# Similar Subreddits Finder (`datamagnet/similar-subreddits-finder`) Actor Find related subreddits based on a community you choose. Discover similar topics and audiences in a clean, easy-to-review list. - **URL**: https://apify.com/datamagnet/similar-subreddits-finder.md - **Developed by:** [Datamagnet](https://apify.com/datamagnet) (community) - **Categories:** Social media, Lead generation - **Stats:** 2 total users, 1 monthly users, 20.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Similar Subreddits Finder โ€” Reddit Similar Subreddits Scraper Find related subreddits for any community you choose and turn one subreddit into a list of nearby audiences, topics, and niche communities. Similar Subreddits Finder helps marketers, researchers, creators, and community managers discover Reddit communities that share interests with the subreddit you enter. It returns a clean, easy-to-review list of similar subreddits with human-readable details, making it simple to compare communities, export results, and plan your next move. If you need a fast Reddit subreddit scraper for audience discovery, topic research, or community expansion, this actor gives you a practical starting point. ### Key Features - **Discover** related subreddits from a single subreddit name and quickly expand your research beyond one community. - **Collect** a clean list of similar communities that is easy to scan, compare, and share with your team. - **Return** human-readable details so you can understand why each subreddit may be relevant at a glance. - **Limit** results with a simple maximum count, helping you focus on the most useful communities first. - **Handle** larger result sets automatically so you do not need to keep rerunning the actor to get more matches. - **Export** results in formats that are easy to review in spreadsheets or use in reporting workflows. - **Support** audience discovery, content planning, competitor research, and niche community mapping in one workflow. ### Use Cases #### Audience Research Marketers and growth teams use this actor to find Reddit communities that resemble a target subreddit, then compare the audiences behind each one. The returned subreddit names and summary details help them identify where similar users spend time and what topics matter to them. This makes it easier to plan campaigns, content angles, and community outreach with more confidence. #### Content Strategy Content creators and social media managers can start with one subreddit and uncover adjacent communities that discuss the same subject from different angles. By reviewing the similar subreddit names and descriptions, they can spot recurring themes, language patterns, and content gaps. That helps them build posts, videos, newsletters, or discussion prompts that fit the interests of multiple communities. #### Community Expansion Community managers and moderators often want to understand where else their audience may gather on Reddit. This actor helps them identify neighboring subreddits that may be relevant for partnerships, cross-community learning, or audience benchmarking. The clean output makes it easy to compare communities without manually searching one by one. #### Competitive Research Businesses researching a niche can use the results to see which subreddits sit close to a competitorโ€™s community or a topic they care about. The subreddit names and available summary details provide a quick map of the surrounding landscape. That can inform positioning, messaging, and where to focus attention first. #### Trend Discovery Researchers and analysts can use similar subreddit results to spot clusters of interest around a topic. When several related communities appear together, it can reveal how a subject branches into subtopics, tools, hobbies, or audience segments. This is useful for identifying emerging interests and understanding how conversations are organized on Reddit. #### Lead and Partnership Prospecting Agencies and consultants can use the output to find communities that align with a clientโ€™s niche and then prioritize outreach or partnership ideas. The subreddit names and summaries help them quickly judge relevance before investing time in deeper review. This saves time and supports more targeted recommendations. ### Input | Field | Type | Required | Description | Example | |---|---|---:|---|---| | Subreddit | string | Yes | Enter the name of the subreddit you want similar communities for. | python | | Max results | integer | No | Set the maximum number of similar subreddits to return. | 10 | ### Output | Field | Type | Description | |---|---|---| | subreddit | string | The name of a subreddit that is similar to the one you searched for. | | summary | string | A short description or helpful note about the subreddit, when available, so you can judge relevance quickly. | | relatedness | string | A plain-language indication of how closely the subreddit matches your search, when provided by the source. | | url | string | A direct link to the subreddit so you can open it and review the community yourself. | | rank | number | The position of the subreddit in the returned list, helping you compare the strongest matches first. | ### Sample Output ```json { "subreddit": "learnpython", "summary": "A community for people learning Python, asking questions, and sharing beginner-friendly resources.", "relatedness": "high", "url": "https://www.reddit.com/r/learnpython/", "rank": 1 } ```` ### How It Works 1. You enter the subreddit name you want to explore. 2. You choose how many similar communities you want to see. 3. The actor searches for related subreddits and gathers the best matches. 4. It keeps collecting results until it reaches your requested limit or runs out of new matches. 5. You receive a clean list that is ready to review, export, or share. ### Getting Started Getting started is simple and does not require coding knowledge. Click **Try for free**, enter the subreddit you want similar communities for, choose how many results you want, and click **Run**. The actor will do the rest and return a clean list of related subreddits. You can review the results directly in Apify and export them as JSON, CSV, or Excel for reporting and analysis. ### Frequently Asked Questions #### Do I need technical skills? No. This actor is designed for business users, researchers, and creators who want results without dealing with complicated setup. Just enter a subreddit name and run it. #### How fast does it run? It usually runs quickly because it focuses on one subreddit and returns a targeted list of similar communities. The exact time depends on how many results you request and how many matches are available. #### What format is the output? The results are delivered in a clean, easy-to-read list and can be exported as JSON, CSV, or Excel. That makes it simple to review the data in a spreadsheet or share it with others. #### Is this legal to use? Yes, when used responsibly and in line with the rules of the platform and your local laws. It is best practice to use the results for legitimate research, analysis, and discovery purposes. #### Can I schedule it to run automatically? Yes. You can set it up to run on a schedule if you want regular updates for a subreddit or topic area. This is useful for ongoing research, monitoring community changes, or tracking new related communities over time. # Actor input Schema ## `subreddit` (type: `string`): Enter the name of the subreddit you want similar communities for. ## `max_results` (type: `integer`): Set the maximum number of similar subreddits to return. The actor will fetch more results automatically if needed. ## Actor input object example ```json { "subreddit": "python", "max_results": 10 } ``` # 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 = { "subreddit": "python", "max_results": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("datamagnet/similar-subreddits-finder").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "subreddit": "python", "max_results": 10, } # Run the Actor and wait for it to finish run = client.actor("datamagnet/similar-subreddits-finder").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "subreddit": "python", "max_results": 10 }' | apify call datamagnet/similar-subreddits-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamagnet/similar-subreddits-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Similar Subreddits Finder", "description": "Find related subreddits based on a community you choose. Discover similar topics and audiences in a clean, easy-to-review list.", "version": "0.0", "x-build-id": "Wih7LWndi1GsBSXUs" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamagnet~similar-subreddits-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamagnet-similar-subreddits-finder", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamagnet~similar-subreddits-finder/runs": { "post": { "operationId": "runs-sync-datamagnet-similar-subreddits-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamagnet~similar-subreddits-finder/run-sync": { "post": { "operationId": "run-sync-datamagnet-similar-subreddits-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "subreddit" ], "properties": { "subreddit": { "title": "Subreddit", "type": "string", "description": "Enter the name of the subreddit you want similar communities for." }, "max_results": { "title": "Max results", "minimum": 1, "type": "integer", "description": "Set the maximum number of similar subreddits to return. The actor will fetch more results automatically if needed." } } }, "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 } } } } } } } } } } ```