# AI Job Search Agent — Web Job Finder (BYOK) (`nomad-jobs/web-search-scraper`) Actor An AI agent (Claude, bring your own Anthropic API key) that searches the open web for live job postings matching your free-text query — beyond any single job board. Returns verified records: title, company, location and a working apply URL. Finds roles other scrapers miss. - **URL**: https://apify.com/nomad-jobs/web-search-scraper.md - **Developed by:** [Nomad.Dev](https://apify.com/nomad-jobs) (community) - **Categories:** Jobs, AI - **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 ## AI Job Search Agent — Web Job Finder (BYOK) A Claude-powered agent (BYO Anthropic key) that hunts the open web for live postings matching your query — company career pages, niche boards, anywhere. > **Bring your own key.** This Actor uses Claude (Anthropic) for AI extraction. Pass your `anthropicApiKey` in the input. Without a key the AI step is skipped and the run returns no items. ### What AI job search data does this scraper extract? Each result is one flat JSON record per job posting: | Field | Meaning | |---|---| | `title` | Job title as posted | | `company` | Hiring company / organisation | | `location` | Location / duty station (may include remote hints) | | `url` | Direct link to the posting | | `postedAt` | Posting date where the source provides it | | `salary` | Salary text where the source provides it | | `snippet` | Short description excerpt | | `id` | Stable source-side identifier | ### How to scrape AI job search with this Actor 1. Click **Try for free** / **Run** — no login to the target site, no cookies, no proxies to configure. 2. Adjust the input (keyword, filters, `maxItems`) or keep the defaults. 3. Run it and export the dataset as JSON, CSV or Excel, or read it over the [API](https://docs.apify.com/api/v2). Run it from your own code: ```python from apify_client import ApifyClient client = ApifyClient("") run = client.actor("nomad-jobs/web-search-scraper").call(run_input={"maxItems": 50}) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["title"], "—", item["company"], item["url"]) ```` Or a single HTTP call that runs the Actor and returns items in one response: ```bash curl -X POST \ "https://api.apify.com/v2/acts/nomad-jobs~web-search-scraper/run-sync-get-dataset-items?token=" \ -H "Content-Type: application/json" \ -d '{"maxItems": 50}' ``` ### Input | Field | Type | Default | Notes | |---|---|---|---| | `anthropicApiKey` | string | `""` | Your Anthropic API key (sk-ant-…). Required. The actor uses it to run a web-searching AI agent that… | | `model` | string | `"claude-haiku-4-5-20251001"` | Claude model to use for the discovery agent. Haiku is fast and inexpensive; switch to Sonnet for… | | `keywords` | array | `""` | Role or technology keywords the agent should search for (e.g. "frontend", "react", "typescript"). Each… | | `locations` | array | `""` | Preferred locations or "remote". The agent will bias results toward these and skip obvious mismatches. | | `remote` | string | `"any"` | Remote work preference communicated to the search agent. | | `seniority` | string | `"any"` | Target seniority level. The agent will prefer postings that match. | | `titleMustMatch` | array | `""` | The agent will prefer postings whose title contains at least one of these terms. | | `titleExclude` | array | `""` | The agent will skip postings whose title contains any of these terms. | | `maxItems` | integer | `15` | Maximum number of job postings to return. The agent may return fewer if it cannot find enough high-quality… | | `maxAgeHours` | integer | `168` | Preferred maximum age of postings in hours. The agent will prefer fresh postings but may include older… | | `userDescription` | string | `""` | Free-text description of what you are looking for. The agent uses this as primary signal alongside the… | ### Output example ```json { "id": "ws-a1b2c3", "title": "Computational Linguist", "company": "DeepJudge", "location": "Zurich / Remote EU", "url": "https://deepjudge.ai/careers/computational-linguist", "postedAt": "2026-06-25", "snippet": "Found on company careers page; verified live." } ``` ### Pricing Pay per event: **$0.05 per Actor start** and **$0.004 per job returned**. 100 jobs ≈ $0.45. No subscription, no rental — you pay only for what you fetch. ### Use cases - Long-tail job discovery beyond big boards - Passive-candidate tooling (find who hires for X) - Niche-role hunting (rare stacks, rare titles) - Backfilling gaps in board coverage ### FAQ **Is it legal to scrape AI job search?** This Actor reads only publicly available job postings — data any visitor can see without logging in. No personal data behind authentication is touched. Review the target site's terms and your local regulations for your specific use case. **Do I need an account on the target site?** No. Postings are fetched from public pages/APIs — no login, cookies or session tokens. **How fresh is the data?** Every run fetches live listings. Results are cached for `cacheTtlSeconds` (default 30 min, set 0 to always hit the source live). **How many jobs can I get?** `maxItems` caps the run (set 0 where supported for no cap). Most sources paginate from newest to oldest. **Something broken or missing?** Open an issue on the Actor's **Issues** tab — it is monitored and reliability fixes ship fast. ### Related Actors - [LinkedIn Jobs Scraper — No Login, No Cookies](https://apify.com/nomad-jobs/linkedin-scraper) - [Research & Academic Jobs Scraper — 10 Sources](https://apify.com/nomad-jobs/researcher-bundle) - [Web Developer Jobs Scraper — 10 Boards in One](https://apify.com/nomad-jobs/web-dev-bundle) # Actor input Schema ## `anthropicApiKey` (type: `string`): Your Anthropic API key (sk-ant-…). Required. The actor uses it to run a web-searching AI agent that discovers job postings. ## `model` (type: `string`): Claude model to use for the discovery agent. Haiku is fast and inexpensive; switch to Sonnet for higher-quality extraction on complex pages. ## `keywords` (type: `array`): Role or technology keywords the agent should search for (e.g. "frontend", "react", "typescript"). Each entry is one keyword or short phrase. ## `locations` (type: `array`): Preferred locations or "remote". The agent will bias results toward these and skip obvious mismatches. ## `remote` (type: `string`): Remote work preference communicated to the search agent. ## `seniority` (type: `string`): Target seniority level. The agent will prefer postings that match. ## `titleMustMatch` (type: `array`): The agent will prefer postings whose title contains at least one of these terms. ## `titleExclude` (type: `array`): The agent will skip postings whose title contains any of these terms. ## `maxItems` (type: `integer`): Maximum number of job postings to return. The agent may return fewer if it cannot find enough high-quality matches. ## `maxAgeHours` (type: `integer`): Preferred maximum age of postings in hours. The agent will prefer fresh postings but may include older ones when fresh results are scarce. ## `userDescription` (type: `string`): Free-text description of what you are looking for. The agent uses this as primary signal alongside the structured filters above. ## Actor input object example ```json { "model": "claude-haiku-4-5-20251001", "keywords": [ "frontend", "react", "typescript" ], "locations": [ "remote", "Europe", "Spain" ], "remote": "any", "seniority": "senior", "titleMustMatch": [ "frontend", "react", "typescript" ], "titleExclude": [ "intern", "manager" ], "maxItems": 15, "maxAgeHours": 168, "userDescription": "Looking for a senior React engineer role at a product company, ideally in fintech or developer tooling, fully remote within Europe." } ``` # 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("nomad-jobs/web-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 = {} # Run the Actor and wait for it to finish run = client.actor("nomad-jobs/web-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 '{}' | apify call nomad-jobs/web-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nomad-jobs/web-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Job Search Agent — Web Job Finder (BYOK)", "description": "An AI agent (Claude, bring your own Anthropic API key) that searches the open web for live job postings matching your free-text query — beyond any single job board. Returns verified records: title, company, location and a working apply URL. Finds roles other scrapers miss.", "version": "0.1", "x-build-id": "D4mZOQrIKOlFFyZgw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nomad-jobs~web-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nomad-jobs-web-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nomad-jobs~web-search-scraper/runs": { "post": { "operationId": "runs-sync-nomad-jobs-web-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nomad-jobs~web-search-scraper/run-sync": { "post": { "operationId": "run-sync-nomad-jobs-web-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "anthropicApiKey" ], "properties": { "anthropicApiKey": { "title": "Anthropic API key", "type": "string", "description": "Your Anthropic API key (sk-ant-…). Required. The actor uses it to run a web-searching AI agent that discovers job postings." }, "model": { "title": "Claude model", "enum": [ "claude-haiku-4-5-20251001", "claude-sonnet-4-5-20251001", "claude-opus-4-5-20251001" ], "type": "string", "description": "Claude model to use for the discovery agent. Haiku is fast and inexpensive; switch to Sonnet for higher-quality extraction on complex pages.", "default": "claude-haiku-4-5-20251001" }, "keywords": { "title": "Keywords / role type", "type": "array", "description": "Role or technology keywords the agent should search for (e.g. \"frontend\", \"react\", \"typescript\"). Each entry is one keyword or short phrase.", "items": { "type": "string" } }, "locations": { "title": "Preferred locations", "type": "array", "description": "Preferred locations or \"remote\". The agent will bias results toward these and skip obvious mismatches.", "items": { "type": "string" } }, "remote": { "title": "Remote preference", "enum": [ "any", "remote-only", "hybrid", "on-site" ], "type": "string", "description": "Remote work preference communicated to the search agent.", "default": "any" }, "seniority": { "title": "Seniority level", "type": "string", "description": "Target seniority level. The agent will prefer postings that match.", "default": "any" }, "titleMustMatch": { "title": "Title must contain", "type": "array", "description": "The agent will prefer postings whose title contains at least one of these terms.", "items": { "type": "string" } }, "titleExclude": { "title": "Title must NOT contain", "type": "array", "description": "The agent will skip postings whose title contains any of these terms.", "items": { "type": "string" } }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 30, "type": "integer", "description": "Maximum number of job postings to return. The agent may return fewer if it cannot find enough high-quality matches.", "default": 15 }, "maxAgeHours": { "title": "Max posting age (hours)", "minimum": 24, "type": "integer", "description": "Preferred maximum age of postings in hours. The agent will prefer fresh postings but may include older ones when fresh results are scarce.", "default": 168 }, "userDescription": { "title": "Your own description / request (optional)", "type": "string", "description": "Free-text description of what you are looking for. The agent uses this as primary signal alongside the structured filters above." } } }, "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 } } } } } } } } } } ```