# Reddit Search Scraper (`backhoe/reddit-search-scraper`) Actor Search Reddit for posts, subreddits, and users by keywords. Supports flexible filtering by time, sorting options, and NSFW content. Automatically handles pagination for large result sets. - **URL**: https://apify.com/backhoe/reddit-search-scraper.md - **Developed by:** [D](https://apify.com/backhoe) (community) - **Categories:** Social media - **Stats:** 165 total users, 10 monthly users, 100.0% runs succeeded, 9 bookmarks - **User rating**: 4.51 out of 5 stars ## 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 ## 🔍 Reddit Search Scraper > **🆓 100% FREE** | **⚡ Fast & Reliable** | **🔒 No API Key Required** A powerful tool for searching Reddit content by keywords. Find posts, subreddits, and users with flexible sorting and time filters. --- ### ✨ Why Choose This Scraper? - ✅ **Completely Free** - No hidden costs or subscriptions - ✅ **No Reddit API Key Needed** - Works out of the box - ✅ **Residential Proxies Included** - Avoid rate limits - ✅ **Automatic Pagination** - Get thousands of results effortlessly - ✅ **Real-time Data** - Fresh content from Reddit --- ### 🚀 Key Features - **Keyword Search**: Find relevant content on Reddit - **Search Types**: Posts (link), subreddits (sr), users (user) - **Time Filters**: hour, day, week, month, year, all - **Sorting Options**: relevance, hot, top, new, comments - **NSFW Content**: Optional inclusion/exclusion - **Pagination**: Automatic handling of large result sets ### 📋 Input Parameters ```json { "searches": ["python", "machine learning"], "type": "link", "sort": "new", "time": "week", "includeNSFW": false, "maxItems": 100, "debugMode": false } ```` #### Parameters: - **searches** (array, required): List of keywords to search for - **type** (string): Content type - `link` (posts), `sr` (subreddits), `user` (users) - **sort** (string): Result sorting method - **time** (string): Time filter for publication date - **includeNSFW** (boolean): Include NSFW content - **maxItems** (integer): Maximum number of results - **debugMode** (boolean): Debug mode with detailed logging ### 📤 Output Data Each result contains: ```json { "dataType": "post", "keyword": "python", "title": "New Python 3.13 features", "url": "https://www.reddit.com/r/Python/comments/...", "id": "abc123", "author": "username", "created_at_text": "2 hours ago", "scrapedAt": "2025-01-20T10:30:00.000Z" } ``` ### 💡 Usage Examples #### Search for Python posts from the last week ```json { "searches": ["python"], "type": "link", "sort": "top", "time": "week", "maxItems": 50 } ``` #### Find AI-related subreddits ```json { "searches": ["artificial intelligence", "machine learning"], "type": "sr", "sort": "relevance", "maxItems": 20 } ``` ### ⚙️ Technical Details - **Memory**: 256 MB (optimized for efficiency) - **Proxy**: Residential (automatic, included free) - **Timeout**: 3600 seconds - **Source**: old.reddit.com (more stable for parsing) ### 📝 Notes - Uses HTML parsing for maximum stability - Automatic pagination handling - Residential proxies to avoid blocking - All results include data collection timestamp *** ### 🎯 Perfect For - 📊 Market research and trend analysis - 🔬 Academic research and data collection - 📈 Social media monitoring - 🤖 Training AI/ML models - 📰 Content discovery and curation *** **💬 Questions or Issues?** Feel free to reach out or check the documentation! ## Actor input object example ```json {} ``` # 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("backhoe/reddit-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("backhoe/reddit-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 backhoe/reddit-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=backhoe/reddit-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Search Scraper", "description": "Search Reddit for posts, subreddits, and users by keywords. Supports flexible filtering by time, sorting options, and NSFW content. Automatically handles pagination for large result sets.", "version": "0.0", "x-build-id": "dn0x8STmCWJVOs2K7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/backhoe~reddit-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-backhoe-reddit-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/backhoe~reddit-search-scraper/runs": { "post": { "operationId": "runs-sync-backhoe-reddit-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/backhoe~reddit-search-scraper/run-sync": { "post": { "operationId": "run-sync-backhoe-reddit-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": {} }, "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 } } } } } } } } } } ```