ChatPaper Guide

Overview

ChatPaper is an open-source tool that leverages large language models to automatically summarize, search, and analyze academic papers from arXiv. It addresses a fundamental challenge in modern research: the overwhelming volume of new publications makes it nearly impossible for researchers to keep up with developments in their fields through manual reading alone.

The tool connects to the arXiv API to retrieve papers based on keyword queries, then uses LLM capabilities to generate structured summaries covering research motivation, methodology, key findings, and limitations. This enables researchers to rapidly triage large batches of papers and identify the most relevant ones for detailed study.

With over 19,000 GitHub stars, ChatPaper has become a widely adopted tool in the research community. It supports multiple LLM backends and offers both command-line and web-based interfaces, making it accessible to researchers with varying levels of technical expertise.

Installation and Setup

Clone the repository and install dependencies:

git clone https://github.com/kaixindelele/ChatPaper.git
cd ChatPaper
pip install -r requirements.txt

Configure your LLM API access by setting environment variables:

# For OpenAI API
export OPENAI_API_KEY=$OPENAI_API_KEY

# Optional: use a custom API endpoint
export OPENAI_BASE_URL=$OPENAI_BASE_URL

Alternatively, edit the configuration directly in the settings file to specify your preferred model and API parameters. The tool supports OpenAI models as well as compatible alternatives.

Verify the installation by running a test query:

python chat_paper.py --query "transformer attention mechanism" --max_results 3

Core Features

Automated Paper Search and Summarization: ChatPaper queries arXiv based on your research interests and generates concise, structured summaries for each paper:

# Search for recent papers on a topic
python chat_paper.py \
  --query "graph neural networks drug discovery" \
  --max_results 10 \
  --sort "Relevance" \
  --language "en"