lintlang

Static linter for AI agent tool descriptions, system prompts, and configs.

Most AI agent bugs aren't code bugs — they're language bugs. Vague tool descriptions make agents pick the wrong tool. Missing constraints cause infinite loops. Schema mismatches break structured output. lintlang catches these at authoring time, in CI, with zero LLM calls.

Install

pip install lintlang

Requires Python 3.10+. One dependency (pyyaml). No API keys, no network access, no LLM calls.

Quick Start

# Scan a single file
lintlang scan agent_config.yaml

# Scan a directory (finds .yaml, .json, .txt, .md, .prompt)
lintlang scan configs/

# JSON output for CI
lintlang scan config.yaml --format json

# Fail CI on CRITICAL/HIGH findings
lintlang scan config.yaml --fail-on fail

# Fail CI on any MEDIUM+ findings
lintlang scan config.yaml --fail-on review

Example Output

  LINTLANG v0.2.0
  bad_tool_descriptions.yaml
  ──────────────────────────────────────────────────

  ❌ FAIL — 1 CRITICAL, 2 HIGH, 6 MEDIUM, 3 LOW

  H1: Tool Description Ambiguity

    !! [CRITICAL] tool:process_ticket
      Tool 'process_ticket' has no description.
      → Add a specific description explaining WHEN to use this tool.

    ! [HIGH] tool:get_user_info
      Tool 'get_user_info' has a very short description (13 chars)
      → Expand to include purpose, when to use, expected input/output.

    ~ [MEDIUM] tool:handle_request
      Tool 'handle_request' starts with vague verb 'handle'.
      → Replace with a specific action verb.

  H2: Missing Constraint Scaffolding

    ! [HIGH] system_prompt
      System prompt defines tools but has no termination conditions.
      → Add: 'Maximum 5 tool calls per task. Stop and report after 2 failures.'

  ──────────────────────────────────────────────────
  lintlang v0.2.0 | H1-H7 structural analysis | Zero LLM calls

How It Works

lintlang gives you a verdict, not a score:

Verdict	Meaning	When
✅ PASS	Ship it	Only LOW/INFO findings or none
⚠️ REVIEW	Has blind spots	MEDIUM findings present
❌ FAIL	Will break in production	CRITICAL or HIGH findings

Each finding includes the pattern (H1-H7), severity, location, and a concrete fix suggestion. No vague "improve your prompt" — specific rewrites you can apply immediately.

Structural Detectors (H1-H7)

Pattern	Name	What Users Report	Severity
H1	Tool Description Ambiguity	"Agent picks wrong tool"	CRITICAL-MEDIUM
H2	Missing Constraint Scaffolding	"Agent loops infinitely"	CRITICAL-HIGH
H3	Schema-Intent Mismatch	"Structured output broken"	CRITICAL-LOW
H4	Context Boundary Erosion	"Agent leaks state across tasks"	HIGH-MEDIUM
H5	Implicit Instruction Failure	"Model doesn't follow instructions"	MEDIUM-LOW
H6	Template Format Contract Violation	"Agent broke after prompt change"	MEDIUM-INFO
H7	Role Confusion	"Chat history is messed up"	CRITICAL-MEDIUM

H5: Context-Aware Negatives

H5 distinguishes between safety constraints and style negatives. Security rules like "Never expose API keys" are correctly exempted. Style issues like "Don't be verbose" are flagged with positive rewrites.

Validated on 26 real-world configs (OpenHands, RAG agents, HIPAA compliance, financial advisors, content moderation, DevOps safety).

CI Integration

GitHub Actions

- name: Lint agent configs
  run: |
    pip install lintlang
    lintlang scan configs/ --fail-on fail

Verdict-Based Gating

Flag	Exits 1 when	Use case
--fail-on fail	Any CRITICAL/HIGH finding	Blocking deploy gate
--fail-on review	Any MEDIUM+ finding	Strict quality gate
--fail-under 80	HERM score < threshold	Legacy score-based gate

Filter by Severity

# Only show CRITICAL and HIGH
lintlang scan config.yaml --min-severity high

# Only check specific patterns
lintlang scan config.yaml --patterns H1 H3

Programmatic API

from lintlang import scan_file, compute_verdict

result = scan_file("config.yaml")
verdict = compute_verdict(result.structural_findings)
print(f"Verdict: {verdict}")  # PASS, REVIEW, or FAIL

for finding in result.structural_findings:
    print(f"  [{finding.severity.value}] {finding.description}")
    print(f"  → {finding.suggestion}")

# Scan a directory
from lintlang import scan_directory, compute_verdict

results = scan_directory("configs/")
for path, result in results.items():
    verdict = compute_verdict(result.structural_findings)
    print(f"{path}: {verdict}")

Supported Formats

lintlang auto-detects file format:

• YAML (.yaml, .yml) — OpenAI function-calling format, tool definitions
• JSON (.json) — OpenAI and Anthropic tool schemas, message arrays
• Plain text (.txt, .md, .prompt) — System prompts, instruction docs

Unknown extensions are tried as JSON → YAML → plain text.

How Is lintlang Different?

Tool	What It Does	How lintlang Differs
promptfoo	Tests prompts via eval suites at runtime	lintlang is static — no LLM calls, catches issues at authoring time
guardrails-ai	Validates LLM outputs at runtime	lintlang catches root causes (bad instructions), not symptoms
NeMo Guardrails	Runtime dialogue rails	lintlang operates on config files, not live conversations
eslint / ruff	Lints source code	lintlang lints natural language in agent configs

lintlang treats tool descriptions, system prompts, and agent configs as lintable artifacts — static analysis for prose, like eslint for JavaScript.

Development

git clone https://github.com/roli-lpci/lintlang.git
cd lintlang
pip install -e ".[dev]"
pytest

Hermes Labs Ecosystem

lintlang is part of the Hermes Labs open-source suite:

• little-canary — Prompt injection detection
• zer0dex — Dual-layer memory for AI agents
• forgetted — Selective memory governance
• zer0lint — mem0 extraction diagnostics
• suy-sideguy — Autonomous agent watchdog
• quickthink — Planning scaffolding for local LLMs

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If lintlang saves you time, please star the repo — it helps others find it.

License

Apache 2.0