the provided files - Zero or more markdown sections delimited by H2 headers, containing “file lists” of URLs where further detail is available - Each “file list” is a markdown list, containing a required markdown hyperlink `[name](url)`, then optionally a `:` and notes about the file. Here is a mock example: ``` markdown # Title > Optional description goes here Optional details go here ## Section name - [Link title](https://link_url): Optional link details ## Optional - [Link title](https://link_url) ``` Note that the “Optional” section has a special meaning—if it’s included, the URLs provided there can be skipped if a shorter context is needed. Use it for secondary information which can often be skipped. ## Existing standards llms.txt is designed to coexist with current web standards. While sitemaps list all pages for search engines, `llms.txt` offers a curated overview for LLMs. It can complement robots.txt by providing context for allowed content. The file can also reference structured data markup used on the site, helping LLMs understand how to interpret this information in context. The approach of standardising on a path for the file follows the approach of `/robots.txt` and `/sitemap.xml`. robots.txt and `llms.txt` have different purposes—robots.txt is generally used to let automated tools know what access to a site is considered acceptable, such as for search indexing bots. On the other hand, `llms.txt` information will often be used on demand when a user explicitly requests information about a topic, such as when including a coding library’s documentation in a project, or when asking a chat bot with search functionality for information. Our expectation is that `llms.txt` will mainly be useful for *inference*, i.e. at the time a user is seeking assistance, as opposed to for *training*. However, perhaps if `llms.txt` usage becomes widespread, future training runs could take advantage of the information in `llms.txt` files too. sitemap.xml is a list of all the indexable human-readable information available on a site. This isn’t a substitute for `llms.txt` since it: - Often won’t have the LLM-readable versions of pages listed - Doesn’t include URLs to external sites, even though they might be helpful to understand the information - Will generally cover documents that in aggregate will be too large to fit in an LLM context window, and will include a lot of information that isn’t necessary to understand the site. ## Example Here’s an example of `llms.txt`, in this case a cut down version of the file used for the FastHTML project (see also the [full version](https://www.fastht.ml/docs/llms.txt)): ``` markdown # FastHTML > FastHTML is a python library which brings together Starlette, Uvicorn, HTMX, and fastcore's `FT` "FastTags" into a library for creating server-rendered hypermedia applications. Important notes: - Although parts of its API are inspired by FastAPI, it is *not* compatible with FastAPI syntax and is not targeted at creating API services - FastHTML is compatible with JS-native web components and any vanilla JS library, but not with React, Vue, or Svelte. ## Docs - [FastHTML quick start](https://fastht.ml/docs/tutorials/quickstart_for_web_devs.html.md): A brief overview of many FastHTML features - [HTMX reference](https://github.com/bigskysoftware/htmx/blob/master/www/content/reference.md): Brief description of all HTMX attributes, CSS classes, headers, events, extensions, js lib methods, and config options ## Examples - [Todo list application](https://github.com/AnswerDotAI/fasthtml/blob/main/examples/adv_app.py): Detailed walk-thru of a complete CRUD app in FastHTML showing idiomatic use of FastHTML and HTMX patterns. ## Optional - [Starlette full documentation](https://gist.githubusercontent.com/jph00/809e4a4808d4510be0e3dc9565e9cbd3/raw/9b717589ca44cedc8aaf00b2b8cacef922964c0f/starlette-sml.md): A subset of the Starlette documentation useful for FastHTML development. ``` To create effective `llms.txt` files, consider these guidelines: - Use concise, clear language. - When linking to resources, include brief, informative descriptions. - Avoid ambiguous terms or unexplained jargon. - Run a tool that expands your `llms.txt` file into an LLM context file and test a number of language models to see if they can answer questions about your content. ## Directories Here are a few directories that list the `llms.txt` files available on the web: - [llmstxt.site](https://llmstxt.site/) - [directory.llmstxt.cloud](https://directory.llmstxt.cloud/) ## Integrations Various tools and plugins are available to help integrate the llms.txt specification into your workflow: - [`llms_txt2ctx`](https://llmstxt.org/intro.html#cli) - CLI and Python module for parsing llms.txt files and generating LLM context - [JavaScript Implementation](./llmstxt-js.html) - Sample JavaScript implementation - [`vitepress-plugin-llms`](https://github.com/okineadev/vitepress-plugin-llms) - VitePress plugin that automatically generates LLM-friendly documentation for the website following the llms.txt specification - [`docusaurus-plugin-llms`](https://github.com/rachfop/docusaurus-plugin-llms) - Docusaurus plugin for generating LLM-friendly documentation following the llmtxt.org standard - [Drupal LLM Support](https://www.drupal.org/project/llm_support) - A Drupal Recipe providing full support for the llms.txt proposal on any Drupal 10.3+ site - [`llms-txt-php`](https://github.com/raphaelstolt/llms-txt-php) - A library for writing and reading llms.txt Markdown files - [`VS Code PagePilot Extension`](https://dmux.github.io/pagepilot) - PagePilot is a VS Code Chat participant that automatically loads external context (documentation, APIs, README files) to provide enhanced responses. ## Next steps The `llms.txt` specification is open for community input. A [GitHub repository](https://github.com/AnswerDotAI/llms-txt) hosts [this informal overview](https://github.com/AnswerDotAI/llms-txt/blob/main/nbs/index.qmd), allowing for version control and public discussion. A [community discord channel](https://discord.gg/aJPygMvPEN) is available for sharing implementation experiences and discussing best practices. --- # Source: https://llmstxt.org/intro.html.md # Python module & CLI Given an `llms.txt` file, this provides a CLI and Python API to parse the file and create an XML context file from it. The input file should follow this format: # FastHTML > FastHTML is a python library which... When writing FastHTML apps remember to: - Thing to remember ## Docs - [Surreal](https://host/README.md): Tiny jQuery alternative with Locality of Behavior - [FastHTML quick start](https://host/quickstart.html.md): An overview of FastHTML features ## Examples - [Todo app](https://host/adv_app.py) ## Optional - [Starlette docs](https://host/starlette-sml.md): A subset of the Starlette docs ## Install ``` sh pip install llms-txt ``` ## How to use ### CLI After installation, `llms_txt2ctx` is available in your terminal. To get help for the CLI: ``` sh llms_txt2ctx -h ``` To convert an `llms.txt` file to XML context and save to `llms.md`: ``` sh llms_txt2ctx llms.txt > llms.md ``` Pass `--optional True` to add the ‘optional’ section of the input file. ### Python module ``` python from llms_txt import * ``` ``` python samp = Path('llms-sample.txt').read_text() ``` Use `parse_llms_file` to create a data structure with the sections of an llms.txt file (you can also add `optional=True` if needed): ``` python parsed = parse_llms_file(samp) list(parsed) ``` ['title', 'summary', 'info', 'sections'] ``` python parsed.title,parsed.summary ``` ('FastHTML', 'FastHTML is a python library which brings together Starlette, Uvicorn, HTMX, and fastcore\'s `FT` "FastTags" into a library for creating server-rendered hypermedia applications.') ``` python list(parsed.sections) ``` ['Docs', 'Examples', 'Optional'] ``` python parsed.sections.Optional[0] ``` ``` json { 'desc': 'A subset of the Starlette documentation useful for FastHTML ' 'development.', 'title': 'Starlette full documentation', 'url': 'https://gist.githubusercontent.com/jph00/809e4a4808d4510be0e3dc9565e9cbd3/raw/9b717589ca44cedc8aaf00b2b8cacef922964c0f/starlette-sml.md'} ``` Use `create_ctx` to create an LLM context file with XML sections, suitable for systems such as Claude (this is what the CLI calls behind the scenes). ``` python ctx = create_ctx(samp) ``` ``` python print(ctx[:300]) ``` Remember: - Use `serve()` for running uvicorn (`if __name__ == "__main__"` is not ### Implementation and tests To show how simple it is to parse `llms.txt` files, here’s a complete parser in \<20 lines of code with no dependencies: ``` python from pathlib import Path import re,itertools def chunked(it, chunk_sz): it = iter(it) return iter(lambda: list(itertools.islice(it, chunk_sz)), []) def parse_llms_txt(txt): "Parse llms.txt file contents in `txt` to a `dict`" def _p(links): link_pat = '-\s*\[(?P[^\]]+)\]\((?P<url>[^\)]+)\)(?::\s*(?P<desc>.*))?' return [re.search(link_pat, l).groupdict() for l in re.split(r'\n+', links.strip()) if l.strip()] start,*rest = re.split(fr'^##\s*(.*?$)', txt, flags=re.MULTILINE) sects = {k: _p(v) for k,v in dict(chunked(rest, 2)).items()} pat = '^#\s*(?P<title>.+?$)\n+(?:^>\s*(?P<summary>.+?$)$)?\n+(?P<info>.*)' d = re.search(pat, start.strip(), (re.MULTILINE|re.DOTALL)).groupdict() d['sections'] = sects return d ``` We have provided a test suite in `tests/test-parse.py` and confirmed that this implementation passes all tests.