# Multilingual Github Scraper (`sincere_spinner/multilingual-github-scraper`) Actor Turn any GitHub file or issue into clear, multilingual insights instantly. This standalone AI actor explains complex code with flowcharts and generates step-by-step solution plans for issues all in your preferred language. - **URL**: https://apify.com/sincere\_spinner/multilingual-github-scraper.md - **Developed by:** [Sk Akram](https://apify.com/sincere_spinner) (community) - **Categories:** AI, Developer tools - **Stats:** 1 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 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 ## Multilingual GitHub Scraper A standalone, AI-powered Apify Actor designed to analyze GitHub repositories. It can explain code files, visually diagram logic with flowcharts, and create solution plans for GitHub issues. It supports multiple languages and two primary usage patterns: Standalone (AI-powered) and App Integration (Raw Data). ### User Guidance #### 1. Raw Data Modes (Fast & Cheap) These modes perform NO AI analysis. They simply fetch the raw content from GitHub. Ideal for feeding data into your own external pipelines. | Mode | Purpose | Required Input | URL Error Policy | |------|---------|----------------|------------------| | `file` | Fetch raw code file content | **Github File URL** (e.g., `.../blob/main/main.ts`) | ❌ **Error:** If you provide an Issue URL, it will fail. | | `issue` | Fetch raw issue data (body, comments, labels) | **Github Issue URL** (e.g., `.../issues/1`) | ❌ **Error:** If you provide a File URL, it will fail. | #### 2. AI-Powered Modes (Deep Analysis) These modes use LLMs (and optionally Lingo.dev) to analyze the content. ##### 📄 File Explainer (`file_explainer`) Analyzes code files to explain purpose, logic, and structure. | Feature | Input | Description | |---------|-------|-------------| | **Base Analysis** | `mode: "file_explainer"` | Standard logic explanation. | | **Flowchart** | `includeFlowchart: true` | Adds a Mermaid.js flowchart visualization. | | **Lingo Translation** | `useLingoTranslation: true` | Uses Lingo.dev for high-quality technical translation (if `language` != `en`). | ##### 🔧 Issue Solver (`issue_solver`) Analyzes GitHub issues to propose actionable step-by-step solutions. | Feature | Input | Description | |---------|-------|-------------| | **Base Analysis** | `mode: "issue_solver"` | Explains the issue context and problem. | | **Solution Plan** | `includeSolutionPlan: true` | Adds a detailed, step-by-step fix plan with code snippets. | | **Lingo Translation** | `useLingoTranslation: true` | Uses Lingo.dev for high-quality technical translation (if `language` != `en`). | --- ### 1. Standalone Modes (AI-Powered) These modes are for users running the Actor directly on Apify to get AI insights. #### Mode: File Explainer (`file_explainer`) Analyzes a single source code file and provides a technical breakdown with a visualization. **Example Input JSON:** ```json { "mode": "file_explainer", "url": "https://github.com/user/project/blob/main/calculate.js", "language": "ja", "includeFlowchart": true, "useLingoTranslation": false } ```` **Example Output (Japanese With Flowchart):** "## ファイルの目的 このファイルは、TypeScriptコンパイラの設定（tsconfig.json）を解析し、ビルドプロセスを初期化するためのロジックを含んでいます。 ### フローチャート image **Full Output JSON Structure:** ```json { "success": true, "mode": "file_explainer", "file": { "path": "calculate.js", "content": "function calculateTotal(price, tax) { ... }", "detectedLanguage": "javascript" }, "explanation": "## ファイルの目的\nこのファイルは...", "flowchart": "flowchart TD\n A[開始] --> B{価格は0未満か？}..." } ``` #### How to Render Flowcharts The `flowchart` field contains [Mermaid.js](https://mermaid.js.org/) syntax. To visualize it: | Tool | How to Use | |------|------------| | **Mermaid Live Editor** | Paste at [mermaid.live](https://mermaid.live) | | **GitHub Markdown** | Wrap in ` ```mermaid ` code blocks in `.md` files | | **VS Code** | Install "Mermaid" extension | | **React/Web Apps** | Use `mermaid` npm package (see below) | **React Example:** ```javascript import mermaid from 'mermaid'; mermaid.initialize({ theme: 'dark' }); const { svg } = await mermaid.render('chart-id', flowchartCode); // Render svg in your component ``` *** #### Mode: Issue Solver (`issue_solver`) Analyzes a GitHub issue, identifies mentioned files, and proposes a fix. **Example Input JSON:** ```json { "mode": "issue_solver", "url": "https://github.com/user/project/issues/123", "language": "hi", "includeSolutionPlan": true, "useLingoTranslation": false } ``` **Example Output (Hindi Solution Plan):** "## समस्या विवरण मोनोरेपो के लिए Vitest टेस्टिंग कॉन्फ़िगरेशन में नए प्रोजेक्ट पाथ को जोड़ने की आवश्यकता है। ### समाधान योजना 1. **चरण 1:** `vitest.config.ts` फ़ाइल खोलें। 2. **चरण 2:** `test.projects` सरणी (array) में नए पैकेज का पाथ जोड़ें। 3. **चरण 3:** परिवर्तनों को सहेजें और `npm test` चलाकर पुष्टि करें। " **Full Output JSON Structure:** ```json { "success": true, "mode": "issue_solver", "issue": { "title": "Config Update", "number": 123, "body": "Need to add new project path..." }, "issueExplanation": "## समस्या विवरण...", "solutionPlan": "## समाधान योजना\n1. चरण 1: ..." } ``` *** ### 2. App Integration Modes (Raw Data) These modes are used by the OSFIT App or other external tools. They perform no AI analysis on the Actor side, instead acting as high-speed data fetchers. #### Mode: File (`file`) Specifically designed to fetch the raw content of a file from GitHub for app-side processing. **Example Input JSON:** ```json { "mode": "file", "url": "https://github.com/user/project/blob/main/src/utils.ts" } ``` **Output Example (JSON):** ```json { "type": "file", "path": "src/utils.ts", "content": "raw file content string...", "detectedLanguage": "typescript", "url": "https://github.com/user/project/blob/main/src/utils.ts" } ``` #### Mode: Issue (`issue`) Scrapes GitHub issue data into a structured format for app-side AI processing. **Example Input JSON:** ```json { "mode": "issue", "url": "https://github.com/user/project/issues/456" } ``` **Output Example (JSON):** ```json { "type": "issue", "title": "Issue Title", "number": 456, "state": "open", "body": "Description text...", "url": "https://github.com/user/project/issues/456", "labels": ["bug", "help wanted"], "comments": [] } ``` *** ### Input Parameters Table | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `mode` | string | Yes | Operation mode: `file_explainer`, `issue_solver`, `file`, or `issue`. | | `url` | string | Yes | The GitHub URL. Must match the mode (File URL for file modes, Issue URL for issue modes). | | `language` | string | No | Target language code for AI output (e.g., `en`, `fr`, `hi`). Default: `en`. | | `includeFlowchart` | boolean | No | (File Explainer only) If true, generates Mermaid.js flowchart (+$0.04). Default: `true`. | | `includeSolutionPlan` | boolean | No | (Issue Solver only) If true, generates solution plan ($0.10 total). If false, only explanation ($0.04). Default: `true`. | | `useLingoTranslation` | boolean | No | If true, uses Lingo.dev for professional-grade translation. Default: `false`. | *** ### Environment Variables This Actor requires the following environment variables. In the hosted version, these are pre-configured. - `GROQ_API_KEY`: Required. API key for Groq (used for AI analysis). - `LINGO_API_KEY`: Optional. API key for Lingo.dev (used if `useLingoTranslation` is true). *** ### Pricing This Actor uses a **pay-per-event** model. You pay for the specific features you use: #### AI-Powered Analysis | Mode | Features | Price | |------|----------|-------| | **File Analysis** | Code explanation in 20+ languages | $0.06 | | **File Analysis + Flowchart** | Explanation + Mermaid.js diagram | $0.10 | | **File Analysis + Flowchart + Lingo** | Above + Lingo.dev translation | $0.50 | | **Issue Explanation** | Issue analysis in 20+ languages | $0.04 | | **Issue Explanation + Solution** | Analysis + step-by-step fix plan | $0.10 | | **Issue Explanation + Solution + Lingo** | Above + Lingo.dev translation | $0.50 | #### Raw Data Fetching | Mode | Description | Price | |------|-------------|-------| | **File Fetcher** | Fetch raw file content | $0.001 | | **Issue Fetcher** | Fetch raw issue data | $0.001 | # Actor input Schema ## `mode` (type: `string`): Operation mode: file\_explainer (AI-powered file analysis), issue\_solver (AI-powered issue solution), file (raw file data for app integration), issue (raw issue data for app integration) ## `url` (type: `string`): GitHub file URL (for file\_explainer/file modes) or GitHub issue URL (for issue\_solver/issue modes) ## `language` (type: `string`): Language for AI-powered explanations and solutions. Only applies to file\_explainer and issue\_solver modes. ## `useLingoTranslation` (type: `boolean`): Enable Lingo.dev for professional-grade translation. Requires LINGO\_API\_KEY environment variable. ## `includeFlowchart` (type: `boolean`): Generate a Mermaid.js flowchart visualizing code execution flow. Only applies when mode is 'file\_explainer'. ## `includeSolutionPlan` (type: `boolean`): Generate step-by-step solution plan for the issue. Only applies when mode is 'issue\_solver'. ## Actor input object example ```json { "mode": "file_explainer", "url": "https://github.com/user/repo/blob/main/src/index.ts", "language": "en", "useLingoTranslation": false, "includeFlowchart": true, "includeSolutionPlan": true } ``` # Actor output Schema ## `overview` (type: `string`): View key analysis results including success status, mode, and summaries ## `fileAnalysis` (type: `string`): View file explanations and flowcharts from file\_explainer mode ## `issueSolutions` (type: `string`): View issue analysis and solution plans from issue\_solver mode ## `fullDataset` (type: `string`): Access all raw data items (all modes) # 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("sincere_spinner/multilingual-github-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("sincere_spinner/multilingual-github-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 sincere_spinner/multilingual-github-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=sincere_spinner/multilingual-github-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Multilingual Github Scraper", "description": "Turn any GitHub file or issue into clear, multilingual insights instantly. This standalone AI actor explains complex code with flowcharts and generates step-by-step solution plans for issues all in your preferred language.", "version": "1.0", "x-build-id": "ypx0ETuNFJ2bXQpZO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/sincere_spinner~multilingual-github-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-sincere_spinner-multilingual-github-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/sincere_spinner~multilingual-github-scraper/runs": { "post": { "operationId": "runs-sync-sincere_spinner-multilingual-github-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/sincere_spinner~multilingual-github-scraper/run-sync": { "post": { "operationId": "run-sync-sincere_spinner-multilingual-github-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "url" ], "properties": { "mode": { "title": "Mode", "enum": [ "file_explainer", "issue_solver", "file", "issue" ], "type": "string", "description": "Operation mode: file_explainer (AI-powered file analysis), issue_solver (AI-powered issue solution), file (raw file data for app integration), issue (raw issue data for app integration)", "default": "file_explainer" }, "url": { "title": "GitHub URL", "type": "string", "description": "GitHub file URL (for file_explainer/file modes) or GitHub issue URL (for issue_solver/issue modes)" }, "language": { "title": "Output Language (AI Modes Only)", "enum": [ "en", "es", "fr", "de", "bn", "hi", "ar", "ja", "ko", "zh", "ru", "pt", "it", "nl", "tr", "vi", "th", "id", "ms", "ta", "te" ], "type": "string", "description": "Language for AI-powered explanations and solutions. Only applies to file_explainer and issue_solver modes.", "default": "en" }, "useLingoTranslation": { "title": "Use Lingo.dev Translation", "type": "boolean", "description": "Enable Lingo.dev for professional-grade translation. Requires LINGO_API_KEY environment variable.", "default": false }, "includeFlowchart": { "title": "Generate Flowchart (File Explainer Only)", "type": "boolean", "description": "Generate a Mermaid.js flowchart visualizing code execution flow. Only applies when mode is 'file_explainer'.", "default": true }, "includeSolutionPlan": { "title": "Generate Solution Plan (Issue Solver Only)", "type": "boolean", "description": "Generate step-by-step solution plan for the issue. Only applies when mode is 'issue_solver'.", "default": true } } }, "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 } } } } } } } } } } ```