Fixes 15 defects found by a max-effort multi-agent review of the last 6 merged PRs (#388, #390, #391, #392, #393, #394), all adversarially verified before fixing. Install queue + dispatcher (the serious cluster): - features.ts: finalize the installer child exactly once. A failed spawn fires both "error" and "close", and the second event released the file lock and active slot that pump() had just handed to the next queued bundle, letting two pip processes write the same venv concurrently. Outcome recording now happens before pump() so the next bundle's first progress frame cannot race the previous install's bookkeeping. - feature-status.ts: keep failed-install errors in a per-bundle map instead of the single progress slot. With the queue auto-starting the next install, the slot was overwritten within seconds and a failed install vanished without ever surfacing to GET /features. - bridge.ts: scope child lifecycle per process (stopped-children set + request generation tags) instead of an instance-wide shuttingDown flag that the next spawn reset. A stale SIGTERMed child's late close event could record a phantom crash (5 of which permanently disable the dispatcher), null out the freshly spawned child, and reject the new child's pending requests. The request-timeout kill path still counts as a real crash. - install_feature.py: the pre-write disk re-check measured ai_dir's filesystem even when budgeting the cross-filesystem copy that lands on the venv's disk; now each budget is checked against the filesystem the bytes actually land on, so ENOSPC cannot strike mid-write and leave site-packages half overwritten. Behavior regressions: - embed-subtitles: preserve pre-existing subtitle tracks (0:s?) and MKV attachments (0:t?) that the -map 0:v:0/0:a? rewrite silently dropped; data streams stay unmapped on purpose (the actual MPEG remux fix). The new subtitle maps first so the language tag hits the right stream. - usage-survey-overlay: fail closed when the settings fetch fails; the fail-open path rendered the blocking survey against an unhealthy API and soft-locked admins, the lock-out class #392 fixed. - features-store: queued bundles poll instead of each holding an SSE connection (Install All could pin 7 EventSources and exhaust the browser's 6-per-origin HTTP/1.1 limit, hanging the whole app); listenToProgress closes any prior stream and stops any poll before subscribing; installAll skips bundles already installing or queued. Contracts, tests, i18n: - openapi.yaml: add "queued" to the features status enum and document downloadBytes/installedBytes (Schemathesis conformance). - feature-lifecycle e2e: queue transcription (~0.5 GB) instead of ocr (~6 GB) and give the test a budget that covers both install drains (the stacked waits exceeded the old 900s timeout). - docker-compose.qa.yml: parameterize the host port (QA_APP_PORT) so QA_PROJECT_NAME concurrent stacks can actually bind. - compare + watermark-image: restore per-input error attribution ("Invalid first/second image", "Invalid watermark image") lost in the shared-handler migration. - ai-features-section: the "{size} on disk" suffix now goes through i18n; key added to all 21 locales. - watermark-image + content-aware-resize: migrate to the shared inputHandlerFor("image") chain like compare/vectorize/compose, fixing drift in the inline copies (no SVG sanitize, no RAW extension hint, no AVIF probe). Verified: typecheck across 9 workspaces, Biome clean on all changed files, 584 targeted unit tests and 249 integration tests green (including real-ffmpeg embed-subtitles runs). One unit test updated to the new poll-while-queued contract with a single-EventSource assertion. Claude-Session: https://claude.ai/code/session_017mR1HiHaf3a1BmUtrHX4j3
Note
SnapOtter v2.0.0 is the current monorepo version, with 200+ tools across image, video, audio, PDF, and files. For published image channels and GPU variants, see the Docker Tags guide.
Self-hosted file toolkit. 200+ tools across image, video, audio, PDF, and files.
The open-source alternative to Smallpdf, iLovePDF, TinyPNG, CloudConvert, and Otter.ai, in one stack you host yourself.
Stirling-PDF stops at PDFs. ConvertX stops at conversions. SnapOtter runs all five, and your files never leave your server. Edit images, convert video, transcribe audio, repair PDFs, batch your files: one Docker stack, on hardware you own.
Key Features
- 200+ tools across 5 modalities:
- Image (105): resize, crop, compress, convert, watermark, color adjust, beautify screenshots, generate memes, vectorize, GIF tools, find duplicates, passport photos, plus dedicated format converters (JPG to PNG, HEIC to JPG, WebP to PNG, image to PDF, and more). Supports 55+ input formats (including 23 camera RAW formats) and 14 output formats
- Video (57): convert, compress, trim, resize, crop, merge, video-to-GIF, extract audio, stabilize, change FPS, burn/extract subtitles, plus dedicated converters (MOV to MP4, MKV to MP4, MP4 to MP3, and more)
- Audio (27): convert, trim, normalize, volume, fade, pitch shift, silence removal, noise reduction, merge/split, waveform, plus dedicated converters (M4A to MP3, AAC to MP3, OGG to WAV, and more)
- PDF (29): merge, split, compress, convert, protect/unlock, redact, sign, watermark, page numbers, OCR, plus PDF to JPG/PNG/TIFF
- Files (23): CSV/JSON/XML/YAML conversion, CSV merge/split, Excel to CSV, chart maker, ZIP create/extract
- Image editor: Layer-based editor with brushes, shapes, adjustments, filters, curves, and keyboard shortcuts. Runs in your browser, processes on your hardware
- Local AI: Remove backgrounds, upscale images, restore and colorize old photos, erase objects, blur faces, enhance faces, extract text (OCR from images and PDFs), transcribe audio, auto-generate video subtitles, expand canvas, and fix transparency. All on your hardware, no internet required
- OIDC / SSO: Login with Google, GitHub, Okta, or any OpenID Connect provider
- 21 languages: English, Arabic, Chinese (Simplified & Traditional), Dutch, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian, Vietnamese. RTL support for Arabic
- Pipelines: Chain tools into reusable workflows with unlimited steps. Import/export as JSON. Batch process unlimited files at once
- REST API: Every tool available via API with API key auth. Interactive docs at
/api/docs - Self-hosted: one
docker runfor a single-container quick start (embedded Postgres + Redis), or a Postgres 17 + Redis 8 Compose stack for production. No external SaaS dependencies - Multi-arch: Runs on AMD64 and ARM64 (Intel, Apple Silicon, Raspberry Pi)
- Privacy first: Your files never leave your network. Basic analytics help us catch bugs and improve tools -- disable anytime by rebuilding with
SNAPOTTER_ANALYTICS=off(Here's how to do it)
Quick Start
One container, no setup. It starts an embedded Postgres + Redis on loopback and stores data in the SnapOtter-data volume:
docker run -d --name SnapOtter -p 1349:1349 -v SnapOtter-data:/data snapotter/snapotter:latest
For production, run the 3-container Compose stack (app + Postgres 17 + Redis 8). Save this as compose.yaml:
services:
snapotter:
image: snapotter/snapotter:latest
ports: ["1349:1349"]
environment:
DATABASE_URL: postgres://snapotter:snapotter@postgres:5432/snapotter
REDIS_URL: redis://redis:6379
volumes:
- snapotter-data:/data
depends_on: [postgres, redis]
restart: unless-stopped
postgres:
image: postgres:17-alpine
environment:
POSTGRES_USER: snapotter
POSTGRES_PASSWORD: snapotter
POSTGRES_DB: snapotter
volumes: ["snapotter-pgdata:/var/lib/postgresql/data"]
restart: unless-stopped
redis:
image: redis:8-alpine
volumes: ["snapotter-redisdata:/data"]
restart: unless-stopped
volumes:
snapotter-data:
snapotter-pgdata:
snapotter-redisdata:
Then start the stack:
docker compose up -d
Have an NVIDIA GPU? Click here for CUDA acceleration.
Use the GPU Compose file for NVIDIA CUDA-accelerated background removal, upscaling, transcription, and OCR. Intel/AMD iGPU acceleration through VA-API, Quick Sync, or OpenCL is not supported for AI inference today; those systems run AI tools on CPU. See Docker Tags for the GPU Compose example and benchmarks.
Default credentials:
| Field | Value |
|---|---|
| Username | admin |
| Password | admin |
You will be asked to change your password on first login.
For Docker Compose, persistent storage, and other setup options, see the Getting Started Guide. For NVIDIA CUDA acceleration and tag details, see Docker Tags.
Documentation
- Getting Started
- Configuration
- OIDC / SSO
- Deployment
- Supported Formats
- Docker Tags
- REST API
- AI Engine
- Image Engine
- Architecture
- Database
- Developer Guide
- Contributing
- Translation Guide
Contributing
We welcome bug reports, feature ideas, and pull requests. See CONTRIBUTING.md for the full guide, or jump in:
- Open an issue
- Submit a PR
- Join Discord for help and discussion
- Sponsor the project to keep SnapOtter free for everyone
Support SnapOtter
SnapOtter is built and maintained independently with no venture capital or corporate backing. Sponsorships fund infrastructure, keep releases flowing, and ensure the project stays free and open for everyone.
If SnapOtter saves you from paying for cloud file-processing services, consider supporting its development:
License
This project is dual-licensed under the AGPLv3 and a commercial license.
- AGPLv3 (free): You may use, modify, and distribute this software under the AGPLv3. If you run a modified version as a network service, you must make your source code available under the AGPLv3.
- Commercial license (paid): For use in proprietary software or SaaS products where AGPLv3 source-disclosure is not suitable, a commercial license is available. Contact us for pricing and terms.
See LICENSING.md for full details on the open-core boundary between AGPLv3 and commercial code.

