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Using Typesafe.AI to generate diff reviews.
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Typesafeai Review
A code reviewer built as AI-powered software rather than an LLM agent: code slices the diff, Jev answers narrow typed questions per hunk, and code composes the verdict and findings.
- Stars
- 0
- Language
- Python
- Reading time
- 3 min
- Published by
- Community
- Author
- rbalch
- Last verified
- Sep 2026
About this project
A code reviewer built as AI-powered software, not an LLM agent. Code runs the checks, slices the diff, asks Jev (TypeSafe's System One model) narrow typed questions per hunk, and composes verdict, score and findings in code. Ships as a standalone dev dependency (uv run review --worktree ). Spec: docs/specs/typesafe-reviewer.md. All work happens inside the devcontainer. uv manages dependencies and the Python toolchain itself — the image ships no system Python.
Quoted from the project's README. Read the full README.
Highlights
- Jev answers narrow typed questions per diff hunk while the verdict, score, and findings are composed in code.
- Ships as a standalone dev dependency invoked with uv run review --worktree <path>; the spec lives at docs/specs/typesafe-reviewer.md.
- All development runs inside a devcontainer where uv manages the Python toolchain; the image ships no system Python.
- A ledger governance harness backs architectural rules with executable controls, and CI fails on any drift.
Quickstart
bash
uv run review --worktree <path>Watch out
No license file, so reuse terms are unclear. Needs a TypeSafe API key to reach Jev, and development happens in the devcontainer with shared Docker volumes for SSH and gh credentials.
Questions about this entry
Short answers drawn from the linked project or page.
There is no agent loop in the judging path. The diff is sliced, Jev answers narrow typed questions per hunk, and the verdict, score, and findings are composed deterministically in code. It runs as a standalone dev command, uv run review --worktree <path>, not as an autonomous session.
The README does not list them and points to the spec at docs/specs/typesafe-reviewer.md instead. What is stated is that each hunk gets a small set of typed questions with composition happening in code, so the shape is per-hunk judgment rather than one verdict question over the whole diff.
Not without work. The README says all development happens in the devcontainer and the image ships no system Python, with uv managing the toolchain. It also leans on external Docker volumes for SSH and gh credentials, which is a lot of setup to reproduce elsewhere.
There is no license file in the repository, so reuse terms are unclear and worth resolving before copying any of it. It does need a TypeSafe API key, which means every review of your own code is a billable call rather than a free local lint step.
Everything runs inside the devcontainer, with uv managing the Python toolchain because the image ships no system Python. After the first make build, run make init KEY=~/.ssh/your-key on the host to create the shared credential volumes, prove the key against GitHub and build the code graph.
make check runs controls, views --check, governance and tests in order. make views regenerates RULES.md and registry.json, make controls runs the fitness scripts plus ruff and ty, and make test runs pytest.
Rules live as decisions under governance/decisions/, each backed by an executable control under controls/, and CI fails on any drift. Agents read the generated governance/views/RULES.md, and a rule changes by supersession, never by edit.
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