openaireview 0.2.5

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 fast PDF processing (requires MISTRAL_API_KEY):

uv pip install openaireview[mistral]

For development:

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

Updates

• --max-pages and --max-tokens to limit input size and save OCR cost
• Mistral OCR and DeepSeek OCR as optional PDF engines (pip install openaireview[mistral])
• openaireview extract subcommand for two-stage OCR + review workflow
• Multi-provider routing: OpenRouter, OpenAI, Anthropic, Gemini, Mistral (--provider)
• Table and figure extraction from arXiv HTML (tables as markdown)
• pymupdf4llm + GNN layout as default PDF fallback (replaces raw PyMuPDF)
• Mobile-responsive visualization UI
• Collapsible resolved comments in viz
• Claude Code skill (/openaireview) with multi-agent pipeline

PDF parsing engines (optional)

PDF extraction quality matters — math symbols, tables, and reading order all affect review quality. Four engines are supported, tried in order:

Engine	Install	Best for	Notes
Mistral OCR	pip install openaireview[mistral] + set MISTRAL_API_KEY	Best overall quality, math, tables	Cloud API, ~$0.001/page
DeepSeek OCR	pip install openaireview[deepseek] + local backend	Privacy-sensitive docs	Local model via Ollama/vLLM
Marker	uv tool install marker-pdf --with psutil	Math-heavy PDFs (offline)	Slow without GPU
pymupdf4llm	(included)	Fallback, always available	No math symbol support

The engine is auto-detected: if MISTRAL_API_KEY is set, Mistral OCR is tried first; then DeepSeek (if installed); then Marker (if on PATH); finally pymupdf4llm. You can force a specific engine with --ocr:

openaireview review paper.pdf --ocr mistral
openaireview review paper.pdf --ocr marker

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 an OCR engine.

Quick Start

First, set an API key for any supported provider:

export OPENROUTER_API_KEY=your_key_here   # OpenRouter (supports all models)
# or
export OPENAI_API_KEY=your_key_here       # OpenAI native
# or
export ANTHROPIC_API_KEY=your_key_here    # Anthropic native
# or
export GEMINI_API_KEY=your_key_here       # Google Gemini native
# or
export MISTRAL_API_KEY=your_key_here     # Mistral native (also enables Mistral OCR)

Or create a .env file in your working directory (see .env.example).

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
--provider	(auto)	LLM provider: openrouter, openai, anthropic, gemini, mistral
--ocr	(auto)	PDF OCR engine: mistral, deepseek, marker, pymupdf
--max-pages	(all)	Only process first N pages of a PDF (saves OCR cost)
--max-tokens	(all)	Truncate input text to first N tokens before review
--output-dir	./review_results	Directory for output JSON files
--name	(from filename)	Paper slug name

openaireview extract <file>

Run OCR extraction only and save as markdown with metadata frontmatter. Useful for a two-stage workflow: extract first, then review the markdown.

Option	Default	Description
-o, --output	<file>.md	Output markdown path
--ocr	(auto)	PDF OCR engine: mistral, deepseek, marker, pymupdf

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) — auto-selects best available engine (Mistral OCR > DeepSeek > Marker > pymupdf4llm); see PDF parsing engines
• 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		OpenRouter API key (supports all models)
OPENAI_API_KEY		OpenAI native API key
ANTHROPIC_API_KEY		Anthropic native API key
GEMINI_API_KEY		Google Gemini native API key
MISTRAL_API_KEY		Mistral API key (also used for Mistral OCR)
MODEL	anthropic/claude-opus-4-6	Default model
REVIEW_PROVIDER	(auto)	Force a specific LLM provider

Set one API key. The provider is auto-detected from whichever key is set (priority: OpenRouter > OpenAI > Anthropic > Gemini > Mistral). 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.

Related Resources

• AI-research-feedback
• OpenEvalProject

License

MIT