| name | literature-review-tools |
| description | Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文（用于「文献综述工具选型」与「一键安装/运行」）。 |
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:
python3 scripts/litrun.py doctor — check toolchain + which API keys are already set.python3 scripts/litrun.py info <id> — confirm what the tool needs (entry, required env).- 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).
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.- 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 corpustopic-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
- Identify which stage of the lit-review workflow the user is on (search → read → extract → synthesize → screen → cite-check → write/review).
- Match it to a category below and recommend the ⭐ editor's pick first, then 1–2 alternatives.
- 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.
- 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)
| Category | Editor's pick ⭐ | When |
|---|
| All-in-one research agents & skills | academic-research-skills | Claude Code user wanting research→write→review→revise, with integrity/citation gates |
| Deep research & auto-survey | STORM / gpt-researcher | Topic → cited survey / report / related-work |
| Autonomous science (idea→paper) | AI-Scientist(-v2) / AutoResearchClaw | Fully automated discovery: lit + hypotheses + experiments + writing |
| Literature Q&A / RAG | paper-qa (PaperQA2) | Citation-backed answers over a PDF corpus |
| Systematic review & screening | ASReview | Active-learning screening of thousands of abstracts (PRISMA) |
| MCP servers | zotero-mcp / arxiv-mcp-server | Wire papers into Claude / Cursor / Cline |
| Zotero / Obsidian integration | zotero-gpt | Chat with your library inside your reference manager |
| PDF → structured extraction | MinerU / docling / marker | Turn PDFs into clean Markdown/JSON for LLMs |
| Citation graphs & API clients | scholarly / pyalex | Citation-network analysis; scripting academic DBs |
| Writing & peer-review assistants | open_reviewer / ai-peer-review | Draft, polish, and pre-submission review |
| Awesome lists | Awesome-Auto-Research-Tools | Browse the whole landscape |
Decision table (map need → recommendation)
| User's need | Recommend |
|---|
| Claude Code, end-to-end research→paper | academic-research-skills (most complete, #1 in space) |
| Generic "research this topic for me" agent | GPT Researcher / STORM |
| Wiki/survey-style long-form with citations | STORM / Co-STORM |
| Fully autonomous "idea → submittable paper" | AI-Scientist-v2 / AutoResearchClaw |
| Cited Q&A over many PDFs | PaperQA / PaperQA2 |
| Rigorous PRISMA systematic review | ASReview or prismAId |
| PDF → clean Markdown for an LLM | MinerU / Docling / marker |
| Lit capabilities in an MCP client | paper-search-mcp / zotero-mcp |
| Chat with library inside Zotero | zotero-gpt / PapersGPT |
| AI pre-review before submission | open_reviewer / ai-peer-review |
| Just want to browse the landscape | The Awesome lists section |
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.
