# Reddit Archive Scraper (`benthepythondev/reddit-archive-scraper`) Actor Reddit Archive Scraper to extract years of historical Reddit posts and comments from the PullPush archive. Reddit's API caps subreddits at ~1000 posts; this Actor pulls months or years from many subreddits by date range and keyword. For historical backfill, research and AI datasets. - **URL**: https://apify.com/benthepythondev/reddit-archive-scraper.md - **Developed by:** [ben](https://apify.com/benthepythondev) (community) - **Categories:** AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 Archive Scraper — Historical Posts & Comments (Years of Data) **Pull MONTHS or YEARS of historical Reddit posts and comments** from one or many subreddits — by date range and keyword. This Actor uses the [PullPush](https://pullpush.io) archive (the public Pushshift successor) to reach data that Reddit's own API simply won't return. ### Why this exists Reddit's official API **hard-caps any subreddit listing at ~1000 posts** — for an active subreddit that's only a few weeks of history. There is no way around that cap with the official API, in any tool. This Actor solves that: it reads from the historical archive, so you can backfill **a full year (or several)** across multiple subreddits in one job. > Need live, up-to-the-minute posts and full threaded comment trees instead? Use the companion **Reddit Scraper** (official API) for fresh data, and this Archive Scraper for deep history. They pair well: archive for backfill, live scraper for ongoing updates. ### What you get **Posts:** title, selftext (body), author, subreddit, score, upvote_ratio, num_comments, created date (epoch + ISO), permalink, url, domain, flair, is_self/is_video/over_18/locked/stickied/spoiler, awards. **Comments (optional):** body, author, subreddit, score, parent_id, link_id, post_id, created date, permalink, is_submitter. Each row has a `type` field (`post` or `comment`) so you can split them easily. ### Input | Field | Type | Description | |---|---|---| | `subreddits` | array | Subreddits to archive (without r/) | | `searchQuery` | string | Optional keyword filter (or search all of Reddit) | | `afterDate` | string | Earliest date `YYYY-MM-DD` (lower bound) | | `beforeDate` | string | Latest date `YYYY-MM-DD` (start point) | | `maxPosts` | integer | Max posts across all subreddits | | `includeComments` | boolean | Also fetch archived comments per post | | `maxCommentsPerPost` | integer | Cap comments per post | #### Example: one year of a subreddit ```json { "subreddits": ["FragranceClones"], "afterDate": "2024-01-01", "beforeDate": "2025-01-01", "maxPosts": 10000, "includeComments": false } ```` #### Example: keyword across all of Reddit, posts + comments ```json { "searchQuery": "dupe", "afterDate": "2024-06-01", "maxPosts": 1000, "includeComments": true, "maxCommentsPerPost": 50 } ``` ### Sample output (post) ```json { "type": "post", "id": "1d8bw4c", "title": "Best clone of Cool Water?", "selftext": "Looking for an affordable alternative...", "author": "someuser", "subreddit": "fragranceclones", "score": 14, "num_comments": 8, "created_iso": "2024-06-02T10:14:00+00:00", "permalink": "https://www.reddit.com/r/fragranceclones/comments/1d8bw4c/..." } ``` ### Use cases - **Historical backfill** — seed a database with years of a subreddit's content - **Research & sentiment datasets** — analyse trends over long time spans - **AI / RAG training data** — large historical corpora by topic - **Brand / product monitoring** — see what was said about a topic over time ### Cost tips - Pay-per-result: you're charged per post/comment returned. - Comments are the bulk of the count — keep `includeComments` off if you only need posts, or cap `maxCommentsPerPost`. - Use `afterDate`/`beforeDate` to scope exactly the window you need. ### Notes & legal - Data comes from the public PullPush archive; coverage and freshness depend on that service. For the most recent posts, pair with the live Reddit Scraper. - Use data only for lawful purposes and in line with Reddit's and PullPush's terms. ### Related actors More scrapers from the same author: - [Reddit Scraper](https://apify.com/benthepythondev/reddit-scraper) — live posts, comments & AI-ready markdown - [OpenAlex Scraper](https://apify.com/benthepythondev/openalex-scraper) — academic papers & citations - [PubMed Scraper](https://apify.com/benthepythondev/pubmed-scraper) — biomedical literature & citations - [arXiv Scraper](https://apify.com/benthepythondev/arxiv-scraper) — 2M+ scientific papers, abstracts & PDFs # Actor input Schema ## `subreddits` (type: `array`): One or more subreddits to archive (without r/). Leave empty if using a keyword across all of Reddit. ## `searchQuery` (type: `string`): Only return posts containing this keyword. Can be combined with subreddits, or used alone to search all of Reddit. ## `afterDate` (type: `string`): Only include posts created on or after this date. Leave empty for no lower bound. ## `beforeDate` (type: `string`): Only include posts created on or before this date. Leave empty to start from the most recent archived posts. ## `maxPosts` (type: `integer`): Maximum number of posts to return (across all subreddits). Comments are additional. ## `includeComments` (type: `boolean`): Also scrape archived comments for each post. Increases result count (and cost) significantly. ## `maxCommentsPerPost` (type: `integer`): Cap comments fetched per post (only used when Include Comments is on). Leave empty for all. ## Actor input object example ```json { "subreddits": [ "FragranceClones" ], "searchQuery": "", "afterDate": "", "beforeDate": "", "maxPosts": 200, "includeComments": false } ``` # 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": [ "FragranceClones" ], "maxPosts": 200 }; // Run the Actor and wait for it to finish const run = await client.actor("benthepythondev/reddit-archive-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": ["FragranceClones"], "maxPosts": 200, } # Run the Actor and wait for it to finish run = client.actor("benthepythondev/reddit-archive-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": [ "FragranceClones" ], "maxPosts": 200 }' | apify call benthepythondev/reddit-archive-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=benthepythondev/reddit-archive-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Archive Scraper", "description": "Reddit Archive Scraper to extract years of historical Reddit posts and comments from the PullPush archive. Reddit's API caps subreddits at ~1000 posts; this Actor pulls months or years from many subreddits by date range and keyword. For historical backfill, research and AI datasets.", "version": "1.0", "x-build-id": "Eky6RwD53fZctaSuN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/benthepythondev~reddit-archive-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-benthepythondev-reddit-archive-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/benthepythondev~reddit-archive-scraper/runs": { "post": { "operationId": "runs-sync-benthepythondev-reddit-archive-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/benthepythondev~reddit-archive-scraper/run-sync": { "post": { "operationId": "run-sync-benthepythondev-reddit-archive-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": "One or more subreddits to archive (without r/). Leave empty if using a keyword across all of Reddit.", "items": { "type": "string" } }, "searchQuery": { "title": "Keyword (optional)", "type": "string", "description": "Only return posts containing this keyword. Can be combined with subreddits, or used alone to search all of Reddit.", "default": "" }, "afterDate": { "title": "After Date (YYYY-MM-DD)", "type": "string", "description": "Only include posts created on or after this date. Leave empty for no lower bound.", "default": "" }, "beforeDate": { "title": "Before Date (YYYY-MM-DD)", "type": "string", "description": "Only include posts created on or before this date. Leave empty to start from the most recent archived posts.", "default": "" }, "maxPosts": { "title": "Max Posts", "minimum": 1, "maximum": 500000, "type": "integer", "description": "Maximum number of posts to return (across all subreddits). Comments are additional.", "default": 500 }, "includeComments": { "title": "Include Comments", "type": "boolean", "description": "Also scrape archived comments for each post. Increases result count (and cost) significantly.", "default": false }, "maxCommentsPerPost": { "title": "Max Comments per Post", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Cap comments fetched per post (only used when Include Comments is on). Leave empty for all." } } }, "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 } } } } } } } } } } ```