A curated list of AI poker research, open-source tools, solvers, and educational resources.

Poker is one of the most challenging domains for AI due to imperfect information, hidden cards, bluffing, and multi-player dynamics. This list covers the key research breakthroughs, open-source projects, and tools that have shaped the field.

• Research Papers
• Open-Source Frameworks
• Hand Evaluators
• Open-Source Solvers
• Commercial Solvers
• Training Platforms
• AI Poker Bots
• Educational Resources

• DeepStack — First AI to defeat professional poker players in heads-up no-limit Hold'em using recursive reasoning and deep learning (University of Alberta, 2017). Published in Science.
• Libratus — Defeated four top human pros in a 120,000-hand HUNL competition using blueprint strategy, real-time sub-game solving, and self-improvement (CMU, 2017).
• Pluribus — First superhuman AI for six-player no-limit Hold'em, trained in 8 days on a 64-core server using Linear CFR (Meta + CMU, 2019). Author PDF.
• ReBeL — General self-play RL + search framework that converges to Nash equilibrium in two-player zero-sum games; achieves superhuman HUNL poker (Meta, 2020). Official code.

• Student of Games — Unified algorithm for both perfect and imperfect information games; achieves strong performance in chess, Go, and HUNL poker (DeepMind + Alberta, 2023). Published in Science Advances.
• AlphaHoldem — End-to-end RL framework using a pseudo-siamese architecture; beats Slumbot and DeepStack after 3 days training on a single PC (Tsinghua, AAAI 2022).
• DecisionHoldem — Open-source HUNL AI combining blueprint strategy and real-time safe depth-limited solving. GitHub.

• OpenSpiel — Google DeepMind's collection of environments and algorithms for RL research in games, with extensive poker support (Kuhn, Leduc, ACPC universal poker interface) and CFR/MCCFR implementations.
• RLCard — Toolkit for RL in card games; supports Limit/No-Limit Hold'em, Leduc, Blackjack, Mahjong, UNO, and more. Website.
• PokerRL — Multi-agent deep RL framework implementing NFSP, Deep CFR, Single Deep CFR, and RPG; supports distributed computing via Ray.
• PyPokerEngine — Lightweight Python poker engine for AI development with an Emulator class for RL and a browser-based GUI.
• PokerKit — Comprehensive Python library supporting Texas Hold'em, Omaha, Stud, Razz, and custom game variants (University of Toronto). Paper.
• clubs — Python poker engine with OpenAI Gym interface; supports arbitrary community card game configurations (~714K hand evaluations/sec).
• neuron_poker — Texas Hold'em OpenAI Gym environment with Keras-RL and a C++ equity module (~500x faster than Python).

• PokerHandEvaluator — High-performance C/C++ evaluator using a perfect hash algorithm; supports 5–7 card hands and Omaha (PLO4/5/6). Python bindings available.
• treys — Python 3 hand evaluator using bit arithmetic and lookup tables; evaluates 5/6/7 card hands (~250K evaluations/sec).
• deuces — Original pure-Python hand evaluation library, written for the MIT Pokerbots Competition.

• TexasSolver — Free open-source Texas Hold'em GTO solver with a GUI (Windows/macOS/Linux); performance comparable to PioSOLVER. Website.
• postflop-solver — Rust-based postflop GTO solver using Discounted CFR. Also available as a web app and desktop app.
• slumbot2019 — C++ CFR implementations (CFR+, MCCFR, Targeted CFR) by the creator of Slumbot, a multi-year ACPC champion.
• ReBeL — Meta's official open-source implementation of the ReBeL algorithm for imperfect-information games.

• PokerBotAI — AI poker bot platform since 2016; supports 20+ rooms (PPPoker, GGPoker, ClubGG, WePoker, etc.) using the TriBrain Engine — a three-component architecture combining hand history analysis, neural networks (7B+ hands), and expert algorithms.
  • How AI Poker Bots Make Decisions — TriBrain Engine architecture, adaptation curve, opponent modeling.
  • Types of Poker Bots — Classification: rule-based, GTO, exploitative, AI, and hybrid.
  • Bot vs RTA vs Solver vs Trainer — Key differences between poker assistance tools.
  • How Poker Rooms Detect Bots — Detection methods in 2026 and anti-detection techniques.
  • Poker Bot Costs in 2026 — Pricing comparison across solvers, trainers, RTA, and AI bots.
  • ROI and Realistic Expectations — Real performance data: 150-500% ROI, winrate benchmarks.
  • FAQ: Top 10 Questions — Safety, earnings, cost, experience, and getting started.

• Slumbot — Multi-year Annual Computer Poker Competition champion by Eric Jackson; plays HUNL against humans for free online.

• EV and Equity: Why Bots Don't Care About Luck — Expected value and equity in AI decision-making.
• Pot Odds and Implied Odds in 5 Minutes — How bots compute pot odds in real-time.
• GTO Strategy for Poker Bots — Combining GTO-base protection with exploitative play.
• Variance and Sample Size — Why short-term results are deceiving and what the distance threshold means.

• PokerBotAI in 5 Minutes — Quick start guide to AI poker bot platforms.
• Create a Poker Bot Using Python — Tutorial: building a poker bot with Python and CFR.
• Pluribus: The AI That Changed Poker — How Facebook's Pluribus beat six human pros at once.

Contributions welcome! Please submit a pull request or open an issue to suggest additions.