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Estimate costs and lint agent workflow YAML files

Project description

agent-lint

CLI tool for cost estimation and anti-pattern detection in agent workflow YAML configs. Catch expensive, fragile, or ungoverned agent workflows before they hit production.

PyPI Python License: MIT CI


The Problem

Agent workflows fail in predictable ways: unbounded loops, no retry limits, missing cost guards, parallel branches with no coordination. Most teams find these problems in production — after a $400 API bill or a stuck workflow that ran for six hours.

agent-lint finds them at config time.


Install

pip install agentlinter

Usage

# Lint a single workflow config
agent-lint lint workflow.yaml

# Lint all workflows in a directory, fail CI if score < 80
agent-lint lint workflows/ --fail-under 80

# Estimate token/cost exposure for a workflow
agent-lint estimate workflow.yaml --provider anthropic

# Generate a full audit report
agent-lint lint workflows/ --format markdown

What It Detects

Anti-patterns:

  • Unbounded retry loops (no max_retries or timeout)
  • Missing cost guards on high-token operations
  • Parallel agent branches with no merge strategy
  • Hard-coded API keys in config (security)
  • Missing checkpointing on long-running workflows
  • Agents with no defined output schema

Cost estimation:

  • Token budget projection per workflow step
  • Worst-case / expected-case / best-case cost ranges
  • Model-aware pricing (Claude, GPT-4, Gemini)

Scoring:

  • 0-100 score per workflow
  • Error (-15 pts), warning (-5 pts), info (-1 pt)
  • CI integration: --fail-under threshold

CI Integration

# .github/workflows/agent-lint.yml
- name: Lint agent workflows
  run: agent-lint lint workflows/ --fail-under 80

Treat your agent configs like production code. Gate them the same way.


Why Static Analysis

No LLM dependency. No API calls. No variance between runs. Agent configs are deterministic artifacts — they should be audited deterministically.


Status

  • Anti-pattern detection (17 rules)
  • Cost estimation (Claude, GPT-4)
  • CI integration with threshold gating
  • Markdown audit reports
  • Gemini pricing model
  • VS Code extension
  • Auto-fix suggestions

Part of the AreteDriver AI tooling ecosystem.

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