litai-research 0.1.3.dev202508091542

AI-powered academic paper synthesis tool

LitAI

AI-powered literature review assistant that understands your research questions and automatically finds papers, extracts insights, and synthesizes findings - all through natural conversation.

Why LitAI?

LitAI accelerates your research by turning hours of paper reading into minutes of focused insights:

• Find relevant papers fast: Natural language search across millions of papers
• Extract key insights: AI reads papers and pulls out claims with evidence
• Synthesize findings: Ask questions across multiple papers and get cited answers
• Build your collection: Manage PDFs locally with automatic downloads from ArXiv

Perfect for:

• Literature reviews for research papers
• Understanding a new field quickly
• Finding solutions to technical problems
• Discovering contradictions in existing work
• Building comprehensive reading lists

💡 Tip: Use the /questions command to see research unblocking questions organized by phase - from debugging experiments to contextualizing results.

Installation

Prerequisites

• Python 3.11 or higher
• API key for OpenAI or Anthropic

Install with pip or uv

# Using pip
pip install litai-research

# Using uv (faster)
uv pip install litai-research

Configuration

Set your API key as an environment variable:

# For OpenAI
export OPENAI_API_KEY=sk-...

# For Anthropic
export ANTHROPIC_API_KEY=sk-ant-...

Advanced Configuration

Configure LitAI using the /config command:

# Show current configuration
/config show

# Set provider and model
/config set llm.provider openai
/config set llm.model gpt-4o-mini

# Reset to auto-detection
/config reset

Configuration is stored in ~/.litai/config.json and persists across sessions.

Quick Start

Launch the interactive interface:

litai

Two Ways to Use LitAI

→ Natural Language Mode (Recommended)

Just ask questions and let AI handle everything. Follow this workflow:

litai
# Step 1: Find papers (builds search results)
> Find papers about vision transformers

# Step 2: Add papers from search results to your collection
> Add the "Attention Is All You Need" paper to my collection

# Step 3: Analyze papers in your collection
> What are the key findings in the BERT paper?

# Step 4: Synthesize across your collection
> How does ViT compare to CNN methods in my papers?

The AI will automatically:

• Search for relevant papers
• Download and read PDFs
• Extract key insights
• Synthesize findings across multiple sources
• Provide citations for all claims

→ Command Mode

For precise control over specific operations:

View Command Reference

# Search for papers
> /find attention mechanisms for computer vision

# Add papers to your collection (by search result number)
> /add 1 3 5

# List papers in your collection
> /list

# Extract key points from a paper
> /distill 1

# Synthesize multiple papers
> /synthesize Compare transformer and CNN architectures

# Clear the screen
> /clear

Features

Paper Discovery

• Natural language search via Semantic Scholar API
• View abstracts and metadata before adding to collection

Paper Management

• Build a local collection of research papers
• Automatic PDF download from ArXiv
• Duplicate detection and organized storage

AI-Powered Analysis

• Extract key claims with supporting evidence
• Automatic section references and quotes
• Generate comprehensive literature reviews
• Proper inline citations (Author et al., Year)

Natural Language Interface

• Chat-based interaction for complex queries
• Context-aware conversations about your research
• Multi-paper analysis and comparison

Data Storage

LitAI stores all data locally in ~/.litai/:

• litai.db - SQLite database with paper metadata and extractions
• pdfs/ - Downloaded PDF files
• logs/ - Application logs for debugging

Development

Project Structure

litai/
├── src/litai/
│   ├── cli.py          # Command-line interface
│   ├── database.py     # Data persistence layer
│   ├── llm.py          # LLM client (OpenAI/Anthropic)
│   ├── papers.py       # Paper search and management
│   ├── pdf.py          # PDF processing
│   ├── synthesis.py    # Literature synthesis
│   └── tools.py        # Extraction tools
├── tests/              # Test suite
├── docs/               # Documentation
└── pyproject.toml      # Project configuration

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=litai

# Run specific test file
pytest tests/test_papers.py

FAQ

Why do paper searches sometimes fail?

Semantic Scholar's public API can experience high load, leading to search failures. If you encounter frequent issues:

• Wait a few minutes and try again
• Consider requesting a free API key for higher rate limits: Semantic Scholar API Key Form

License

This project is open source and available under the MIT License.

Acknowledgments

• Built with Semantic Scholar API
• Powered by OpenAI/Anthropic language models

Support

• Report issues: GitHub Issues
• Logs for debugging: ~/.litai/logs/litai.log