# PDF Extractor: PDF → Clean Markdown + JSON for LLM/RAG (`boxbox10/pdf-extractor`) Actor Turn any PDF URL into clean, LLM-ready Markdown + structured JSON (title, metadata, per-page text, page count, word count, token count). Perfect for RAG pipelines, AI agents, and LLM document ingestion. - **URL**: https://apify.com/boxbox10/pdf-extractor.md - **Developed by:** [Marvin Eguilos](https://apify.com/boxbox10) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## PDF Extractor — PDF → Clean Markdown + JSON for LLM & RAG **Give it a PDF URL. Get back clean, LLM-ready Markdown and structured JSON.** Title, metadata, per-page text, page count, word count, and an accurate token count — every document, one tidy result. Built for RAG pipelines, AI agents, and anyone who needs the *text* of a PDF without wrestling with binary parsing, broken encodings, or page-layout noise. > Feed your PDFs to your LLM the way it wants to be fed: as clean Markdown, chunkable by page, with the token budget already counted. --- ### ✨ What it does - **Robust PDF text extraction** — powered by Mozilla's `pdf.js` (via `unpdf`), the same battle-tested engine that renders PDFs in Firefox. Handles multi-page documents, embedded fonts, and complex layouts. - **Clean Markdown** — normalized whitespace, collapsed noise, optional per-page separators (`--- Page N ---`) so you can chunk on natural document boundaries. - **Structured JSON** — `title`, `metadata` (author, subject, keywords, creator, producer, creation/modification dates, PDF version), `pageCount`, `pages[]` (per-page text), `wordCount`, `tokenCount`, `fileSizeBytes`, `fetchedAt`. - **Accurate token counts** — counted with the GPT/`cl100k`-family tokenizer so you know exactly how much context each PDF costs *before* you send it to a model. - **Token budgeting** — optional `maxTokens` truncates output to fit your context window. - **Page-level chunking** — the `pages[]` array gives you clean text per page, ideal for granular RAG retrieval and citations. - **Robust by design** — one bad URL never kills the run. Failed downloads/parses return a clean error record (and are **never charged**). - **Safe & efficient** — file-size guard, `%PDF-` magic-byte validation, and content-type checks reject non-PDFs before wasting compute. --- ### 🎯 Use cases | You want to… | This Actor gives you… | |---|---| | **Build a RAG knowledge base from PDFs** | Clean Markdown + per-page text with token counts, ready to embed. | | **Ingest research papers (arXiv, journals)** | Structured text + metadata your pipeline can index and cite. | | **Feed reports / whitepapers to an LLM** | Pre-counted tokens so you never blow the context window. | | **Give an AI agent document context** | Structured JSON your agent can reason over — no binary noise. | | **Convert PDF docs to Markdown** | Portable, diff-friendly Markdown you can drop into a repo or wiki. | | **Chunk documents by page for citations** | A `pages[]` array with clean text and page numbers. | --- ### 📥 Input | Field | Type | Default | Description | |---|---|---|---| | `urls` | string[] | — **(required)** | One or more direct links to PDF files. Each successful PDF is one result. | | `outputFormat` | `both` \| `markdown` \| `json` | `both` | Include Markdown, the JSON fields, or both. | | `includePageText` | boolean | `true` | Include a `pages[]` array with per-page text in the JSON output. | | `pageSeparators` | boolean | `true` | Insert `--- Page N ---` markers between pages in the Markdown. | | `maxPagesPerRun` | integer | `1000` | Safety cap on PDF URLs processed per run. | | `maxTokens` | integer | `0` | Truncate Markdown to ~N tokens (`0` = no limit). | | `maxFileSizeMb` | integer | `100` | Skip PDFs larger than this size (recorded as failures, never charged). | #### Example input ```json { "urls": [ "https://arxiv.org/pdf/1706.03762", "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" ], "outputFormat": "both", "includePageText": true, "pageSeparators": true, "maxTokens": 0 } ```` *** ### 📤 Output One dataset item per PDF. Successful example (truncated): ```json { "url": "https://arxiv.org/pdf/1706.03762", "finalUrl": "https://arxiv.org/pdf/1706.03762", "statusCode": 200, "title": "Attention Is All You Need", "metadata": { "title": "Attention Is All You Need", "author": "Ashish Vaswani et al.", "creator": "LaTeX with hyperref", "producer": "pdfTeX", "creationDate": "D:20170612...", "pdfVersion": "1.5" }, "pageCount": 15, "fileSizeBytes": 2215244, "wordCount": 8823, "tokenCount": 13140, "fetchedAt": "2026-07-19T02:17:19.954Z", "markdown": "Attention Is All You Need\n\n...\n\n---\n\n**Page 2**\n\n...", "pages": [ { "page": 1, "text": "Attention Is All You Need\n\n..." } ] } ``` Failed URL (returned to a separate `failures` dataset, **not charged**): ```json { "url": "https://not-a-real-domain-xyz.com/file.pdf", "finalUrl": "https://not-a-real-domain-xyz.com/file.pdf", "statusCode": null, "error": "getaddrinfo ENOTFOUND not-a-real-domain-xyz.com", "fetchedAt": "2026-07-19T02:17:19.831Z" } ``` *** ### 🔌 Call it from code (Apify API) ```bash curl -X POST "https://api.apify.com/v2/acts/YOUR_USERNAME~pdf-extractor/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"urls":["https://arxiv.org/pdf/1706.03762"]}' ``` ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_TOKEN' }); const { defaultDatasetId } = await client .actor('YOUR_USERNAME/pdf-extractor') .call({ urls: ['https://arxiv.org/pdf/1706.03762'] }); const { items } = await client.dataset(defaultDatasetId).listItems(); console.log(items[0].markdown); ``` *** ### 💸 Pricing (Pay-Per-Event) | Event | Price | |---|---| | Actor start | **$0.05** per run | | Extracted PDF | **$0.003** per successfully extracted PDF | - **You only pay for PDFs that succeed** — failed URLs are never charged. - **🎁 Free tier:** free-plan users' platform usage is covered by Apify, so you can try it and run small jobs at **no cost** before scaling up. - Structured per-page JSON + accurate token counts, purpose-built for LLM/RAG ingestion — at a low, predictable price point. *** ### ⚖️ Acceptable use This is a general-purpose format-conversion tool: **you supply the PDF URLs and are responsible for having the right to download and use the content you submit.** The Actor identifies itself with a descriptive User-Agent and only extracts text from documents you point it at. It does not target any single platform's private API and does not harvest personal data as a feature. Please comply with each source's terms of service and applicable law. *** ### 🧱 Under the hood Node.js · [unpdf](https://github.com/unjs/unpdf) (Mozilla `pdf.js` core, serverless-friendly) · [gpt-tokenizer](https://github.com/niieani/gpt-tokenizer) · Apify SDK. Pure-JS extraction — no headless browser, no system libraries. Stateless — nothing is stored between runs. # Actor input Schema ## `urls` (type: `array`): One or more direct links to PDF files. Each successfully extracted PDF is one billable result. ## `outputFormat` (type: `string`): What to include per result: Markdown only, JSON fields only, or both. ## `includePageText` (type: `boolean`): Include a structured `pages[]` array with the raw text of each page in the JSON output. Useful for page-level chunking in RAG. ## `pageSeparators` (type: `boolean`): Insert a horizontal-rule page marker (e.g. '--- Page 2 ---') between pages in the Markdown output. ## `maxPagesPerRun` (type: `integer`): Safety cap on how many PDF URLs are processed in a single run. ## `maxTokens` (type: `integer`): Optional. Truncate the Markdown output to approximately this many tokens (0 = no limit). Useful for fitting LLM context windows. ## `maxFileSizeMb` (type: `integer`): Skip PDFs larger than this size (in megabytes) to protect compute. Oversized files are recorded as failures and never charged. ## Actor input object example ```json { "urls": [ "https://arxiv.org/pdf/1706.03762" ], "outputFormat": "both", "includePageText": true, "pageSeparators": true, "maxPagesPerRun": 1000, "maxTokens": 0, "maxFileSizeMb": 100 } ``` # 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 = { "urls": [ "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf", "https://arxiv.org/pdf/1706.03762" ] }; // Run the Actor and wait for it to finish const run = await client.actor("boxbox10/pdf-extractor").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 = { "urls": [ "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf", "https://arxiv.org/pdf/1706.03762", ] } # Run the Actor and wait for it to finish run = client.actor("boxbox10/pdf-extractor").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 '{ "urls": [ "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf", "https://arxiv.org/pdf/1706.03762" ] }' | apify call boxbox10/pdf-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=boxbox10/pdf-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PDF Extractor: PDF → Clean Markdown + JSON for LLM/RAG", "description": "Turn any PDF URL into clean, LLM-ready Markdown + structured JSON (title, metadata, per-page text, page count, word count, token count). Perfect for RAG pipelines, AI agents, and LLM document ingestion.", "version": "0.1", "x-build-id": "smDCjQhX0hUciwhD6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/boxbox10~pdf-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-boxbox10-pdf-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/boxbox10~pdf-extractor/runs": { "post": { "operationId": "runs-sync-boxbox10-pdf-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/boxbox10~pdf-extractor/run-sync": { "post": { "operationId": "run-sync-boxbox10-pdf-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "urls" ], "properties": { "urls": { "title": "PDF URLs", "type": "array", "description": "One or more direct links to PDF files. Each successfully extracted PDF is one billable result.", "items": { "type": "string" } }, "outputFormat": { "title": "Output format", "enum": [ "both", "markdown", "json" ], "type": "string", "description": "What to include per result: Markdown only, JSON fields only, or both.", "default": "both" }, "includePageText": { "title": "Include per-page text array", "type": "boolean", "description": "Include a structured `pages[]` array with the raw text of each page in the JSON output. Useful for page-level chunking in RAG.", "default": true }, "pageSeparators": { "title": "Page separators in Markdown", "type": "boolean", "description": "Insert a horizontal-rule page marker (e.g. '--- Page 2 ---') between pages in the Markdown output.", "default": true }, "maxPagesPerRun": { "title": "Max PDFs per run", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Safety cap on how many PDF URLs are processed in a single run.", "default": 1000 }, "maxTokens": { "title": "Max tokens per result", "minimum": 0, "maximum": 2000000, "type": "integer", "description": "Optional. Truncate the Markdown output to approximately this many tokens (0 = no limit). Useful for fitting LLM context windows.", "default": 0 }, "maxFileSizeMb": { "title": "Max PDF file size (MB)", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Skip PDFs larger than this size (in megabytes) to protect compute. Oversized files are recorded as failures and never charged.", "default": 100 } } }, "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 } } } } } } } } } } ```