# Lemmy Scraper (`opendata-labs/lemmy-scraper`) Actor Scrape posts, comments, communities and search results from any Lemmy instance via the official API. Clean structured data (JSON/CSV), no login required. - **URL**: https://apify.com/opendata-labs/lemmy-scraper.md - **Developed by:** [Joao Paulo](https://apify.com/opendata-labs) (community) - **Categories:** Social media, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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 ## Lemmy Scraper **Scrape posts, comments, communities, and search results from any Lemmy instance — fast, structured, and auth-free.** Built on the official public **Lemmy API v3** (ActivityPub / fediverse), so it works on `lemmy.world` or any other decentralized server. ### What it does Lemmy Scraper turns the public Lemmy REST API into clean, flat dataset rows you can export to JSON, CSV, or Excel. Point it at any instance, pick a mode, and collect data from the fediverse without logging in. Four modes: - **Community posts** — every post in a community, sorted by Hot / Active / New / Top. - **Search** — keyword search across the instance. - **Community info** — metadata for a single community (subscribers, post count, description). - **Post comments** — all comments under a specific post. ### Features - Works on **any Lemmy instance** — just change the `instance` input. - No authentication, no cookies, no tokens — pure public API reads. - **Flattened output** — clean columns, not raw nested JSON blobs. - Automatic pagination with a `maxItems` cap. - Polite request pacing plus automatic retries on transient HTTP errors. - Pay-per-result pricing friendly (PPE `item-scraped` events). ### Input | Field | Type | Description | |---|---|---| | `mode` | enum | `community_posts`, `search`, `community_info`, or `post_comments`. | | `instance` | string | Lemmy host, e.g. `lemmy.world` (default). | | `communityName` | string | Community to scrape — `name` or `name@instance.tld`. Required for community modes. | | `query` | string | Search keywords. Required for `search` mode. | | `postId` | integer | Post ID. Required for `post_comments` mode. | | `sort` | enum | `Hot`, `Active`, `New`, `TopDay`, `TopWeek` (community posts). | | `maxItems` | integer | Max rows to collect (default `1000`). | #### Example input ```json { "mode": "community_posts", "instance": "lemmy.world", "communityName": "technology", "sort": "Hot", "maxItems": 500 } ```` ### Output example Each post becomes one flat row: ```json { "id": 12345678, "title": "Open-source project hits 1.0", "body": "Release notes inside...", "url": "https://example.com/release", "creatorName": "dev_user", "creatorActorId": "https://lemmy.world/u/dev_user", "communityName": "technology", "score": 842, "upvotes": 870, "downvotes": 28, "commentsCount": 134, "published": "2026-06-20T14:03:11.000Z", "postUrl": "https://lemmy.world/post/12345678" } ``` Comments and community-info modes produce their own flat schemas (content, creator, score, subscribers, etc.). ### Use cases - **OSINT & research** — monitor communities and discussions across the fediverse. - **Journalism** — track emerging stories and public sentiment on decentralized platforms. - **Brand monitoring** — find mentions of your product or company via search mode. - **AI / ML training data** — collect open social text and threaded discussions at scale. ### Why this actor Lemmy exposes a **stable, official public REST API** backed by ActivityPub. This scraper talks to that API directly instead of fragile HTML parsing, so it keeps working through UI changes and runs against **any Lemmy server** in the fediverse. No login, no rate-limit gymnastics, no brittle selectors — just structured decentralized social data. *** *Keywords: Lemmy scraper, Lemmy API, scrape Lemmy, ActivityPub, fediverse data, decentralized social, Lemmy posts, Lemmy comments, federated Reddit alternative.* # Actor input Schema ## `mode` (type: `string`): What to scrape. 'community\_posts' lists posts of a community, 'search' runs a keyword search, 'community\_info' fetches community metadata, 'post\_comments' lists comments of a single post. ## `instance` (type: `string`): Hostname of the Lemmy instance to target (without https://). Works with any Lemmy server in the fediverse. ## `communityName` (type: `string`): Community to scrape. Use 'name' for a local community or 'name@instance.tld' for a remote one. Required for 'community\_posts' and 'community\_info' modes. ## `query` (type: `string`): Keyword(s) to search for. Required for 'search' mode. ## `postId` (type: `integer`): Numeric ID of the post whose comments you want. Required for 'post\_comments' mode. ## `sort` (type: `string`): Sort order for community posts. ## `maxItems` (type: `integer`): Maximum number of rows to push to the dataset. Scraping stops once this is reached. ## Actor input object example ```json { "mode": "community_posts", "instance": "lemmy.world", "communityName": "technology", "sort": "Hot", "maxItems": 1000 } ``` # 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 = { "instance": "lemmy.world", "communityName": "technology" }; // Run the Actor and wait for it to finish const run = await client.actor("opendata-labs/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 = { "instance": "lemmy.world", "communityName": "technology", } # Run the Actor and wait for it to finish run = client.actor("opendata-labs/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 '{ "instance": "lemmy.world", "communityName": "technology" }' | apify call opendata-labs/lemmy-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=opendata-labs/lemmy-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Lemmy Scraper", "description": "Scrape posts, comments, communities and search results from any Lemmy instance via the official API. Clean structured\ndata (JSON/CSV), no login required.", "version": "0.1", "x-build-id": "9OI1cfY6cDayYULSS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/opendata-labs~lemmy-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-opendata-labs-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/opendata-labs~lemmy-scraper/runs": { "post": { "operationId": "runs-sync-opendata-labs-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/opendata-labs~lemmy-scraper/run-sync": { "post": { "operationId": "run-sync-opendata-labs-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": [ "mode", "instance" ], "properties": { "mode": { "title": "Scrape mode", "enum": [ "community_posts", "search", "community_info", "post_comments" ], "type": "string", "description": "What to scrape. 'community_posts' lists posts of a community, 'search' runs a keyword search, 'community_info' fetches community metadata, 'post_comments' lists comments of a single post.", "default": "community_posts" }, "instance": { "title": "Lemmy instance", "type": "string", "description": "Hostname of the Lemmy instance to target (without https://). Works with any Lemmy server in the fediverse.", "default": "lemmy.world" }, "communityName": { "title": "Community name", "type": "string", "description": "Community to scrape. Use 'name' for a local community or 'name@instance.tld' for a remote one. Required for 'community_posts' and 'community_info' modes." }, "query": { "title": "Search query", "type": "string", "description": "Keyword(s) to search for. Required for 'search' mode." }, "postId": { "title": "Post ID", "type": "integer", "description": "Numeric ID of the post whose comments you want. Required for 'post_comments' mode." }, "sort": { "title": "Sort order", "enum": [ "Hot", "Active", "New", "TopDay", "TopWeek" ], "type": "string", "description": "Sort order for community posts.", "default": "Hot" }, "maxItems": { "title": "Max items", "minimum": 1, "type": "integer", "description": "Maximum number of rows to push to the dataset. Scraping stops once this is reached.", "default": 1000 } } }, "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 } } } } } } } } } } ```