# X Twitter Search Scraper - Tweets, People & Media (`khadinakbar/x-twitter-search-scraper`) Actor Search public X/Twitter results by keyword, hashtag, advanced operator, or tab. Export tweets, profiles, photos, and media with engagement metrics, authors, URLs, cursors, and clean summaries. No cookies required. - **URL**: https://apify.com/khadinakbar/x-twitter-search-scraper.md - **Developed by:** [Khadin Akbar](https://apify.com/khadinakbar) (community) - **Categories:** Social media, Lead generation, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 x/twitter search results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## X Twitter Search Scraper Search public X/Twitter results by keyword, hashtag, advanced operator, or search tab and export normalized records for monitoring, lead research, market intelligence, and AI-agent workflows. ### What it does This actor searches the public X/Twitter search surface through a provider-backed API. It does not require user cookies, browser sessions, login credentials, or an X developer account. Use it to collect: - Tweets from the Top or Latest search tabs - Hashtag results such as `#buildinpublic` - Advanced X search operators such as `from:openai min_faves:100` - People/profile results for account discovery - Photos and Media search results ### Input The most important fields are: - `queries` - one or more search queries, hashtags, usernames, or advanced operators - `searchType` - `Top`, `Latest`, `People`, `Photos`, or `Media` - `maxResults` - maximum records to save across all queries - `maxPagesPerQuery` - pagination depth per query - `startCursor` - optional cursor from `RUN_SUMMARY.nextCursors` for continuation - `includeRaw` - include provider payloads for debugging field drift Example: ```json { "queries": ["ai agents", "#buildinpublic", "from:openai min_faves:100"], "searchType": "Latest", "maxResults": 100, "maxPagesPerQuery": 10, "dedupeResults": true } ```` ### Output Each dataset item is normalized into a stable schema: - `recordType` - `searchQuery` - `searchType` - `tweetId` - `tweetUrl` - `text` - `createdAt` - `authorUsername` - `authorName` - `authorId` - `authorUrl` - `authorVerified` - `authorFollowers` - `replyCount` - `retweetCount` - `quoteCount` - `likeCount` - `viewCount` - `hashtags` - `mentions` - `urls` - `mediaUrls` - `listName` - `listUrl` - `provider` - `providerPage` - `scrapedAt` The actor also writes: - `OUTPUT` - concise machine-readable summary - `RUN_SUMMARY` - detailed run telemetry, warnings, status codes, and next cursors ### Public data only This actor is designed for public X/Twitter search results. It does not unlock private accounts, hidden posts, deleted posts, DMs, private follower graphs, or login-only data. ### Pricing The actor uses pay-per-event pricing: - Actor start: `$0.00005` - X/Twitter search result saved: `$0.003` ### Reliability The actor uses provider retries, status-code telemetry, clean empty-result handling, dedupe, cursor persistence, and graceful terminal summaries. Missing provider credentials or upstream failures are written to `OUTPUT` and `RUN_SUMMARY` instead of crashing without context. ### Common use cases - Brand monitoring on X/Twitter - Trend and hashtag discovery - Competitor tracking - Creator and influencer research - Social listening pipelines - AI-agent research tools - Sales and recruiting signal discovery ### Tips - Use `Latest` for fresh monitoring. - Use `Top` for higher-signal results. - Use `People` when you need accounts rather than tweets. - Use narrower queries and X search operators for cleaner datasets. - Use `startCursor` only for continuing one query/search type from a previous run. # Actor input Schema ## `queries` (type: `array`): X/Twitter search queries, hashtags, usernames, or advanced operators. Examples: ai agents, #buildinpublic, from:openai min\_faves:100, (marketing OR sales) lang:en. ## `searchType` (type: `string`): Which X search tab to scrape. Top and Latest return tweet-like results. People returns account-like results. Photos and Media return media-heavy results. ## `maxResults` (type: `integer`): Maximum dataset records to save across all queries. This is also the hard cap for billable result events. ## `maxPagesPerQuery` (type: `integer`): Advanced pagination safety valve. Lower this for quick tests; increase it for deeper searches when the provider returns a next cursor. ## `startCursor` (type: `string`): Optional provider cursor from a previous run. Use this only when continuing one query/search type from RUN\_SUMMARY.nextCursors. ## `includeRaw` (type: `boolean`): Attach the raw provider item to each dataset record for debugging field drift. Leave disabled for smaller production datasets. ## `dedupeResults` (type: `boolean`): Skip duplicate tweet/account/list records across pages and queries. ## Actor input object example ```json { "queries": [ "ai agents" ], "searchType": "Latest", "maxResults": 100, "maxPagesPerQuery": 10, "includeRaw": false, "dedupeResults": true } ``` # Actor output Schema ## `dataset` (type: `string`): No description ## `summary` (type: `string`): No description ## `runSummary` (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": [ "ai agents" ] }; // Run the Actor and wait for it to finish const run = await client.actor("khadinakbar/x-twitter-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 = { "queries": ["ai agents"] } # Run the Actor and wait for it to finish run = client.actor("khadinakbar/x-twitter-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 '{ "queries": [ "ai agents" ] }' | apify call khadinakbar/x-twitter-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=khadinakbar/x-twitter-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "X Twitter Search Scraper - Tweets, People & Media", "description": "Search public X/Twitter results by keyword, hashtag, advanced operator, or tab. Export tweets, profiles, photos, and media with engagement metrics, authors, URLs, cursors, and clean summaries. No cookies required.", "version": "1.1", "x-build-id": "XzfizABRgwO1BCCSJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/khadinakbar~x-twitter-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-khadinakbar-x-twitter-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/khadinakbar~x-twitter-search-scraper/runs": { "post": { "operationId": "runs-sync-khadinakbar-x-twitter-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/khadinakbar~x-twitter-search-scraper/run-sync": { "post": { "operationId": "run-sync-khadinakbar-x-twitter-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "queries" ], "properties": { "queries": { "title": "Search queries", "minItems": 1, "maxItems": 50, "type": "array", "description": "X/Twitter search queries, hashtags, usernames, or advanced operators. Examples: ai agents, #buildinpublic, from:openai min_faves:100, (marketing OR sales) lang:en.", "items": { "type": "string" } }, "searchType": { "title": "Search type", "enum": [ "Top", "Latest", "People", "Photos", "Media" ], "type": "string", "description": "Which X search tab to scrape. Top and Latest return tweet-like results. People returns account-like results. Photos and Media return media-heavy results.", "default": "Latest" }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum dataset records to save across all queries. This is also the hard cap for billable result events.", "default": 100 }, "maxPagesPerQuery": { "title": "Maximum pages per query", "minimum": 1, "maximum": 100, "type": "integer", "description": "Advanced pagination safety valve. Lower this for quick tests; increase it for deeper searches when the provider returns a next cursor.", "default": 10 }, "startCursor": { "title": "Start cursor", "maxLength": 500, "type": "string", "description": "Optional provider cursor from a previous run. Use this only when continuing one query/search type from RUN_SUMMARY.nextCursors." }, "includeRaw": { "title": "Include raw provider payload", "type": "boolean", "description": "Attach the raw provider item to each dataset record for debugging field drift. Leave disabled for smaller production datasets.", "default": false }, "dedupeResults": { "title": "Dedupe results", "type": "boolean", "description": "Skip duplicate tweet/account/list records across pages and queries.", "default": true } } }, "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 } } } } } } } } } } ```