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Jacquard
Pricing
- Model
- Free
Summary
When most of your code comes out of a model, you have no language-level mechanism to ask what effects it produces or what authority it assumes — you are reading output and hoping. Jacquard was built for exactly that regime.
Jacquard is a small programming language with a surface syntax (.jac files), an OCaml type-checker, a CPS interpreter, and a C-emitting AOT backend — the full stack for running, reviewing, and simulating model-written programs. Its core differentiator is language-level effect tracking: the runtime can surface what a program touches and what authority it claims before you let it run. The Warp tool lets you execute code against multiple simulated or real worlds, which means policy and risk scenarios become testable rather than theoretical. The project is Apache-2.0 licensed with free binaries and a self-hosted install path. This is a research project — the community is small, the ecosystem is thin, and production support does not exist.
Bottom line: Pick Jacquard when you are researching or prototyping a review pipeline for model-written code and need effect visibility the host language cannot give you — abandon it when your team needs a production runtime with a support contract, a plugin ecosystem, or a language that general-purpose contributors already know.
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Pros
Sign in to edit- Language-level effect tracking lets reviewers inspect what authority a model-written program claims before it executes, which means you catch over-privileged code at review time rather than after it runs in production.
- Warp simulation runs the same program against multiple environments, so policy and risk edge cases become testable artifacts rather than thought experiments.
- Apache-2.0 license with self-hosted install path means no vendor dependency and no data leaves your infrastructure — critical when reviewing proprietary or sensitive model output.
- CPS interpreter and C-emitting AOT backend in the same toolchain, so you can prototype in interpreted mode and then compile without switching environments.
- Free and open-source with no paid tier gating any features, so the full capability set is available to a research team without a procurement process.
Cons
Sign in to edit- Jacquard is its own language with its own syntax — any team that wants to review Python, TypeScript, or Go output from a model gets nothing here. Teams with existing codebases in mainstream languages will hit this wall immediately and route around it by staying in their host language's static analysis ecosystem.
- The community is tiny: 73 stars and no open issues or pull requests at the time of scrape. When you hit a bug or an underdocumented behavior, there is no forum, no Stack Overflow tag, and no vendor support line — you read the source or file an issue into the void.
- The C-emitting AOT backend is described as a work in progress in the scrape ('cur' is where the description cuts off). Teams that need a stable compilation target for anything approaching production will find themselves blocked and move to a language with a mature compiler toolchain.
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About
- Platforms
- Linux x86-64, macOS Intel, macOS Apple Silicon
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-07-14T06:25:11.175Z
Best For
Who it's for
- AI-assisted code review and verification
- Effectful program simulation
- Research into ML-written code regimes
What it does well
- Reviewing ML-generated programs for effects and uncertainty
- Running code against multiple simulated or real worlds
- Language-level effect tracking and authority grants
- Testing with Warp for policy and risk scenarios
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Frequently Asked Questions
- Is Jacquard free?
- Yes — Jacquard is fully free to use. There is no paid tier.
- Is Jacquard open source?
- Yes. Jacquard is open source.
- Can I self-host Jacquard?
- Yes. Jacquard supports self-hosting on your own infrastructure.
- What platforms does Jacquard support?
- Jacquard is available on: Linux x86-64, macOS Intel, macOS Apple Silicon.
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Curated lists that include this category
Jacquard addresses the problem of trusting code you did not write: it gives reviewers a language-level view of what a program does, not just what it says. The core workflow is writing or receiving .jac source files, running them through the OCaml checker to surface effects and authority grants, and then executing or simulating them through the CPS interpreter or the C-emitting AOT backend. The Warp tooling runs programs against multiple simulated or real worlds, turning ‘what would this do if the environment looked different’ into a concrete, repeatable test.
The differentiating feature is effect tracking at the language level. Rather than auditing output after the fact, Jacquard’s type system encodes what effects a program is permitted to produce — so a reviewer can inspect authority grants and decide whether to approve them before execution, not after. This is not a linting pass on top of another language; it is baked into the type system and interpreter.
Jacquard fits teams doing research into ML-written code pipelines, academics studying capability and authority models, or engineers prototyping a review layer they plan to build infrastructure around. It does not fit teams that need a stable, supported production runtime. The contributor base is small (the repository shows no open issues or pull requests at time of scrape, and 73 stars), documentation is described as a ‘human-friendly introduction’ rather than a reference manual, and there is no indication of a paid tier or commercial support path.
The project is implemented in OCaml and uses a dune build system. Binaries and an install script are provided. The repository includes a bench directory, a spec, a corpus, and demos — suggesting it is far enough along for experimentation but positioned explicitly as a research artifact, not a production dependency.