coarse

Free, open-source AI academic paper reviewer that outperforms popular paid AI reviewers.

You provide your own API key and pay the LLM provider directly — typically under $2 per review.

Don't want to run it locally? Use the web interface instead.

Quickstart

Get an API key from OpenRouter (free to sign up), then:

uvx coarse-ink review paper.pdf --api-key sk-or-v1-YOUR_KEY

That's it. The review is written to paper_review.md in the current directory.

Prerequisites

coarse requires Python 3.12+. If you don't have uvx, install uv first:

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

uvx runs coarse in a temporary environment with no permanent install. To install permanently:

uv tool install coarse-ink    # or: pip install coarse-ink

Why coarse-ink and not coarse? The bare coarse name on PyPI is held by an unrelated package, so we ship under coarse-ink. The Python import name (import coarse) and the coarse CLI command are unchanged — installing coarse-ink puts both coarse and coarse-ink on your PATH.

Save your API key

To avoid passing --api-key every time, create a .env file in your working directory:

OPENROUTER_API_KEY=sk-or-v1-YOUR_KEY

Or run coarse setup to store keys in ~/.coarse/config.toml.

Use your coding-agent subscription

If you already have a claude, codex, or gemini CLI logged into its subscription, coarse-review routes every pipeline LLM call through the local CLI (claude -p, codex exec, gemini -p) so reviews bill your Claude Max / ChatGPT Pro / Google AI Pro subscription instead of a pay-per-token API. Only the ~$0.05–0.15 Mistral OCR step on PDF sources still uses OPENROUTER_API_KEY (skip it with --pre-extracted paper.md). Non-PDF sources (.tex, .md, .txt, .docx, .html, .epub) extract locally, so they run entirely on your subscription with no OpenRouter key at all.

coarse-review paper.pdf                          # auto-detects claude → codex → gemini
coarse-review paper.pdf --host codex --model gpt-5.6-sol
coarse-review paper.tex                          # .tex/.md/...: no OpenRouter key needed
coarse-review paper.tex --deep-literature-search # deeper Perplexity pass (key required)

Run coarse install-skills to register a /coarse-review skill in each CLI's skills directory, so you can trigger reviews from inside the coding agent itself.

Supported formats

PDF, TXT, Markdown, LaTeX, DOCX, HTML, and EPUB. PDFs use Mistral OCR; other formats use Docling (if installed) with lightweight fallbacks. Only PDF extraction touches OpenRouter — every other format is parsed locally. Install optional format support:

pip install coarse-ink[formats]   # DOCX, HTML, EPUB fallbacks
pip install coarse-ink[docling]   # Docling for PDF/DOCX/HTML/LaTeX

How it works

paper.pdf (or .txt, .md, .tex, .docx, .html, .epub)
  -> Mistral OCR (Docling fallback)      Extract text as markdown
  -> Vision LLM spot-check               Optional QA (auto-triggers on garbled text)
  -> Structure analysis                   Parse sections, detect math content, classify domain
  -> Domain calibration + lit search      Parallel: criteria + Perplexity Pro/optional Deep Research
  -> Overview agent                       Single macro-level review pass
  -> Completeness agent                   Structural-gap pass merged into overview
  -> Section agents + proof verification  Parallel: 15-25 detailed comments; math sections get adversarial proof check
  -> Cross-section synthesis              Results vs discussion consistency check
  -> Editorial filter                     Primary deduplication, contradiction, and quality pass
  -> Legacy crossref/critique fallback    Only used if the editorial pass fails
  -> Quote verification                   Fuzzy-match quotes against paper text (stricter for math)
  -> Synthesis                            Render final paper_review.md

The pipeline extracts text, classifies the paper's domain and structure, then generates domain-specific review criteria and searches for related literature (via Perplexity Sonar Pro Search, with arXiv fallback). Pass --deep-literature-search to use Sonar Deep Research for a broader, multi-step source search. It normally adds about $0.30 to the buffered estimate and can take a few minutes; the standard pass remains the default. A single overview pass produces macro-level feedback, completeness can add structural gaps, and section agents run in parallel with adversarial proof verification for math-heavy sections. A conditional cross-section pass checks whether discussion claims are supported by formal results, and an editorial pass is the primary deduplication and quality gate. All quotes are programmatically verified against the source text, with stricter thresholds for math content.

Model selection

Pass any litellm-compatible model string with --model:

coarse review paper.pdf --model openai/gpt-4o
coarse review paper.pdf --model anthropic/claude-sonnet-5
coarse review paper.pdf --model gemini/gemini-3-flash-preview

The package default is qwen/qwen3.7-plus routed via OpenRouter. The coarse.ink form defaults to Claude Opus 5 and also features Sonnet 5, GPT-5.6, Gemini 3.6 Flash, Kimi K3, Grok 4.5, and other current frontier models. Any model supported by litellm works. With only OPENROUTER_API_KEY set, all models (including vision QA) route through OpenRouter automatically.

Use --cheap to automatically select the cheapest model for which you have an API key.

API keys

Only OPENROUTER_API_KEY is needed. This covers everything: review agents, literature search, and PDF extraction (Mistral OCR is always routed through OpenRouter's file-parser plugin, so there's no separate key for it). For step-by-step setup instructions, see the API key guide.

Set your OpenRouter per-key spend limit to at least $10 (ideally matching the max_cost_usd default of $10). If the limit is hit mid-review the run will fail and you'll need to raise the limit and resubmit. Cost estimates shown before each review are approximate (~30% buffer) — they're a guide, not a hard ceiling, so leave yourself headroom.

For direct provider access to chat models (lower latency, separate billing), you can set the provider-specific key instead:

Cost

coarse estimates cost before running and asks for confirmation. The estimate includes a 30% buffer to account for variance.

Actual costs can run materially above the estimate on complex papers depending on model reasoning depth, editorial filtering behavior, and proof-verification chains for math-heavy sections. The built-in buffer is conservative, not a ceiling. Make sure your OpenRouter per-key spend limit has headroom above the estimate.

The default spending cap is $10 per review (max_cost_usd in config). Use --yes to skip the confirmation prompt. Use --no-qa to skip the post-extraction quality check (vision LLM). Scanned PDFs are supported via Docling's built-in OCR (pip install coarse-ink[docling]).

You can also load API keys from any .env file with --env-file path/to/.env.

Output format

The review is written as a structured markdown file:

# Paper Title

**Date**: MM/DD/YYYY
**Domain**: social_sciences/economics
**Taxonomy**: academic/research_paper
**Filter**: Active comments

---

## Overall Feedback

4-6 macro issues with titles and body paragraphs.

---

## Detailed Comments (N)

20+ numbered comments, each with a verbatim quote from the paper and
actionable feedback.

Python API

from coarse import review_paper
from pathlib import Path

review, markdown, paper_text = review_paper(
    pdf_path=Path("paper.pdf"),  # accepts any supported format
    model="openai/gpt-4o",       # optional; uses config default if omitted
)

print(markdown)                         # full review as markdown string
print(review.detailed_comments[0].feedback)  # access structured fields

review_paper returns a (Review, str, PaperText) tuple: the structured Review model, rendered markdown, and the extracted paper text. The pdf_path parameter accepts any supported file format (PDF, TXT, MD, TeX, DOCX, HTML, EPUB).

Configuration

Settings are stored in ~/.coarse/config.toml:

default_model = "qwen/qwen3.7-plus"
vision_model = "gemini/gemini-3-flash-preview"
extraction_qa = true
max_cost_usd = 10.0

[api_keys]
openai = "sk-..."
anthropic = "sk-ant-..."

Run coarse setup for an interactive prompt that writes this file.

Development

git clone https://github.com/Davidvandijcke/coarse.git
cd coarse
uv sync --extra dev
uv run pytest tests/ -v

Contributing

See CONTRIBUTING.md for development setup, project structure, and guidelines.

Version

1.9.0

License

MIT