cherry-studio 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.
cherry-studio
Cross-platform desktop chat client for many cloud and local LLM providers
screenpipe
Records screen and audio locally, makes it searchable for you and your agents
Verdict
cherry-studio ranks higher, 80 to 75. Of the 5 score parts we measure for both, cherry-studio leads on 1, screenpipe on 1 and 3 are even.
- Adoptioncherry-studio leads by 33 points, 91 to 58: a wide gap.
- FreshnessEven at 100.
- Maintenancescreenpipe leads by 11 points, 97 to 86.
- Easy to runEven at 50.
- Agent-readyEven at 85.
Pick cherry-studio if you are
Individuals wanting one desktop client for many LLM providers and local models
- Supports cloud providers plus local Ollama and LM Studio in one client
- Runs the same prompt against multiple models in a single conversation
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 | cherry-studio | screenpipe |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 91, widely used | 58, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 86, healthy | 97, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 85, ready | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 52.8k | 22k |
| License | AGPL-3.0 (copyleft) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | TypeScript | Rust |
| Docker | No | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
cherry-studio
Cherry Studio is a desktop app for Windows, macOS and Linux that talks to cloud providers such as OpenAI, Gemini and Anthropic, and to local models through Ollama and LM Studio. It includes 300+ preset assistants, simultaneous multi-model chats, document handling (text, images, Office, PDF), WebDAV backup and an MCP server integration. An Enterprise Edition with admin backend and shared knowledge bases is sold separately.
Who it is for: Individuals wanting one desktop client for many LLM providers and local models
Strengths
- Supports cloud providers plus local Ollama and LM Studio in one client
- Runs the same prompt against multiple models in a single conversation
- Installs as a desktop app with no environment setup, on Windows, Mac and Linux
- 300+ preconfigured assistants plus custom assistant creation
Weaknesses
- AGPL-3.0; commercial use outside AGPL terms needs a separate license
- Admin backend, team knowledge base and access control are Enterprise-only
- Plugin system, OCR, TTS and ASR are listed as roadmap items, not shipped
- No Docker or web deployment; desktop only, so no multi-user server in Community Edition
- GPU optional
- Needs Ollama or LM Studio (optional, for local models), WebDAV (optional, for backup)
- Models: OpenAI, Gemini, Anthropic, Ollama, LM Studio
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
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