# Reddit Posts Scraper — Subreddit, Search, User & Thread JSON (`tugelbay/reddit-posts-scraper`) Actor Scrape Reddit posts, comments, user submissions and search results via public JSON endpoints. PPE pricing, MCP-native, clean markdown output for RAG pipelines. - **URL**: https://apify.com/tugelbay/reddit-posts-scraper.md - **Developed by:** [Tugelbay Konabayev](https://apify.com/tugelbay) (community) - **Categories:** Social media, AI - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 reddit posts 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 ## Reddit Posts Scraper API - Subreddit, Search, User & Thread JSON > **Fast first run** — the default input returns a small 10-post sample from public subreddit JSON. > **No OAuth required** for public listings, search, users, and direct post IDs. Reddit may still rate-limit or block some runs. > **Pay-per-use after** — $0.003/post, with a single clean schema covering subreddits, search, user streams, and full comment trees in one input. Scrape public Reddit posts and comments without setting up OAuth apps. This actor wraps Reddit's public `.json` endpoints behind a single Apify-platform input with proxy rotation, incremental dataset writes, and a clean canonical output schema. Perfect for **AI agents, RAG pipelines, market research, competitive intelligence, trend tracking**, and **content monitoring** — returns clean Markdown that plugs straight into LangChain document loaders or Claude MCP tools. ### How it works This actor supports four input sources, used in priority order (first non-empty wins): 1. **`postIds`** — direct fetch of specific posts by ID (e.g. `1k0abc`). Returns full content + optional comment tree. 2. **`users`** — each user's submitted-posts stream. 3. **`search`** — Reddit-wide keyword search. Supports Reddit's native operators: `author:`, `subreddit:`, `site:`, etc. 4. **`subreddits`** — one or more subreddit listings with `sort` (hot/new/top/rising/controversial) and `timeframe` (hour/day/week/month/year/all). Posts are pushed to the dataset **incrementally** — even if the run is aborted, everything fetched so far is already stored. ### Why use this instead of alternatives? | Feature | Reddit Posts Scraper | Reddit PRAW (self-hosted) | Reddit official API (paid) | Other Apify scrapers | | ------------------------ | ----------------------------- | ------------------------- | -------------------------- | -------------------- | | **Auth required** | No | Yes (OAuth app) | Yes (app + tier limits) | Varies | | **IP rotation** | Yes — Apify proxy | Manual | N/A | Varies | | **Output formats** | full / minimal / rag-markdown | custom code | custom code | full only | | **Comment trees** | Optional, depth-limited | Manual pagination | Manual pagination | Varies | | **Search** | Yes — same input | Yes | Yes | Often separate actor | | **User streams** | Yes — same input | Yes | Yes | Often separate actor | | **Direct post-ID fetch** | Yes — same input | Yes | Yes | Rarely | | **MCP compatible** | Yes (PPE = agent-friendly) | No | No | Rarely | | **Pay model** | PPE — only what you scrape | Free but self-host | Token quotas | Varies | **Key advantage:** one input schema instead of four separate actors. Drop in subreddit names OR a search query OR user handles OR specific post IDs and the actor just works. ### Input examples #### Trending in two subreddits (default prefill) ```json { "subreddits": ["MachineLearning", "webscraping"], "sort": "hot", "maxItems": 10 } ```` #### Top of the week, with comments, for RAG ```json { "subreddits": ["LocalLLaMA"], "sort": "top", "timeframe": "week", "maxItems": 50, "includeComments": true, "maxComments": 20, "maxCommentDepth": 2, "outputFormat": "rag" } ``` #### Search across all of Reddit for a product mention ```json { "search": "notebooklm vs chatgpt", "sort": "new", "maxItems": 100, "minScore": 2 } ``` #### Pull specific posts by ID (faster than listing) ```json { "postIds": ["1k0abc", "1jzxy9"], "includeComments": true } ``` #### A user's submitted posts ```json { "users": ["spez"], "maxItems": 50 } ``` ### Input parameters | Parameter | Type | Default | Description | | -------------------- | ------------- | ----------------------------------- | ----------------------------------------------------------------- | | `subreddits` | Array\[String] | `["MachineLearning","webscraping"]` | Subreddit names without `r/` | | `search` | String | — | Reddit-wide search query (overrides subreddits) | | `users` | Array\[String] | — | Usernames without `u/` | | `postIds` | Array\[String] | — | Direct post IDs (highest priority) | | `sort` | Enum | `hot` | hot / new / top / rising / controversial | | `timeframe` | Enum | `day` | For `top`/`controversial`: hour / day / week / month / year / all | | `maxItems` | Integer | `10` | Max posts across all sources (1–10,000) | | `includeComments` | Boolean | `false` | Fetch comment tree per post | | `maxComments` | Integer | `20` | Per-post comment cap (1–500) | | `maxCommentDepth` | Integer | `3` | Reply-tree depth (0–10) | | `outputFormat` | Enum | `full` | full / minimal / rag | | `skipNsfw` | Boolean | `false` | Drop posts with `over_18=true` | | `minScore` | Integer | `0` | Drop posts below this upvote count | | `proxyConfiguration` | Object | Apify proxy | Proxy rotation (recommended) | ### Choosing the right source mode Use `subreddits` when you know exactly where the conversation happens. This is the best mode for routine monitoring because subreddit listings are predictable and easy to schedule. Use `search` when you are discovering conversations across Reddit. Search is useful for brand names, competitor names, product categories, and buyer-intent phrases, but Reddit search can be less complete than focused subreddit monitoring. Use `users` when you want posts from known accounts. This is useful for creator/influencer research, executive monitoring, and tracking official company accounts. Use `postIds` when you already have URLs or IDs and need clean JSON, Markdown, or comments for specific threads. Priority order matters: if `postIds` is set, the actor uses post IDs first. If `users` is set, it uses user streams before search/subreddit listings. Keep one source mode per run for the cleanest reporting. ### Recommended workflows #### Brand and competitor monitoring 1. Start with `search` for your brand and 2-3 competitors. 2. Export dataset rows to Google Sheets or BigQuery. 3. Filter by `score`, `num_comments`, and subreddit. 4. Add the best subreddits to a scheduled `subreddits` run. 5. Use `includeComments=true` only for posts that need deeper analysis. Example: ```json { "search": "\"example product\" OR \"competitor product\"", "sort": "new", "maxItems": 100, "minScore": 1, "outputFormat": "full" } ``` #### Buyer-intent research Use Reddit search operators and RAG output to collect real purchase language: ```json { "search": "subreddit:SaaS \"looking for\" \"CRM\"", "sort": "new", "maxItems": 100, "outputFormat": "rag" } ``` Then send the `markdown` field into your LLM workflow to extract: - pain points - feature requests - products mentioned - objections - exact customer language - competitor alternatives #### Weekly subreddit digest ```json { "subreddits": ["LocalLLaMA", "MachineLearning", "artificial"], "sort": "top", "timeframe": "week", "maxItems": 75, "outputFormat": "minimal" } ``` This is the simplest scheduled monitoring pattern. It avoids comments by default and keeps cost predictable. #### RAG dataset for an agent ```json { "subreddits": ["webscraping", "dataengineering"], "sort": "top", "timeframe": "month", "maxItems": 100, "includeComments": true, "maxComments": 20, "maxCommentDepth": 2, "outputFormat": "rag" } ``` Use this when you want thread context, not just post metadata. ### Output format Each post is one dataset item. Field set depends on `outputFormat`: #### `full` (default) ```json { "id": "1k0abc", "subreddit": "MachineLearning", "title": "[D] Why RAG is harder than it looks", "author": "ml_practitioner", "score": 412, "upvote_ratio": 0.97, "num_comments": 89, "created_utc": "2026-04-15T12:34:56+00:00", "url": "https://arxiv.org/abs/2504.01234", "permalink": "https://www.reddit.com/r/MachineLearning/comments/1k0abc/...", "selftext": "", "selftext_markdown": "", "link_flair_text": "Discussion", "over_18": false, "stickied": false, "is_self": false, "is_video": false, "is_gallery": false, "thumbnail": "https://b.thumbs.redditmedia.com/...", "media_url": null, "gallery_urls": [] } ``` #### `minimal` ```json { "id": "1k0abc", "subreddit": "MachineLearning", "title": "[D] Why RAG is harder than it looks", "author": "ml_practitioner", "score": 412, "num_comments": 89, "created_utc": "2026-04-15T12:34:56+00:00", "url": "https://arxiv.org/abs/2504.01234", "permalink": "https://www.reddit.com/r/...", "selftext_markdown": "" } ``` #### `rag` Single `markdown` field per post, ready for vector-DB ingestion: ```json { "id": "1k0abc", "subreddit": "MachineLearning", "title": "[D] Why RAG is harder than it looks", "permalink": "https://www.reddit.com/r/MachineLearning/comments/1k0abc/...", "created_utc": "2026-04-15T12:34:56+00:00", "score": 412, "markdown": "## [D] Why RAG is harder than it looks\n\n**r/MachineLearning** · u/ml_practitioner · score 412 · 2026-04-15T12:34:56+00:00\n[link](https://www.reddit.com/r/...)\n\n..." } ``` ### Output field reference | Field | Type | Description | | --- | --- | --- | | `id` | string | Reddit post ID without the `t3_` prefix. | | `subreddit` | string | Subreddit name. | | `title` | string | Post title. | | `author` | string/null | Reddit username when public. | | `score` | integer | Reddit score at fetch time. | | `upvote_ratio` | number/null | Upvote ratio when available. | | `num_comments` | integer | Comment count reported by Reddit. | | `created_utc` | string | UTC timestamp converted to ISO format. | | `url` | string/null | External URL for link posts, or null for self posts. | | `permalink` | string | Canonical Reddit permalink. | | `selftext` | string/null | Plain self-post body where present. | | `selftext_markdown` | string/null | Markdown body where Reddit exposes it. | | `link_flair_text` | string/null | Link flair label. | | `author_flair_text` | string/null | Author flair label. | | `over_18` | boolean | NSFW marker from Reddit. | | `spoiler` | boolean | Spoiler marker. | | `stickied` | boolean | Whether the post is stickied. | | `locked` | boolean | Whether comments are locked. | | `is_self` | boolean | Whether it is a self/text post. | | `is_video` | boolean | Whether Reddit marks it as video. | | `is_gallery` | boolean | Whether gallery media is present. | | `thumbnail` | string/null | Thumbnail URL when available. | | `media_url` | string/null | Main media URL when available. | | `gallery_urls` | array | Gallery image URLs. | | `comments` | array | Present when `includeComments=true`; each item has author/body/score/depth. | | `markdown` | string/null | Present when `outputFormat=rag`. | ### Comment extraction Comments are optional because every post with comments requires an additional request and more parsing. Leave comments off for feed scanning. Turn them on only when the comment discussion matters. Recommended settings: | Goal | `includeComments` | `maxComments` | `maxCommentDepth` | | --- | --- | ---: | ---: | | Feed monitoring | `false` | 20 | 3 | | RAG summaries | `true` | 20 | 2 | | Deep thread analysis | `true` | 100 | 3 | | Large historical run | `false` | 20 | 3 | Deep comment trees can be noisy and expensive. For most research workflows, top-level and near-top-level comments carry enough context. ### Cost estimation Approximate prompt-sized run costs: | Use case | Input | Approx. cost | | ----------------------------------- | ---------------------------- | ------------ | | 10 posts from 2 subs, no comments | default | ~$0.03 | | 100 posts from 5 subs, no comments | `maxItems=100` | ~$0.30 | | 100 posts with top-20 comments each | `includeComments=true` | ~$0.40 | | 1,000 posts from search, RAG output | search mode, `maxItems=1000` | ~$3.00 | Billed as **PPE** (pay-per-event): one `reddit-post` event per item written + a small `actor-start` overhead. ### Scheduling and automation The most useful Reddit workflows are scheduled: - hourly: brand crisis or launch monitoring - daily: product category monitoring - weekly: trend and content research - monthly: RAG corpus refresh Recommended Task setup: ```json { "subreddits": ["webscraping", "dataengineering"], "sort": "top", "timeframe": "week", "maxItems": 100, "outputFormat": "minimal", "skipNsfw": true } ``` Attach a webhook to send finished datasets to Slack, Google Sheets, BigQuery, or your own API. ### Integrations #### Python ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("tugelbay/reddit-posts-scraper").call(run_input={ "subreddits": ["LocalLLaMA"], "sort": "top", "timeframe": "week", "maxItems": 50, "outputFormat": "rag", }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["markdown"]) ``` #### JavaScript ```javascript import { ApifyClient } from "apify-client"; const client = new ApifyClient({ token: "YOUR_APIFY_TOKEN" }); const run = await client.actor("tugelbay/reddit-posts-scraper").call({ search: "notebooklm vs chatgpt", sort: "new", maxItems: 100, }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); ``` #### LangChain document loader ```python from langchain_community.document_loaders import ApifyDatasetLoader from langchain_core.documents import Document loader = ApifyDatasetLoader( dataset_id=run["defaultDatasetId"], dataset_mapping_function=lambda item: Document( page_content=item["markdown"], metadata={"subreddit": item["subreddit"], "url": item["permalink"]}, ), ) docs = loader.load() ``` #### Claude MCP / Apify MCP Server Works out of the box. Any Claude/GPT agent using the Apify MCP Server can call this actor as a tool and pipe the output straight into its context. ### Data quality checklist Before using results for analysis: 1. Filter out posts with very low `score` if you only want meaningful discussions. 2. Use `skipNsfw=true` for business/brand monitoring. 3. Prefer `sort=new` for alerting and `sort=top` for research. 4. Keep one source mode per run so downstream reports are easier to interpret. 5. Store the `permalink` field so analysts can inspect the original thread. 6. For sentiment or topic extraction, use `outputFormat=rag` so the title, body, and comments stay together. ### Use cases - **RAG knowledge base** — scrape relevant subreddit threads into a vector DB - **Competitive intelligence** — monitor mentions of your product or competitors - **Trend research** — top posts of the week across 20 subreddits, one run - **Content gap analysis** — aggregate questions people ask in niche subs - **Academic data collection** — snapshots of subreddit discourse over time - **AI agent tooling** — on-demand Reddit search as an MCP tool - **Brand safety monitoring** — NSFW filter built in - **Influencer research** — pull recent public submitted posts for known users ### FAQ **Do I need a Reddit account or OAuth app?** No. This actor uses Reddit's public `.json` endpoints — the exact same data Reddit serves to logged-out visitors. **Why do I need a proxy?** Reddit rate-limits by IP. At small volumes you may be fine without one, but larger runs should keep the default residential proxy enabled to reduce block/rate-limit risk. **Can I get removed posts or deleted comments?** No. If Reddit has removed content from the public API, this actor sees the same `[deleted]` placeholder. Use Pushshift for historical archives. **How fresh is the data?** Live — each run hits Reddit directly. Default sort is `hot` which reflects the current front page at run time. **What about NSFW content?** Set `skipNsfw: true` to filter posts marked `over_18`. Comment filtering is not per-comment. **Can it handle quarantined or private subreddits?** Quarantined subs: partial results. Private subs: no access without OAuth. **Can I scrape comments only?** Use `postIds` with `includeComments=true` for the specific threads you care about. The actor still returns the post row as the parent item. **Can I scrape Reddit profiles?** Yes, use `users`. It fetches submitted posts for each public username. **Can I search inside one subreddit?** Yes. Either set `subreddits` and choose a listing sort, or use Reddit search syntax such as `subreddit:LocalLLaMA "fine tuning"`. **Why do results differ from Reddit's UI?** Reddit personalizes and experiments with ranking. The actor reads public JSON endpoints at run time, so output can differ from a logged-in browser session. **Does it bypass Reddit restrictions?** No. It reads public pages/data. Private, removed, deleted, or login-only content is outside scope. ### Troubleshooting | Issue | Cause | Fix | | -------------------- | --------------------------------------- | ---------------------------------------------------- | | Empty dataset | Subreddit name typo or banned sub | Check the name on Reddit first | | `HTTP 403` in logs | Reddit temporarily blocked the proxy IP | Leave `proxyConfiguration` on — session auto-rotates | | Missing comments | `includeComments: false` | Set to true | | Posts look truncated | `outputFormat: minimal` | Switch to `full` or `rag` | | Search misses posts | Reddit search ranking/coverage varies | Monitor key subreddits directly when possible | | Run takes too long | Comments enabled on many posts | Lower `maxItems`, `maxComments`, or comment depth | | Duplicate topics | Same story cross-posted across subreddits | Deduplicate downstream by URL/title/permalink | ### Limitations - **No OAuth-only data** — private subs, user inbox, friends, subscribed feed - **No historical archives** — Reddit JSON returns live data only; for posts older than a few weeks on active subs, pagination may stop early - **Comment depth capped** — default 3, max 10 (Reddit itself caps around 10) - **Public-data only** — no inbox, private communities, mod-only data, or logged-in recommendations - **Search is not exhaustive** — Reddit search can miss posts; direct subreddit monitoring is more predictable ### Privacy and compliance This actor is designed for public Reddit content. Do not use it to collect private messages, bypass access controls, or infer sensitive personal data. For business reporting, store only the fields you need and respect deletion/removal signals from Reddit. ### Changelog - **0.1.10** (2026-04-26) — reduced first-run default to 10 posts, added quality contract, and expanded README with workflow, output, scheduling, and troubleshooting guidance. - **0.1** (2026-04-19) — initial release: subreddits, search, users, postIds; optional comment trees; full / minimal / rag output # Actor input Schema ## `subreddits` (type: `array`): Subreddit names (without r/). The scraper will fetch posts from each using the selected sort. ## `search` (type: `string`): Reddit-wide keyword search. If set, subreddits are ignored. Supports Reddit search operators (author:, subreddit:, etc). ## `users` (type: `array`): Reddit usernames (without u/). Fetches each user's submitted posts. If set, subreddits/search are ignored. ## `postIds` (type: `array`): Specific post IDs (e.g. '1k0abc') to fetch directly with full content. If set, all other sources are ignored. ## `sort` (type: `string`): How to sort listings (ignored for postIds). ## `timeframe` (type: `string`): Only used when sort is 'top' or 'controversial'. ## `maxItems` (type: `integer`): Maximum number of posts to return across all sources. Each post counts as one PPE event. Increase after trying the free sample. ## `includeComments` (type: `boolean`): Fetch the comment tree for each post. Slower and more requests. Leave off for feed-scanning use cases. ## `maxComments` (type: `integer`): Only used when Include comments is on. ## `maxCommentDepth` (type: `integer`): How deep to traverse reply threads. 0 = top-level only, 3 = includes replies-of-replies-of-replies. ## `outputFormat` (type: `string`): 'full' includes every Reddit field. 'minimal' keeps just the key fields. 'rag' outputs a single Markdown block per post, ready to ingest into a vector DB. ## `skipNsfw` (type: `boolean`): Filter out posts marked over\_18 by Reddit. ## `minScore` (type: `integer`): Drop posts with fewer upvotes than this. 0 = no filter. ## `proxyConfiguration` (type: `object`): Reddit blocks datacenter IPs — residential is required. The default uses Apify's RESIDENTIAL group. ## Actor input object example ```json { "subreddits": [ "MachineLearning", "webscraping" ], "sort": "hot", "timeframe": "day", "maxItems": 10, "includeComments": false, "maxComments": 20, "maxCommentDepth": 3, "outputFormat": "full", "skipNsfw": false, "minScore": 0, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # 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 = { "subreddits": [ "MachineLearning", "webscraping" ], "sort": "hot", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("tugelbay/reddit-posts-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "subreddits": [ "MachineLearning", "webscraping", ], "sort": "hot", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("tugelbay/reddit-posts-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "subreddits": [ "MachineLearning", "webscraping" ], "sort": "hot", "maxItems": 10 }' | apify call tugelbay/reddit-posts-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=tugelbay/reddit-posts-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Posts Scraper — Subreddit, Search, User & Thread JSON", "description": "Scrape Reddit posts, comments, user submissions and search results via public JSON endpoints. PPE pricing, MCP-native, clean markdown output for RAG pipelines.", "version": "0.1", "x-build-id": "PPm3pSm6gLkzN4qn0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/tugelbay~reddit-posts-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-tugelbay-reddit-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/tugelbay~reddit-posts-scraper/runs": { "post": { "operationId": "runs-sync-tugelbay-reddit-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/tugelbay~reddit-posts-scraper/run-sync": { "post": { "operationId": "run-sync-tugelbay-reddit-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "subreddits": { "title": "Subreddits", "type": "array", "description": "Subreddit names (without r/). The scraper will fetch posts from each using the selected sort.", "items": { "type": "string" } }, "search": { "title": "Search query", "type": "string", "description": "Reddit-wide keyword search. If set, subreddits are ignored. Supports Reddit search operators (author:, subreddit:, etc)." }, "users": { "title": "User handles", "type": "array", "description": "Reddit usernames (without u/). Fetches each user's submitted posts. If set, subreddits/search are ignored.", "items": { "type": "string" } }, "postIds": { "title": "Post IDs", "type": "array", "description": "Specific post IDs (e.g. '1k0abc') to fetch directly with full content. If set, all other sources are ignored.", "items": { "type": "string" } }, "sort": { "title": "Sort", "enum": [ "hot", "new", "top", "rising", "controversial" ], "type": "string", "description": "How to sort listings (ignored for postIds).", "default": "hot" }, "timeframe": { "title": "Timeframe (for top/controversial)", "enum": [ "hour", "day", "week", "month", "year", "all" ], "type": "string", "description": "Only used when sort is 'top' or 'controversial'.", "default": "day" }, "maxItems": { "title": "Max posts", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of posts to return across all sources. Each post counts as one PPE event. Increase after trying the free sample.", "default": 10 }, "includeComments": { "title": "Include comments", "type": "boolean", "description": "Fetch the comment tree for each post. Slower and more requests. Leave off for feed-scanning use cases.", "default": false }, "maxComments": { "title": "Max comments per post", "minimum": 1, "maximum": 500, "type": "integer", "description": "Only used when Include comments is on.", "default": 20 }, "maxCommentDepth": { "title": "Max comment depth", "minimum": 0, "maximum": 10, "type": "integer", "description": "How deep to traverse reply threads. 0 = top-level only, 3 = includes replies-of-replies-of-replies.", "default": 3 }, "outputFormat": { "title": "Output format", "enum": [ "full", "minimal", "rag" ], "type": "string", "description": "'full' includes every Reddit field. 'minimal' keeps just the key fields. 'rag' outputs a single Markdown block per post, ready to ingest into a vector DB.", "default": "full" }, "skipNsfw": { "title": "Skip NSFW posts", "type": "boolean", "description": "Filter out posts marked over_18 by Reddit.", "default": false }, "minScore": { "title": "Minimum score", "minimum": 0, "type": "integer", "description": "Drop posts with fewer upvotes than this. 0 = no filter.", "default": 0 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Reddit blocks datacenter IPs — residential is required. The default uses Apify's RESIDENTIAL group.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```