# Conda Repodata Extractor (`datamule/conda-repodata-extractor`) Actor Point at any Conda channel's repodata.json and get one structured row per package: name, version, build, subdir, license, md5/sha256, size, timestamp, depends and constrains. - **URL**: https://apify.com/datamule/conda-repodata-extractor.md - **Developed by:** [Datamule](https://apify.com/datamule) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.35 / 1,000 packages 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 ## Conda Repodata Extractor **Point at any Conda channel's `repodata.json` and get one clean, structured row per package.** Every Conda channel — [conda-forge](https://conda.anaconda.org/conda-forge), [bioconda](https://conda.anaconda.org/bioconda), [PyTorch](https://conda.anaconda.org/pytorch), [NVIDIA](https://conda.anaconda.org/nvidia), the Anaconda `pkgs/main` **defaults**, and the tens of thousands of self‑hosted, org and [anaconda.org](https://anaconda.org) channels — publishes the **identical `repodata.json`** index for each platform subdir. This actor speaks that one standard, so **a single actor spans every Conda channel in existence** — no per‑channel scraper, no host enumeration. Give it a channel and a subdir; get back a flat, fully‑typed dataset row for every package build, ready for a spreadsheet, a database, a dependency‑graph, a license audit or an SBOM. ### What it does - **Any channel, one input.** Pass a channel base (`https://conda.anaconda.org/pytorch`) + a `subdir`, or paste a full `...//repodata.json` URL directly. A compressed `.json.bz2` / `.json.zst` URL is normalized to the plain `.json`. - **Both package maps, merged.** conda ships `.tar.bz2` builds under `packages` and the newer `.conda` builds under `packages.conda`. This actor merges **both** into one globally‑sorted stream — nothing is missed, nothing is double‑counted. - **Memory‑flat streaming.** `repodata.json` for a big subdir (e.g. `conda-forge/linux-64`) is hundreds of megabytes. The feed is streamed to disk and parsed incrementally, so RAM stays flat regardless of channel size, and `maxRecords` stops early. - **Deterministic under a cap.** Records are emitted in sorted filename order, and `repodata.json` is an immutable artifact — so a capped run always returns the same first *N* records. ### Input | Field | Required | Description | |---|---|---| | `channelUrl` | ✅ | A conda channel base (`https://conda.anaconda.org/pytorch`) **or** a full `repodata.json` URL. | | `subdir` | | Platform subdir used to complete a channel base. Default `noarch`. One of `noarch`, `linux-64`, `linux-aarch64`, `linux-ppc64le`, `osx-64`, `osx-arm64`, `win-64`, `win-arm64`. Ignored when `channelUrl` is already a full repodata URL. | | `packageFilter` | | Case‑insensitive substring matched against each package **name**; non‑matching records are skipped. | | `maxRecords` | | Global cap on emitted rows (each row = one package record = one billable event). | | `bearer` | | Bearer token for a private/gated channel. Never logged. | | `extraHeaders` | | Extra HTTP headers as a JSON object for gated channels. Never logged. | #### Example inputs ```json { "channelUrl": "https://conda.anaconda.org/pytorch", "subdir": "noarch" } ```` ```json { "channelUrl": "https://conda.anaconda.org/nvidia", "subdir": "linux-64", "packageFilter": "cuda", "maxRecords": 500 } ``` ```json { "channelUrl": "https://repo.anaconda.com/pkgs/main/osx-arm64/repodata.json" } ``` ### Output One row per package record (from **both** the `.tar.bz2` and `.conda` maps): | Column | Description | |---|---| | `filename` | The package filename (the repodata map key), e.g. `botorch-0.1.0-0.tar.bz2`. | | `name` | Package name. | | `version` | Package version. | | `build` / `buildNumber` | Build string and build number. | | `subdir` | Platform subdir. | | `license` | Declared license (SPDX‑ish string, when present). | | `md5` / `sha256` | Artifact checksums. | | `size` | Artifact size in bytes. | | `timestamp` | Build timestamp, normalized to ISO‑8601 UTC. | | `depends` | Array of runtime dependency specs (`"pytorch >=1.1"`, …). | | `constrains` | Array of optional‑constraint specs. | | `track_features` | Feature flags (space‑delimited string), when present. | | `_raw` | The complete original record — nothing is ever dropped (`license_family`, `noarch`, `platform`, …). | | `_source` / `_subdir` / `_packageCount` | The exact repodata URL, the subdir, and the total record count. | Every field is **nullable** and read by key presence — a channel that omits `license` / `depends` / `constrains` / `track_features` simply yields `null` for that column, never an error. ### Notes & scope - **Public metadata only.** This reads a channel's published package index — it does **not** download the packages themselves. - **GTFS‑style honesty.** An empty subdir, or a `packageFilter` that excludes everything, returns **0 records** and a clean success. A URL that isn't conda repodata (an HTML error page, a 404 body, JSON without a `packages` key) fails fast with a clear message. - **Find channels** on [anaconda.org](https://anaconda.org). Common bases: `https://conda.anaconda.org/` and the Anaconda defaults at `https://repo.anaconda.com/pkgs/main`. ### Pricing Pay‑per‑event: **$0.0005 per package record** returned. A capped run charges only for what it emits. # Actor input Schema ## `channelUrl` (type: `string`): A conda channel base OR a full repodata.json URL. Every conda channel publishes the identical repodata standard, so one actor works against any of them: https://conda.anaconda.org/pytorch (channel base — completed with the subdir below), https://conda.anaconda.org/bioconda, https://conda.anaconda.org/nvidia, https://conda.anaconda.org/conda-forge, https://repo.anaconda.com/pkgs/main (Anaconda defaults), or any self-hosted / org / anaconda.org channel. You can also paste a full URL like https://conda.anaconda.org/pytorch/noarch/repodata.json. A compressed .json.bz2 / .json.zst URL is rewritten to the plain .json. ## `subdir` (type: `string`): The platform subdir to read when channelUrl is a channel BASE (ignored when channelUrl already points at a repodata.json). Common values: noarch (default, cross-platform packages), linux-64, linux-aarch64, osx-64, osx-arm64, win-64. Each subdir has its own repodata.json. ## `packageFilter` (type: `string`): Optional case-insensitive substring matched against each record's package name; records whose name does not contain it are skipped. Leave empty to extract every package in the subdir. Example: torch (matches pytorch, botorch, torchvision, …). ## `maxRecords` (type: `integer`): A GLOBAL cap on the number of package-record rows to emit (each row is one package record and one billable event). Records are emitted in globally-sorted filename order and repodata.json is an immutable file, so a small cap is deterministic. Leave empty to emit every record — note a big channel subdir (e.g. conda-forge/linux-64) can hold hundreds of thousands of records. ## `bearer` (type: `string`): Optional bearer token for a private/gated conda channel (sent as Authorization: Bearer \*\*\*). Not required for public channels. Never logged. ## `extraHeaders` (type: `object`): Optional extra HTTP headers as a JSON object, e.g. {"x-api-key": "..."} for gated/authenticated channels. Not required for public channels. Header values are never logged. ## Actor input object example ```json { "channelUrl": "https://conda.anaconda.org/pytorch", "subdir": "noarch" } ``` # 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 = { "channelUrl": "https://conda.anaconda.org/pytorch" }; // Run the Actor and wait for it to finish const run = await client.actor("datamule/conda-repodata-extractor").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 = { "channelUrl": "https://conda.anaconda.org/pytorch" } # Run the Actor and wait for it to finish run = client.actor("datamule/conda-repodata-extractor").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 '{ "channelUrl": "https://conda.anaconda.org/pytorch" }' | apify call datamule/conda-repodata-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datamule/conda-repodata-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Conda Repodata Extractor", "description": "Point at any Conda channel's repodata.json and get one structured row per package: name, version, build, subdir, license, md5/sha256, size, timestamp, depends and constrains.", "version": "0.1", "x-build-id": "EbESuhKpIwaaOZcNu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datamule~conda-repodata-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datamule-conda-repodata-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datamule~conda-repodata-extractor/runs": { "post": { "operationId": "runs-sync-datamule-conda-repodata-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datamule~conda-repodata-extractor/run-sync": { "post": { "operationId": "run-sync-datamule-conda-repodata-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "channelUrl" ], "properties": { "channelUrl": { "title": "Conda channel URL (or repodata.json URL)", "type": "string", "description": "A conda channel base OR a full repodata.json URL. Every conda channel publishes the identical repodata standard, so one actor works against any of them: https://conda.anaconda.org/pytorch (channel base — completed with the subdir below), https://conda.anaconda.org/bioconda, https://conda.anaconda.org/nvidia, https://conda.anaconda.org/conda-forge, https://repo.anaconda.com/pkgs/main (Anaconda defaults), or any self-hosted / org / anaconda.org channel. You can also paste a full URL like https://conda.anaconda.org/pytorch/noarch/repodata.json. A compressed .json.bz2 / .json.zst URL is rewritten to the plain .json." }, "subdir": { "title": "Subdir (platform)", "enum": [ "noarch", "linux-64", "linux-aarch64", "linux-ppc64le", "osx-64", "osx-arm64", "win-64", "win-arm64" ], "type": "string", "description": "The platform subdir to read when channelUrl is a channel BASE (ignored when channelUrl already points at a repodata.json). Common values: noarch (default, cross-platform packages), linux-64, linux-aarch64, osx-64, osx-arm64, win-64. Each subdir has its own repodata.json.", "default": "noarch" }, "packageFilter": { "title": "Package name filter (substring)", "type": "string", "description": "Optional case-insensitive substring matched against each record's package name; records whose name does not contain it are skipped. Leave empty to extract every package in the subdir. Example: torch (matches pytorch, botorch, torchvision, …)." }, "maxRecords": { "title": "Max records (global cap)", "minimum": 1, "type": "integer", "description": "A GLOBAL cap on the number of package-record rows to emit (each row is one package record and one billable event). Records are emitted in globally-sorted filename order and repodata.json is an immutable file, so a small cap is deterministic. Leave empty to emit every record — note a big channel subdir (e.g. conda-forge/linux-64) can hold hundreds of thousands of records." }, "bearer": { "title": "Bearer token", "type": "string", "description": "Optional bearer token for a private/gated conda channel (sent as Authorization: Bearer ***). Not required for public channels. Never logged." }, "extraHeaders": { "title": "Extra request headers", "type": "object", "description": "Optional extra HTTP headers as a JSON object, e.g. {\"x-api-key\": \"...\"} for gated/authenticated channels. Not required for public channels. Header values are never logged." } } }, "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 } } } } } } } } } } ```