# Exercism Scraper (`crawlerbros/exercism-scraper`) Actor Scrape Exercism.org - free open-source coding education platform. List all programming language tracks, browse exercises by track, filter by difficulty, type, and more. No auth required. - **URL**: https://apify.com/crawlerbros/exercism-scraper.md - **Developed by:** [Crawler Bros](https://apify.com/crawlerbros) (community) - **Categories:** Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Exercism Scraper Scrape **[Exercism.org](https://exercism.org)** — a free, open-source platform for learning 70+ programming languages through hands-on exercises. Extract programming language track metadata, exercise lists, difficulty levels, types (concept, practice, tutorial), and direct URLs. No API key, no login, no proxy required. Uses the public Exercism REST API. ### What you can scrape - **All 80+ programming language tracks** with exercise counts, concept counts, tags, and URLs - **Exercises for any specific track** with difficulty, type (concept/practice/tutorial), and blurb - **Exercises across all tracks** (or a subset) in a single run - Filter by difficulty (easy, medium, hard) and exercise type ### Use cases - Build a curriculum explorer or language comparison tool - Analyze exercise difficulty distribution across languages - Generate learning roadmaps based on concept availability - Research which languages have concept-based vs practice-based tracks - Track exercise inventory for specific programming languages ### Input | Field | Type | Description | |-------|------|-------------| | `mode` | select | What to fetch: `tracks`, `exercises`, or `allExercises` | | `trackId` | select | Track slug (e.g. `python`, `rust`) — required for `mode=exercises` | | `trackIds` | array | List of track slugs for `mode=allExercises` (leave empty for all tracks) | | `difficulty` | select | Filter: `easy`, `medium`, `hard`, or empty for all | | `exerciseType` | select | Filter: `tutorial`, `concept`, `practice`, or empty for all | | `maxItems` | integer | Maximum records to return (1–10,000, default 100) | #### Example inputs **List all programming language tracks:** ```json { "mode": "tracks", "maxItems": 100 } ```` **Get all Python exercises:** ```json { "mode": "exercises", "trackId": "python", "maxItems": 200 } ``` **Get medium-difficulty practice exercises for Rust:** ```json { "mode": "exercises", "trackId": "rust", "difficulty": "medium", "exerciseType": "practice" } ``` **Get exercises for multiple tracks:** ```json { "mode": "allExercises", "trackIds": ["python", "javascript", "rust"], "maxItems": 500 } ``` ### Output #### Track record (mode=tracks) ```json { "trackId": "python", "trackName": "Python", "numExercises": 146, "numConcepts": 16, "isCourse": true, "tags": ["Interpreted", "Object-oriented", "Dynamic", "Strong"], "trackUrl": "https://exercism.org/tracks/python", "exercisesUrl": "https://exercism.org/tracks/python/exercises", "conceptsUrl": "https://exercism.org/tracks/python/concepts", "iconUrl": "https://assets.exercism.org/tracks/python.svg", "recordType": "track", "scrapedAt": "2026-06-10T12:00:00+00:00" } ``` #### Exercise record (mode=exercises / allExercises) ```json { "exerciseSlug": "two-fer", "exerciseName": "Two Fer", "trackId": "python", "trackName": "Python", "difficulty": "easy", "type": "practice", "blurb": "Create a sentence of the form 'One for X, one for me.'", "exerciseUrl": "https://exercism.org/tracks/python/exercises/two-fer", "iconUrl": "https://assets.exercism.org/exercises/two-fer.svg", "recordType": "exercise", "scrapedAt": "2026-06-10T12:00:00+00:00" } ``` ### Supported tracks (sample) 8th, ABAP, ARM64 Assembly, AWK, Ballerina, Bash, C, CFML, Clojure, COBOL, C++, Crystal, C#, D, Dart, Delphi, Elixir, Elm, Erlang, Fortran, F#, Gleam, Go, Groovy, Haskell, Idris, Java, JavaScript, Julia, Kotlin, Lua, MIPS Assembly, Nim, OCaml, Pascal, Perl 5, PHP, PL/SQL, PowerShell, Prolog, PureScript, Python, R, Racket, Raku, ReasonML, Ruby, Rust, Scala, Scheme, Standard ML, Solidity, Swift, Tcl, TypeScript, Vimscript, Wren, x86-64 Assembly, Zig, and more. ### FAQ **Do I need an API key or account?** No. This scraper uses Exercism's completely public REST API (`exercism.org/api/v2`). **How many exercises are there?** Each track has 40–200+ exercises. Python alone has 146+ exercises. Total across all tracks is 5,000+. **What is the difference between exercise types?** - `tutorial`: Introductory exercises (usually just "Hello World") - `concept`: Exercises teaching a specific language concept - `practice`: Traditional algorithmic exercises **What does `isCourse` mean?** When `true`, the track has a structured learning path with concept-based progression. When `false`, it's a practice-only track. **Can I get community solutions or user data?** No. Community solutions require authentication. This scraper only accesses public, unauthenticated endpoints. **How fast is the scraper?** Tracks mode returns 80+ records in a few seconds. Exercise mode for a single track (100–200 exercises) completes in 1–2 seconds. ### Data source All data is sourced from [exercism.org](https://exercism.org) via their public REST API. Exercism is an open-source, not-for-profit platform. # Actor input Schema ## `mode` (type: `string`): What to fetch from Exercism: all tracks, exercises for a specific track, or exercises for multiple tracks. ## `trackId` (type: `string`): The programming language track slug (e.g. python, javascript, rust). Required for mode=exercises. ## `trackIds` (type: `array`): List of track slugs to fetch exercises for. Leave empty to scrape all tracks. ## `difficulty` (type: `string`): Only return exercises matching this difficulty level. ## `exerciseType` (type: `string`): Only return exercises of this type. ## `maxItems` (type: `integer`): Hard cap on emitted records. ## Actor input object example ```json { "mode": "tracks", "trackIds": [], "difficulty": "", "exerciseType": "", "maxItems": 100 } ``` # Actor output Schema ## `data` (type: `string`): Dataset containing all scraped Exercism tracks and exercises. # 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": "tracks", "trackIds": [], "difficulty": "", "exerciseType": "", "maxItems": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("crawlerbros/exercism-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 = { "mode": "tracks", "trackIds": [], "difficulty": "", "exerciseType": "", "maxItems": 100, } # Run the Actor and wait for it to finish run = client.actor("crawlerbros/exercism-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 '{ "mode": "tracks", "trackIds": [], "difficulty": "", "exerciseType": "", "maxItems": 100 }' | apify call crawlerbros/exercism-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crawlerbros/exercism-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Exercism Scraper", "description": "Scrape Exercism.org - free open-source coding education platform. List all programming language tracks, browse exercises by track, filter by difficulty, type, and more. No auth required.", "version": "1.0", "x-build-id": "3R4XP9JZpY2Nf8nVO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crawlerbros~exercism-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crawlerbros-exercism-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crawlerbros~exercism-scraper/runs": { "post": { "operationId": "runs-sync-crawlerbros-exercism-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crawlerbros~exercism-scraper/run-sync": { "post": { "operationId": "run-sync-crawlerbros-exercism-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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" ], "properties": { "mode": { "title": "Mode", "enum": [ "tracks", "exercises", "allExercises" ], "type": "string", "description": "What to fetch from Exercism: all tracks, exercises for a specific track, or exercises for multiple tracks.", "default": "tracks" }, "trackId": { "title": "Track slug (mode=exercises)", "enum": [ "8th", "abap", "arm64-assembly", "awk", "ballerina", "bash", "c", "cfml", "clojure", "cobol", "coffeescript", "common-lisp", "cpp", "crystal", "csharp", "d", "dart", "delphi", "elixir", "elm", "emacs-lisp", "erlang", "fortran", "fsharp", "gleam", "go", "groovy", "haskell", "idris", "java", "javascript", "julia", "kotlin", "lua", "mips", "nim", "ocaml", "pascal", "perl5", "php", "plsql", "powershell", "prolog", "purescript", "python", "r", "racket", "raku", "reasonml", "ruby", "rust", "scala", "scheme", "sml", "solidity", "swift", "tcl", "typescript", "vimscript", "wren", "x86-64-assembly", "zig" ], "type": "string", "description": "The programming language track slug (e.g. python, javascript, rust). Required for mode=exercises." }, "trackIds": { "title": "Track slugs (mode=allExercises)", "type": "array", "description": "List of track slugs to fetch exercises for. Leave empty to scrape all tracks.", "default": [], "items": { "type": "string" } }, "difficulty": { "title": "Filter by difficulty", "enum": [ "", "easy", "medium", "hard" ], "type": "string", "description": "Only return exercises matching this difficulty level.", "default": "" }, "exerciseType": { "title": "Filter by exercise type", "enum": [ "", "tutorial", "concept", "practice" ], "type": "string", "description": "Only return exercises of this type.", "default": "" }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Hard cap on emitted records.", "default": 100 } } }, "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 } } } } } } } } } } ```