SCIOS

Your Own Personal AI Research Assistant

An intelligent, locally-run academic agent designed to automate literature exploration, continuously monitor research fields, and assist in academic writing and data analysis.

🌟 Features

• Deep Research (Topic Exploration) Generate professional, concise academic reports for any given topic. SCIOS intelligently routes queries to the most relevant academic databases, retrieves and synthesizes papers, and provides structured insights.

• Field Tracking (Topic Monitoring) Periodically track specific research fields or keywords for new developments. SCIOS runs automated scheduled tasks to fetch the latest papers and delivers summarized daily/weekly briefs directly to your dashboard and email.

• Interactive Academic Assistant A powerful AI agent equipped with a local sandbox workspace. It can help you search for literature, analyze experimental data, and even write and compile LaTeX documents autonomously.

📸 Screenshots

Explore Mode

Generate structured topic exploration reports with core concepts, key scholars, must-read papers, and trend analysis.

Assistant Mode

Use the interactive assistant to search papers, edit LaTeX files, and compile PDFs directly in the local workspace.

Monitor Mode

Subscribe to topics and receive periodic research briefs with newly published papers and concise summaries.

🧰 Built-in Agent Tools

The Interactive Assistant is equipped with a variety of powerful tools to perform complex tasks:

• Academic Search (search_academic_papers): Search for papers across multiple academic databases.
• Web Search (web_search): Access the internet via Tavily to find the latest news, tutorials, and general knowledge.
• Workspace Operations (read_file, write_file, edit_file, glob_search): Read, create, and precisely edit files within a secure local sandbox.
• Persistent Shell (run_bash_command): Execute shell commands, manage files, and install dependencies in a persistent bash session.
• Python REPL (run_python_code): Execute Python code for data analysis, plotting, or testing algorithms.
• LaTeX Compiler (compile_latex): Automatically compile .tex files into PDF documents and fix compilation errors.
• Data Parser (parse_csv_log): Quickly extract and analyze metrics from experimental CSV logs.

📚 Supported Academic Sources

SCIOS integrates a hybrid intelligent routing mechanism to query the most appropriate sources for your topic. Supported sources include:

• ArXiv (Computer Science, Physics, Math)
• PubMed & PMC & EuropePMC (Biomedical, Life Sciences)
• bioRxiv & medRxiv (Biology and Medicine Preprints)
• Semantic Scholar (General academic graph)
• Crossref & OpenAlex (Broad scholarly metadata)
• CORE (Open access research papers)
• dblp (Computer Science bibliography)
• DOAJ (Directory of Open Access Journals)

---

🚀 Getting Started

SCIOS is split into a Python backend (FastAPI) and a Next.js frontend. Below are the instructions to set up and run the project.

Prerequisites

• Python >= 3.10
• Node.js >= 18.x
• uv (Recommended Python package manager)
• pdflatex (Optional, required only if you want the Assistant to compile LaTeX)

1. Backend Setup

# Navigate to the backend directory
cd backend

# Copy the environment template
cp .env.example .env

Configuration (.env)

You must configure the API keys in your .env file for SCIOS to function properly:

• LLM_API_KEY: API key for your selected provider.
• LLM_BASE_URL: API base URL. Keep the default for OpenAI; set it for OpenAI-compatible providers.
• LLM_MODEL: Model name in LiteLLM format.
• TAVILY_API_KEY: API key for Tavily Web Search.
• (Optional) Add other specific source keys like CORE_API_KEY, DOAJ_API_KEY, or your email for Unpaywall/Crossref to enhance search stability.
• (Optional) Configure SMTP_* variables if you want to receive monitoring reports via email.

Multi-model provider examples (via LiteLLM)

# OpenAI
LLM_BASE_URL=https://api.openai.com/v1
LLM_MODEL=gpt-4o
LLM_API_KEY=<OPENAI_API_KEY>

# Anthropic Claude
LLM_BASE_URL=
LLM_MODEL=anthropic/claude-3-5-sonnet-20241022
LLM_API_KEY=<ANTHROPIC_API_KEY>

# Google Gemini
LLM_BASE_URL=
LLM_MODEL=gemini/gemini-1.5-pro
LLM_API_KEY=<GEMINI_API_KEY>

# DeepSeek
LLM_BASE_URL=https://api.deepseek.com/v1
LLM_MODEL=deepseek-chat
LLM_API_KEY=<DEEPSEEK_API_KEY>

Run the Backend

Instead of using development hot-reloading, run the backend using standard FastAPI commands:

# Install dependencies
uv sync

# Run the backend server
uv run fastapi run src/main.py --host 0.0.0.0 --port 8000

The backend API will be available at http://localhost:8000. API documentation is at http://localhost:8000/docs.

2. Frontend Setup

Open a new terminal window to start the Next.js frontend.

# Navigate to the frontend directory
cd frontend

# Install dependencies
npm install

# Build the project for production
npm run build

# Start the production server
npm run start

The frontend interface will be accessible at http://localhost:3000.

---

💡 How to Use

1. Deep Research: Open http://localhost:3000, enter a complex academic query (e.g., "Reinforcement Learning from Human Feedback in LLMs"), and let the agent retrieve, synthesize, and format a comprehensive report for you.
2. Monitoring: Navigate to the "Monitor" tab, add a research field, and SCIOS will automatically track and compile daily/weekly literature briefs.
3. Assistant: Engage with the Interactive Assistant. Ask it to "Search for the latest papers on graph neural networks, summarize the top 3, write a brief LaTeX introduction about them, and compile it to PDF." Watch it plan, execute tools, and fix errors autonomously in the workspace.