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Self-host Paperless-AI next

v2026.10.02

AI document tagging for Paperless-ngx with OCR rescue

ReleaseOpen SourceMIT
Updated9 hours ago
LanguageJavaScript
Setup difficultyModerate
ThemeDark mode
Stars
264
RAM
~512 MB
Port
3000 (Web UI)
Requires
AI provider (OpenAI, Ollama, Azure, or custom), Paperless-NGX
Docker
admonstrator/zettelrobbe
Install with Docker Compose
Zettelrobbe dashboard in dark mode showing KPI tiles, task runner, document type chart, entities and token usage widgets

What is Paperless-AI next?

Zettelrobbe is a fork of Paperless-AI that automatically classifies documents in Paperless-ngx using AI. It connects to a Paperless-ngx instance and uses a chosen AI provider — OpenAI, Ollama, Azure OpenAI, or any OpenAI-compatible endpoint — to analyze new documents and assign tags, correspondents, document types, titles, and custom fields. Key capabilities include an OCR rescue queue that re-processes poorly scanned documents using Mistral OCR or local vision models, a duplicates page that finds and merges similar tags and correspondents with AI assistance, a history page with one-click restore of original metadata, manual tagging for on-demand processing, and a simplify-tags feature to consolidate tag vocabularies. The web UI includes MFA login, OIDC single sign-on support, server-side pagination for large archives, and a guided 7-step installer. It runs as a single Docker image (~500-700 MB) with a bundled SQLite database for history and user accounts, and requires a running Paperless-ngx instance.

Should you self-host Paperless-AI next?

Paperless-AI next scores 49/100 on selfhost.directory (how scoring works) and ranks #31 of 46 in Document Management. It has 264 GitHub stars.

It shipped 21 stable releases in the past year. The latest, v2026.10.02, came out on 22 October 2026. New versions land roughly every 8 days.

Setup is moderate: expect some configuration beyond a single command. An official Docker image is available. It is MIT-licensed and written mostly in JavaScript.

Its closest alternative on selfhost.directory is PaperCortex (35/100, 31 stars). Paperless-AI next scores higher overall. See Paperless-AI next vs PaperCortex side by side.

Features

  • Automatic AI document tagging
  • Custom fields extraction
  • Duplicate detection and merging
  • External API enrichment
  • Guided installer
  • History and one-click restore
  • Local AI operation
  • Manual tagging
  • MFA login
  • Multi-Provider AI Support
  • OCR rescue queue
  • OIDC Single Sign-On
  • Rate Limiting
  • Server-side pagination and tag caching
  • Simplify tags

Paperless-AI next README

How to install Paperless-AI next

Install Paperless-AI next with Docker Compose

Environment variables
PAPERLESS_API_URL, PAPERLESS_API_TOKEN, AI_PROVIDER, OPENAI_API_KEY/OPENAI_MODEL or OLLAMA_API_URL/OLLAMA_MODEL, JWT_SECRET, PAPERLESS_AI_PORT=3000, COOKIE_SECURE_MODE, TRUST_PROXY

services: zettelrobbe: image: admonstrator/zettelrobbe:latest container_name: zettelrobbe restart: unless-stopped ports: - "3000:3000" volumes: - data:/app/data

volumes: data:

Requires COOKIE_SECURE_MODE=never for local HTTP access without TLS. Reverse proxy with HTTPS recommended for production.

Paperless-AI next releases & version history

FAQ

Does this support a specific niche AI provider?

If it's OpenAI-compatible, it probably works. Otherwise, open an issue and the author may implement it.

Why should I use this instead of the original Paperless-AI?

If the original works for you, stay there. Zettelrobbe offers better performance, improved AI and OCR, and a more polished experience.

What if I find a bug?

The author welcomes pull requests from all contributors and is happy to learn from code reviews.

Why was RAG (semantic search/chat) support removed?

The RAG subsystem required a secondary Python container with ChromaDB, adding massive image sizes, high RAM requirements, and architectural overhead. The project now focuses on core document tagging.

Is this project stable if it's vibe-coded?

The author uses it daily for actual documents and has a strong incentive to keep it stable. Code is reviewed and tested before merging.

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