pywiggum 0.1.0

AI Agent Orchestrator with Dashboard - oversee your autonomous coding agents

PyWiggum — AI Agent Orchestrator with Dashboard

"Me fail English? That's unpossible!" — Ralph Wiggum

Chief Wiggum oversees Ralph. PyWiggum oversees your AI coding agents.

What Is PyWiggum?

PyWiggum is a Python-based autonomous AI coding agent orchestrator with a built-in web dashboard. It runs a configurable loop that feeds tasks from a kanban to an LLM-powered coding agent, tracks velocity and progress, detects stalls, and gives humans real-time controls to steer the process — pause, resume, inject hints, and adjust iteration limits — all from a web UI.

Key differentiators from existing Ralph implementations:

1. Local-model-first: Native support for OpenAI-compatible APIs (vLLM, Ollama, llama.cpp) — not just Claude CLI
2. Web dashboard: Real-time kanban, velocity tracking, stall detection, baseline drift monitoring, ETA predictions
3. Human-in-the-loop via web UI: Pause/resume, hint injection, iteration control — no Telegram bot or CLI-only interaction
4. Kanban-driven: Uses a structured kanban.json (not PRD/user-stories), supports milestones with dependency ordering
5. 🚔 Springfield PD routing: Multi-agent hierarchy (Ralph → Eddie → Lou → Chief Matt) with automatic escalation
6. Smart task routing: Route different task types to different agents based on complexity

Installation

pip install pywiggum

# Or with uv (recommended)
uv pip install pywiggum

Quick Start

# Initialize a new project with kanban
pywiggum init

# Edit wiggum.yaml and kanban.json to customize your project

# Start the autonomous loop
pywiggum run --max-iterations 50

# Start the dashboard (in another terminal)
pywiggum dash --port 3333

# Open http://localhost:3333 in your browser

Usage

Initialize Project

pywiggum init

This creates two files:

• wiggum.yaml - Configuration file
• kanban.json - Kanban board with example tasks

Run the Agent Loop

# Basic usage
pywiggum run

# With options
pywiggum run --max-iterations 100 --agent claude_code --dash

# Available agents: opencode, claude_code, api

Control the Runner

# Check status
pywiggum status

# Pause/resume
pywiggum pause
pywiggum resume

# Send a hint to the agent
pywiggum hint "The control IDs need zero-padding normalization"

# Add more iterations
pywiggum add-iterations 25

Start the Dashboard

# Default (port 3333)
pywiggum dash

# Custom port/host
pywiggum dash --port 8080 --host 0.0.0.0

Configuration

Edit wiggum.yaml to customize your project:

# Project metadata
project:
  name: "My Awesome Project"
  kanban: "kanban.json"
  work_dir: "."

# Agent configuration
agent:
  backend: "opencode"  # opencode | claude_code | api
  model: "vllm/qwen3-coder-next"
  timeout: 600

# Runner settings
runner:
  max_iterations: 50
  sleep_between: 3
  commit_after_task: true
  commit_format: "{task_id}: {task_title}"

# Dashboard
dashboard:
  port: 3333
  host: "0.0.0.0"
  refresh_interval: 15

# Prompt customization
prompt:
  tech_stack: |
    SvelteKit 5, TypeScript, better-sqlite3, Tailwind CSS
    Use $state(), $derived(), $effect() (Svelte 5 runes)
  conventions: |
    All database code in src/lib/server/db.ts
    Types in src/lib/types.ts
  extra_context: ""

Kanban Format

The kanban.json file defines your project structure:

{
  "milestones": [
    {
      "id": "M1",
      "name": "Project Setup",
      "blocked_by": [],
      "tasks": [
        {
          "id": "M1.1",
          "title": "Initialize SvelteKit project",
          "description": "Run sv create to scaffold the project",
          "acceptance_criteria": [
            "package.json exists",
            "npm run dev works"
          ],
          "status": "todo"
        }
      ]
    },
    {
      "id": "M2",
      "name": "Core Implementation",
      "blocked_by": ["M1"],
      "tasks": [
        {
          "id": "M2.1",
          "title": "Create database schema",
          "description": "Set up SQLite database with initial tables",
          "acceptance_criteria": [
            "Database file created",
            "Tables exist"
          ],
          "status": "todo"
        }
      ]
    }
  ]
}

Task status values: todo, done, failed

Agent Backends

OpenCode (Local Models)

Use with vLLM, Ollama, or other OpenAI-compatible endpoints:

agent:
  backend: "opencode"
  model: "vllm/qwen3-coder-next"

Requires: opencode CLI installed

Claude Code

Use with Claude Code CLI:

agent:
  backend: "claude_code"

Requires: claude CLI installed and authenticated

API (Direct)

Make direct API calls to OpenAI-compatible endpoints:

agent:
  backend: "api"
  model: "gpt-4"
  api_base_url: "http://localhost:8000/v1"
  api_key_env: "OPENAI_API_KEY"

Requires: Set environment variable specified in api_key_env

Dashboard Features

The web dashboard provides:

• Real-time status: Running/paused/stopped with pulsing indicator
• Progress tracking: Visual progress bar and statistics
• Velocity metrics: Average task duration and recent velocity
• ETA predictions: Estimated completion time
• Drift detection: Alerts when behind schedule
• Stall detection: Warnings when tasks take too long
• Kanban view: Milestones and tasks with status
• Git log: Recent commits
• Runner log: Recent activity
• Live controls: Pause/resume, add iterations, send hints
• Claude context blob: Copyable status summary for Claude conversations

How It Works

1. Runner reads kanban.json and finds the first todo task whose milestone is not blocked
2. Builds a prompt with project context, tech stack, conventions, and any human hints
3. Invokes the agent backend (OpenCode, Claude Code, or API)
4. Agent completes the task and updates kanban.json status to done or failed
5. Optionally commits the changes with a formatted message
6. Records completion in history for velocity tracking
7. Repeats until all tasks are done or max iterations reached

The dashboard runs independently and provides real-time visibility and control.

Human-in-the-Loop Controls

PyWiggum uses file-based IPC for simple, debuggable control:

File	Purpose
.wiggum-pause	Exists = runner paused
.wiggum-max	Current max iteration count
.wiggum-hint	Text hint for next iteration
.wiggum-hints-archive/	Consumed hints with timestamps

You can manually create/edit these files or use the CLI/dashboard.

Examples

Basic SvelteKit Project

pywiggum init
# Edit kanban.json to add your SvelteKit tasks
# Edit wiggum.yaml to set tech stack
pywiggum run --max-iterations 100 --dash

Using Local Model with vLLM

agent:
  backend: "opencode"
  model: "vllm/qwen3-coder-next"

# In one terminal: start vLLM server
vllm serve Qwen/Qwen2.5-Coder-32B-Instruct

# In another terminal: run PyWiggum
pywiggum run

Using Claude Code

agent:
  backend: "claude_code"

pywiggum run --agent claude_code

🚔 Springfield PD: Multi-Agent Routing

PyWiggum includes a hierarchical agent routing system inspired by Springfield's police department:

👮 Ralph (Wiggum)  →  👮‍♂️ Eddie  →  👨‍✈️ Lou  →  👨‍💼 Chief Matt
   Local model      Better local   Claude     Human

The Team:

• Ralph: Local model (Qwen 3), handles basic tasks
• Eddie: Better local model (Qwen 32B), moderate complexity
• Lou: Frontier model (Claude), complex reasoning
• Chief Matt: Human in the loop, final authority

Example configuration:

routing:
  agents:
    ralph:
      backend: "opencode"
      model: "vllm/qwen3-coder-next"
    eddie:
      backend: "opencode"
      model: "vllm/qwen3-32b-instruct"
    lou:
      backend: "claude_code"
    matt:
      backend: "human"

  rules:
    - task_type: "planning"
      agent_level: "lou"
    - task_type: "test"
      agent_level: "ralph"

  escalation:
    enabled: true
    trigger_after_iterations: 3
    escalation_chain: ["ralph", "eddie", "lou", "matt"]

How it works:

1. Ralph starts with most tasks (cheap, fast)
2. If Ralph fails 3 times → escalate to Eddie
3. If Eddie fails → escalate to Lou (Claude)
4. If Lou fails → escalate to Chief Matt (you!)

See SPRINGFIELD_PD.md for full documentation.

Development

# Clone the repo
git clone https://github.com/pywiggum/pywiggum.git
cd pywiggum

# Install with dev dependencies
uv pip install -e ".[dev]"

# Run tests
pytest

# Lint
ruff check .

# Type check
mypy src/pywiggum

Roadmap

✅ Layer 2 & 3: Springfield PD (Complete!)

Multi-agent routing with automatic escalation. See SPRINGFIELD_PD.md.

Layer 4: Future Ideas

These are potential future enhancements. Vote or suggest via GitHub issues!

• Parallel execution: Run independent tasks simultaneously
• Self-improving prompts: Learn from successful completions
• Multi-agent collaboration: Agents work together on complex tasks
• Checkpoint/resume: Save and restore runner state
• Remote runners: Distribute work across machines
• Plugin system: Custom agents and integrations

Design Principles

1. Files as IPC: No message queues, no WebSockets for control. Files are simple, debuggable, and work for single-user local setups.
2. Single HTML dashboard: No React, no build step, no node_modules. One HTML file with embedded CSS/JS.
3. Config over code: Everything customizable via wiggum.yaml. No need to edit Python to use it.
4. Local-first: Designed for local models on local hardware. Cloud APIs are optional escalation, not the default.
5. One task per iteration: Clean context per task. Memory persists via git history and kanban state, not model context.

Contributing

Contributions welcome! Please open an issue or PR on GitHub.

License

MIT License - see LICENSE file for details.

Credits

Inspired by the Ralph pattern and all the Ralph implementations in the wild. PyWiggum brings the pattern to local models with a focus on observability and control.

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"That's unpossible!" — Ralph Wiggum