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EvaluationExperimental
lintlang
by roli-lpci
Static linter for agent configs, prompts, and tool-call schemas
Python
Updated Jul 20, 2026
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What It Does
Analyze and lint agent configurations, tool descriptions, and system prompts to catch safety, format, and semantic issues before deployment. lint checks into CI Runs static checks against a ruleset (HERM v1.1 scoring) to surface risky prompts, malformed tool-calls, and configuration anti-patterns. Provides a Agent Protocol and Python hooks for integrating lint checks into CI and pre-deploy pipelines.
Why It Matters
As agents are composed and delegated across systems, subtle prompt and config errors become systemic failure modes that compound across agents. context confusion Lintlang gives teams early, repeatable signal about configuration quality and safety so reputation and reliability are based on vetted inputs, not blind trust. Until now, much of this validation was ad-hoc; a static linter makes agent-to-agent evaluation and pre-production testing tractable and automatable.
When to Use
Teams who need automated, pre-deploy checks on agent prompts, tool descriptions, and configuration to reduce failure modes and safety risks, guided by the Mutual Verification Pattern.
Use Cases
- Validate system prompts and tool descriptions before deployment to prevent unsafe or ambiguous behavior
- Enforce schema and function-calling conventions for tool integrations in agent pipelines
- Add static checks to CI to catch formatting and semantic config errors during pre-production testing
Topics
agent-configurationagent-testingai-agentsai-safetyai-securityclicontext-engineeringfunction-callinglinterllm+5 more
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Keywords
prompt-lintingagent-configurationmulti-agent trustagent-evaluation