# Fiverr Search Pro ⭐️ (`sentry/fiverr-search-pro`) Actor The fastest, cheapest way to scrape Fiverr search results by keyword. Collect gigs, seller details, prices, ratings, reviews, delivery times, packages, and ranking data for competitor research, lead generation, pricing analysis, and market validation. - **URL**: https://apify.com/sentry/fiverr-search-pro.md - **Developed by:** [Sentry](https://apify.com/sentry) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $1.00 / 1,000 results 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 ## Fiverr Search Pro Fiverr Search Pro helps you search Fiverr gigs by keyword and turn search results into clean, structured data you can actually use for sales, market research, pricing analysis, offer positioning, and competitor tracking. If you need a Fiverr scraper for lead generation, freelance market research, service validation, agency prospecting, or keyword-based competitor discovery, this actor is built for that workflow. ### What You Can Use It For - Fiverr keyword research - Fiverr competitor analysis - Fiverr pricing research - Fiverr seller and gig discovery - service market validation before launching a new offer - tracking top Fiverr gigs in a niche over time - lead generation for outreach, prospecting, and market mapping - comparing delivery times, reviews, ratings, and package positioning across sellers ### What Data You Can Collect Depending on your input settings, the actor can return: - Fiverr gig titles - Fiverr gig URLs - gig IDs and slugs - thumbnails and listing images - seller usernames and display names - seller country and seller level - rating score and review count - starting price and delivery time - package and offer metadata - tags and gallery data - search query and ranking context ### Why Customers Use Fiverr Search Pro - Find the best-performing Fiverr gigs in any niche - Understand how freelancers price similar services - Compare service positioning across keywords and categories - Build prospect lists from Fiverr search results - Monitor changes in rankings, reviews, and offer quality - Validate demand before investing in a new service ### Best Use Cases - agencies researching Fiverr competitors - founders validating freelance service demand - marketers analyzing niche saturation - freelancers benchmarking their own gigs - lead generation teams collecting seller data - operators building Fiverr market intelligence datasets ### Example Input ```json { "queries": ["full stack developer", "medical poster design"], "maxPages": 2, "maxResultsPerQuery": 48, "maxResultsTotal": 96, "includeDetails": true, "includeSellerProfile": false } ```` ### Output Each dataset item represents a Fiverr listing with structured search and seller data. Common fields include: - `gigId` - `gigUrl` - `title` - `sellerName` - `sellerDisplayName` - `sellerCountry` - `sellerLevel` - `sellerRatingScore` - `sellerRatingCount` - `price` - `duration` - `metadata` - `tags` - `gallery` - `scraped_at` ### Who This Is For Fiverr Search Pro is for customers who want Fiverr data without wasting time manually searching, copying listings, comparing sellers, or checking pricing one page at a time. It is especially useful if you want a Fiverr dataset for: - Fiverr SEO research - gig research and keyword expansion - competitor monitoring - sales research - B2B prospecting - freelance marketplace intelligence ### Summary Fiverr Search Pro is a Fiverr scraper for keyword-based gig discovery, seller research, pricing intelligence, and marketplace analysis. Use it to collect Fiverr gig data at scale and make faster decisions about offers, niches, pricing, positioning, and outreach. # Actor input Schema ## `queries` (type: `array`): One or more Fiverr search queries. Each query runs independently. ## `maxPages` (type: `integer`): Number of search result pages to fetch per query. Each page returns up to 48 gigs. ## `sortBy` (type: `string`): How to order search results. ## `includeDetails` (type: `boolean`): Fetch each gig's detail page for richer data: packages with features, FAQ, reviews with comments, seller bio and member since date, and full gallery. Slower but much richer output. ## `minPrice` (type: `integer`): Only return gigs with a starting price at or above this amount. ## `maxPrice` (type: `integer`): Only return gigs with a starting price at or below this amount. ## `sellerLevel` (type: `string`): Filter by seller level. Leave empty to include all levels. ## Actor input object example ```json { "queries": [ "logo design" ], "maxPages": 2, "sortBy": "relevance", "includeDetails": false } ``` # Actor output Schema ## `dataset` (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 = { "queries": [ "logo design" ] }; // Run the Actor and wait for it to finish const run = await client.actor("sentry/fiverr-search-pro").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 = { "queries": ["logo design"] } # Run the Actor and wait for it to finish run = client.actor("sentry/fiverr-search-pro").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 '{ "queries": [ "logo design" ] }' | apify call sentry/fiverr-search-pro --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sentry/fiverr-search-pro", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fiverr Search Pro ⭐️", "description": "The fastest, cheapest way to scrape Fiverr search results by keyword. Collect gigs, seller details, prices, ratings, reviews, delivery times, packages, and ranking data for competitor research, lead generation, pricing analysis, and market validation.", "version": "1.0", "x-build-id": "iMl9AXDqSXVng4paf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sentry~fiverr-search-pro/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sentry-fiverr-search-pro", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/sentry~fiverr-search-pro/runs": { "post": { "operationId": "runs-sync-sentry-fiverr-search-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/sentry~fiverr-search-pro/run-sync": { "post": { "operationId": "run-sync-sentry-fiverr-search-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "queries" ], "properties": { "queries": { "title": "Search Queries", "type": "array", "description": "One or more Fiverr search queries. Each query runs independently.", "items": { "type": "string" } }, "maxPages": { "title": "Max Pages", "minimum": 1, "maximum": 20, "type": "integer", "description": "Number of search result pages to fetch per query. Each page returns up to 48 gigs.", "default": 2 }, "sortBy": { "title": "Sort By", "enum": [ "relevance", "best_selling", "newest", "price_asc", "price_desc" ], "type": "string", "description": "How to order search results.", "default": "relevance" }, "includeDetails": { "title": "Fetch Gig Detail Pages", "type": "boolean", "description": "Fetch each gig's detail page for richer data: packages with features, FAQ, reviews with comments, seller bio and member since date, and full gallery. Slower but much richer output.", "default": false }, "minPrice": { "title": "Min Price (USD)", "minimum": 0, "type": "integer", "description": "Only return gigs with a starting price at or above this amount." }, "maxPrice": { "title": "Max Price (USD)", "minimum": 0, "type": "integer", "description": "Only return gigs with a starting price at or below this amount." }, "sellerLevel": { "title": "Seller Level", "enum": [ "", "level_one_seller", "level_two_seller", "top_rated_seller" ], "type": "string", "description": "Filter by seller level. Leave empty to include all levels." } } }, "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 } } } } } } } } } } ```