juvenal 0.4.0

Who guards the agents? A framework for orchestrating AI coding agents through verified implementation phases.

Juvenal

Quis custodiet ipsos custodes? — Who guards the agents?

Juvenal is a framework for orchestrating AI coding agents through verified implementation phases. It prevents agents from cheating on success criteria by separating implementation from verification, helps agents implement complex projects in phases, etc.

How It Works

A non-agentic Python script orchestrates AI coding agents (Claude or Codex) through alternating steps:

1. Implementation — an agent executes a prompt to build/modify code
2. Verification — separate checkers (scripts, agents, or both) verify the work
3. Bounce — if verification fails, the pipeline bounces back (to a configurable target phase or the most recent implement phase) with failure context injected. A global bounce limit (max_bounces) prevents infinite loops.

The implementing agent and the checking agent are separate processes, so the implementer can't cheat by weakening tests.

Other Such Frameworks

Juvenal is conceptually similar to ralph, but it works slightly better for my exact purposes and reinventing the wheel is cheap now!

Install

pip install -e ".[dev]"

Claude Code Skill

Juvenal ships as a Claude Code plugin, so you can use it directly from Claude Code with /juvenal.

Install the plugin

From the marketplace (pending approval):

/plugin install juvenal

From source (works now):

claude --plugin-dir /path/to/juvenal/plugin

Usage

Once installed, invoke the skill in Claude Code:

/juvenal add authentication to the Flask app

Claude will create a Juvenal workflow for your goal and run it. You can also ask for help with workflow formats or run existing workflows.

Quick Start

# Scaffold a workflow
juvenal init my-project

# Run a workflow
juvenal run workflow.yaml

# Generate a workflow from a goal
juvenal plan "implement a REST API with tests" -o workflow.yaml

# Plan and immediately run
juvenal do "add authentication to the Flask app"

Workflow Formats

YAML

name: "my-workflow"
backend: claude
max_bounces: 999

phases:
  - id: implement
    prompt: "Implement the feature."
    checkers:
      - type: script
        run: "pytest tests/ -x"
      - type: agent
        role: tester

Directory Convention

my-workflow/
  phases/
    01-setup/
      prompt.md            # implementation prompt
      check-build.sh       # script checker (exit 0 = pass)
      check-quality.md     # agent checker
    02-implement/
      prompt.md
      check-tests.sh       # paired with .md = composite
      check-tests.md       # gets {script_output} injected

Bare Markdown

phases/
  01-setup.md              # single phase, default tester checker

Checker Types

Type	Description
script	Shell command; exit 0 = PASS, nonzero = FAIL
agent	AI agent that emits VERDICT: PASS or VERDICT: FAIL: reason
composite	Script runs first, output fed to agent via {script_output}

Built-in Roles

Agent checkers can use built-in verification personas:

• tester — runs tests, checks for build errors
• architect — validates design, checks for circular dependencies
• pm — confirms requirements are met, no TODOs remain
• senior-tester — checks test integrity, looks for cheating
• senior-engineer — reviews code quality, completeness, security

CLI

juvenal run <workflow> [--resume] [--phase X] [--max-retries N] [--backend claude|codex] [--dry-run]
juvenal plan "goal" [-o output.yaml] [--backend claude|codex]
juvenal do "goal" [--backend claude|codex] [--max-retries N]
juvenal status [--state-file path]
juvenal init [directory] [--template name]

License

MIT