pongogo 0.3.18

AI agent knowledge routing system for software development

Pongogo

Portable AI agent knowledge routing system. Install Pongogo on any repository to get intelligent instruction routing for AI coding assistants.

Quick Start

# Install Pongogo
pip install pongogo

# Initialize in your repository
cd your-project
pongogo init

This creates a .pongogo/ directory with configuration and seeded instruction files that help AI assistants understand your project's patterns and practices. The MCP server and hooks run natively — no Docker required.

Installation

pip (Recommended)

pip install pongogo

Installs the CLI, MCP server, and all hooks as native entry points. No Docker needed.

Homebrew (macOS)

brew tap pongogo/pongogo
brew install pongogo

Docker

For users who prefer container isolation:

curl -sSL https://get.pongogo.com | bash

The installer pulls the Docker image and configures Claude Code to run the MCP server inside a container.

Requirements

• Python 3.12+
• Claude Code installed
• Docker (optional — only needed for Docker installation method)

Usage

Initialize Pongogo

pongogo init

Creates a .pongogo/ directory with:

• config.yaml - Configuration for enabling/disabling instruction categories
• instructions/ - Seeded instruction files (42 files across 14 categories)

Command Options

Flag	Short	Description
--minimal	-m	Install only core instruction categories
--force	-f	Overwrite existing .pongogo/ directory
--no-interactive	-y	Accept all defaults without prompting

Configuration

After initialization, customize .pongogo/config.yaml:

# Enable/disable instruction categories
categories:
  software_engineering: true
  project_management: true
  agentic_workflows: true
  # ... set to false to disable

# Customize placeholders for your project
placeholders:
  wiki_path: wiki/
  docs_path: docs/
  owner_repo: your-org/your-repo

Instruction Categories

Category	Files	Description
software_engineering	3	Git safety, commit formats, Python standards
project_management	6	Work logging, scope prevention, task management
agentic_workflows	4	Agent decision making, compliance patterns
safety_prevention	3	Validation-first execution, systematic prevention
trust_execution	3	Trust-based execution, feature development
_pongogo_core	10	Core Pongogo workflows
+ 8 more categories

MCP Server Integration

Pongogo includes an MCP (Model Context Protocol) server that routes instructions to AI coding assistants.

Configure Claude Code

pongogo init automatically configures the MCP server and hooks. For manual configuration:

pongogo setup-mcp

# Preview configuration without changes
pongogo setup-mcp --dry-run

Upgrading

# Upgrade via CLI (auto-detects pip, Homebrew, or Docker)
pongogo upgrade

# Or from within Claude Code
/pongogo-upgrade

Architecture

Pongogo routes instructions based on context:

User message → MCP Server → Semantic Router → Matched Instructions → Agent

The routing engine uses:

• Pattern matching: Keyword and regex patterns
• Context disambiguation: Positive/negative pattern weights
• Lexicon database: 329 entries for guidance, friction, and hedging detection

Status

• ✅ pongogo init CLI
• ✅ Seeded instruction files (42 files, 14 categories)
• ✅ MCP server (native via pip/Homebrew, or Docker)
• ✅ Claude Code integration
• ✅ CI/CD with release train (alpha/beta/stable)
• ✅ Three distribution channels: pip, Homebrew, Docker

License

MIT License - see LICENSE

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Named after "Pongo" — the scientific name for the orangutan genus — reflecting the intelligent, collaborative nature of the system.