# Reddit Topic Watcher - Intent, Sentiment, B2B Triggers, MCP (`seibs.co/reddit-topic-watcher`) Actor Reddit monitoring with intent classification (recommendation/comparison/alternative-seek/complaint), sentiment, B2B trigger scoring, author profile signals (karma, age, bot filter), competitor mention tracking, MCP-ready output. For SDR teams, brand monitoring, AI agents. - **URL**: https://apify.com/seibs.co/reddit-topic-watcher.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** Social media, AI, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 post records 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 Topic Watcher Reddit monitoring for B2B intent capture, brand tracking, and AI-agent ingestion - sentiment, 8-label intent, B2B trigger score, MCP markdown. ### What does Reddit Topic Watcher do? It runs multi-keyword x multi-subreddit topic watch in a single run, classifies every post (and optionally each top comment) for sentiment, intent, and B2B-readiness, optionally enriches the author with karma / age / bot signals, and emits MCP-friendly Markdown summary blocks ready to feed an LLM agent or RAG store. ### AI / RAG / Agent Reddit signal stream pre-shaped for AI sales agents and brand-monitoring bots. Set `output_mode=mcp` to get clean Markdown summary blocks an LLM can consume directly - no extra prompt-engineering. Intent labels (`recommendation`, `comparison`, `alternative_seek`, `complaint`, ...) and `b2b_trigger_score` let an agent filter for buying-intent posts before it even calls the model. Compatible with **MCP** runtimes (Claude Desktop, Cursor), **LangChain**, **LlamaIndex**, **Pinecone**, **Weaviate**, **Chroma**. ```python from apify_client import ApifyClient from langchain.schema import Document from langchain_community.vectorstores import Pinecone from langchain_openai import OpenAIEmbeddings client = ApifyClient("APIFY_TOKEN") run = client.actor("you/reddit-topic-watcher").call(run_input={ "keywords": ["alternative to zendesk", "looking for crm"], "subreddits": ["SaaS", "smallbusiness", "Entrepreneur"], "output_mode": "mcp", "min_b2b_score": 0.6, }) docs = [ Document( page_content=item["mcp_block"], metadata={ "subreddit": item["subreddit"], "intent": item["intent"], "sentiment": item["sentiment"], "b2b_score": item["b2b_trigger_score"], "permalink": item["permalink"], }, ) for item in client.dataset(run["defaultDatasetId"]).iterate_items() ] Pinecone.from_documents(docs, OpenAIEmbeddings(), index_name="reddit-intent") ```` ### Features - Multi-keyword x multi-subreddit topic watch with cross-job dedupe and a hard cap on emitted records. - Sentiment classification (positive / neutral / negative) with -1..+1 magnitude, lexicon + 2-token negation lookback. - 8-label intent classifier (recommendation\_request, alternative\_seek, comparison\_request, complaint, frustration, question, praise, neutral\_discussion) with `intent_keywords_matched` for explainability. - Composite B2B trigger score (0-1) blending intent, recency, subreddit relevance, author quality. - Author profile enrichment - karma, account age, mod / verified flags, `probable_bot` indicator. - Brand mention mode - counts competitor names across post + comments and emits per-topic aggregated `topic_summary` records with competitor leaderboards. - MCP-friendly markdown output - drop into an agent's tool response, MCP resource, or vector store. - Top-comments unwind view for one-row-per-comment analysis. ### Use cases - SDR teams - surface recommendation\_request and alternative\_seek leads in real time across Reddit's vertical communities. - RevOps / GTM analysts - track competitor mentions, sentiment trends, category demand signals. - Brand managers - monitor brand mentions with sentiment split + reach (subscriber-weighted), alert on complaints. - AI agent builders - feed MCP markdown summaries straight into an agent's context window or a RAG vector store. - Market researchers - quantify category sentiment over a lookback window across any vertical community. ### FAQ **Q: Is this legal?** A: Yes - we use Reddit's public JSON endpoints (e.g. `reddit.com/r//search.json`), which are publicly accessible without authentication. Use the data per Reddit's Terms of Service and applicable law. We do not scrape NSFW or quarantined subreddits (those require login). **Q: Why might a run fail?** A: (1) Reddit 429/503 - we self-throttle to ~30 req/min and respect `Retry-After`, but very high concurrency on a single keyword can still trip it. (2) Quarantined or login-walled subreddits return empty - skip them. (3) Misspelled subreddit names silently return zero results - double-check casing on edge cases like `r/Accounting`. **Q: How fresh is the data?** A: Live at crawl time. Posts and comments are pulled directly from Reddit's public JSON at run time - typically minutes-fresh on active subreddits. **Q: Can I schedule this daily or weekly?** A: Yes - daily for high-volume B2B intent watchlists (CRM, ERP, sales tools), hourly for crisis brand monitoring, weekly for category-trend research. Use Apify Schedules; combine with cross-job dedupe so re-runs do not re-emit the same posts. **Q: How do I push results into a CRM or alerting tool?** A: SDR-priority leads (records where `intent_classification` in {`recommendation_request`, `alternative_seek`} and `b2b_trigger_score >= 0.7`) forward to HubSpot, Salesforce, or Apollo via Zapier/Make/n8n. Brand-manager `complaint` alerts can webhook straight into a Slack channel. The MCP markdown output can also feed an LLM-based qualification step before CRM write. **Q: What's MCP output?** A: Model Context Protocol - the open spec for feeding structured tool results into LLM agents (Claude, GPT, LangChain, custom agents). Set `output_format: mcp` and each `topic_summary` record includes a `mcp_summary_md` markdown block ready to drop directly into an agent's tool response, an MCP resource, or a RAG vector store - no reformatting required. Lets you build "watch Reddit for X and tell my agent about it" in one step. **Q: How is `b2b_trigger_score` computed?** A: `0.50 x intent_weight + 0.20 x recency_score + 0.20 x subreddit_relevance + 0.10 x author_quality`. A score `>= 0.7` triggers a `high_intent_alert` PPE event - treat that threshold as the SDR-action floor. **Q: How does PPE pricing actually work here?** A: $0.003 per post, $0.001 per comment, $0.002 per unique author profile enrichment (cached per run), $0.008 per topic\_summary record, $0.005 surcharge per high-intent alert. A typical "3 keywords x 5 subreddits x 30 days" run with comments on is roughly $1-3. ### Related Actors - [`b2b-sales-triggers`](../b2b-sales-triggers/) - score company-level intent (hiring, funding, exec changes) alongside the post-level Reddit intent signals from this actor. - [`google-maps-reviews-pro`](../google-maps-reviews-pro/) - extend reputation monitoring from Reddit threads into Google Maps reviews on the same brand for full off-site coverage. - [`youtube-intelligence`](../youtube-intelligence/) - layer YouTube comment / video sentiment on top of Reddit signals for cross-platform category research. ### Integrations - Zapier - push to HubSpot/Salesforce/Pipedrive/Apollo/Klaviyo - Make.com - workflow automation - n8n - self-hosted automation - Apify webhooks - POST to your endpoint - API + dataset export (JSON/CSV/Excel/XML) - MCP / AI agents - call from Claude/GPT/LangChain ### Modes | Mode | Use case | |---|---| | `topic_watch` | Track N keywords across M subreddits (or all of Reddit). | | `brand_mention` | Like topic\_watch plus counts competitor names and emits per-topic summaries. | | `subreddit_dump` | Pull latest posts from each listed subreddit, no keyword filter. | | `user_profile` | Bulk-enrich Reddit usernames with karma, account age, mod / verified, bot indicator. | ### Intent taxonomy | Label | What it catches | Why SDRs / brand managers care | |---|---|---| | `recommendation_request` | "looking for", "anyone recommend", "what's the best" | Highest-signal SDR lead - active vendor evaluation. | | `alternative_seek` | "alternatives to", "switching from", "tired of" | Conquest opportunity. | | `comparison_request` | "X vs Y", "which is better", "pros and cons" | Mid-funnel evaluation. | | `complaint` | "doesn't work", "broken", "ripoff" | Brand managers want these instantly. | | `frustration` | "frustrating", "fed up" | Soft churn signal. | | `question` | Generic interrogative | Lower priority, flagged for support. | | `praise` | "highly recommend", "game-changer" | UGC for social proof. | | `neutral_discussion` | Everything else | Default fallback. | ### Input See `.actor/INPUT_SCHEMA.json`. Sample - track CRM intent across sales subreddits: ```json { "mode": "topic_watch", "keywords": ["best CRM", "CRM alternative", "tired of HubSpot"], "subreddits": ["sales", "saas", "smallbusiness"], "lookback_days": 30, "min_b2b_trigger_score": 0.5, "include_comments": true, "max_comments_per_post": 10, "include_author_profiles": true, "output_format": "json", "concurrency": 4 } ``` ### Output **Sample output:** [`.actor/sample-output.json`](./.actor/sample-output.json) รขโ‚ฌโ€ copy-paste-ready preview of real-looking records. First record inline: ```json { "record_type": "post", "mode": "topic_watch", "scraped_at": "2026-05-13T17:11:48Z", "available": true, "reason": null, "query": "best CRM for small accounting firm", "post_id": "1d4kb78", "comment_id": null, "user_id": null, "permalink": "https://www.reddit.com/r/Accounting/comments/1d4kb78/best_crm_for_small_accounting_firm", "subreddit": "Accounting", "subreddit_subscribers": 514212, "title": "Best CRM for a 4-person CPA firm? Karbon vs TaxDome vs Canopy?", "body": "We're growing past spreadsheets and email. Currently 4 CPAs + 2 admin. Need: client task tracking, document portal, e-sign, and ideally something that talks to QBO. Karbon looks polished but pricey. TaxDome is half the price. Anyone moved from Karbon to TaxDome or vice versa?", "author": "ledgerlife_cpa", "author_karma": 8421, "author_account_age_days": 2188, "author_is_mod": false, "author_verified": false, "score": 142, "upvote_ratio": 0.94, "comment_count": 73, "created_utc": 1747165908, "is_self": true, "link_url": null, "is_video": false, "num_crossposts": 1, "sentiment": "neutral", "sentiment_score": 0.04, "intent_classification": "comparison_request", "intent_keywords_matched": [ "best", "vs", "anyone moved from" ], "b2b_trigger_score": 0.88, "competitors_mentioned": [ { "name": "karbon", "mention_count": 4 }, { "name": "taxdome", "mention_count": 3 }, { "name": "canopy", "mention_count": 1 } ], "top_comments": [ { "comment_id": "l9k2m4a", "author": "small_firm_partner", "body": "We moved from Karbon to TaxDome 6 months ago. TaxDome is messier UX but the e-sign and portal are way better for tax-heavy practices. Karbon is built for advisory, TaxDome is built for tax compliance.", "score": 84, "created_utc": 1747171200, "sentiment": "neutral", "sentiment_score": 0.12, "intent_classification": "recommendation_request", "intent_keywords_matched": [ "moved from", "way better" ] }, { "comment_id": "l9k4nq2", "author": "boutiquetax", "body": "Canopy got expensive after the rebuild and the 2024 outage scared us off. Stuck with Karbon for now.", "score": 41, "created_utc": 1747173600, "sentiment": "negative", "sentiment_score": -0.32, "intent_classification": "frustration", "intent_keywords_matched": [ "expensive", "scared us off" ] } ], "author_profile": null, "mcp_summary_md": null } ``` Sample post record: ```json { "record_type": "post", "mode": "topic_watch", "subreddit": "sales", "title": "Tired of HubSpot pricing - what are people moving to?", "author": "salesguy42", "score": 187, "comment_count": 53, "sentiment": "negative", "sentiment_score": -0.42, "intent_classification": "alternative_seek", "intent_keywords_matched": ["tired of"], "b2b_trigger_score": 0.86, "permalink": "https://www.reddit.com/r/sales/comments/...", "available": true, "scraped_at": "2026-05-14T12:00:00Z" } ``` ### Pricing Pay-per-event: | Event | Price | When charged | |---|---|---| | `post_record` | $0.003 | Per emitted post | | `comment_record` | $0.001 | Per top comment fetched | | `author_profile_enrichment` | $0.002 | Per unique author (cached per run) | | `topic_summary` | $0.008 | Per topic\_summary record | | `high_intent_alert` | $0.005 | Surcharge when `b2b_trigger_score >= 0.7` | ### FAQ **Q: Do I need a Reddit API key?** A: No - we use Reddit's public JSON endpoints, which work without authentication. We self-throttle to ~30 req/min and respect `Retry-After` on 429/503. **Q: Does this scrape NSFW or quarantined subreddits?** A: No - quarantined subreddits require login. NSFW community results may be filtered by Reddit's anonymous endpoint based on the request session. **Q: How is `b2b_trigger_score` computed?** A: `0.50 * intent_weight + 0.20 * recency_score + 0.20 * subreddit_relevance + 0.10 * author_quality`. A score >= 0.7 triggers a `high_intent_alert` PPE event. **Q: Can the MCP output be used directly with Claude / GPT-4?** A: Yes - set `output_format=mcp` and the `topic_summary` records carry a `mcp_summary_md` markdown block ready for an LLM context window or RAG ingestion. ### Save your input as an Apify Task Apify Tasks let you save a configured input once and re-run it with a single click - no need to re-type search terms, locations, filters, or tier settings every time. Tasks are the foundation for everything that comes next: schedules, monitor mode, and webhook routing all attach to a saved Task, not to the raw actor. Steps to save your current input as a Task: 1. On this actor's Apify Store page, click `Run` with your input fully configured. 2. Click the `Save as task` button at the top of the run page. 3. Name the task something memorable (e.g. `Watch r/sysadmin for ConnectWise mentions - daily`). 4. Reload the task page and click `Start` anytime to re-run with the same inputs. Tasks unlock the next two features below: scheduling and monitor mode. ### Run this weekly with Apify Schedules Apify Schedules cron-run any saved Task automatically. Pair this with the saved Task above and you get hands-off recurring runs with no manual clicks, no missed weeks, and a steady stream of fresh data into your CRM or warehouse. Steps to schedule a Task: 1. Save your input as a Task (see above). 2. Go to https://console.apify.com/schedules and click `Create new schedule`. 3. Pick your Task and set the cron expression. Common patterns: - Daily at 9am UTC: `0 9 * * *` - Weekly on Mondays at 9am: `0 9 * * 1` - Monthly on the 1st: `0 9 1 * *` 4. Save. Apify will run your Task on that schedule automatically, push the dataset to whatever integrations you have wired up, and fire run-completion webhooks for downstream automation. Run daily to monitor for new mentions, complaints, and shifting sentiment across the subreddits you track. ### Monitor mode (v2, beta) Monitor mode is the v2 evolution of this actor and is currently in BETA. It turns a recurring schedule into a true change-feed instead of a firehose of duplicate records. How it works: - When this actor runs under an Apify Schedule, monitor mode is enabled automatically. - Instead of emitting ALL records every run, it emits ONLY records that are NEW or CHANGED since the last scheduled run. - A digest record summarizes the delta (X new, Y changed, Z removed) at the top of every run. - Optional: provide a Slack or email webhook URL in the `monitor_webhook_url` input field and the digest fires there too, so your team gets the delta in their inbox or channel without polling the dataset. - Cost: a single `scheduled_delta_run` event ($0.05) per scheduled run, plus standard PPE on emitted delta records only. Predictable monthly cost, no surprise bills from re-charging for unchanged records. Monitor mode is rolling out to the top 3 actors first (this one included if it's hotel-motel-lead-finder, google-maps-reviews-pro, or mcp-accounting-firm-leads). Full portfolio coverage by end of June. ### Support Open an issue on the actor's GitHub or contact via Apify Store. Include the run ID and input config. ### Changelog See [CHANGELOG.md](./CHANGELOG.md). ### Found this useful? If this actor saved you time or money, please consider leaving a quick review on the Apify Store. Reviews help other buyers find work that solves their problem and let me prioritize the features paying customers actually use. Leave a review: https://apify.com/seibs.co/reddit-topic-watcher#reviews # Actor input Schema ## `mode` (type: `string`): topic\_watch: monitor N keywords across M subreddits. brand\_mention: track a primary brand + competitor list with mention counts, sentiment split, top mentioning subs. subreddit\_dump: pull latest posts from each subreddit (no keyword filter). user\_profile: enrich a list of usernames with karma + account age signals. ## `keywords` (type: `array`): Search queries used in topic\_watch and brand\_mention modes. In brand\_mention, the FIRST entry is treated as the primary brand. Examples: \['best CRM alternative', 'HubSpot vs Salesforce']. ## `subreddits` (type: `array`): Subreddit names without the r/ prefix. Leave empty in topic\_watch / brand\_mention to search across all of Reddit. Example: \['sales', 'saas', 'marketing']. ## `competitor_names` (type: `array`): List of competing brand names to count alongside the primary brand. Mentions are case-insensitive whole-word matches. Example: \['Salesforce', 'HubSpot', 'Pipedrive']. ## `usernames` (type: `array`): Reddit usernames to enrich (without u/ prefix). Returns karma, account age, mod/verified flags, plus a probable-bot indicator for very new low-karma accounts. ## `lookback_days` (type: `integer`): Drop posts older than this many days. Reddit search returns up to ~1000 results regardless; this filters post-fetch. ## `min_score` (type: `integer`): Drop posts/comments with Reddit score below this. Filters out junk and downvoted threads. ## `min_comments` (type: `integer`): Drop posts with fewer comments than this. Use to focus on engaged threads. ## `min_author_karma` (type: `integer`): Drop posts whose author has less combined karma than this. Requires include\_author\_profiles=true to take effect; otherwise ignored. ## `min_author_account_age_days` (type: `integer`): Drop posts whose author's account is younger than this. Default 30 filters most bot accounts. Requires include\_author\_profiles=true; ignored otherwise. ## `intent_filter` (type: `array`): If set, only emit posts whose intent\_classification matches one of these. Leave empty to emit all intents. ## `min_b2b_trigger_score` (type: `string`): Drop posts whose composite b2b\_trigger\_score is below this. Leave empty to emit all. Score combines intent type weight, recency, subreddit relevance, author quality. 0.7+ = high-intent SDR lead. ## `include_comments` (type: `boolean`): When true, fetches the top N comments for each emitted post and classifies sentiment + intent per comment. Adds latency but provides much richer signal. ## `max_comments_per_post` (type: `integer`): Cap on comments fetched per post. Ignored when include\_comments is false. ## `include_author_profiles` (type: `boolean`): Fetch each post author's /about.json: karma, account age, mod/verified flags. Slower but enables bot filtering, karma filtering, and author\_quality scoring. Cached per username across the run. ## `output_format` (type: `string`): json: structured records (default; recommended for code). mcp: structured records PLUS topic\_summary records with markdown-formatted summary blocks ready for LLM RAG / agent ingestion. csv\_friendly: flattens nested fields and joins arrays with ' | ' for spreadsheet export. ## `use_apify_proxy` (type: `boolean`): Route Reddit requests through Apify Proxy. Strongly recommended - Reddit blocks datacenter IPs aggressively. ## `apify_proxy_groups` (type: `array`): Apify Proxy groups. RESIDENTIAL is most reliable for Reddit. Defaults to RESIDENTIAL. ## `concurrency` (type: `integer`): Max parallel Reddit requests. Reddit allows ~60/min unauthenticated; default 4 stays well under that. ## `max_results` (type: `integer`): Hard cap on total post records emitted. Comments and topic\_summary records do not count against this. ## Actor input object example ```json { "mode": "topic_watch", "keywords": [ "best CRM alternative" ], "subreddits": [ "sales", "saas", "marketing" ], "competitor_names": [ "Salesforce", "HubSpot", "Pipedrive" ], "usernames": [], "lookback_days": 30, "min_score": 5, "min_comments": 0, "min_author_karma": 0, "min_author_account_age_days": 30, "intent_filter": [], "min_b2b_trigger_score": "", "include_comments": true, "max_comments_per_post": 10, "include_author_profiles": false, "output_format": "json", "use_apify_proxy": true, "apify_proxy_groups": [ "RESIDENTIAL" ], "concurrency": 4, "max_results": 200 } ``` # Actor output Schema ## `datasetItems` (type: `string`): Narrow, token-efficient slice of every record. Consumer: LLM agents (Claude, GPT, LangChain tools), MCP hosts, dashboards. Reddit fields: subreddit, title, score, comment count, intent classification, B2B trigger score, permalink. ## `datasetItemsDetailed` (type: `string`): All fields for every record. Consumer: humans browsing the dataset in the Apify UI, RAG ingest pipelines, and full backups. Larger payload โ€” not recommended as a direct LLM tool response. ## `datasetItemsUnwind` (type: `string`): Same records, but each element of the `comments` array is promoted to its own row. Consumer: Zapier / Make / n8n / Google Sheets users who want one row per item, and LLM agents that prefer flat rows over nested arrays. ## `datasetItemsMcp` (type: `string`): First 50 overview records as a clean JSON array. Wrap on the agent side in an MCP tool-call response envelope, e.g. `{ "ok": true, "data": , "meta": { "actor": "reddit-topic-watcher", "count": , "view": "overview" } }`. Consumer: MCP servers, Claude Desktop, Cursor, OpenAI Assistants tool calls. ## `datasetItemsCsv` (type: `string`): Spreadsheet-friendly export of the overview view. Consumer: humans, sales-ops teams, Excel / Google Sheets users. # 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 = { "mode": "topic_watch", "keywords": [ "best CRM alternative" ], "subreddits": [ "sales", "saas", "marketing" ], "competitor_names": [ "Salesforce", "HubSpot", "Pipedrive" ], "usernames": [] }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/reddit-topic-watcher").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 = { "mode": "topic_watch", "keywords": ["best CRM alternative"], "subreddits": [ "sales", "saas", "marketing", ], "competitor_names": [ "Salesforce", "HubSpot", "Pipedrive", ], "usernames": [], } # Run the Actor and wait for it to finish run = client.actor("seibs.co/reddit-topic-watcher").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 '{ "mode": "topic_watch", "keywords": [ "best CRM alternative" ], "subreddits": [ "sales", "saas", "marketing" ], "competitor_names": [ "Salesforce", "HubSpot", "Pipedrive" ], "usernames": [] }' | apify call seibs.co/reddit-topic-watcher --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/reddit-topic-watcher", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Topic Watcher - Intent, Sentiment, B2B Triggers, MCP", "description": "Reddit monitoring with intent classification (recommendation/comparison/alternative-seek/complaint), sentiment, B2B trigger scoring, author profile signals (karma, age, bot filter), competitor mention tracking, MCP-ready output. For SDR teams, brand monitoring, AI agents.", "version": "0.1", "x-build-id": "f1mrrkez03a7uTjpJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~reddit-topic-watcher/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-reddit-topic-watcher", "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/seibs.co~reddit-topic-watcher/runs": { "post": { "operationId": "runs-sync-seibs.co-reddit-topic-watcher", "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/seibs.co~reddit-topic-watcher/run-sync": { "post": { "operationId": "run-sync-seibs.co-reddit-topic-watcher", "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": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "topic_watch", "brand_mention", "subreddit_dump", "user_profile" ], "type": "string", "description": "topic_watch: monitor N keywords across M subreddits. brand_mention: track a primary brand + competitor list with mention counts, sentiment split, top mentioning subs. subreddit_dump: pull latest posts from each subreddit (no keyword filter). user_profile: enrich a list of usernames with karma + account age signals.", "default": "topic_watch" }, "keywords": { "title": "Keywords / search queries", "type": "array", "description": "Search queries used in topic_watch and brand_mention modes. In brand_mention, the FIRST entry is treated as the primary brand. Examples: ['best CRM alternative', 'HubSpot vs Salesforce'].", "items": { "type": "string" } }, "subreddits": { "title": "Subreddits", "type": "array", "description": "Subreddit names without the r/ prefix. Leave empty in topic_watch / brand_mention to search across all of Reddit. Example: ['sales', 'saas', 'marketing'].", "items": { "type": "string" } }, "competitor_names": { "title": "Competitor names (brand_mention mode)", "type": "array", "description": "List of competing brand names to count alongside the primary brand. Mentions are case-insensitive whole-word matches. Example: ['Salesforce', 'HubSpot', 'Pipedrive'].", "items": { "type": "string" } }, "usernames": { "title": "Usernames (user_profile mode)", "type": "array", "description": "Reddit usernames to enrich (without u/ prefix). Returns karma, account age, mod/verified flags, plus a probable-bot indicator for very new low-karma accounts.", "items": { "type": "string" } }, "lookback_days": { "title": "Lookback window (days)", "minimum": 1, "maximum": 365, "type": "integer", "description": "Drop posts older than this many days. Reddit search returns up to ~1000 results regardless; this filters post-fetch.", "default": 30 }, "min_score": { "title": "Minimum post score", "minimum": 0, "type": "integer", "description": "Drop posts/comments with Reddit score below this. Filters out junk and downvoted threads.", "default": 5 }, "min_comments": { "title": "Minimum comment count", "minimum": 0, "type": "integer", "description": "Drop posts with fewer comments than this. Use to focus on engaged threads.", "default": 0 }, "min_author_karma": { "title": "Minimum author karma", "minimum": 0, "type": "integer", "description": "Drop posts whose author has less combined karma than this. Requires include_author_profiles=true to take effect; otherwise ignored.", "default": 0 }, "min_author_account_age_days": { "title": "Minimum author account age (days)", "minimum": 0, "type": "integer", "description": "Drop posts whose author's account is younger than this. Default 30 filters most bot accounts. Requires include_author_profiles=true; ignored otherwise.", "default": 30 }, "intent_filter": { "title": "Intent filter (emit only matching)", "uniqueItems": true, "type": "array", "description": "If set, only emit posts whose intent_classification matches one of these. Leave empty to emit all intents.", "items": { "type": "string", "enum": [ "question", "complaint", "recommendation_request", "comparison_request", "alternative_seek", "praise", "frustration", "neutral_discussion" ] }, "default": [] }, "min_b2b_trigger_score": { "title": "Minimum B2B trigger score (0-1)", "type": "string", "description": "Drop posts whose composite b2b_trigger_score is below this. Leave empty to emit all. Score combines intent type weight, recency, subreddit relevance, author quality. 0.7+ = high-intent SDR lead.", "default": "" }, "include_comments": { "title": "Fetch top comments per post", "type": "boolean", "description": "When true, fetches the top N comments for each emitted post and classifies sentiment + intent per comment. Adds latency but provides much richer signal.", "default": true }, "max_comments_per_post": { "title": "Max comments per post", "minimum": 0, "maximum": 50, "type": "integer", "description": "Cap on comments fetched per post. Ignored when include_comments is false.", "default": 10 }, "include_author_profiles": { "title": "Fetch author profiles", "type": "boolean", "description": "Fetch each post author's /about.json: karma, account age, mod/verified flags. Slower but enables bot filtering, karma filtering, and author_quality scoring. Cached per username across the run.", "default": false }, "output_format": { "title": "Output format", "enum": [ "json", "mcp", "csv_friendly" ], "type": "string", "description": "json: structured records (default; recommended for code). mcp: structured records PLUS topic_summary records with markdown-formatted summary blocks ready for LLM RAG / agent ingestion. csv_friendly: flattens nested fields and joins arrays with ' | ' for spreadsheet export.", "default": "json" }, "use_apify_proxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Route Reddit requests through Apify Proxy. Strongly recommended - Reddit blocks datacenter IPs aggressively.", "default": true }, "apify_proxy_groups": { "title": "Proxy groups", "type": "array", "description": "Apify Proxy groups. RESIDENTIAL is most reliable for Reddit. Defaults to RESIDENTIAL.", "default": [ "RESIDENTIAL" ], "items": { "type": "string" } }, "concurrency": { "title": "Concurrency", "minimum": 1, "maximum": 6, "type": "integer", "description": "Max parallel Reddit requests. Reddit allows ~60/min unauthenticated; default 4 stays well under that.", "default": 4 }, "max_results": { "title": "Max results per run", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Hard cap on total post records emitted. Comments and topic_summary records do not count against this.", "default": 200 } } }, "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 } } } } } } } } } } ```