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Documentation Index

Fetch the complete documentation index at: https://hubify.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

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Frequently Asked Questions

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General

Hubify Labs is a Scientific Discovery Platform, an IDE-like environment for running experiments, managing multi-agent AI teams, writing papers, and publishing research. Think Cursor for research instead of code.
Researchers, scientists, and research engineers who want to:
  • Run GPU-powered experiments with automated QC
  • Use AI agents to accelerate analysis and paper writing
  • Publish research with a public lab site
  • Manage reproducibility infrastructure
It works for any research domain: cosmology, machine learning, biology, physics, and more.
Hubify Labs is a hosted service. The web UI and API run on our infrastructure. GPU compute is provisioned through your own RunPod (or Modal) account, so you control your compute costs directly.
Three equivalent surfaces:
  • Web UI at hubify.com, full-featured research IDE
  • Desktop App, native macOS application (Windows coming soon)
  • CLI, hubify command for terminal-first researchers
All three share the same features, data, and agents.

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Agents

  • Claude Opus 4.7 / 4.6, primary orchestrator and lead models
  • Claude Sonnet 4.6, fast worker tier
  • Claude Haiku 4.5, cheapest tier for high-volume tasks
  • OpenAI GPT-5.4 and GPT-5.4 Mini, for cross-model review and worker tasks
  • Gemini 3.1 Pro and Gemini 3.1 Flash, for cross-model review and long-context cross-checks
  • Grok 4, for contrarian first-principles audit
  • Sonar Pro (Perplexity), for live citation and fact verification
The orchestrator uses Claude Opus 4.7 by default. Workers can use any supported model. Every paper gets reviewed by five reviewer models from five different labs.
No single model should review its own output. Cross-model review prevents echo chambers by having GPT review Claude’s work, Gemini review GPT’s work, Grok question both, and Sonar live-fact-check the citations. This catches model-specific biases and improves research quality.
Yes. You provide your own API keys for OpenAI, Google, xAI, and Perplexity. Hubify never stores these on our servers, they are configured locally in your CLI or browser.

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Compute

GPU costs are billed directly by your compute provider (RunPod). Hubify does not mark up compute costs. Typical rates:
  • H100: ~$2.49/hr
  • H200: ~$3.89/hr
  • A100: ~$1.64/hr
Set budget limits with hubify pod budget --monthly 500.
Yes. Connect your existing RunPod account with an API key. Hubify manages pod lifecycle but billing goes through your RunPod account.
No. Many features work without GPU compute: agent chat, paper writing, knowledge base, lab site, task management. GPU is only needed for running experiments on pods.

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Data and Privacy

  • Lab metadata (experiment configs, task queues, agent settings), Hubify servers
  • Experiment outputs (chain files, figures, results), your compute provider’s storage, synced to Hubify
  • Papers, stored in your lab, compiled on pods
  • Chat messages, stored in your lab for continuity
Yes. Export everything with:
hubify lab export my-lab --output ./backup.tar.gz
This includes experiments, papers, knowledge base, figures, and configuration.
Yes. Public lab sites include SEO metadata, structured data for Google Scholar, and sitemaps. Private labs are not indexed.

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Papers

All papers use revtex4-2 (Physical Review D style) by default. This is the standard format for physics journals. Other templates can be configured.
Hubify packages your paper into an arXiv-ready tarball with source, figures, and bibliography. You upload this package to arXiv manually. Direct API submission is planned for a future release.