Get This Tool
Maith
Summary
The hardest failure mode in AI-assisted mathematics isn't hallucination — it's not knowing whether you're looking at a proof, a plausible narrative, or a lucky numerical coincidence. Maith is a research workspace built around that exact distinction.
Maith organizes AI exploration of open problems — Riemann Hypothesis, P vs NP, Collatz, Goldbach, and roughly twenty others — into a structured workflow that keeps generated ideas, numerical evidence, and symbolic output in separate lanes, so you can't accidentally treat one as the other. Each conjecture lives in its own directory, which means your lemma dependencies, small-case experiments, and falsification attempts stay auditable rather than buried in a chat thread. The workspace is self-hosted and open-source with no license file published, so production use requires legal review before deployment in institutional settings. There is no API, no autonomous agent loop, and no GUI — this is a code-and-file workflow, not a drag-and-drop canvas.
Bottom line: Pick Maith if you need a reproducible, auditable scaffolding for AI-assisted conjecture exploration on known open problems — but if your work requires formal proof verification integrated into the same system, you will hit the ceiling fast and reach for something like Lean or Coq instead.
Community Performance Report Card
No community ratings yet. Be the first to rate this tool!
Community Benchmarks Community
Sign in to submit a benchmarkNo community benchmarks yet. Be the first to share a real-world data point.
Pros
Sign in to edit- Separates AI-generated ideas from numerical evidence and symbolic output into distinct artifacts, so a plausible narrative never gets mistaken for a proof step during review.
- Pre-structured directories for roughly twenty named open problems ship with the repo, so you start with a scaffold rather than designing your own organizational scheme from scratch.
- Self-hosted and file-based, which means your conjecture work, lemma notes, and experiment outputs stay on your infrastructure — no data leaves to a third-party service.
- Adversarial falsification is built into the workflow design, so small-case counterexample searches and reproducible CAS experiments are first-class activities rather than afterthoughts.
- No proprietary lock-in to a specific AI provider — you wire in your own model or tool, so the workspace survives provider changes without restructuring your research artifacts.
Cons
Sign in to edit- There is no formal proof verification integration: when your workflow requires machine-checked proofs rather than structured human review, Maith offers no path to Lean, Coq, or Isabelle, and teams doing formal verification abandon it for those environments immediately.
- No license file exists in the repository, so institutional or commercial use requires legal clarification before deployment — teams under compliance constraints cannot use it without resolving that gap first.
- The workflow is entirely file-and-code-based with no GUI, which means onboarding any collaborator who is not comfortable in a code environment requires building your own interface layer on top.
- Coverage is limited to roughly twenty pre-structured open problems — researchers working outside that set get no scaffold and must design their own directory conventions, at which point the reproducibility guarantees depend entirely on their own discipline rather than the tool's structure.
Community Reviews
Sign in to write a reviewNo reviews yet. Be the first to share your experience.
About
- Platforms
- GitHub, Python
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-07-22T06:23:08.254Z
Best For
Who it's for
- Mathematicians seeking structured AI assistance on open problems
- Researchers emphasizing reproducibility and adversarial review
- Users building auditable workflows combining AI with formal verification
What it does well
- Exploring open problems like Riemann Hypothesis or P vs NP with AI-generated ideas under verification
- Generating and validating small cases or counterexamples for mathematical conjectures
- Organizing lemmas and proof dependencies while requiring independent checks
- Designing reproducible CAS or numerical experiments for falsification
Discussion Community
Sign in to commentNo discussion yet. Sign in to start the conversation.
Spotted incorrect or missing data? Join our community of contributors.
Sign Up to ContributeCommunity Notes & Tips Community
Sign in to contributeBe the first to contribute. General notes, observations, gotchas, and tips from people who use this tool day-to-day.
Frequently Asked Questions
- Is Maith free?
- Yes — Maith is fully free to use. There is no paid tier.
- Is Maith open source?
- Yes. Maith is open source.
- Can I self-host Maith?
- Yes. Maith supports self-hosting on your own infrastructure.
- What platforms does Maith support?
- Maith is available on: GitHub, Python.
Hours Saved & ROI Stories Community
Sign in to contributeBe the first to contribute. Concrete time/cost savings, with context. e.g. "Cut my code review backlog from 4h to 45m per week."
Curated lists that include this category
Maith is an open-source research workspace from clidey that structures AI-assisted exploration of open mathematical problems. The core workflow is file-and-directory-based: each open problem gets its own folder containing generated ideas, lemma sketches, small-case numerical experiments, and falsification attempts as distinct artifacts. The discipline is explicit — the system is designed so that plausible AI text, CAS output, and symbolic reasoning never get conflated with one another or with proof. You bring your own AI tooling; Maith provides the scaffolding that keeps outputs categorized and independently verifiable.
The differentiating feature is its adversarial review posture. Rather than treating AI output as a draft to be polished, the workspace treats it as a claim to be falsified. The repo ships pre-structured directories for roughly twenty named open problems — ABC conjecture, Birch–Swinnerton-Dyer, Navier-Stokes regularity, Hodge conjecture, and others — so researchers working on established problems get a starting scaffold rather than a blank slate. This is the opposite of a general-purpose AI chat interface.
Maith fits researchers who already know the difference between numerical evidence and proof, and want tooling that enforces that distinction rather than glossing over it. It does not fit teams looking for formal proof verification — there is no integration with Lean, Coq, or Isabelle described in the repository. It also does not fit teams that need a shared collaborative interface or a GUI: the workflow is code-native, self-hosted, and entirely local.
No license file is listed among the repository contents, which means the legal status of the code is unresolved. The repository has no releases or published packages as of the available data. There is no API surface and no agent loop — Maith does not take autonomous actions or call external tools on your behalf.