# Regex Helper (`rl1987/regex-helper`) Actor Apply named regular expressions to a list of strings and extract structured matches. Handy for contact info extraction and other text-processing workflows. - **URL**: https://apify.com/rl1987/regex-helper.md - **Developed by:** [R.L.](https://apify.com/rl1987) (community) - **Categories:** Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / actor invocation 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 ## Regex Helper **Regex Helper** applies a list of **named regular expressions** to a list of **free-form strings** and returns structured, ready-to-use matches. It's a small building block for plugging regex-based extraction — **contact info, IDs, URLs, SKUs, hashtags, anything** — into a larger data-processing workflow on the [Apify platform](https://apify.com/actors). Because it runs as an Actor, you get an HTTP API, scheduling, and [integrations](https://apify.com/integrations) (Make, Zapier, n8n, Google Drive, ...) for free — so you can chain it after a scraper that produces raw text and before whatever consumes the cleaned-up fields. **Built to be chained.** Regex Helper has a dedicated **pipeline mode**: point it at another Actor's output dataset and it reads the text straight from there — no glue code, no manual export/import. Combined with sensible defaults, this lets you drop it into a two-step Apify pipeline (for example **[RAG Web Browser](https://apify.com/apify/rag-web-browser) → Regex Helper**) where an upstream run finishes, fires a webhook, and Regex Helper extracts contacts from every page it scraped. ### Why use Regex Helper? - **Drop-in pipeline step** — read input directly from any upstream Actor's dataset and post-process scraped text without writing a single line of glue code. - **Reusable extraction step** — define your patterns once and run them over thousands of strings. - **No code required** — paste strings and regexes into the input form, hit run, download results. - **Structured output** — every match comes with its value, position, and capture groups, grouped by the pattern name you chose. - **Works out of the box** — omit the patterns and Regex Helper falls back to a built-in contact-extraction set (`email`, `phone`, `url`, `linkedin`), so a chained run needs almost no configuration. A typical use case is **contact information extraction**: chain it after a scraper, and get back a tidy record of emails, phones, and URLs for every page that was crawled. ### How to use Regex Helper #### Standalone 1. Open the **Input** tab. 2. Add the **strings** you want to process (one per line). 3. Add your **named regular expressions** — each needs a `name` and a `regex` (Python `re` syntax), with optional `flags`. 4. Click **Start** and download the results from the **Output** tab. #### Pipeline mode — chain it after another Actor Instead of pasting strings, you can have Regex Helper read its input straight from another Actor's output dataset: 1. Run any Actor that produces a dataset of text (a scraper, **[RAG Web Browser](https://apify.com/apify/rag-web-browser)**, an LLM Actor, etc.). 2. Start Regex Helper with **`inputDatasetId`** set to that run's dataset ID. When set, it overrides the inline `strings` list. 3. Set **`textField`** to the dataset field that holds the text to match against (defaults to `markdown`, the field RAG Web Browser produces). 4. Leave `patterns` empty to use the built-in contact-extraction set, or supply your own. The cleanest way to wire this up is a **webhook / integration** on the upstream Actor: on run success, start Regex Helper and map the upstream dataset into `inputDatasetId`. For example, in an upstream Actor's **Integrations → Webhook** configuration, set the payload so that: ```json { "inputDatasetId": "{{resource.defaultDatasetId}}", "textField": "markdown" } ```` Now every time the upstream run finishes, Regex Helper automatically extracts matches from its output — a native, two-step Apify pipeline with no code in between. You can chain it the same way via the [API](https://docs.apify.com/api/v2), [Apify CLI](https://docs.apify.com/cli), scheduled tasks, or any of the supported [integrations](https://apify.com/integrations). ### Input All fields are optional, but you must supply text to process one way or another — either inline `strings` **or** an `inputDatasetId` (pipeline mode). | Field | Type | Description | | --- | --- | --- | | `strings` | array of strings | The free-form strings to process. Each one produces a single output record. Ignored when `inputDatasetId` is set. | | `inputDatasetId` | string | **Pipeline mode.** ID of an upstream dataset to read input strings from. When set, each item's `textField` is matched against the patterns, overriding `strings`. Map `{{resource.defaultDatasetId}}` from an upstream run to chain Actors. | | `textField` | string | When reading from `inputDatasetId`, the dataset item field whose value is matched. Defaults to `markdown` (the field produced by RAG Web Browser). Items without the field are skipped. | | `patterns` | array of objects | The named regexes to apply. Each item is `{ "name", "regex", "flags" }`. If omitted, a default contact-extraction set (`email`, `phone`, `url`, `linkedin`) is used. | | `firstMatchOnly` | boolean | If `true`, only the first match of each pattern per string is returned. Defaults to `false` (all matches). | Each entry in `patterns` accepts: - `name` (string, required) — used as the key under `matches` in the output. Must be unique. - `regex` (string, required) — a [Python `re`](https://docs.python.org/3/library/re.html) pattern. - `flags` (string, optional) — any combination of `i` (ignore case), `m` (multiline), `s` (dotall), `x` (verbose), `a` (ASCII). #### Example input ```json { "strings": [ "John Doe, john.doe@example.com, +1 (415) 555-0132, https://example.com", "Reach Jane at jane_smith@work.co.uk or call 020 7946 0958." ], "patterns": [ { "name": "email", "regex": "[\\w.+-]+@[\\w-]+\\.[\\w.-]+", "flags": "i" }, { "name": "phone", "regex": "\\+?\\d[\\d\\s().-]{7,}\\d" }, { "name": "url", "regex": "https?://[^\\s]+" } ], "firstMatchOnly": false } ``` #### Example input (pipeline mode) Read text from an upstream run's dataset and apply the default contact-extraction patterns — no `strings` or `patterns` needed: ```json { "inputDatasetId": "{{resource.defaultDatasetId}}", "textField": "markdown" } ``` ### Output The Actor pushes one record per input string to the dataset. You can download the dataset in various formats such as JSON, HTML, CSV, or Excel. ```json { "index": 0, "input": "John Doe, john.doe@example.com, +1 (415) 555-0132, https://example.com", "matchCount": 3, "matches": { "email": [ { "value": "john.doe@example.com", "start": 10, "end": 30, "groups": [], "namedGroups": {} } ], "phone": [ { "value": "+1 (415) 555-0132", "start": 32, "end": 49, "groups": [], "namedGroups": {} } ], "url": [ { "value": "https://example.com", "start": 51, "end": 70, "groups": [], "namedGroups": {} } ] } } ``` #### Data table | Field | Description | | --- | --- | | `index` | Zero-based position of the string in the input list. | | `input` | The original input string. | | `matchCount` | Total number of matches found across all patterns for this string. | | `matches` | Object keyed by pattern name; each value is a list of match objects. | | `matches.[].value` | The matched text (full match, group 0). | | `matches.[].start` / `end` | Character offsets of the match within the input string. | | `matches.[].groups` | Numbered capture groups, if the pattern defines any. | | `matches.[].namedGroups` | Named capture groups, e.g. `(?P\d{3})`. | ### Cost estimation This Actor does plain in-memory text processing — no proxies, no browsers — so it's cheap to run. Cost scales with the number and length of input strings and the complexity of your regexes. Most small-to-medium batches comfortably fit within the Apify free tier. ### Tips - Use **named capture groups** (`(?P...)`) to pull sub-parts of a match into `namedGroups`. - Enable `firstMatchOnly` when you only expect one hit per pattern (e.g. a single primary email) to keep output compact. - Remember to escape backslashes when writing regexes in JSON (`\\d`, `\\w`, ...). - Patterns are validated before processing — an invalid regex or a duplicate pattern name fails the run early with a clear message. - In **pipeline mode**, double-check that `textField` matches the field your upstream Actor actually writes (e.g. `text`, `markdown`, `html`); items lacking that field are skipped and logged. ### FAQ and support **Which regex dialect is used?** Python's built-in [`re`](https://docs.python.org/3/library/re.html) module. **What happens if a string has no matches?** You still get a record for it, with empty match lists and `matchCount: 0`. **How do I chain this after my scraper?** Use pipeline mode: set `inputDatasetId` to the scraper's output dataset (e.g. `{{resource.defaultDatasetId}}` in a webhook payload) and `textField` to the field holding the text. See [Pipeline mode](#pipeline-mode--chain-it-after-another-actor) above. **Disclaimer:** Make sure you have the right to process any text and personal data (such as emails or phone numbers) you pass through this Actor, in line with applicable laws and the source's terms of service. Found a bug or have a feature request? Open an issue on the Actor's **Issues** tab. # Actor input Schema ## `strings` (type: `array`): List of free-form strings to run the regular expressions against. Each string produces one output record. Ignored when `inputDatasetId` is set. ## `inputDatasetId` (type: `string`): ID of a dataset to read input strings from (pipeline mode). When set, the Actor reads each item's `textField` instead of using the inline `strings` list. Use this to chain another Actor's output into Regex Helper via a webhook/integration, e.g. map `{{resource.defaultDatasetId}}` from an upstream run. ## `textField` (type: `string`): When reading from `inputDatasetId`, the dataset item field whose value is matched against the patterns. Defaults to `markdown` (the field produced by RAG Web Browser). ## `patterns` (type: `array`): List of named regular expressions to apply. Each item has a `name` (used as the result key), a `regex` (Python `re` syntax), and optional `flags` (any of i, m, s, x). If omitted, a default contact-extraction set (email, phone, url, linkedin) is used. ## `firstMatchOnly` (type: `boolean`): If enabled, only the first match of each pattern per string is returned. Otherwise all matches are returned. ## Actor input object example ```json { "strings": [ "John Doe, john.doe@example.com, +1 (415) 555-0132, https://example.com", "Reach Jane at jane_smith@work.co.uk or call 020 7946 0958.", "No contact details in this line." ], "textField": "markdown", "patterns": [ { "name": "email", "regex": "[\\w.+-]+@[\\w-]+\\.[\\w.-]+", "flags": "i" }, { "name": "phone", "regex": "\\+?\\d[\\d\\s().-]{7,}\\d" }, { "name": "url", "regex": "https?://[^\\s)\\]]+" } ], "firstMatchOnly": false } ``` # Actor output Schema ## `dataset` (type: `string`): Dataset with one item per input string, each holding the matches found by every pattern. ## `keyValueStore` (type: `string`): Default key-value store for this run, including the INPUT record. # 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 = { "strings": [ "John Doe, john.doe@example.com, +1 (415) 555-0132, https://example.com", "Reach Jane at jane_smith@work.co.uk or call 020 7946 0958.", "No contact details in this line." ], "patterns": [ { "name": "email", "regex": "[\\w.+-]+@[\\w-]+\\.[\\w.-]+", "flags": "i" }, { "name": "phone", "regex": "\\+?\\d[\\d\\s().-]{7,}\\d" }, { "name": "url", "regex": "https?://[^\\s)\\]]+" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("rl1987/regex-helper").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 = { "strings": [ "John Doe, john.doe@example.com, +1 (415) 555-0132, https://example.com", "Reach Jane at jane_smith@work.co.uk or call 020 7946 0958.", "No contact details in this line.", ], "patterns": [ { "name": "email", "regex": "[\\w.+-]+@[\\w-]+\\.[\\w.-]+", "flags": "i", }, { "name": "phone", "regex": "\\+?\\d[\\d\\s().-]{7,}\\d", }, { "name": "url", "regex": "https?://[^\\s)\\]]+", }, ], } # Run the Actor and wait for it to finish run = client.actor("rl1987/regex-helper").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 '{ "strings": [ "John Doe, john.doe@example.com, +1 (415) 555-0132, https://example.com", "Reach Jane at jane_smith@work.co.uk or call 020 7946 0958.", "No contact details in this line." ], "patterns": [ { "name": "email", "regex": "[\\\\w.+-]+@[\\\\w-]+\\\\.[\\\\w.-]+", "flags": "i" }, { "name": "phone", "regex": "\\\\+?\\\\d[\\\\d\\\\s().-]{7,}\\\\d" }, { "name": "url", "regex": "https?://[^\\\\s)\\\\]]+" } ] }' | apify call rl1987/regex-helper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=rl1987/regex-helper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Regex Helper", "description": "Apply named regular expressions to a list of strings and extract structured matches. Handy for contact info extraction and other text-processing workflows.", "version": "0.0", "x-build-id": "lWBW29hdbLdTh9rHu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/rl1987~regex-helper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-rl1987-regex-helper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/rl1987~regex-helper/runs": { "post": { "operationId": "runs-sync-rl1987-regex-helper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/rl1987~regex-helper/run-sync": { "post": { "operationId": "run-sync-rl1987-regex-helper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "strings": { "title": "Input strings", "type": "array", "description": "List of free-form strings to run the regular expressions against. Each string produces one output record. Ignored when `inputDatasetId` is set.", "items": { "type": "string" } }, "inputDatasetId": { "title": "Input dataset ID", "type": "string", "description": "ID of a dataset to read input strings from (pipeline mode). When set, the Actor reads each item's `textField` instead of using the inline `strings` list. Use this to chain another Actor's output into Regex Helper via a webhook/integration, e.g. map `{{resource.defaultDatasetId}}` from an upstream run." }, "textField": { "title": "Text field", "type": "string", "description": "When reading from `inputDatasetId`, the dataset item field whose value is matched against the patterns. Defaults to `markdown` (the field produced by RAG Web Browser).", "default": "markdown" }, "patterns": { "title": "Named regular expressions", "type": "array", "description": "List of named regular expressions to apply. Each item has a `name` (used as the result key), a `regex` (Python `re` syntax), and optional `flags` (any of i, m, s, x). If omitted, a default contact-extraction set (email, phone, url, linkedin) is used." }, "firstMatchOnly": { "title": "First match only", "type": "boolean", "description": "If enabled, only the first match of each pattern per string is returned. Otherwise all matches are returned.", "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 } } } } } } } } } } ```