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What you can do with Bootloops

BootLoops is a Python harness of certified scientific computing tools and house engines that LLM agents can run for exact and high-precision physics and quantitative science.

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BootLoops-ai/bootloops on GitHub

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What do you want to do?

4 use cases

  • Validate a fresh BootLoops clone before trusting it

    Run the validation command to confirm the toolkit packages pass their own batteries on your machine.

    View prompt
    In the bootloops repository, install the core Python dependencies, then run `python3 run_selftests.py --par 8`. Report how many packages come back green, which 3 stop with a named error, and what each error says is missing. Verify that no failure is caused by a Python version below 3.12.

  • Reproduce the one-loop box with the Landau Alphabet engine

    Run the Landau Alphabet test to reproduce the solver's reference results for a one-loop box integral.

    View prompt
    In the bootloops repository, run `python3 tools/landau-alphabet/test_landau_alphabet.py`. Report whether it reproduces the solver's reference results for the one-loop box and how long it took. Verify that the reported values match the reference values in the test, not values you computed yourself.

  • Have an agent map a precision problem to the right tools

    Point an agent at the tools index so it proposes a plan for your problem and waits for your approval.

    View prompt
    Read tools/README.md and the tool guides it points to in the bootloops repository. For the problem <describe the integral, dataset, or published number>, propose a plan listing the instruments that apply, or state that none do, with the acceptance test each result must pass. Verify that every tool you name appears in tools/README.md.

  • Audit a draft's prose and bibliography with the skills

    Install the skills repository so an agent can check claims, quoted numbers, and bibliography entries.

    View prompt
    Using the BootLoops skills repository at ../skills, apply the prose-editing and bibliography-verification skills to my draft <path to draft>. Report each unsupported claim, each quoted number that cannot be traced to a source, and each bibliography entry that fails verification. Verify that you did not add any claim that is absent from the draft.

How to use Bootloops

  1. Check you have what it needs: Git to clone the repository; Python 3.12; Python packages mpmath, sympy, numpy, python-flint, and pytest.
  2. Open BootLoops-ai/bootloops on GitHub and read the README and license.
  3. Clone the repository: git clone https://github.com/BootLoops-ai/bootloops (inferred, check the README)
  4. Enter the directory: cd bootloops (inferred, check the README)
  5. Install the core dependencies: pip install mpmath sympy numpy python-flint pytest (inferred, check the README)
  6. Run the validation command: python3 run_selftests.py --par 8

These steps come from our top use case, Validate a fresh BootLoops clone before trusting it, which also has a copy-paste agent prompt.

Bootloops alternatives · Best AI repos for Research

About this repo

  • Who it is forResearchers and physicists who need checkable numerical or symbolic results, and the agents they direct, should use this when a computation must be trusted.
  • Why it is hot
  • LanguagePython
  • Sourcegithub.com/BootLoops-ai/bootloops · Back to Hot repos

Requirements

  • Git to clone the repository
  • Python 3.12
  • Python packages mpmath, sympy, numpy, python-flint, and pytest
  • Julia only for packages that ship Julia components
  • An LLM agent such as Claude Code, Codex, or Cursor, or any agent you prefer
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Questions about Bootloops

What is Bootloops?

BootLoops is a Python harness of certified scientific computing tools and house engines that LLM agents can run for exact and high-precision physics and quantitative science. It is an open-source project on GitHub: https://github.com/BootLoops-ai/bootloops.

Who is Bootloops for?

Researchers and physicists who need checkable numerical or symbolic results, and the agents they direct, should use this when a computation must be trusted.

What can I do with Bootloops?

4 use cases so far, for example: Validate a fresh BootLoops clone before trusting it; Reproduce the one-loop box with the Landau Alphabet engine; Have an agent map a precision problem to the right tools. Each has steps and a copy-paste agent prompt.

What do I need to run Bootloops?

Git to clone the repository; Python 3.12; Python packages mpmath, sympy, numpy, python-flint, and pytest; Julia only for packages that ship Julia components.

What are alternatives to Bootloops?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is Bootloops safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.