deep-live-cam 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.
deep-live-cam
Real-time webcam face swap from a single source image
screenpipe
Records screen and audio locally, makes it searchable for you and your agents
Verdict
deep-live-cam ranks higher, 76 to 75. Of the 5 score parts we measure for both, deep-live-cam leads on 1, screenpipe on 2 and 2 are even.
- Adoptiondeep-live-cam leads by 40 points, 98 to 58: a wide gap.
- FreshnessEven at 100.
- Maintenancescreenpipe leads by 9 points, 97 to 88.
- Easy to runEven at 50.
- Agent-readyscreenpipe leads by 85 points, 85 to 0: a wide gap.
Pick deep-live-cam if you are
Creators and streamers wanting local real-time face swapping on a webcam
- Live webcam mode plus image and video file processing from one GUI
- Execution providers for CPU, CUDA, CoreML, DirectML and OpenVINO
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 | deep-live-cam | screenpipe |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 58, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 88, healthy | 97, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 0, none | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 97.2k | 22k |
| License | AGPL-3.0 (copyleft) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Python | Rust |
| Docker | No | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
deep-live-cam
Deep-Live-Cam is a Python desktop GUI that swaps a face from one source image onto a webcam feed, video or image, using ONNX models (inswapper_128_fp16, gfpgan-1024). It supports mouth masking, mapping different faces to multiple subjects, and CPU, CUDA, CoreML, DirectML and OpenVINO execution providers. A paid pre-built edition with extra features is sold on the project website.
Who it is for: Creators and streamers wanting local real-time face swapping on a webcam
Strengths
- Live webcam mode plus image and video file processing from one GUI
- Execution providers for CPU, CUDA, CoreML, DirectML and OpenVINO
- Mouth mask and face mapping for multiple subjects in one frame
- Needs only a single source image, no per-face training
Weaknesses
- Manual install needs Python, ffmpeg, CUDA/cuDNN pinning; README says not for beginners
- CPU-only mode is slower; live preview takes 10-30 seconds to appear
- Command-line arguments are marked unmaintained in the README
- insightface models are limited to non-commercial research use
- GPU optional
- Needs Python 3.11-3.14, ffmpeg, onnxruntime, CUDA Toolkit 12.8 (NVIDIA), tkinter, OBS (for streaming output)
- Models: inswapper_128_fp16.onnx, gfpgan-1024.onnx, ONNX
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
More in Desktop AI apps
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.