# Lemmy Scraper - Federated Reddit Alternative (`legend006/lemmy-scraper`) Actor Scrape posts and comments from any Lemmy instance (the open, federated Reddit alternative). Filter by community, search keyword, or pull instance-wide feeds. No login required. Built for AI training datasets, fediverse research, and community monitoring. - **URL**: https://apify.com/legend006/lemmy-scraper.md - **Developed by:** [NIJ KANANI](https://apify.com/legend006) (community) - **Categories:** AI, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.30 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## 🐭 Lemmy Scraper Scrape posts and comments from **any Lemmy instance** — the federated, open-source Reddit alternative. No login. No rate-limit nightmares. Works with `lemmy.world`, `lemmy.ml`, `beehaw.org`, `sh.itjust.works`, and any other instance. > 🎯 Built for AI/LLM training datasets, fediverse research, brand monitoring on emerging platforms, and Reddit alternatives analysis. --- ### ✨ What you can do - 🏘️ **Community posts** — pull all posts from one or many communities - 🔍 **Search** — keyword search across an instance - 🌐 **Instance feed** — top/hot/new across the whole instance - 💬 **Optional comment trees** — flattened with paths for tree reconstruction - 🔁 **Sort options** — Hot, Active, New, Top (multiple ranges), MostComments - 🌍 Cross-instance federation aware (`asklemmy@lemmy.ml`) --- ### 🚀 Quick start ```json { "instance": "lemmy.world", "mode": "community", "communities": ["technology@lemmy.world", "asklemmy@lemmy.ml"], "sort": "Top", "topRange": "TopWeek", "maxItems": 200 } ```` *** ### 📥 Input | Field | Description | |---|---| | `instance` | Hostname (e.g. `lemmy.world`) | | `mode` | `community` / `search` / `instance` | | `communities` | Names like `tech` or `tech@lemmy.world` | | `searchQueries` | Keywords | | `sort` | `Hot`, `Active`, `New`, `Top`, `MostComments`, `NewComments` | | `topRange` | When sort = `Top`: `TopHour` … `TopAll` | | `maxItems` | Cap per target | | `includeComments` | Fetch comment trees | *** ### 📤 Output (per post) ```json { "instance": "lemmy.world", "community": "technology", "title": "Some headline", "body": "Body text or empty", "creator": "username", "creatorActor": "https://lemmy.world/u/username", "score": 123, "upvotes": 130, "downvotes": 7, "comments": 42, "publishedAt": "2026-04-15T...", "url": "https://example.com/article", "thumbnailUrl": "https://...", "nsfw": false, "apId": "https://lemmy.world/post/123456", "postUrl": "https://lemmy.world/post/123456", "commentsList": [ { "id": 9999, "creator": "commenter", "content": "Reply text", "score": 12, "publishedAt": "...", "path": "0.123.456" } ] } ``` *** ### 🎯 Use cases | Who | Why | |---|---| | 🤖 AI/LLM teams | Reddit-style training data without Reddit's API gate | | 📚 Researchers | Federation studies, online community migration patterns | | 📊 Marketers | Track brand mentions on emerging platforms | | 📰 Journalists | Source mining on Reddit-alternative communities | *** ### ⚙️ Tech notes - Uses Lemmy's official `/api/v3` REST endpoints — fully open, no key required - Federation-aware: `community@instance` syntax works for any cross-instance pull - Pagination via `page` parameter; auto-stops when no new posts returned - Comment trees fetched separately and capped per post for performance # Actor input Schema ## `instance` (type: `string`): Hostname of the Lemmy instance to scrape, e.g. 'lemmy.world', 'lemmy.ml', 'beehaw.org', 'sh.itjust.works'. ## `mode` (type: `string`): 'community' = posts in specific community/communities. 'search' = keyword search. 'instance' = top-level feed of the whole instance. ## `communities` (type: `array`): Lemmy community names. Local: 'asklemmy'. Cross-instance: 'asklemmy@lemmy.ml'. Used when mode = community. ## `searchQueries` (type: `array`): Keywords/phrases to search across the instance. Used when mode = search. ## `sort` (type: `string`): Sort order for posts: Hot/Active/New/Top/MostComments/NewComments. ## `topRange` (type: `string`): Time window when sort = Top (e.g. TopWeek = top posts of last 7 days). ## `maxItems` (type: `integer`): Cap per community / search query / instance feed. ## `includeComments` (type: `boolean`): Fetch comment trees for each post (slower, richer data). ## `maxCommentsPerPost` (type: `integer`): Cap on comments per post when includeComments is on. ## Actor input object example ```json { "instance": "lemmy.world", "mode": "community", "communities": [ "technology@lemmy.world" ], "searchQueries": [], "sort": "Hot", "topRange": "TopWeek", "maxItems": 100, "includeComments": false, "maxCommentsPerPost": 50 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("legend006/lemmy-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 = {} # Run the Actor and wait for it to finish run = client.actor("legend006/lemmy-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 '{}' | apify call legend006/lemmy-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=legend006/lemmy-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Lemmy Scraper - Federated Reddit Alternative", "description": "Scrape posts and comments from any Lemmy instance (the open, federated Reddit alternative). Filter by community, search keyword, or pull instance-wide feeds. No login required. Built for AI training datasets, fediverse research, and community monitoring.", "version": "0.1", "x-build-id": "wZf7A8HJXdUW1tsf6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/legend006~lemmy-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-legend006-lemmy-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/legend006~lemmy-scraper/runs": { "post": { "operationId": "runs-sync-legend006-lemmy-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/legend006~lemmy-scraper/run-sync": { "post": { "operationId": "run-sync-legend006-lemmy-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "instance", "mode" ], "properties": { "instance": { "title": "Lemmy instance host", "type": "string", "description": "Hostname of the Lemmy instance to scrape, e.g. 'lemmy.world', 'lemmy.ml', 'beehaw.org', 'sh.itjust.works'.", "default": "lemmy.world" }, "mode": { "title": "Scrape mode", "enum": [ "community", "search", "instance" ], "type": "string", "description": "'community' = posts in specific community/communities. 'search' = keyword search. 'instance' = top-level feed of the whole instance.", "default": "community" }, "communities": { "title": "Community names", "type": "array", "description": "Lemmy community names. Local: 'asklemmy'. Cross-instance: 'asklemmy@lemmy.ml'. Used when mode = community.", "default": [ "technology@lemmy.world" ], "items": { "type": "string" } }, "searchQueries": { "title": "Search queries", "type": "array", "description": "Keywords/phrases to search across the instance. Used when mode = search.", "default": [], "items": { "type": "string" } }, "sort": { "title": "Sort order", "enum": [ "Hot", "Active", "New", "Top", "MostComments", "NewComments" ], "type": "string", "description": "Sort order for posts: Hot/Active/New/Top/MostComments/NewComments.", "default": "Hot" }, "topRange": { "title": "Top range (when sort=Top)", "enum": [ "TopHour", "TopSixHour", "TopTwelveHour", "TopDay", "TopWeek", "TopMonth", "TopThreeMonths", "TopSixMonths", "TopNineMonths", "TopYear", "TopAll" ], "type": "string", "description": "Time window when sort = Top (e.g. TopWeek = top posts of last 7 days).", "default": "TopWeek" }, "maxItems": { "title": "Max posts per target", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Cap per community / search query / instance feed.", "default": 100 }, "includeComments": { "title": "Include comments", "type": "boolean", "description": "Fetch comment trees for each post (slower, richer data).", "default": false }, "maxCommentsPerPost": { "title": "Max comments per post", "minimum": 0, "maximum": 1000, "type": "integer", "description": "Cap on comments per post when includeComments is on.", "default": 50 } } }, "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 } } } } } } } } } } ```