# Reddit AI Tool Mention Monitor (`fabian_projects/reddit-ai-tool-mention-monitor`) Actor Monitor AI-tool subreddits for product complaints, competitor comparisons, buying intent, and switching signals across tools like ChatGPT, Claude, Cursor, Gemini, and Perplexity. - **URL**: https://apify.com/fabian\_projects/reddit-ai-tool-mention-monitor.md - **Developed by:** [Fabian Projects](https://apify.com/fabian_projects) (community) - **Categories:** Automation, Developer tools, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 attention item founds 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 ## Reddit AI Tool Mention Monitor An Apify-ready Python Actor that scans one AI-tool subreddit for a target product plus competitors, then returns a ranked attention queue of complaints, comparisons, purchase intent, and switching signals. ### Store-ready positioning #### Who this is for - founders and PMs tracking product friction, churn risk, and competitor pull - support and success teams that want complaint spikes before they become ticket volume - growth and research operators harvesting real comparison language from AI-tool communities #### Core promise Give me one AI-tool subreddit, one product, and a competitor set — I return the posts most likely to matter for retention, positioning, and conversion. #### Buyer personas - **Founder / PM:** wants fast signal on reliability complaints, feature frustration, pricing pain, and competitor pull - **Support lead:** wants recurring bug and outage patterns before customer frustration compounds - **Growth / research operator:** wants side-by-side language and switching intent to improve copy, sales research, and launch messaging #### ROI angle - catch complaint clusters before they become churn or reputation drag - see when users openly compare you against Claude, Gemini, Cursor, Windsurf, or Perplexity - collect real buyer wording from public communities instead of guessing from internal debate #### Why this is more sellable than a generic AI wrapper - one narrow input domain: one AI-tool subreddit at a time - one clear use case: product and competitor monitoring for AI tools - one output shape buyers understand: ranked attention items - pay-per-event pricing maps cleanly to **one attention item = one monetizable event** ### What it does - fetches recent posts from `arctic-shift.photon-reddit.com` - matches a target tool, aliases, and competitor names inside posts - samples comments for lightweight confirmation and sentiment context - classifies each matched post into `complaint`, `purchase_intent`, `comparison`, `switching_intent`, or `general_mention` - scores each item by urgency and commercial relevance - pushes one dataset item per ranked attention item - exposes a custom charge event name: `attention-item-found` ### Inputs | Field | Purpose | |---|---| | `subreddit` | subreddit name without `/r/` | | `brand` | primary AI tool or product to monitor | | `aliases` | alternative names or short forms for the tool | | `competitors` | alternatives you want compared against | | `postLimit` | number of recent posts to scan | | `commentsPerPost` | comments sampled per matched post | | `minAttentionScore` | drop low-value items below this threshold | | `maxItems` | cap dataset output size | | `minPostScore` | ignore low-score posts | | `painKeywords` | complaint/friction signals | | `intentKeywords` | buying/evaluation signals | | `comparisonKeywords` | side-by-side evaluation signals | | `switchingKeywords` | migration/replacement signals | ### Output shape Each dataset item includes fields like: - `brand` - `entityName` - `entityType` - `mentionType` - `attentionScore` - `urgencyLabel` - `confidence` - `matchedCompetitors` - `score` - `commentCount` - `title` - `url` - `commentSnippets` - `summary` The run-level `summary` includes: - `attentionItemCount` - `matchedPostCount` - `typeCounts` - `topEntities` - `executiveSummary` ### Practical Store listing angle Better positioning: - Reddit AI tool mention monitor - competitor comparison radar for AI product teams - complaint and switching-intent scout for coding assistants and chat apps - founder attention queue for fast-moving AI startups ### Known limitations - depends on Arctic Shift availability rather than official Reddit API access - classification is keyword-based and intentionally lightweight - best for fast monitoring and triage, not final market research by itself - noisy subreddits may need threshold and keyword tuning ### Pricing This Actor is designed around **Pay Per Event** pricing so buyers pay for returned attention items rather than vague AI output. ### Example use cases #### Example 1: chat assistant monitoring - `subreddit`: `ChatGPT` - `brand`: `ChatGPT` - `competitors`: `Claude`, `Gemini` - good for: tracking reliability complaints, switching intent, and side-by-side model comparisons #### Example 2: coding-tool monitoring - `subreddit`: `Cursor` - `brand`: `Cursor` - `competitors`: `Windsurf`, `Claude Code` - good for: spotting frustration, migration intent, and competitor pull in coding-tool workflows ### What makes it useful - surfaces the most actionable posts instead of a raw feed dump - highlights buying, comparison, and switching language buyers actually care about - works well for manual review, support triage, founder research, and downstream automation # Actor input Schema ## `subreddit` (type: `string`): Subreddit name without the /r/ prefix. ## `brand` (type: `string`): Primary brand or product you want to monitor, such as ChatGPT, Cursor, Notion, or Linear. ## `aliases` (type: `array`): Alternative spellings, product names, or common short forms for the target brand. ## `competitors` (type: `array`): Competitors or alternatives you want compared against the target brand. ## `postLimit` (type: `integer`): How many recent posts to fetch before mention matching. ## `commentsPerPost` (type: `integer`): How many comments to inspect per matched post for confirmation or pain signals. ## `minAttentionScore` (type: `integer`): Only items at or above this score are returned. ## `maxItems` (type: `integer`): Caps the number of ranked attention items pushed to the dataset. ## `minPostScore` (type: `integer`): Ignore posts below this Reddit score before analysis. ## `painKeywords` (type: `array`): Words or phrases that hint at problems, friction, or negative experiences. ## `intentKeywords` (type: `array`): Words or phrases that signal buying, evaluating, or looking for a solution. ## `comparisonKeywords` (type: `array`): Words or phrases that imply a side-by-side evaluation. ## `switchingKeywords` (type: `array`): Words or phrases that imply migration or replacement intent. ## Actor input object example ```json { "subreddit": "macapps", "brand": "ChatGPT", "aliases": [ "chatgpt", "openai" ], "competitors": [ "Claude", "Gemini" ], "postLimit": 30, "commentsPerPost": 12, "minAttentionScore": 45, "maxItems": 15, "minPostScore": 0, "painKeywords": [ "bug", "broken", "issue", "problem", "expensive", "slow" ], "intentKeywords": [ "looking for", "need", "recommend", "what should i use" ], "comparisonKeywords": [ "vs", "versus", "compare", "better than", "alternative to" ], "switchingKeywords": [ "switching", "migrate", "moving from", "replace", "ditched" ] } ```` # 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 = { "aliases": [ "chatgpt", "openai" ], "competitors": [ "Claude", "Gemini" ], "painKeywords": [ "bug", "broken", "issue", "problem", "expensive", "slow" ], "intentKeywords": [ "looking for", "need", "recommend", "what should i use" ], "comparisonKeywords": [ "vs", "versus", "compare", "better than", "alternative to" ], "switchingKeywords": [ "switching", "migrate", "moving from", "replace", "ditched" ] }; // Run the Actor and wait for it to finish const run = await client.actor("fabian_projects/reddit-ai-tool-mention-monitor").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 = { "aliases": [ "chatgpt", "openai", ], "competitors": [ "Claude", "Gemini", ], "painKeywords": [ "bug", "broken", "issue", "problem", "expensive", "slow", ], "intentKeywords": [ "looking for", "need", "recommend", "what should i use", ], "comparisonKeywords": [ "vs", "versus", "compare", "better than", "alternative to", ], "switchingKeywords": [ "switching", "migrate", "moving from", "replace", "ditched", ], } # Run the Actor and wait for it to finish run = client.actor("fabian_projects/reddit-ai-tool-mention-monitor").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 '{ "aliases": [ "chatgpt", "openai" ], "competitors": [ "Claude", "Gemini" ], "painKeywords": [ "bug", "broken", "issue", "problem", "expensive", "slow" ], "intentKeywords": [ "looking for", "need", "recommend", "what should i use" ], "comparisonKeywords": [ "vs", "versus", "compare", "better than", "alternative to" ], "switchingKeywords": [ "switching", "migrate", "moving from", "replace", "ditched" ] }' | apify call fabian_projects/reddit-ai-tool-mention-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fabian_projects/reddit-ai-tool-mention-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit AI Tool Mention Monitor", "description": "Monitor AI-tool subreddits for product complaints, competitor comparisons, buying intent, and switching signals across tools like ChatGPT, Claude, Cursor, Gemini, and Perplexity.", "version": "0.1", "x-build-id": "DcXUcbY4mHXxib5Y4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fabian_projects~reddit-ai-tool-mention-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fabian_projects-reddit-ai-tool-mention-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/fabian_projects~reddit-ai-tool-mention-monitor/runs": { "post": { "operationId": "runs-sync-fabian_projects-reddit-ai-tool-mention-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/fabian_projects~reddit-ai-tool-mention-monitor/run-sync": { "post": { "operationId": "run-sync-fabian_projects-reddit-ai-tool-mention-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "brand" ], "properties": { "subreddit": { "title": "Subreddit", "type": "string", "description": "Subreddit name without the /r/ prefix.", "default": "ChatGPT" }, "brand": { "title": "Target brand", "type": "string", "description": "Primary brand or product you want to monitor, such as ChatGPT, Cursor, Notion, or Linear.", "default": "ChatGPT" }, "aliases": { "title": "Brand aliases", "type": "array", "description": "Alternative spellings, product names, or common short forms for the target brand.", "items": { "type": "string" } }, "competitors": { "title": "Competitors", "type": "array", "description": "Competitors or alternatives you want compared against the target brand.", "items": { "type": "string" } }, "postLimit": { "title": "Recent posts to scan", "minimum": 5, "maximum": 100, "type": "integer", "description": "How many recent posts to fetch before mention matching.", "default": 30 }, "commentsPerPost": { "title": "Comments sampled per matched post", "minimum": 0, "maximum": 50, "type": "integer", "description": "How many comments to inspect per matched post for confirmation or pain signals.", "default": 12 }, "minAttentionScore": { "title": "Minimum attention score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only items at or above this score are returned.", "default": 45 }, "maxItems": { "title": "Max attention items returned", "minimum": 1, "maximum": 50, "type": "integer", "description": "Caps the number of ranked attention items pushed to the dataset.", "default": 15 }, "minPostScore": { "title": 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"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": 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"example": 0 } } } } } } } } } } ```