Flag mixed output-format instructions
Surface references to multiple output formats that may lead to hybrid output.
A linter for agent instructions
Make your agents mean it.
LintLang flags vague tool descriptions, missing operational limits, mixed output-format instructions, vague qualifiers, and context-boundary risks before runtime review.
Illustrative rendering of an exercised LintLang 0.4.1 scan
1
Keep trying until it works.2
Respond in JSON and Markdown.Clear instructions✦Better review✦Fewer surprises✦Clear instructions✦Better review✦Fewer surprises✦
THE INSTRUCTION GAP
Natural language is a beautiful interface. It is also a very loose one.
Agent configurations are becoming application code, but they are still written and reviewed like prose. LintLang gives your instructions the same disciplined feedback loop as the rest of your stack.
Explore the approach →Expose bounded risks
Spot vague tool descriptions and qualifiers, missing operational limits, and context-boundary risks.
Set a baseline
Bring repeatable checks to every change, from a local prompt edit to a CI pull request.
A TIGHTER FEEDBACK LOOP
Write. Check. Ship with intent.
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Point at supported instructions
Scan recognized JSON/YAML agent-config shapes, .txt, .md, and .prompt files, or Python files with extractable prompts.
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Read findings in context
Each finding reports a detected pattern, severity, logical location, and suggested review action. Findings may remain at file or logical-location level.
- 3
Keep the standard in CI
Turn configuration review from a heroic habit into a dependable part of your delivery flow.
START IN A MINUTE
Put a check between the draft and the deploy.
Install it locally, run it against the instruction file in front of you, then bring the same command into CI.
Working with a coding agent? Tell it: “Install LintLang and run it against our agent instructions before changing them.”
IN THE FIELD
Language is part of the runtime.
LintLang brings static analysis to the language agents execute: before runtime review and evaluation.
Character.AI
Added to its Larch consolidated third-party CI lint job: 236 files passed with no HIGH or CRITICAL findings.
Read the merged PR ↗8,508 downloads
All-time PyPI downloads including mirrors; 4,269 downloads excluding known mirrors. Coverage through August 21, 2026.
Inspect the PyPI stats ↗Gentoo-ready
Packaged in the community-maintained Haven overlay for Gentoo, with ebuilds through version 0.4.1.
Browse the ebuilds ↗Built in public
An Apache-2.0 package with public source, designed to fit a local-first engineering workflow.
Explore the repository ↗Evidence, not endorsements:Public receipts checked August 22, 2026; PyPI all-time counts cover through August 21.
A DIFFERENT KIND OF LINTER
Built for the messy parts of language.
LintLang is designed around a simple idea: instructions deserve clear, actionable feedback without pretending that language is perfectly deterministic.
Read the docs ↗01
Human-readable findings
Feedback for the people who write and maintain agent behavior.
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Severity with judgment
Focus attention where an instruction failure can carry real consequences.
03
Local-first workflow
Check the configuration in front of you, then bring it into your pipeline.
START WITH THE WORDS