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Real-time quality guardrails for AI coding agents

Project description

agentlint

CI PyPI Python License: MIT

Real-time quality guardrails for AI coding agents.

AI coding agents drift during long sessions — they introduce API keys into source, skip tests, force-push to main, and leave debug statements behind. AgentLint catches these problems as they happen, not at review time.

What it catches

AgentLint ships with 10 universal rules that work with any tech stack:

Rule Severity What it does
no-secrets ERROR Blocks writes containing API keys, tokens, passwords
no-env-commit ERROR Blocks writing .env and credential files
no-force-push ERROR Blocks git push --force to main/master
no-destructive-commands WARNING Warns on rm -rf, DROP TABLE, git reset --hard
dependency-hygiene WARNING Warns on ad-hoc pip install / npm install
max-file-size WARNING Warns when a file exceeds 500 lines
drift-detector WARNING Warns after many edits without running tests
no-debug-artifacts WARNING Detects console.log, print(), debugger left in code
test-with-changes WARNING Warns if source changed but no tests were updated
no-todo-left INFO Reports TODO/FIXME comments in changed files

ERROR rules block the agent's action. WARNING rules inject advice into the agent's context. INFO rules appear in the session report.

Quick start

pip install agentlint
cd your-project
agentlint setup

That's it! AgentLint hooks are now active in Claude Code.

When AgentLint blocks a dangerous action, the agent sees:

⛔ [no-secrets] Possible secret token detected (prefix 'sk_live_')
💡 Use environment variables instead of hard-coded secrets.

The agent's action is blocked before it can write the secret into your codebase.

The setup command:

  • Installs hooks into .claude/settings.json
  • Creates agentlint.yml with auto-detected settings (if it doesn't exist)

To remove AgentLint hooks:

agentlint uninstall

Installation options

# Install to project (default)
agentlint setup

# Install to user-level settings (~/.claude/settings.json)
agentlint setup --global

Claude Code marketplace

Add the AgentLint marketplace and install the plugin:

/plugin marketplace add mauhpr/agentlint
/plugin install agentlint@agentlint

Local plugin (development)

claude --plugin-dir /path/to/agentlint/plugin

Manual hook configuration

Add to your project's .claude/settings.json:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash|Edit|Write",
        "hooks": [{ "type": "command", "command": "agentlint check --event PreToolUse" }]
      }
    ],
    "PostToolUse": [
      {
        "matcher": "Edit|Write",
        "hooks": [{ "type": "command", "command": "agentlint check --event PostToolUse" }]
      }
    ],
    "Stop": [
      {
        "hooks": [{ "type": "command", "command": "agentlint report" }]
      }
    ]
  }
}

Configuration

Create agentlint.yml in your project root (or run agentlint init):

# Auto-detect tech stack or list packs explicitly
stack: auto

# strict: warnings become errors
# standard: default behavior
# relaxed: warnings become info
severity: standard

packs:
  - universal

rules:
  max-file-size:
    limit: 300          # Override default 500-line limit
  drift-detector:
    threshold: 5        # Warn after 5 edits without tests (default: 10)
  no-secrets:
    enabled: false      # Disable a rule entirely

# Load custom rules from a directory
# custom_rules_dir: .agentlint/rules/

Custom rules

Create a Python file in your custom rules directory:

# .agentlint/rules/no_direct_db.py
from agentlint.models import Rule, RuleContext, Violation, Severity, HookEvent

class NoDirectDB(Rule):
    id = "custom/no-direct-db"
    description = "API routes must not import database layer directly"
    severity = Severity.WARNING
    events = [HookEvent.POST_TOOL_USE]
    pack = "custom"

    def evaluate(self, context: RuleContext) -> list[Violation]:
        if not context.file_path or "/routes/" not in context.file_path:
            return []
        if context.file_content and "from database" in context.file_content:
            return [Violation(
                rule_id=self.id,
                message="Route imports database directly. Use repository pattern.",
                severity=self.severity,
                file_path=context.file_path,
            )]
        return []

Then set custom_rules_dir: .agentlint/rules/ in your config.

See docs/custom-rules.md for the full guide.

How it works

AgentLint hooks into Claude Code's lifecycle events:

  1. PreToolUse — Before Write/Edit/Bash calls. Can block the action (exit code 2).
  2. PostToolUse — After Write/Edit. Injects warnings into the agent's context.
  3. Stop — End of session. Generates a quality report.

Each invocation loads your config, evaluates matching rules, and returns JSON that Claude Code understands. Session state persists across invocations so rules like drift-detector can track cumulative behavior.

Comparison with alternatives

Project How AgentLint differs
guardrails-ai Validates LLM I/O. AgentLint validates agent tool calls in real-time.
claude-code-guardrails Uses external API. AgentLint is local-first, no network dependency.
Custom hooks Copy-paste scripts. AgentLint is a composable engine with config + plugins.
Codacy Guardrails Commercial, proprietary. AgentLint is fully open source.

FAQ

Does AgentLint slow down Claude Code? No. Rules evaluate in <10ms. AgentLint runs locally as a subprocess — no network calls, no API dependencies.

What if a rule is too strict for my project? Disable it in agentlint.yml: rules: { no-secrets: { enabled: false } }. Or switch to severity: relaxed to downgrade warnings to informational.

Is my code sent anywhere? No. AgentLint is fully offline. It reads stdin from Claude Code's hook system and evaluates rules locally. No telemetry, no network requests.

Can I use AgentLint outside Claude Code? The CLI works standalone — you can pipe JSON to agentlint check in any CI pipeline. However, the hook integration (blocking actions in real-time) is specific to Claude Code.

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT

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