# Conda Package Scraper - Anaconda Metadata (`benthepythondev/conda-package-scraper`) Actor Scrape Anaconda and Conda package metadata with package name, summary, package types, owner, downloads, labels and latest version data. - **URL**: https://apify.com/benthepythondev/conda-package-scraper.md - **Developed by:** [ben](https://apify.com/benthepythondev) (community) - **Categories:** Developer tools, Business, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 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 ## Conda Package Scraper - Anaconda Metadata Scrape Anaconda and Conda package metadata with package name, summary, package types, owner, downloads, labels and latest version data. Export results to JSON, CSV, Excel, webhooks, Make, Zapier, n8n, Google Sheets or your own API. This actor is part of a portfolio of lightweight public-data scrapers designed for reliable scheduled runs. It uses the public Anaconda endpoint directly, so it does not need a browser, residential proxy, login session or API key. That makes it inexpensive to run and suitable for Apify Store daily auto-tests. ### What does this actor do? The actor searches Anaconda for a keyword such as `numpy` and normalizes the returned records into a flat dataset. The exact fields depend on the registry, but the output is designed to preserve the commercially useful metadata: names, descriptions, owners/authors, popularity counters, download counts, versions, URLs, tags, licenses and source-specific identifiers. ### Input ```json { "query": "numpy", "maxResults": 25 } ```` Use broad terms for market mapping and exact terms for monitoring. For example, developer-tool companies can search for a framework or technology category, agencies can monitor plugins and themes around a vertical, and researchers can watch package ecosystems over time. ### Output Each output item includes a stable package or record identifier, the source name and the search query. A typical record looks like this: ```json { "package_id": "example", "name": "example", "description": "Example description", "source": "Anaconda", "search": "numpy" } ``` The actor keeps the output intentionally flat so it works well in spreadsheets, BI tools and enrichment pipelines. ### Use cases Developer-relations teams can identify projects and ecosystems that match their product. SaaS companies can find integration, migration and partnership opportunities. Security teams can monitor widely used packages or plugins. Market researchers can track popularity, downloads and version activity over time. Agencies can build lead lists from technology adoption signals. ### Commercial value Public registry metadata is a buying-intent signal. A project using a package, plugin, formula, module or theme often has an active maintainer or company behind it. Combine this output with website/contact enrichment to turn package metadata into sales research, partner discovery, competitive tracking or ecosystem intelligence. ### Scheduling Create a saved task for each keyword and run it weekly. Over time, you can compare new results, download counters, versions and metadata changes. Webhooks can send new records to your CRM, spreadsheet or database. ### Reliability The actor uses direct HTTP requests with retries and small defaults. It avoids fragile browser automation and anti-bot-heavy sites. If the source API returns a temporary error, the actor retries before finishing. ### Pricing This actor uses pay-per-result pricing. You pay only for records written to the default dataset, plus a small actor start event. ### FAQ #### Does it need an API key? No. It uses public endpoints from Anaconda. #### Can I run it daily? Yes. The actor is built for scheduled monitoring and small reliable runs. #### Can I enrich the output? Yes. Combine URLs, names and package IDs with website contact extraction, GitHub enrichment or company enrichment actors. #### Is this useful for lead generation? Yes, especially for developer tools, agencies, security products, hosting providers, integration vendors and technical consultants. ### You might also like - [PyPI Scraper](https://apify.com/benthepythondev/pypi-scraper) - [Docker Hub Scraper](https://apify.com/benthepythondev/dockerhub-scraper) - [RemoteOK Jobs Scraper](https://apify.com/benthepythondev/remoteok-jobs-scraper) - [Website Contact Extractor](https://apify.com/benthepythondev/website-contact-extractor) ### Keywords Anaconda scraper, metadata scraper, developer tools data, package registry scraper, technology intelligence, open source intelligence, lead generation data, Apify scraper. ### Data quality and field coverage The actor preserves source-specific metadata instead of forcing every source into a thin generic schema. This is important because commercial value often lives in the details: download counters, active installs, ratings, verified flags, repository URLs, author names, version numbers, dependency lists, license fields and homepage links. When a source does not provide a field, the actor leaves it empty rather than inventing data. For analytics workflows, keep the `source` and `search` fields in your exports. They make it easy to combine multiple runs, compare several keywords and trace each row back to its origin. For CRM workflows, use the package name, homepage, repository or author fields as enrichment starting points. ### Recommended workflow Start with a broad search to understand the market, then create separate saved tasks for high-value subcategories. For example, a security vendor might track authentication, secrets, OAuth, SSO and vulnerability terms separately. A hosting provider might track database, queue, cache, deployment and observability terms. A developer-relations team might track framework names, competitor names and integration keywords. After the first export, deduplicate records by package ID and enrich only the rows that matter. This keeps downstream costs low and makes the dataset more useful for sales, partnerships, research and content planning. ### Why run this on Apify? Apify gives you scheduling, API access, datasets, webhooks, integrations and pay-per-result billing in one place. You can run the actor manually, trigger it from your backend, schedule it weekly, send results to a webhook or connect it to no-code tools. That makes this actor useful as both a quick data export and a small building block inside a larger data pipeline. ### Maintenance approach This actor intentionally avoids scraping brittle private pages. It targets public endpoints that are stable, lightweight and fast. That makes it more reliable than a browser-based scraper and keeps run costs predictable. If the source changes its response shape, the actor can usually be updated with a small parser change rather than a full anti-bot rebuild. # Actor input Schema ## `query` (type: `string`): Search keyword or package term. ## `maxResults` (type: `integer`): Maximum number of records to return. ## Actor input object example ```json { "query": "numpy", "maxResults": 10 } ``` # 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 = { "query": "numpy", "maxResults": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("benthepythondev/conda-package-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 = { "query": "numpy", "maxResults": 10, } # Run the Actor and wait for it to finish run = client.actor("benthepythondev/conda-package-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 '{ "query": "numpy", "maxResults": 10 }' | apify call benthepythondev/conda-package-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=benthepythondev/conda-package-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Conda Package Scraper - Anaconda Metadata", "description": "Scrape Anaconda and Conda package metadata with package name, summary, package types, owner, downloads, labels and latest version data.", "version": "1.0", "x-build-id": "tVdjkbYuDUKqv8zQT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/benthepythondev~conda-package-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-benthepythondev-conda-package-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/benthepythondev~conda-package-scraper/runs": { "post": { "operationId": "runs-sync-benthepythondev-conda-package-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/benthepythondev~conda-package-scraper/run-sync": { "post": { "operationId": "run-sync-benthepythondev-conda-package-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "query": { "title": "Search query", "type": "string", "description": "Search keyword or package term.", "default": "numpy" }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of records to return.", "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 } } } } } } } } } } ```