# Resume / CV Parser (Claude → Structured JSON) (`gochujang/resume-parser`) Actor Pass a PDF resume URL (or text). Returns structured JSON: name, email, phone, location, current title, skills, education, experience (with highlights), languages, links. Powered by Claude with strict schema. BYO Anthropic API key. $0.02 per resume. - **URL**: https://apify.com/gochujang/resume-parser.md - **Developed by:** [Hojun Lee](https://apify.com/gochujang) (community) - **Categories:** Developer tools, Automation, Other - **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 ## Resume / CV Parser (Claude) > Pass a PDF resume URL (or text). Returns structured JSON: **name, email, phone, location, skills, education, experience with highlights, languages, links**. Powered by Claude with strict schema. **BYO Anthropic API key. $0.02 per resume.** --- ### Why this exists Recruiters waste hours copying resume fields into ATS / spreadsheets. Existing solutions: - **Sovren Parser**: $100+/mo - **Affinda**: $99-499/mo - **Daxtra**: enterprise quote - **HireAbility**: $$$ This actor: $0.02 per resume + Anthropic tokens (~$0.01 per resume at Opus 4.7). Total ~$0.03 per resume — 50-100x cheaper than commercial parsers. --- ### What you get ```json { "source_url": "https://...resume.pdf", "parsed": { "name": "Jane Doe", "email": "jane@example.com", "phone": "+1-555-1234", "location": "San Francisco, CA", "headline": "Senior Software Engineer", "current_title": "Senior Software Engineer", "current_company": "Apple Inc.", "years_experience": 8, "skills": ["Python", "TypeScript", "AWS", "Kubernetes"], "languages": ["English (native)", "Mandarin (fluent)"], "links": { "linkedin": "https://linkedin.com/in/janedoe", "github": "https://github.com/janedoe", "portfolio": "https://janedoe.dev" }, "education": [ {"school":"MIT","degree":"BS","field":"Computer Science","start_year":"2014","end_year":"2018"} ], "experience": [ { "company":"Apple Inc.","title":"Senior Software Engineer", "location":"Cupertino, CA", "start_date":"2022-01","end_date":null,"is_current":true, "highlights":["Shipped X feature reducing latency 35%","Led team of 6"] }, ... ], "certifications": ["AWS Solutions Architect"], "publications": [] }, "model": "claude-opus-4-7", "input_chars": 4523, "usage": {"input_tokens": 1200, "output_tokens": 850} } ```` *** ### Quick start #### Single resume by URL ```json { "resumeUrl": "https://example.com/resumes/jane.pdf", "anthropicApiKey": "sk-ant-..." } ``` #### Paste text (skip download) ```json { "resumeText": "JANE DOE\nSenior Software Engineer...\n", "model": "claude-haiku-4-5", "anthropicApiKey": "sk-ant-..." } ``` #### Bulk via Apify Task Create a Task with the API key + chain it: scrape resume URLs from a job board → for each URL, run this actor. *** ### Pricing **Pay-Per-Event**: `$0.02 per resume` (Apify-side). Anthropic tokens charged separately: | Model | Approx Anthropic cost | |---|---| | Claude Opus 4.7 | $0.008-0.015 | | Claude Sonnet 4.6 | $0.002-0.005 | | Claude Haiku 4.5 | $0.0006-0.002 | **Total** = $0.022-0.035 per resume. Vs commercial parsers ($0.50-2.00 per resume), this is 20-50x cheaper. *** ### Use cases 1. **Recruiting agency** — Bulk parse incoming PDFs into your ATS 2. **HR / TA team** — Stop pasting resume fields into Greenhouse / Lever / Workday 3. **VC scout** — Quickly process LinkedIn export PDFs of portfolio company candidates 4. **Outbound sales** — Enrich Sales Navigator exports with structured skill data 5. **Research** — Build a labor-market dataset from public CV samples *** ### Schema reference The actor uses a strict schema covering: - `name`, `email`, `phone`, `location`, `headline`, `summary` - `current_title`, `current_company`, `years_experience` (int) - `skills` (array of normalized strings) - `languages` (array) - `links` (linkedin, github, portfolio, twitter) - `education` (array: school, degree, field, start\_year, end\_year) - `experience` (array: company, title, location, start\_date, end\_date, is\_current, summary, highlights) - `certifications` (array) - `publications` (array) Fields not found are set to `null` (not omitted). *** ### Setting your Anthropic API key See [Article Summarizer README](https://apify.com/gochujang/article-summarizer) for the BYO key guide. *** ### Limitations - **Scanned PDFs (image-only)** — Will return empty text → parser returns null fields. - **Non-English resumes** — Claude handles 100+ languages, but normalize the `language` input accordingly. - **Heavy formatting** — Tables/columns may yield slightly mangled order, but Claude usually recovers. *** ### Related actors (same author) - [PDF Text Extractor](https://apify.com/gochujang/pdf-text-extractor) — Just the raw text, no parsing - [PDF → RAG Chunks](https://apify.com/gochujang/pdf-rag-chunker) — Chunk a corpus - [Article Summarizer](https://apify.com/gochujang/article-summarizer) *** ### Feedback A short review helps recruiters / HR ops find it: [Leave a review on Apify Store](https://apify.com/gochujang/resume-parser#reviews) # Actor input Schema ## `resumeUrl` (type: `string`): URL of the resume PDF. ## `resumeText` (type: `string`): Raw resume text (used if URL is empty). ## `model` (type: `string`): Claude model. ## `anthropicApiKey` (type: `string`): Get one at console.anthropic.com. ## `userAgent` (type: `string`): Custom UA. ## Actor input object example ```json { "resumeUrl": "", "resumeText": "", "model": "claude-opus-4-7", "userAgent": "" } ``` # Actor output Schema ## `dataset` (type: `string`): No description ## `summary` (type: `string`): No description # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("gochujang/resume-parser").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 = {} # Run the Actor and wait for it to finish run = client.actor("gochujang/resume-parser").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 '{}' | apify call gochujang/resume-parser --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gochujang/resume-parser", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Resume / CV Parser (Claude → Structured JSON)", "description": "Pass a PDF resume URL (or text). Returns structured JSON: name, email, phone, location, current title, skills, education, experience (with highlights), languages, links. Powered by Claude with strict schema. BYO Anthropic API key. $0.02 per resume.", "version": "0.1", "x-build-id": "4rdzzi7RmWThpOLku" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gochujang~resume-parser/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gochujang-resume-parser", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/gochujang~resume-parser/runs": { "post": { "operationId": "runs-sync-gochujang-resume-parser", "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/gochujang~resume-parser/run-sync": { "post": { "operationId": "run-sync-gochujang-resume-parser", "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": [ "anthropicApiKey" ], "properties": { "resumeUrl": { "title": "Resume PDF URL", "type": "string", "description": "URL of the resume PDF.", "default": "" }, "resumeText": { "title": "Resume text (alternative)", "type": "string", "description": "Raw resume text (used if URL is empty).", "default": "" }, "model": { "title": "Claude model", "enum": [ "claude-opus-4-7", "claude-sonnet-4-6", "claude-haiku-4-5" ], "type": "string", "description": "Claude model.", "default": "claude-opus-4-7" }, "anthropicApiKey": { "title": "Anthropic API Key", "type": "string", "description": "Get one at console.anthropic.com." }, "userAgent": { "title": "User-Agent (for PDF download)", "type": "string", "description": "Custom UA.", "default": "" } } }, "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 } } } } } } } } } } ```