PaperPilot

A Fully Open-Source, AI-Native Paper Reading & Management Platform

English | 中文

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This project is developed based on the open-source project Resophy. Special thanks to the original author for their interesting creation.

📖 Introduction

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PaperPilot is a next-generation research assistant designed to streamline your academic workflow. By integrating advanced AI capabilities with a robust document management system, PaperPilot helps you discover, read, understand, and manage research papers more efficiently than ever before.

Whether you are tracking the latest ArXiv preprints or deep-diving into complex PDFs, PaperPilot acts as your intelligent co-pilot.

Note: This project was built using Trae Coding Agent via Vibe Coding, leveraging two models (Gemini-3-Pro-Preview for feature development and GPT-5.2 for bug fixes and performance optimization). It is truly incredible for someone like me who hasn't written modern Web frontend code 🤯 (the last time I wrote Web code was manually coding HTML and CSS during my undergraduate years).

✨ Key Features

📚 Smart Paper Management

• Seamless Upload: Drag & drop PDF uploads with automatic metadata extraction.
• Organization: Custom categories, folders, and full-text search.
• Zotero Integration: One-click import from Zotero RDF libraries.
• Reading Heatmap: Visualize your reading habits with a GitHub-style contribution graph.

🤖 AI-Powered Reading Assistant

• AI Translation: Generate pixel-perfect English-to-Chinese (and other languages) translations using BabelDOC, preserving original layout and charts.
• AI Interpretation: Deep analysis of papers using MinerU (PDF-to-Markdown) and LLMs to generate structured summaries (Abstract, Methods, Experiments, Conclusions).
• Chat with Paper: Interactive Q&A with your documents to clarify concepts and details.

📡 Daily ArXiv Radar

• Automated Tracking: Schedule daily fetches from specific ArXiv categories (e.g., cs.CV, cs.AI).
• Smart Filtering: Filter papers by keywords, institution weights, and more.
• AI Summarization: Automatically generate concise summaries for new arrivals.
• Offline Capable: Works even without LLM connections (skips summary/institution details).

📸 Feature Showcase

📡 Daily ArXiv Tracking

Automated daily paper fetching with AI summaries to keep you updated.

🤖 AI Interpretation & Chat

Deep full-text analysis and interactive Q&A to bridge language and understanding gaps.

📚 Management & Configuration

Efficient reading list management and flexible system configuration.

🔐 Secure Authentication & Access Control

Supports user authentication for secure private access and public network deployment.

🛠️ Tech Stack

• Backend: Python 3.10+, Flask
• Frontend: HTML5, CSS3, Vanilla JS (Responsive)
• Database: SQLite (Metadata), Supabase (Optional Auth)
• AI Core:
  • MinerU (High-fidelity PDF parsing)
  • BabelDOC (Document Translation)
  • OpenAI-compatible LLM Interface

🚀 Installation

We recommend using uv for fast and reliable dependency management.

Prerequisites

• Python 3.10 or higher
• uv package manager

Steps

1. Clone the Repository

   git clone https://github.com/flyflypeng/PaperPilot
   cd PaperPilot

2. Initialize Environment

   uv venv
   source .venv/bin/activate  # Linux/macOS
   # .venv\Scripts\activate   # Windows

3. Install Dependencies

   Option A: Standard (Client-only) Suitable if you use external APIs for AI tasks.

   uv pip install -e ".[local]"

   Option B: Full Server (Local AI) Includes dependencies for local MinerU and VLM inference.

   uv pip install -e ".[server]"

4. Supabase Auth (Optional) PaperPilot’s login/sign-up is powered by Supabase Auth (Email + Password). When enabled, the frontend uses supabase-js in the browser to obtain a session token and attaches Authorization: Bearer <access_token> to /api/* requests; the backend validates the token via Supabase /auth/v1/user. If SUPABASE_URL and SUPABASE_ANON_KEY are not configured, auth is disabled by default: the login overlay is hidden, and /api/* endpoints do not require an Authorization header, so you can enter the management UI directly.

   1. Create a Supabase project and get API values
   • Create a project at https://supabase.com/
   • In the Supabase dashboard, open Project Settings → API
   • Copy Project URL as SUPABASE_URL
   • Copy Project API keys → anon public as SUPABASE_ANON_KEY
   2. Enable Email auth
   • Go to Authentication → Providers
   • Enable Email (Email/Password)
   • For local/private deployments, you can disable email confirmations in Authentication → Settings to avoid requiring email verification after sign-up
   3. Configure redirect URLs (important)
   • Go to Authentication → URL Configuration
   • Set Site URL to your site origin, for example:
   • Local: http://localhost:7191
   • Production: https://your-domain.com
   • Add allowed callback URLs in Redirect URLs (at least include your site root), for example:
   • http://localhost:7191/
   • https://your-domain.com/
   4. Enable auth in PaperPilot Copy and edit environment variables in the project root:

   cp .env.example .env
   # Edit .env and fill in SUPABASE_URL and SUPABASE_ANON_KEY

   Restart the server. If configured correctly, you will see the login/sign-up entry on the page.

   Security notes

   • Use only the anon public key; never put service_role keys into .env or ship them to the browser
   • This project injects SUPABASE_URL and SUPABASE_ANON_KEY into pages for browser-side login, which is expected

5. Run the Application

   Then start the application:

   python app.py

   Access the web interface at http://localhost:7191 (default port).

   Custom Launch Arguments: app.py supports the following command-line arguments for custom configuration:

   Argument	Default	Description
   --papers-dir	./papers	Path to the papers directory (absolute or relative)
   --host	0.0.0.0	Server listening address
   --port	7191	Server listening port
   --debug	False	Enable debug mode (for development)

   Typical Configuration Examples:

   • Specify Data Storage Location (useful for mounted data volumes):

     python app.py --papers-dir /mnt/data/my_papers

   • Change Server Port (if the default port is occupied):

     python app.py --port 8080

   • Allow Local Access Only (for enhanced security):

     python app.py --host 127.0.0.1

⚙️ Configuration

PaperPilot is designed to be configurable directly from the Web UI.

Agentic Settings

Navigate to the Settings tab to configure:

• LLM Provider: Set your API Key, Base URL, and Model Name (e.g., GPT-4, Qwen, DeepSeek).
• MinerU: Choose between Local instance or Cloud API.

Daily ArXiv

Configure your research interests:

• Categories: Select ArXiv categories to monitor.
• Keywords: Define keywords for filtering and highlighting.
• Schedule: Set the automatic fetch interval.

⚠️ Important Notes

• Multi-User Support: The current version of PaperPilot is designed for individuals or small teams and does not yet fully support multi-tenancy. While it supports authentication via Supabase, all users share the same backend configuration and paper library. It is recommended to deploy in a private network or trusted environment.
• BabelDOC Translation: The English-Chinese parallel translation feature based on BabelDOC has high memory consumption and a long processing time. It is recommended to use this feature primarily for papers that require intensive reading.

🗺️ Roadmap

Contributions are welcome! This roadmap is a living document and will evolve with user needs and available time.

Near-Term (Next)

• Reading annotations: highlights, comments, bookmarks, and one-click quote snippets
• Paper chat improvements: grounded answers with page/paragraph/snippet references
• Daily ArXiv rules upgrade: keyword combinations, exclusions, regex
• Deployment & ops: Docker Compose, automated backup/restore, health checks

Mid-Term (Mid)

• Semantic search: local embeddings index (optional vector store) with cross-library search
• Personal knowledge base: turn notes/summaries into a queryable research log (topics/timeline)
• Job queue & progress center: unified queue for translation/interpretation/indexing with retries and priorities

Long-Term (Future)

• Multi-tenancy: isolate libraries and settings per user/team
• Paper recommendations & graph: citation-network and reading-behavior signals
• Collaborative workspace: shared folders, team annotations, access control
• Multimodal understanding: structured extraction for figures/equations/tables and searchability
• Mobile/PWA: offline reading and cross-device sync

Performance & UX (Ongoing)

• Faster PDF rendering, page cache, and on-demand loading for long documents
• Incremental indexing for global search (avoid full rescans)
• Configuration validation and one-click diagnostics (LLM/MinerU/BabelDOC)
• Observability: job timings, failure reasons, and basic metrics

📄 License

This project is licensed under the CC BY-NC 4.0 License. See the LICENSE file for details.

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Made with ❤️ by the PaperPilot Team