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EvaluationExperimental
lintlang
by hermes-labs-ai
Static, zero-LLM linter for agent configs, prompts, and tool specs
Python
Updated Jul 20, 2026
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What It Does
Analyze and lint agent configs, tool descriptions, and system prompts without calling an LLM. static CI gate Runs as a static CI gate that enforces prompt and tool-calling hygiene, flagging risky patterns, broken tool specs, and mis‑aligned instructions. Distinctive zero-LLM design makes it fast, auditable, and suitable for pre-production checks in CI pipelines.
Why It Matters
As agents become more autonomous and chain tool calls, small config mistakes can produce large downstream failures or unsafe behavior. A [multi-agent trust]-friendly approach via a static linter provides early, deterministic checks that catch unsafe prompts, misdeclared functions, and configuration drift before runtime. This matters for building multi-agent trust and maintaining an agent track record because it reduces noisy evaluations and prevents avoidable failures during agent-to-agent evaluation.
When to Use
Teams who want deterministic CI gates for agent configurations and prompt/tool hygiene before running LLM-based evaluations.
Use Cases
- Catch malformed tool descriptions and function-calling mismatches before deployment
- Enforce prompt safety and style rules in CI to reduce unsafe agent behaviors
- Gate agent configuration changes in pull requests with deterministic, zero-LLM checks
Works With
pythoncligithub-actions
Topics
agent-config-linteragent-configurationagent-testingai-agentsai-safetyai-securityclicontext-engineeringfunction-callinglinter+7 more
Similar Tools
prompt-lintpre-commit
Keywords
multi-agent trustagent-configurationcontinuous agent evaluationprompt-linting