A full-featured Kotlin Multiplatform download manager — run locally, remotely, or embedded in your app. Supports Android, iOS, Desktop, and Web.

• Embed it — Add downloads to your Android, iOS, or Desktop app with a simple API
• Run it as a daemon — Self-hosted download server with REST API and real-time SSE events
• Control it remotely — Manage a daemon from any client (mobile app, web UI, CLI, or AI agent)
• Extend it — Pluggable architecture for custom protocols (FTP, BitTorrent, HLS, and more on the roadmap)

Features

• Multi-platform ✅ -- Works on Android, iOS, Desktop, and Web
• Segmented downloads ✅ -- Accelerate downloads by splitting files into multiple parallel connections
• Pause / Resume ✅ -- Pause and pick up where you left off, even after restarting your app
• Queue management ✅ -- Manage multiple downloads with priorities and concurrency limits
• Speed limiting ✅ -- Control bandwidth usage per task or globally
• Scheduling ✅ -- Schedule downloads for a specific time, after a delay, or based on conditions
• Automatic retry ✅ -- Automatically retry failed downloads with smart backoff
• Daemon server ✅ -- Run as a background service with REST API and real-time events
• Remote control ✅ -- Manage a remote server from any client (mobile, desktop, web, or CLI)
• Pluggable architecture ✅ -- Swap out HTTP engines, storage backends, and download sources
• FTP/FTPS ✅ -- Download from FTP servers with segmented parallel transfers and resume
• BitTorrent & Magnet 🚧 -- Peer-to-peer file sharing with .torrent files and magnet links
• Metalink 🔜 -- Multi-source downloads with mirrors, checksums, and chunk verification
• WebDAV 🔜 -- Download from WebDAV servers with resume support
• HLS streaming 🔜 -- Download and merge HTTP Live Streaming videos
• Media extraction 🔜 -- Extract and download media from websites (like yt-dlp)
• Resource sniffer 🔜 -- Detect downloadable resources from web pages
• Browser extension 🔜 -- Intercept and manage downloads directly from your browser
• AI-driven discovery 🚧 -- Find download links from natural language queries using an LLM agent
• MCP server 🔜 -- Expose Ketch capabilities as tools for AI agents via Model Context Protocol

Getting Started

Embed in Your App

Add the SDK to your Kotlin Multiplatform project:

// build.gradle.kts
dependencies {
  implementation("com.linroid.ketch:core:<latest-version>")
  implementation("com.linroid.ketch:ktor:<latest-version>")
}

Start downloading:

val ketch = Ketch(
  httpEngine = KtorHttpEngine(),
  config = DownloadConfig(
    maxConnectionsPerDownload = 4,
    maxConcurrentDownloads = 3,
  )
)

val task = ketch.download(
  DownloadRequest(
    url = "https://example.com/large-file.zip",
    directory = "/path/to/downloads",
  )
)

// Observe progress
task.state.collect { state ->
  when (state) {
    is DownloadState.Downloading -> {
      val p = state.progress
      println("${(p.percent * 100).toInt()}%  ${p.bytesPerSecond / 1024} KB/s")
    }
    is DownloadState.Completed -> println("Done: ${state.filePath}")
    is DownloadState.Failed -> println("Error: ${state.error}")
    else -> {}
  }
}

See Installation for version catalog setup, optional modules (SQLite persistence, Kermit logging, remote client), and the full API reference.

Download the App

Download the latest apps from GitHub Releases:

CLI / Server

Install the native CLI to run Ketch as a daemon on your server:

curl -fsSL https://raw.githubusercontent.com/linroid/Ketch/main/install.sh | bash

Then start the daemon:

# Start the server with REST API + web UI on port 8642
ketch server

# Download a file directly
ketch https://example.com/file.zip

# Use a TOML config file
ketch server --config /path/to/config.toml

Supported platforms: macOS (arm64), Linux (x64, arm64), Windows (x64). See the CLI documentation for all commands, flags, and config file reference.

How It Works

1. Resolve -- Query the download source (HEAD request for HTTP) to get size, range support, identity headers
2. Plan -- If ranges are supported, split the file into N segments; otherwise use a single connection
3. Queue -- If max concurrent downloads reached, queue with priority ordering
4. Download -- Each segment downloads its byte range concurrently and writes to the correct file offset
5. Throttle -- Token-bucket speed limiter controls bandwidth per task and globally
6. Persist -- Segment progress is saved to TaskStore so pause/resume works across restarts
7. Resume -- On resume, validates server identity (ETag/Last-Modified) and file integrity, then continues

Documentation

• Architecture -- Modules, dependency graph, download pipeline, and multi-backend design
• API Reference -- Installation, module interfaces, configuration, error handling, and logging
• Logging -- Logging system and configuration
• CLI -- Command-line interface for downloads and running the daemon

Contributing

Contributions are welcome! Please open an issue to discuss your idea before submitting a PR. See the code style rules for formatting guidelines.

License

Apache-2.0

Built with Claude Code by Anthropic.