Maverick - A Poker Library in Python

Version: 0.6.0

Useful links: Installation | Getting Started | Poker Fundamentals | Hand Rankings | Issue Tracker | Source Repository

Supporting Maverick

Maverick is developed and maintained in the open. If you want to see it thrive and grow, you can help sustain its development.

Maverick is a comprehensive Python library for simulating poker games. It provides a complete game engine with a state machine architecture, making it easy to create poker games, develop AI players, and analyze poker scenarios.

Features

• Configurable Poker Games: Full rules and mechanics supporting a variety of flavours

• State Machine Architecture: Clean separation of game states and transitions

• Flexible Player System: Protocol-based player interface for custom implementations

• Hand Evaluation: Built-in poker hand scoring and comparison

• Event System: Track all game events for analysis and replay

• Well Documented: Comprehensive documentation of rules and APIs

• Thoroughly Tested: Features are heavily tested with high code coverage

Contents

• Overview
  • What You Can Do With Maverick
  • Core Concepts
  • A Minimal Flow
  • Where To Go Next
• Getting Started
  • Installation
  • A minimal working example
  • Next Steps
• Poker Fundamentals
  • The Basic Unit of Play: A “Hand”
  • What Are Players Trying to Win?
  • The Table, Seats, and Turn Order
  • Starting Chips in the Pot: Forced Bets
  • Cards and Stages Within a Hand
  • What a Player Can Do on Their Turn
  • Common Poker Game Families (Variants)
  • Betting Structures (How Big Can Bets Be?)
  • All-Ins and Side Pots (When Players Have Different Chip Stacks)
  • Hand Rankings (Standard 5-Card Poker)
  • Glossary
  • How This Connects to Maverick
• Teach Your LLM to Use Maverick
  • Option 1: Full documentation bundle (llms-full.txt)
  • Option 2: The index file (llms.txt)
  • Option 3: Per-page Markdown

• User Guide
  • Core Concepts Illustrated Through a Simple Game
  • Configuring and Running Games
  • Scoring and Hand Strength Estimation
  • Creating Custom Players
  • Betting Rules - Implemented Semantics & Edge Cases
  • Events and Listeners
  • Playing Deterministic Games

• Examples
  • Finding the Most Successful Strategy
  • Create and Custom Player with Complex Strategy
  • LLM as Player
  • Create Game History for an LLM

• Developer Guide
  • Installation for Developers
  • GitFlow and Branching Strategy
  • Coding Conventions
  • Documenting
  • LLM-Friendly Documentation
  • Git Cookbook
  • Testing and Coverage
  • Changes and Versioning
  • Release Flow

• API Reference
  • Core Classes
  • Card and Deck
  • Players
  • Events and Event Handling
  • Rules
  • Hand Evaluation
  • Utilities
  • Enumerations

Indices and tables

• Index

• Module Index

• Search Page