Literature Review Tools

Literature Review Tools — Select & Run

A curated, use-case-organized catalog of the strongest open-source AI tools for literature review — plus a launcher that actually installs and runs the top ones. Covers: end-to-end research agents, deep-research / auto-survey generators, autonomous "idea→paper" systems, citation-backed RAG over PDFs, PRISMA screening, MCP servers, Zotero/Obsidian integrations, PDF→structured extraction, citation graphs, and paper-writing / peer-review assistants.

Full source of truth (README, always current star counts): https://github.com/brycewang-stanford/lit-review-agent-tools

Two modes

• Recommend — user asks "what should I use to …". Route with the tables below; cite the catalog for details.
• Run — user asks to install / run / use a specific tool ("turn this PDF into Markdown with MinerU", "ask PaperQA2 about these papers", "set up the arXiv MCP server"). Drive scripts/litrun.py via Bash — do not hand the user raw pip commands to copy.

Run mode — how to drive scripts/litrun.py

The launcher installs each supported tool into its own venv under ~/.lit-review-tools/ (uses uv if present, else python -m venv) and reads API keys from one shared ~/.lit-review-tools/.env. Machine-readable recipes: recipes/recipes.json.

Typical flow when the user wants to use a tool:

1. python3 scripts/litrun.py doctor — check toolchain + which API keys are already set.
2. python3 scripts/litrun.py info <id> — confirm what the tool needs (entry, required env).
3. If a required key is missing, ask the user for it, then litrun.py env --set KEY=VALUE (never echo the value back in full).
4. python3 scripts/litrun.py run <id> -- <tool args> — installs on first use, then runs. For PDF tools pass the real file path; e.g. run mineru -- -p paper.pdf -o ./out -b pipeline.
5. For MCP servers, don't "run" them — litrun.py mcp <id> prints the client config block to register in Claude Code / Cursor.

Commands: list [--category C] [--kind K] · info <id> · doctor · env [--set K=V] · install <id> · run <id> -- <args> · mcp <id> [--storage PATH] [--client claude|cursor] · ui <id>.

Runnable ids by kind:

• python-cli (auto install+run): mineru, marker, docling (PDF→Markdown) · paper-qa (cited Q&A) · asreview (PRISMA screening UI)
• python-script (bundled, auto install+run): arxiv-fetch (search arXiv & download PDFs, no key)
• python-lib (install + run example): gpt-researcher, storm (deep research; need API keys) · scholarly, pyalex (API clients)
• mcp-server (install + mcp config): arxiv-mcp-server, paper-search-mcp, zotero-mcp

For gpt-researcher and storm, litrun.py ui <id> clones the repo and launches the full web UI (GPT Researcher → FastAPI at :8000; STORM → Streamlit at :8501). These are long-running servers — launch them with a background Bash call and tell the user the URL. gpt-researcher's UI needs OPENAI_API_KEY + TAVILY_API_KEY set first (litrun writes them into the repo's .env); STORM takes its keys in the app sidebar.

Chained pipelines

For multi-tool tasks, prefer a named workflow over hand-wiring steps: litrun.py workflow list then litrun.py workflow run <id> [--input PATH] [--query "..."] [--question "..."] [--max N]. Built-ins:

• pdf-to-markdown — a PDF/folder → clean Markdown (MinerU)
• pdf-corpus-qa — a folder of PDFs → citation-backed answer (PaperQA2)
• pdf-md-then-qa — convert to Markdown and answer a question over the corpus
• topic-to-pdfs — arXiv query → download top-N PDFs (arxiv-fetch, no key)
• topic-to-review — arXiv query → download PDFs → citation-backed answer (PaperQA2). The end-to-end "retrieve then review" pipeline; no MCP client needed. Needs OPENAI_API_KEY for the QA step.

Add --dry-run first to show the exact resolved step commands without executing — good for confirming paths with the user before a heavy run. Workflows fail fast if a required API key is missing.

Guardrails: installs and downloads happen under the user's home and hit the network — for a heavy first install (marker/docling pull in PyTorch) say so before running. Never fabricate API keys. If a run fails, show the real error rather than claiming success. Paths in this file (scripts/…, recipes/…) are relative to this skill's directory.

Recommend mode — how to route

1. Identify which stage of the lit-review workflow the user is on (search → read → extract → synthesize → screen → cite-check → write/review).
2. Match it to a category below and recommend the ⭐ editor's pick first, then 1–2 alternatives.
3. For anything beyond the top pick — full star counts, every project in a category, or a category not summarized here — read reference/catalog.md. Do not guess project names or URLs; pull them from the catalog.
4. Give a one-line "why this one" tied to the user's constraint (Claude Code vs. standalone, open vs. commercial, privacy/local, medical, etc.). If the pick is a runnable id above, offer to install/run it.

⚡ 30-second picker

Use Claude Code, want end-to-end research→paper ──────────▶ academic-research-skills ⭐
Want AI to research a topic → cited report ───────────────▶ GPT Researcher / STORM
Want fully autonomous "idea → submittable paper" ────────▶ AI-Scientist-v2 / AutoResearchClaw
Citation-backed Q&A over a pile of PDFs ──────────────────▶ PaperQA2
Rigorous PRISMA review (thousands of abstracts) ─────────▶ ASReview / prismAId
Clean Markdown from PDFs to feed an LLM ─────────────────▶ MinerU / Docling / marker
Lit capabilities inside Claude / Cursor (MCP) ───────────▶ paper-search-mcp / zotero-mcp
Chat with your library inside Zotero ────────────────────▶ zotero-gpt / PapersGPT
Pre-submission AI peer review ───────────────────────────▶ open_reviewer / ai-peer-review

Categories (top pick per category)

Decision table (map need → recommendation)

Notes & caveats

• Open-source is prioritized. Commercial/closed tools (Elicit, Consensus, Scite, SciSpace, Research Rabbit, Connected Papers) are listed for reference only — see the catalog's commercial section.
• Star counts drift. The catalog's numbers are periodic GitHub-API snapshots — treat as rough popularity signals, not exact. For live numbers, point the user at the repo.
• Match the constraint, not just the task. Privacy/local → local-deep-research; medical → medsci-skills / paperai; Codex instead of Claude → academic-research-skills-codex.

Full catalog with every project, star count, and one-line description: reference/catalog.md.