# llms.txt Generator — Make Any Website AI-Readable (`darknezz/llms-txt-generator`) Actor Crawl any website and generate llms.txt and llms-full.txt files following the llms.txt standard. BFS crawler with URL filtering and markdown/plaintext output. For AI readiness, SEO for AI, documentation portals, and archiving. - **URL**: https://apify.com/darknezz/llms-txt-generator.md - **Developed by:** [Oaida Adrian](https://apify.com/darknezz) (community) - **Categories:** AI, SEO tools - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## llms.txt Generator — Make Any Website AI-Readable Crawl any website and generate `llms.txt` and `llms-full.txt` files following the [llms.txt standard](https://llmstxt.org). ### What is llms.txt? The `llms.txt` convention is a proposed standard for providing AI models and LLMs with structured information about a website. Similar to `robots.txt`, but for AI readability. ### Features - **Full website crawl** — BFS crawler with configurable depth and page limits - **Content extraction** — Uses [trafilatura](https://github.com/adbar/trafilatura) for precise main content extraction - **Dual output** — Generates both `llms.txt` (summary) and `llms-full.txt` (complete content) - **URL filtering** — Include/exclude URL patterns with glob syntax - **Markdown or plaintext** — Choose output format for compatibility - **Structured output** — Page-level dataset with titles, descriptions, and word counts ### Input Parameters | Parameter | Type | Default | Description | |---|---|---|---| | `startUrls` | array | *required* | URLs to start crawling from | | `maxPages` | integer | 50 | Maximum pages to crawl | | `globs` | array | [] | Include URL patterns (e.g. `/docs/**`) | | `excludes` | array | [] | Exclude URL patterns (e.g. `/blog/**`) | | `outputFormat` | string | "markdown" | Content format: `markdown` or `plaintext` | | `proxyConfiguration` | object | Apify proxy | Proxy settings | ### Output | Output | Description | |---|---| | **llms.txt** (KV store) | Summary file with page titles, descriptions, and links | | **llms-full.txt** (KV store) | Full content file with all extracted text | | **Dataset** | Per-page records with URL, title, description, content, word count | ### Use Cases - **AI/LLM readiness** — Make your website content available to AI assistants - **SEO for AI** — Ensure AI models can understand and reference your content - **Documentation portals** — Generate AI-readable docs for your product - **Content archiving** — Create structured snapshots of website content ### Pricing This actor uses **pay-per-event** pricing. You are charged per page successfully crawled and processed. # Actor input Schema ## `startUrls` (type: `array`): URLs to start crawling from. ## `maxPages` (type: `integer`): Maximum number of pages to crawl. ## `globs` (type: `array`): Glob patterns for URLs to include (e.g. /docs/**). ## `excludes` (type: `array`): Glob patterns for URLs to exclude (e.g. /blog/**). ## `outputFormat` (type: `string`): Format for content extraction. ## `proxyConfiguration` (type: `object`): Proxy settings for crawling. ## Actor input object example ```json { "maxPages": 50, "globs": [], "excludes": [], "outputFormat": "markdown", "proxyConfiguration": { "useApifyProxy": true } } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("darknezz/llms-txt-generator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "proxyConfiguration": { "useApifyProxy": True } } # Run the Actor and wait for it to finish run = client.actor("darknezz/llms-txt-generator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "proxyConfiguration": { "useApifyProxy": true } }' | apify call darknezz/llms-txt-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=darknezz/llms-txt-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "llms.txt Generator — Make Any Website AI-Readable", "description": "Crawl any website and generate llms.txt and llms-full.txt files following the llms.txt standard. BFS crawler with URL filtering and markdown/plaintext output. For AI readiness, SEO for AI, documentation portals, and archiving.", "version": "0.1", "x-build-id": "jjVQyTZJrdVCUeC3u" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/darknezz~llms-txt-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-darknezz-llms-txt-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/darknezz~llms-txt-generator/runs": { "post": { "operationId": "runs-sync-darknezz-llms-txt-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/darknezz~llms-txt-generator/run-sync": { "post": { "operationId": "run-sync-darknezz-llms-txt-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "startUrls" ], "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "URLs to start crawling from.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "title": "URL", "description": "Website URL to crawl.", "type": "string" } } } }, "maxPages": { "title": "Max Pages", "minimum": 1, "type": "integer", "description": "Maximum number of pages to crawl.", "default": 50 }, "globs": { "title": "Include URL patterns", "type": "array", "description": "Glob patterns for URLs to include (e.g. /docs/**).", "items": { "type": "string" }, "default": [] }, "excludes": { "title": "Exclude URL patterns", "type": "array", "description": "Glob patterns for URLs to exclude (e.g. /blog/**).", "items": { "type": "string" }, "default": [] }, "outputFormat": { "title": "Output Format", "enum": [ "markdown", "plaintext" ], "type": "string", "description": "Format for content extraction.", "default": "markdown" }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Proxy settings for crawling." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```