litsurvey

A research assistant for the literature: ask a question, get a cited state-of-the-art survey; state a claim, get a prior-art verdict; give a paper title, get everything that cites it. It runs on your laptop, from the command line or a small browser page, and uses the LLM you already have, whether that is a Claude, ChatGPT or Gemini subscription, a local model, or an API key. One Python package, no runtime dependencies, Python 3.10 or newer.

What it does

LLM-driven (the main draw)

• research "question": the model breaks the question into sub-questions, runs searches across six scholarly indexes, walks the citation graph of the central papers, reads one or two arXiv papers in full, and writes a survey organised by theme with numbered citations, open problems, and an honest coverage caveat. Every citation comes from a real search result, and the report ends with a log of every query that was run.
• novelty "claim": the same machinery aimed at one claim: closest prior work, what is new versus known, a verdict (clearly novel / incremental / substantially anticipated / cannot determine) with confidence, and citations a reviewer can use.
• Runs on any of: your subscription's command-line agent (Claude Code, Codex CLI, Gemini CLI; no API key), a local model through Ollama (nothing leaves the machine), or a cloud API key (OpenAI, OpenRouter, Anthropic, any OpenAI-compatible server).

Search and lookup (no LLM)

• Fused search over OpenAlex, Semantic Scholar, arXiv, TechRxiv, Research Square and the IACR ePrint archive, de-duplicated, sortable by relevance, citations or year.
• For any paper, by title or id: who cites it (most-cited first), what it cites, similar papers from a recommender, its full record and BibTeX, and a legal open-access copy.
• Export to BibTeX, RIS, CSV, JSON or Markdown for Zotero, Overleaf, Mendeley or a spreadsheet.
• A history of every completed run, shared by the command line and the browser page, with the same downloads.

Install

From GitHub (works today):

pipx install git+https://github.com/udaykdk/litsurvey
# or
pip install git+https://github.com/udaykdk/litsurvey

From PyPI, after the first release is published:

pipx install litsurvey

Or clone and run without installing: python3 -m litsurvey --help.

Then, optional but recommended: get a free Semantic Scholar API key (a one-minute form, see docs/api-keys.md) and store it with litsurvey init. Without it everything works, only slower. litsurvey doctor checks the setup and prints a RESULT: verdict.

Examples

A state-of-the-art survey, written by the model from live search results:

litsurvey research "uncertainty quantification methods for physics-informed neural networks" --out uq-survey.md

A prior-art check on a claim, phrased as a generic topic (never a sentence from a confidential manuscript):

litsurvey novelty "adaptive sampling of collocation points in physics-informed neural networks" --out claim.md

Choosing where the model runs:

litsurvey research "..." --backend cli --model claude          # Claude Code, under your Claude Pro/Max plan
litsurvey research "..." --backend ollama --model qwen3:30b    # local; nothing leaves the machine
litsurvey research "..." --backend openai --base-url https://openrouter.ai/api --model anthropic/claude-sonnet-4.5

Search and lookup, with titles instead of ids:

litsurvey search "physics informed neural networks inverse problems" --year-from 2022 --sort citations
litsurvey cites "Superior thermal conductivity of single-layer graphene"   # picks the paper, lists the most-cited citers
litsurvey related "Embedding deep learning in inverse scattering problems"
litsurvey paper "KAN: Kolmogorov-Arnold Networks" --out ref.bib          # BibTeX for a paper you know by title
litsurvey oa "10.1016/j.cma.2022.114823"                                 # legal free PDF
litsurvey web                                                            # the same, in your browser

When a title matches several papers, litsurvey shows the candidates and asks; scripts and agents get the list and use --pick N.

Typical times: a search takes a few seconds; novelty and research take two to fifteen minutes depending on the model.

What needs an LLM, and what leaves your machine

For confidential work (a manuscript under review, an unpublished result) use a local model and phrase the input as generic topic terms. Completed runs, including inputs and results, are stored under ~/.litsurvey/ on your disk. LLM reports are aids, not verified reviews: check the cited paper and DOI before using any claim. Details in docs/confidentiality.md.

Documentation

• docs/cli.md: every command, option and output format, with sample output
• docs/web.md: the browser interface and the History tab
• docs/use-cases.md: seven scenarios with the exact commands, from student discovery to confidential peer review
• docs/llm-integration.md: LLM backends and models; using litsurvey as a tool from Claude Code, Codex and other agents
• docs/confidentiality.md: what leaves the machine, per mode and backend
• docs/api-keys.md: getting and storing the Semantic Scholar key; running without one
• CONTRIBUTING.md: reporting bugs, running tests, adding a source

Coverage, and Google Scholar

Six sources are searched by default: OpenAlex (about 250 million works), Semantic Scholar (about 220 million), arXiv, and three preprint portals: TechRxiv and Research Square (through Crossref, by their DOI prefixes) and the IACR Cryptology ePrint Archive (which has no search API, so litsurvey reads its search page; if that page changes, only that source goes quiet). --sources restricts the set; crossref (all DOI-registered works) can be added but overlaps OpenAlex. Together they cover most of what Google Scholar shows, except some grey literature (technical reports, theses, standards). Google Scholar has no API and its terms of service forbid automated access, so litsurvey does not scrape it; litsurvey search --scholar prints the matching Google Scholar URL for a manual comparison, and the web page has the same link.

Data attribution

Results come from OpenAlex (CC0), Semantic Scholar (Semantic Scholar Open Data Platform, Allen Institute for AI), arXiv (thank you to arXiv for use of its open access interoperability), Crossref, the IACR Cryptology ePrint Archive and Unpaywall. If you publish work that used these results, please credit them.

Author

Designed by Uday Khankhoje, Department of Electrical Engineering, IIT Madras; implemented with Anthropic Claude. Questions and bug reports: the issue tracker.

Credits

The banner of the web page uses slivers of an aerial beach photograph by Lance Asper on Unsplash.

Citing

See CITATION.cff. GitHub shows a "Cite this repository" button on the project page.

License

MIT. See LICENSE.