# LinkedIn Easy Apply Bot — Auto-Apply with AI Filters (`sunny_spade/linkedin-easy-apply-bot`) Actor Automatically applies to LinkedIn Easy Apply jobs matching your profile. Filters by language, role relevance, and Language fluency requirements. Fills all form fields using your profile data. Requires a valid LinkedIn session cookie. - **URL**: https://apify.com/sunny\_spade/linkedin-easy-apply-bot.md - **Developed by:** [NoCanDo](https://apify.com/sunny_spade) (community) - **Categories:** Jobs, Automation, Agents - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $200.00 / 1,000 application submitteds 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.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 ## LinkedIn Easy Apply Bot — Auto-Apply with AI Filters Automatically applies to LinkedIn **Easy Apply** jobs that match your profile. The bot authenticates with your LinkedIn session cookies, searches jobs by keyword and location, filters irrelevant or German-language postings, fills every application form field from your profile data, and submits — all without manual effort. **Powered by [Browserbase](https://browserbase.com)** — a real Chrome browser with a residential IP runs in the cloud. LinkedIn cannot distinguish it from a human user, so bot-detection does not fire. --- ### How it works 1. **Authenticates** using your LinkedIn session cookies (no password stored) 2. **Launches** a real Chrome browser via Browserbase (residential IP, genuine fingerprint) 3. **Searches** across multiple job keywords in your chosen location 4. **Filters** each posting by language and role relevance 5. **Fills the Easy Apply form** — text fields, dropdowns, radio buttons, salary, cover letter 6. **Submits** and saves every result to the Apify dataset --- ### Before you start #### 1 — Create a Browserbase account 1. Sign up at [browserbase.com](https://www.browserbase.com) 2. Go to **Settings** and copy your **API Key** and **Project ID** 3. Paste both into the Actor input fields `browserbaseApiKey` and `browserbaseProjectId` > Browserbase provides the real Chrome browser with a residential IP. No separate proxy configuration is needed. #### 2 — Export your LinkedIn cookies 1. Log in to [linkedin.com](https://www.linkedin.com) in your browser 2. Open **DevTools** → **Application** → **Cookies** → `https://www.linkedin.com` 3. Export all cookies as a JSON array (use the [EditThisCookie](https://www.editthiscookie.com/) extension or any similar tool) 4. Paste the JSON into the **LinkedIn Session Cookies** input field > The cookie array must include `li_at`. Without it the bot cannot authenticate. --- ### Input configuration | Field | Type | Description | |---|---|---| | `linkedinCookies` | string (JSON) | Your LinkedIn session cookies as a JSON array | | `browserbaseApiKey` | string (secret) | Your Browserbase API key from browserbase.com/settings | | `browserbaseProjectId` | string | Your Browserbase project ID | | `profile` | object | Your personal info — name, email, phone, city, current title, etc. | | `experience` | object | Total years of experience, management years, domain-specific years | | `skillsYears` | object | Map of skill → years (e.g. `{"python": 3, "sql": 4}`) | | `preferences` | object | Salary expectation and other form-answer preferences | | `searchConfig` | object | Keywords, location, max jobs per run, date filter, delay settings | | `dryRun` | boolean | If `true`, identifies matching jobs but does **not** submit. Use this first. | #### Example `profile` ```json { "first_name": "John", "last_name": "Doe", "email": "john.doe@example.com", "phone": "+49123456789", "city": "Berlin", "linkedin_url": "https://www.linkedin.com/in/johndoe", "current_title": "Operations Manager", "nationality": "Indian", "gender": "Male", "age": 35, "cover_letter": "I am excited to apply for this role..." } ```` #### Example `searchConfig` ```json { "keywords": ["Operations Manager", "Program Manager", "Supply Chain Manager"], "location": "Germany", "maxJobsPerRun": 50, "datePosted": "r604800", "minDelayBetweenAppsSeconds": 45, "maxDelayBetweenAppsSeconds": 90, "dailyApplicationCap": 40 } ``` > `datePosted` values: `r86400` = last 24 h, `r604800` = last week, `r2592000` = last month *** ### Output Each applied or skipped job is saved to the **Apify dataset**: ```json { "title": "Operations Manager EMEA", "company": "Acme GmbH", "url": "https://www.linkedin.com/jobs/view/1234567890/", "status": "applied", "reason": "", "duration": 47.2 } ``` `status` values: `applied` · `dry_run` · `skipped` · `error` Skipped and errored jobs include a `reason` field: `"German-language posting"`, `"No Easy Apply button"`, `"Relevance filter: wrong role type"`, etc. *** ### Session persistence On the first run, the bot creates a **Browserbase Context** (a persistent browser profile) and stores its ID in the Apify key-value store. Every subsequent run reuses the same context — same cookies, same fingerprint, same residential IP pool. This means: - LinkedIn's session stays alive without re-authentication between runs - The browser identity is stable, which greatly reduces the chance of security challenges - **Do not delete the key-value store** between runs if you want to preserve the session *** ### Tips & limitations - **Run `dryRun: true` first** to verify your cookies and filters before submitting real applications - **Fresh cookies** — LinkedIn session cookies expire every few days. Re-export when the bot reports an authentication failure - **Rate limiting** — the bot waits 45–90 seconds between applications by default to mimic human behaviour - **Complex forms** — some employers add file-upload steps or redirect to external sites. The bot skips these gracefully and logs the reason - **LinkedIn ToS** — automated job applications may violate LinkedIn's Terms of Service. Use responsibly and at your own risk *** ### Browserbase pricing Browserbase charges per browser-hour (~$0.10–$0.12/hr depending on plan). A typical run of 40 jobs takes 30–45 minutes ≈ **$0.05–$0.10 per run**. See [browserbase.com/pricing](https://www.browserbase.com/pricing) for current rates. *** ### Changelog - **0.2.0** — Complete architecture overhaul: replaced Apify Residential Proxy + headless Playwright with Browserbase cloud-browser (real Chrome, residential IP, CDP connection). LinkedIn bot-detection no longer fires. Switched back to card-click SPA navigation (most human-like). Added Browserbase Context persistence across runs via Apify KV store. Added `JobResult` dataclass, exponential-backoff retry helper, per-job 180 s timeout, `Actor.set_status_message()` progress reporting, and structured summary log. - **0.1.30** — Switched to direct URL navigation for job processing: collect all IDs from the search page in one scroll pass, then navigate to each `/jobs/view/{id}/` directly. Fixed crash on `net::ERR_TUNNEL_CONNECTION_FAILED`. - **0.1.29** — Completely new "Search Once, Process In-Place" strategy. All jobs processed by clicking their cards to trigger AJAX right-panel updates. Added comprehensive stealth init script. - **0.1.28** — Reload search page before every card click. Removed route interceptor. - **0.1.27** — Wrapped all `page.goto` calls in try/except; increased navigation timeout to 90 s. - **0.1.26** — Replaced `dispatchEvent` with Playwright's trusted CDP `locator.click()`. - **0.1.25** — Replaced `page.goto(¤tJobId=)` with `dispatchEvent(MouseEvent)` on the job card. - **0.1.22** — Replaced direct `/jobs/view/{id}/` navigation with card-click. - **0.1.15** — Fixed LinkedIn job title and company selectors for 2025 redesign. - **0.1.8** — Fixed cookie format normalization (`sameSite`, `expirationDate`). - **0.1.5** — Fixed proxy credential parsing for Playwright. # Actor input Schema ## `linkedinCookies` (type: `string`): Your LinkedIn cookies as a JSON array. Export from browser DevTools → Application → Cookies → linkedin.com. Must include 'li\_at'. ## `browserbaseApiKey` (type: `string`): Your Browserbase API key from browserbase.com/settings. The bot uses Browserbase to run a real Chrome browser with a residential IP — this is what allows it to bypass LinkedIn's bot detection. ## `browserbaseProjectId` (type: `string`): Your Browserbase project ID from browserbase.com/settings. Find it in the project settings panel. ## `profile` (type: `object`): Personal and contact information used to fill application forms. ## `experience` (type: `object`): Total and domain-specific years of experience — used to answer numeric form fields like 'how many years of X experience?' ## `skillsYears` (type: `object`): Map of skill keyword to years of experience. Used for form fields asking about specific tools or technologies. ## `preferences` (type: `object`): Salary expectations and other form-answer preferences. ## `searchConfig` (type: `object`): Keywords, location, and pacing limits for the job search. ## `dryRun` (type: `boolean`): If true, the bot finds and filters matching jobs but does NOT submit applications. Run this first to verify your configuration. ## Actor input object example ```json { "profile": { "first_name": "John", "last_name": "Doe", "email": "john.doe@example.com", "phone": "+49123456789", "city": "Berlin", "linkedin_url": "https://www.linkedin.com/in/johndoe", "current_title": "Operations Manager", "current_employer": "", "nationality": "Indian", "gender": "Male", "age": 35, "cover_letter": "" }, "experience": { "total_years": 8, "management_years": 5, "amazon_years": 6 }, "skillsYears": { "sql": 4, "python": 3, "tableau": 4, "excel": 7, "power bi": 3, "salesforce": 2, "sap": 1 }, "preferences": { "salary_expectation_eur": 65000 }, "searchConfig": { "keywords": [ "Operations Manager", "Program Manager", "Supply Chain Manager", "Logistics Manager", "Team Lead Operations" ], "location": "Germany", "maxJobsPerRun": 50, "datePosted": "r604800", "minDelayBetweenAppsSeconds": 45, "maxDelayBetweenAppsSeconds": 90, "dailyApplicationCap": 40 }, "dryRun": false } ``` # 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("sunny_spade/linkedin-easy-apply-bot").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("sunny_spade/linkedin-easy-apply-bot").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 sunny_spade/linkedin-easy-apply-bot --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sunny_spade/linkedin-easy-apply-bot", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Easy Apply Bot — Auto-Apply with AI Filters", "description": "Automatically applies to LinkedIn Easy Apply jobs matching your profile. Filters by language, role relevance, and Language fluency requirements. Fills all form fields using your profile data. Requires a valid LinkedIn session cookie.", "version": "0.1", "x-build-id": "5BEkg2nMpPrqLjdsO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sunny_spade~linkedin-easy-apply-bot/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sunny_spade-linkedin-easy-apply-bot", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/sunny_spade~linkedin-easy-apply-bot/runs": { "post": { "operationId": "runs-sync-sunny_spade-linkedin-easy-apply-bot", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/sunny_spade~linkedin-easy-apply-bot/run-sync": { "post": { "operationId": "run-sync-sunny_spade-linkedin-easy-apply-bot", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "linkedinCookies", "browserbaseApiKey", "browserbaseProjectId", "profile" ], "properties": { "linkedinCookies": { "title": "LinkedIn Session Cookies (JSON)", "type": "string", "description": "Your LinkedIn cookies as a JSON array. Export from browser DevTools → Application → Cookies → linkedin.com. Must include 'li_at'." }, "browserbaseApiKey": { "title": "Browserbase API Key", "type": "string", "description": "Your Browserbase API key from browserbase.com/settings. The bot uses Browserbase to run a real Chrome browser with a residential IP — this is what allows it to bypass LinkedIn's bot detection." }, "browserbaseProjectId": { "title": "Browserbase Project ID", "type": "string", "description": "Your Browserbase project ID from browserbase.com/settings. Find it in the project settings panel." }, "profile": { "title": "Your Profile", "type": "object", "description": "Personal and contact information used to fill application forms.", "default": { "first_name": "John", "last_name": "Doe", "email": "john.doe@example.com", "phone": "+49123456789", "city": "Berlin", "linkedin_url": "https://www.linkedin.com/in/johndoe", "current_title": "Operations Manager", "current_employer": "", "nationality": "Indian", "gender": "Male", "age": 35, "cover_letter": "" } }, "experience": { "title": "Experience Summary", "type": "object", "description": "Total and domain-specific years of experience — used to answer numeric form fields like 'how many years of X experience?'", "default": { "total_years": 8, "management_years": 5, "amazon_years": 6 } }, "skillsYears": { "title": "Skills → Years Map", "type": "object", "description": "Map of skill keyword to years of experience. Used for form fields asking about specific tools or technologies.", "default": { "sql": 4, "python": 3, "tableau": 4, "excel": 7, "power bi": 3, "salesforce": 2, "sap": 1 } }, "preferences": { "title": "Preferences", "type": "object", "description": "Salary expectations and other form-answer preferences.", "default": { "salary_expectation_eur": 65000 } }, "searchConfig": { "title": "Job Search Configuration", "type": "object", "description": "Keywords, location, and pacing limits for the job search.", "default": { "keywords": [ "Operations Manager", "Program Manager", "Supply Chain Manager", "Logistics Manager", "Team Lead Operations" ], "location": "Germany", "maxJobsPerRun": 50, "datePosted": "r604800", "minDelayBetweenAppsSeconds": 45, "maxDelayBetweenAppsSeconds": 90, "dailyApplicationCap": 40 } }, "dryRun": { "title": "Dry Run (no actual submissions)", "type": "boolean", "description": "If true, the bot finds and filters matching jobs but does NOT submit applications. Run this first to verify your configuration.", "default": false } } }, "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 } } } } } } } } } } ```