# GitHub Context Extractor (LLM-Ready Markdown, 5k req/hr) (`automationnation/github-context-extractor`) Actor Extract clean README markdown, stars, contributors, and release info for LLMs and RAG pipelines. Bring your own GitHub token to get 5,000 requests/hour instead of the 60/hour unauthenticated limit most scrapers hit after ~15 repos. - **URL**: https://apify.com/automationnation/github-context-extractor.md - **Developed by:** [Nathan Carter](https://apify.com/automationnation) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 github repo context bundle extracteds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## GitHub Context Extractor (LLM-Ready) Instant access to GitHub repository context for AI developers and RAG pipelines. This Actor bypasses the clutter of the GitHub UI to provide structured metadata and raw, sanitized Markdownโ€”perfect for feeding into LLMs. ### ๐ŸŽฏ Why use this Actor? Instead of manually navigating repositories or dealing with slow headless scrapers, this tool provides an instant "Context Bundle" for any public repo. * **LLM-Optimized:** Extracts the README as raw Markdown, stripped of broken image links. * **High Reliability:** Built on GitHub's public APIโ€”no fragile HTML selectors or client-side rendering issues. * **Developer-Centric:** Focuses on metrics that matter for AI evaluation (Stars, Issues, Last Commit, Contributors). ### ๐Ÿ“Š What Data Do You Get? Flat JSON, ready to drop straight into a prompt or vector store: - `repoName`, `repoDescription`, `repoUrl`: Core repo identity. - `readmeMarkdown`: Sanitized Markdown ready for prompt injection. - `stars`, `forks`, `issuesCount`, `primaryLanguage`, `lastCommitDate`: Core repo metadata. - `topContributors`: Top 5 contributors, each with `username` and `avatarUrl`. - `latestReleaseTag`, `latestReleaseUrl`: The latest release tag and its release notes link. ### ๐Ÿ’ก Use Cases - **RAG Pipelines:** Automatically scrape repo documentation to index it into your vector database. - **Library Evaluation:** Feed a repo's README into Claude/GPT to see if it fits your project needs before installing. - **Competitor Intelligence:** Track the development velocity and contribution activity of open-source projects. ### โšก Pricing * **Pay Per Event:** $1.00 per 1,000 results. # Actor input Schema ## `repoUrls` (type: `array`): GitHub repository URLs to extract an LLM context bundle from. ## `githubToken` (type: `string`): Raises GitHub's API rate limit from 60 requests/hour (unauthenticated) to 5,000/hour. A fine-grained token with public-repo read access is enough โ€” no scopes/permissions needed for private repos. Without this, each repo costs ~4 API calls, so you'll hit GitHub's limit after ~15 repos/hour. ## `proxyConfiguration` (type: `object`): Proxy settings. Only relevant without a GitHub token โ€” rotating proxies help spread load across GitHub's per-IP unauthenticated rate limit. ## Actor input object example ```json { "repoUrls": [ "https://github.com/apify/crawlee" ], "proxyConfiguration": { "useApifyProxy": true } } ```` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "repoUrls": [ "https://github.com/apify/crawlee" ] }; // Run the Actor and wait for it to finish const run = await client.actor("automationnation/github-context-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "repoUrls": ["https://github.com/apify/crawlee"] } # Run the Actor and wait for it to finish run = client.actor("automationnation/github-context-extractor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "repoUrls": [ "https://github.com/apify/crawlee" ] }' | apify call automationnation/github-context-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automationnation/github-context-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GitHub Context Extractor (LLM-Ready Markdown, 5k req/hr)", "description": "Extract clean README markdown, stars, contributors, and release info for LLMs and RAG pipelines. Bring your own GitHub token to get 5,000 requests/hour instead of the 60/hour unauthenticated limit most scrapers hit after ~15 repos.", "version": "0.1", "x-build-id": "dscwzDhe9JQQP4z5G" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automationnation~github-context-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automationnation-github-context-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automationnation~github-context-extractor/runs": { "post": { "operationId": "runs-sync-automationnation-github-context-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automationnation~github-context-extractor/run-sync": { "post": { "operationId": "run-sync-automationnation-github-context-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "repoUrls" ], "properties": { "repoUrls": { "title": "Repository URLs", "type": "array", "description": "GitHub repository URLs to extract an LLM context bundle from.", "items": { "type": "string" } }, "githubToken": { "title": "GitHub Personal Access Token (optional, recommended)", "type": "string", "description": "Raises GitHub's API rate limit from 60 requests/hour (unauthenticated) to 5,000/hour. A fine-grained token with public-repo read access is enough โ€” no scopes/permissions needed for private repos. Without this, each repo costs ~4 API calls, so you'll hit GitHub's limit after ~15 repos/hour." }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings. Only relevant without a GitHub token โ€” rotating proxies help spread load across GitHub's per-IP unauthenticated rate limit.", "default": { "useApifyProxy": 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 } } } } } } } } } } ```