openaireview 0.1.10

AI-powered academic paper reviewer

OpenAIReview

Our goal is provide thorough and detailed reviews to help researchers conduct the best research. See more examples here.

Installation

uv venv && uv pip install openaireview
# or: pip install openaireview

For development:

git clone https://github.com/ChicagoHAI/OpenAIReview.git
cd OpenAIReview
uv venv && uv pip install -e .
# or: pip install -e .

PDF math support (optional)

For math-heavy PDFs, install Marker separately to get accurate LaTeX extraction. Without Marker, PDFs are processed with PyMuPDF which cannot extract math symbols correctly.

# Install Marker CLI in an isolated environment (avoids dependency conflicts)
uv tool install marker-pdf --with psutil

Marker is used automatically when available on PATH. It is most useful for math-heavy PDFs, but runs very slowly without a GPU. For papers with math, we recommend using .tex source, .md, or arXiv HTML URLs instead of PDF when possible — these always produce correct output without needing Marker.

Quick Start

First, set your OpenRouter API key (get one at openrouter.ai/keys):

export OPENROUTER_API_KEY=your_key_here

Or create a .env file in your working directory:

OPENROUTER_API_KEY=your_key_here

Then review a paper and visualize results:

# Review a local file
openaireview review paper.pdf

# Or review directly from an arXiv URL
openaireview review https://arxiv.org/html/2602.18458v1

# Visualize results
openaireview serve
# Open http://localhost:8080

CLI Reference

openaireview review <file_or_url>

Review an academic paper for technical and logical issues. Accepts a local file path or an arXiv URL.

Option	Default	Description
--method	progressive	Review method: zero_shot, local, progressive, progressive_full
--model	anthropic/claude-opus-4-6	Model to use
--output-dir	./review_results	Directory for output JSON files
--name	(from filename)	Paper slug name

openaireview serve

Start a local visualization server to browse review results.

Option	Default	Description
--results-dir	./review_results	Directory containing result JSON files
--port	8080	Server port

Supported Input Formats

• PDF (.pdf) — uses Marker for high-quality extraction with LaTeX math; falls back to PyMuPDF if Marker is not installed
• DOCX (.docx) — via python-docx
• LaTeX (.tex) — plain text with title extraction from \title{}
• Text/Markdown (.txt, .md) — plain text
• arXiv HTML — fetch and parse directly from https://arxiv.org/html/<id> or https://arxiv.org/abs/<id>

Environment Variables

Variable	Default	Description
OPENROUTER_API_KEY	(required)	Your OpenRouter API key
MODEL	anthropic/claude-opus-4-6	Default model

These can be set as environment variables or in a .env file. See .env.example for a template.

Supported Models & Pricing

All models available on OpenRouter are supported — use any model ID via --model. The following models have built-in pricing for accurate cost tracking in the visualization:

Model	Input ($/1M tokens)	Output ($/1M tokens)
anthropic/claude-opus-4-6	$5.00	$25.00
anthropic/claude-opus-4-5	$5.00	$25.00
openai/gpt-5.2-pro	$21.00	$168.00
google/gemini-3.1-pro-preview	$2.00	$12.00

For models not listed above, a default rate of $5.00/$25.00 per 1M tokens is used.

Review Methods

• zero_shot — single prompt asking the model to find all issues
• local — deep-checks each chunk with surrounding window context (no filtering)
• progressive — sequential processing with running summary, then consolidation
• progressive_full — same as progressive but returns all comments before consolidation

Claude Code Skill

A deep-review skill is bundled with the package. It runs a multi-agent pipeline — one sub-agent per paper section plus cross-cutting agents — and produces severity-tiered findings (major / moderate / minor).

Install once:

pip install openaireview
openaireview install-skill

Then in any Claude Code project:

/openaireview paper.pdf
/openaireview https://arxiv.org/abs/2602.18458

Finally, run openaireview serve to see results.

Development

Install with dev dependencies (includes pytest):

uv pip install -e ".[dev]"

Run tests:

pytest tests/

Integration tests that call the API require OPENROUTER_API_KEY and are skipped automatically when it's not set.

Benchmarks

Benchmark data and experiment scripts are in benchmarks/. See benchmarks/REPORT.md for results.