# Hh Resume Search Scraper (`soft_alexist/hh-resume-search-scraper`) Actor Scrape resume profiles from hh.ru's search engine with precision. Collect candidate names, skills, salary expectations, job search status, and 20+ professional attributes in structured JSON — perfect for recruiters, HR teams, and talent acquisition platforms. - **URL**: https://apify.com/soft\_alexist/hh-resume-search-scraper.md - **Developed by:** [Soft Alexist](https://apify.com/soft_alexist) (community) - **Categories:** Automation, Developer tools, Jobs - **Stats:** 2 total users, 1 monthly users, 100.0% 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 ## HH.ru Resume Search Scraper: Extract Candidate Data at Scale --- ### About HH.ru HH.ru is Russia's largest job search platform, connecting millions of job seekers with employers across industries. Its resume search feature allows deep mining of candidate data — but manual extraction is slow and error-prone. The **HH.ru Resume Search Scraper** automates this workflow, delivering structured candidate profiles ready for analysis, recruitment workflows, or talent database integration. --- ### Overview The **HH.ru Resume Search Scraper** extracts candidate resume profiles from HH.ru's resume search results pages. It transforms unstructured HTML into clean JSON records containing 26+ candidate attributes. Ideal for: - **Recruiters** sourcing candidates by skills, experience, and location - **HR automation platforms** building talent pools for active hiring - **Talent acquisition teams** conducting market research and competitor benchmarking - **HR analytics providers** creating candidate datasets for insights - **Career platform developers** aggregating resume data for job matching engines The scraper handles pagination gracefully, supports error tolerance via `ignore_url_failures`, and scales to hundreds of profiles per search query. --- ### Input Format The scraper accepts a JSON configuration: ```json { "urls": [ "https://hh.ru/search/resume?from=footer_new&hhtmFromLabel=footer_new&hhtmFrom=main" ], "ignore_url_failures": true, "max_items_per_url": 200 } ```` #### Input Field Definitions | Parameter | Type | Description | Example | |---|---|---|---| | `urls` | Array | URLs of HH.ru resume search result pages to scrape. Add filtered search URLs (by skills, experience, location, salary) | `https://hh.ru/search/resume?...` | | `ignore_url_failures` | Boolean | If `true`, the scraper continues running even if some URLs fail. Useful for bulk scraping. | `true` | | `max_items_per_url` | Integer | Maximum number of resume profiles to extract per URL. Limits data volume and API costs. | `200` | **Pro tip:** Use HH.ru's native filters to create targeted search URLs—by skill (Python, JavaScript), location (Moscow, St. Petersburg), salary range, or job search status. Paste multiple filtered URLs to build comprehensive candidate datasets. *** ### Output Format **Sample output** ```json { "forbidden": null, "age": [ { "string": 50 } ], "fields_view_status": [ { "contact_view_status": null, "vacancy_permissions": [], "role_permissions": [] } ], "gender": [ { "string": "male" } ], "has_photo": [ { "string": true } ], "hidden_fields": [], "job_search_status": [ { "job_search_status": { "name": "active_search", "last_change_time": "2026-03-12T12:58:10.704538Z" } } ], "key_skills": [ { "string": "Ремонтные работы" }, { "string": "Сварочные работы" }, { "string": "Слесарные работы" }, { "string": "каркасное строительство" }, { "string": "штукатурка стен" }, { "string": "Водительское удостоверение категории B" }, { "string": "Точность и внимательность к деталям" }, { "string": "Работа в команде" }, { "string": "Кайдзен" } ], "lang": [ { "string": "ru" } ], "last_activity_time": "2026-07-09T20:28:03.052+03:00", "last_change_time_details": [ { "year": "2026", "month": "7", "day": "9", "hour": "20", "minute": "28", "date": 1783618080000 } ], "last_topic": [], "phone_preview": [ { "type": "cell", "country": "7", "city": "903", "verified": true } ], "predicted_salary": [ { "amount": 33816, "currency": "RUR" } ], "salary": [ { "amount": 75000, "currency": "RUR" } ], "title": [ { "string": "Оператор линии,разнорабочий,подсобный рабочий" } ], "total_experience": [ { "string": 80 } ], "folders": [], "favorite_folders": [], "search_rid": "1783619489027c5ea6b90d352b43d006", "negotiation_links": { "invite": { "state_links": [], "default_link": "/employer/negotiations/change_topic?r=8f9df6e700033f211c0039ed1f537878384951" }, "invite_another": { "state_links": [], "default_link": "/employer/negotiations/change_topic?r=8f9df6e700033f211c0039ed1f537878384951&allVacancies=True&toAnother=True" } }, "is_online": false, "resume_contacts_opening": null, "can_open_contacts": false, "has_unpaid_pfp_topics": false, "webcall_enabled": false, "from_url": "https://hh.ru/search/resume?from=footer_new&hhtmFromLabel=footer_new&hhtmFrom=main" } ``` Each resume profile returns a rich record with 26+ candidate attributes: #### Profile Identification | Field | Meaning | Example | |---|---|---| | `Title` | Candidate's professional title or job target | "Senior Python Developer" | | `Age` | Candidate's age (if publicly disclosed) | `28` | | `Has Photo` | Whether the profile includes a profile photo | `true` | | `Gender` | Candidate's gender (if disclosed) | "M" or "F" | | `Is Online` | Whether the candidate is currently active on HH.ru | `true` | #### Experience & Skills | Field | Meaning | Example | |---|---|---| | `Total Experience` | Years of professional work experience | `5` | | `Key Skills` | Array of candidate's listed professional competencies | `["Python", "Django", "PostgreSQL"]` | | `Language` | Languages spoken by the candidate | `["Russian", "English"]` | | `Job Search Status` | Current job search intent (active, passive, not searching) | `"LOOKING_FOR_JOB"` | #### Compensation & Preferences | Field | Meaning | Example | |---|---|---| | `Salary` | Expected salary explicitly stated by candidate | `"150000 RUB"` | | `Predicted Salary` | HH.ru's algorithm-estimated salary based on skills/experience | `"160000 RUB"` | | `Conditions` | Work conditions the candidate is open to | `["Remote", "Relocation"]` | #### Contact & Access | Field | Meaning | Example | |---|---|---| | `Can Open Contacts` | Whether you have permission to view contact details | `true` | | `Resume Contacts Opening` | Type of contact opening (anonymous, by request, etc.) | `"OPEN"` | | `Phone Preview` | Partially masked phone number (privacy protection) | `"+7 (9XX) XXX-XX-45"` | | `Negotiation Links` | Links to initiate salary/offer negotiations | `[URL]` | | `Webcall Enabled` | Whether the candidate accepts video interview requests | `true` | #### Activity & Visibility | Field | Meaning | Example | |---|---|---| | `Last Activity Time` | Timestamp of candidate's last HH.ru login | `"2024-01-15T10:30:00Z"` | | `Last Change Time Details` | When the resume was last updated | `"2024-01-10T14:22:00Z"` | | `Last Topic` | Last message topic or interaction | `"Job inquiry from recruiter"` | | `Fields View Status` | Which resume fields are visible to non-registered users | `{...}` | #### Account Features & Attributes | Field | Meaning | Example | |---|---|---| | `Attributes` | Special profile badges (e.g., "Top candidate", "Premium resume") | `["Top Professional"]` | | `Defaults` | Default settings for profile visibility and contact opening | `{...}` | | `Hidden Fields` | Which resume sections are hidden from public view | `["Desired Salary"]` | | `Has Unpaid PFP Topics` | Whether candidate used unpaid premium features | `false` | | `Forbidden` | Access restrictions on the profile | `null` or restrictions object | | `Folders` | Candidate's saved job search folders | `["Data Science Jobs"]` | | `Favorite Folders` | Most-used or starred folders | `["Data Science Jobs"]` | | `Search RID` | Unique request ID for tracking this search result | `"abc123def456"` | *** ### How to Use 1. **Build a search URL** — Visit HH.ru/search/resume and use filters (skills, location, experience, salary). Copy the resulting URL. 2. **Add URLs to input** — Paste one or more search URLs into the `urls` array. Add multiple filters for segmented datasets. 3. **Set max items** — Adjust `max_items_per_url` (default: 200) based on your data needs and time constraints. 4. **Enable error tolerance** — Set `ignore_url_failures: true` for production runs to avoid interruptions. 5. **Run the scraper** — Start the actor and monitor the run log in real time. 6. **Export results** — Download data as JSON, CSV, or Excel for CRM, ATS, or analytics platforms. **Best practices:** - Create separate runs for different job functions (backend, frontend, data science) using filtered URLs. - Use salary and experience filters to target mid-to-senior level candidates. - Check `Can Open Contacts` field before reaching out—some profiles restrict direct contact. - Respect `Forbidden` and `Hidden Fields`—do not attempt to bypass privacy settings. *** ### Use Cases & Business Value - **Active recruitment:** Build talent pools by skill set and experience level for faster hiring - **Competitive analysis:** Monitor candidate availability and salary expectations in your market - **Talent pipelining:** Create evergreen candidate databases for future vacancies - **Salary benchmarking:** Analyze predicted salaries to set competitive compensation - **Candidate research:** Identify passive candidates and market trends in technical talent - **API integration:** Feed resume data into ATS platforms, recruitment workflows, or ML models The HH.ru Resume Search Scraper eliminates weeks of manual candidate research, enabling data-driven hiring decisions and scaled talent acquisition operations. *** ### Conclusion The **HH.ru Resume Search Scraper** is a powerful tool for anyone recruiting talent in Russia or Eastern Europe. With support for 200+ profiles per search and rich candidate attributes, it transforms HH.ru's resume database into actionable intelligence. Combine with targeted search filters to build qualified talent pipelines faster than manual sourcing. # Actor input Schema ## `urls` (type: `array`): Add the URLs of the resume list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list. ## `ignore_url_failures` (type: `boolean`): If true, the scraper will continue running even if some URLs fail to be scraped. ## `max_items_per_url` (type: `integer`): The maximum number of items to scrape per URL. ## Actor input object example ```json { "urls": [ "https://hh.ru/search/resume?from=footer_new&hhtmFromLabel=footer_new&hhtmFrom=main" ], "ignore_url_failures": true, "max_items_per_url": 20 } ``` # 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://hh.ru/search/resume?from=footer_new&hhtmFromLabel=footer_new&hhtmFrom=main" ], "ignore_url_failures": true, "max_items_per_url": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("soft_alexist/hh-resume-search-scraper").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://hh.ru/search/resume?from=footer_new&hhtmFromLabel=footer_new&hhtmFrom=main"], "ignore_url_failures": True, "max_items_per_url": 20, } # Run the Actor and wait for it to finish run = client.actor("soft_alexist/hh-resume-search-scraper").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://hh.ru/search/resume?from=footer_new&hhtmFromLabel=footer_new&hhtmFrom=main" ], "ignore_url_failures": true, "max_items_per_url": 20 }' | apify call soft_alexist/hh-resume-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=soft_alexist/hh-resume-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hh Resume Search Scraper", "description": "Scrape resume profiles from hh.ru's search engine with precision. Collect candidate names, skills, salary expectations, job search status, and 20+ professional attributes in structured JSON — perfect for recruiters, HR teams, and talent acquisition platforms.", "version": "0.0", "x-build-id": "J6h6zMghjCzariUSc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/soft_alexist~hh-resume-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-soft_alexist-hh-resume-search-scraper", "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/soft_alexist~hh-resume-search-scraper/runs": { "post": { "operationId": "runs-sync-soft_alexist-hh-resume-search-scraper", "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/soft_alexist~hh-resume-search-scraper/run-sync": { "post": { "operationId": "run-sync-soft_alexist-hh-resume-search-scraper", "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", "properties": { "urls": { "title": "URLs of the resume list urls to scrape", "type": "array", "description": "Add the URLs of the resume list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list.", "items": { "type": "string" } }, "ignore_url_failures": { "title": "Continue running even if some URLs fail to be scraped", "type": "boolean", "description": "If true, the scraper will continue running even if some URLs fail to be scraped." }, "max_items_per_url": { "title": "Max items per URL", "type": "integer", "description": "The maximum number of items to scrape per URL." } } }, "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 } } } } } } } } } } ```