dsh-desktop vs screenpipe
Two of the top projects in Desktop AI apps, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
dsh-desktop
Native Windows and macOS desktop client for DeepSeek Harness, built on plugins
screenpipe
Records screen and audio locally, makes it searchable for you and your agents
Verdict
Both score 88; dsh-desktop ranks higher on stars, 34,369 to 22,199. Of the 4 score parts we measure for both, dsh-desktop leads on 1, screenpipe on 1 and 2 are even.
- Adoptiondsh-desktop leads by 13 points, 79 to 66.
- FreshnessEven at 100.
- Maintenancescreenpipe leads by 11 points, 98 to 87.
- Agent-readyEven at 85.
Pick dsh-desktop if you are
Windows and macOS users who want DeepSeek Harness as an installable desktop app
- Installers for Windows x64 and macOS Universal; the app starts and manages the local Harness service
- Runs a pinned, unmodified upstream version, with desktop features added as plugins
Pick screenpipe if you are
Developers and teams wanting searchable screen history as agent context
- Event-driven capture uses the accessibility tree first, OCR only as fallback
- Local REST API on port 3030, raw SQLite access and an MCP server
| What we compare | dsh-desktop | screenpipe |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 79, popular | 66, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 87, healthy | 98, healthy |
| Agent-ready | 85, ready | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 34.4k | 22.2k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | TypeScript | Rust |
| Docker | No | No |
| GPU | Not stated | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
dsh-desktop
Wraps the pinned upstream DeepSeek Harness local Web UI, host service and plugin system in a desktop app with window, tray, terminal, auto-update and work profiles. The desktop shell is itself a DSH plugin. It includes a plugin market and phone remote control (2.0.9 and later). Installers cover Windows x64 and macOS Universal, and no Node.js install is needed.
Who it is for: Windows and macOS users who want DeepSeek Harness as an installable desktop app
Strengths
- Installers for Windows x64 and macOS Universal; the app starts and manages the local Harness service
- Runs a pinned, unmodified upstream version, with desktop features added as plugins
- Built-in plugin market and iOS/Android remote control from 2.0.9
- Web service listens on loopback only by default
Weaknesses
- No Linux build listed
- Optional LAN access has no authentication; anyone on the network can operate the machine
- Independent community project, not affiliated with DeepSeek; tied to the pinned upstream version
- Update checks send version, channel and a persistent random installation ID
- Needs DeepSeek Harness (bundled, pinned)
- Models: unknown
screenpipe
Screenpipe captures screen content (accessibility tree, with OCR as fallback), system audio and microphone with local Whisper transcription, and stores it in a local SQLite database. It exposes a localhost REST API on port 3030, an MCP server, and pipes, which are scheduled agents defined in markdown that query this history. Newer versions also target teams, with central config and per-pipe data permissions.
Who it is for: Developers and teams wanting searchable screen history as agent context
Strengths
- Event-driven capture uses the accessibility tree first, OCR only as fallback
- Local REST API on port 3030, raw SQLite access and an MCP server
- Pipes are markdown files with per-pipe app, window and endpoint permissions
- Runs on macOS, Windows and Linux; works with local models such as Ollama
Weaknesses
- Source-available license: commercial use requires a paid commercial license
- Continuous capture of everything on screen raises privacy and compliance concerns
- Capture alone takes about 5-20% CPU and 0.5-3 GB RAM; storage use is unspecified
- Some features depend on paid plans; Linux capture varies by desktop environment
- RAM ≥ 8 GB
- GPU optional
- Needs Node.js (npx CLI), Ollama (optional local LLM), Deepgram (optional cloud transcription)
- Models: Whisper Large-V3-Turbo, Deepgram, Ollama, OpenAI-compatible servers
- port 3030
- README: alternative to Otter.ai, Granola
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