Research Hub

research-hub

A workspace on your computer for researchers to organize papers, evidence, and drafts, with AI-assisted proposals you review.

繁體中文 · Workspace guide · Setup guide · Developer architecture

[PyPI] [Python 3.10+] [MIT license]

Why research-hub?

Research is spread across papers, notes, analysis, and manuscripts. A new paper can change a question; an updated file can make a conclusion or section worth revisiting. research-hub gives researchers and AI assistants a local workspace to record those connections, hand off tasks, and review proposals before accepting them.

• Keep context: bring questions, sources, evidence, results, and manuscripts into one project rather than leaving them only in a conversation.
• Track changes: register file paths and a file fingerprint (hash) to help detect changes, then inspect work that may need review when inputs change.
• Keep decisions human: AI proposals require review; a local delivery requires separate approval.

[Concept diagram: research questions connect literature, evidence, analysis, manuscript proposals, and human review; changed inputs return work for review.]

Install and get started

Try the local workspace

Start with the workspace below. If you only want to try the older published literature dashboard, see the separate option below.

You need Python 3.10+ and Git. These commands install a specific source revision and create teaching fixtures in a new or empty workspace-demo directory. The source workspace is separate from the PyPI release: a package version does not establish that the installed wheel contains current source features.

git clone -c core.autocrlf=false https://github.com/WenyuChiou/research-hub.gitcd research-hubgit fetch origin c1ab382e1bc4bfdb1d5df6a230d1ab4509a50d2agit checkout --detach c1ab382e1bc4bfdb1d5df6a230d1ab4509a50d2a

Choose the block for your system. They call the virtual environment's Python directly, without activation or changing PowerShell execution policy. Dependency installation needs network access.

macOS / Linux

python3 -m venv .venv./.venv/bin/python -m pip install -e ../.venv/bin/python -m research_hub project demo --root ./workspace-demo --json./.venv/bin/python -m research_hub serve --workspace --root ./workspace-demo

Windows PowerShell

One Windows test environment using the earlier 14ddc070 source encountered WinError 5 during demo creation; its cause is not yet known. See verification and environment notes.

py -3 -m venv .venv.\.venv\Scripts\python.exe -m pip install -e ..\.venv\Scripts\python.exe -m research_hub project demo --root ./workspace-demo --json.\.venv\Scripts\python.exe -m research_hub serve --workspace --root ./workspace-demo

Open http://127.0.0.1:8765/app/. The demo contains two teaching projects. In the Projects dropdown, choose DEMO: three floating-point summation cases (summation-demo) or DEMO: floating-point reading and review outline (review-demo), then open Literature to see the literature page. These are teaching inputs, not research findings, and need no account. Stop the foreground server with Ctrl+C when finished.

For your own files, go directly to bring your own project; the guide’s earlier installation pin is historical.

python3 -m venv .dashboard-venv./.dashboard-venv/bin/python -m pip install research-hub-pipeline==1.2.0mkdir -p "$HOME/research-hub-preview"RESEARCH_HUB_ROOT="$HOME/research-hub-preview" ./.dashboard-venv/bin/research-hub dashboard --sample

Work in the workspace

[Historical workspace UI capture showing local demo project navigation and research records.]

Historical demo capture, reviewed 2026-09-08; the current interface may differ.

Your editor remains responsible for changes to original files; the workspace registers their paths and hashes. A resolvable identifier does not prove a source supports a claim: check the evidence and scientific judgments yourself.

Discover literature

In a compatible AI tool (an AI host) that can load the research-hub skill and has search and reading tools, start with a concrete request, for example:

Find literature on LLMs simulating human decisions; include original links, relevance, versions, and limits on available full text.

Then ask to focus on economic decisions and compare human baselines and evaluation methods. Review the sources, reading coverage, and open questions before deciding what to do next. Search snippets are not full-text reading. This research approach can be used independently of Hub; Zotero, Obsidian, NotebookLM, and Hub storage are optional. The Hub visual workspace and saved-result view do require Hub.

The native reader can organize supplied research summaries, source indexes, and bibliographies, and attach a PDF explicitly supplied by the user. The PDF is copied unchanged for download; that does not mean it was parsed, translated, verified, or read in full. The reader can display caller-supplied translations alongside original observations, labeled unverified; it does not generate translations or verify their accuracy. See the native reader export guide for the supplied-translation format.

See AI Research Skills, the AI host support matrix, and MCP/REST integration. The optional saved-result contract describes what a Hub handoff stores and its platform boundaries.

Write, review, and deliver

[Concept diagram: a researcher prepares a task, uses a portable AI handoff or a connected Codex prose worker, checks the proposal, and explicitly accepts or requests changes.]

The optional writing setup explains how to verify and configure the adapter. Connected Codex receives bounded text excerpts, not binary document contents. Use your editor for Word formatting, LaTeX compilation, and final document changes. Accepted proposals can be packaged into a local ZIP after separate approval; this does not authorize publication or cloud upload. Workspace approval does not automatically protect existing direct-write CLI, MCP, dashboard, or cloud tools. See boundaries and recovery.

Optional integrations

• Zotero: manage references; the connection requires an API key and library ID.
• Obsidian: organize notes in a local vault, or begin with local files.
• NotebookLM: create source-based briefings; it requires browser sign-in and a working session. See setup and troubleshooting.
• AI hosts: connect through MCP/REST or portable skills; MCP requires the optional [mcp] extra.

Choose integrations as needed and run ./.venv/bin/python -m research_hub doctor (macOS/Linux) or .\.venv\Scripts\python.exe -m research_hub doctor (Windows PowerShell) to inspect them individually. Setup or installation does not itself verify a service. The setup guide covers extras and credentials; see also local file import, EZproxy, and the live smoke checklist.

Documentation and contributing

• Workspace guide · Workspace architecture · Capability baseline · Verification history
• CLI reference · MCP tools · Stable API · File formats · Workflow runtime · OpenWiki
• Recorded dashboard demo · Full-resolution video · MCP flow diagram
• Contributing · Changelog · Release process · MIT license