A curated list of AI poker research, open-source tools, solvers, and educational resources. Updated for 2026.
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
- LLMs vs Poker: The 2025-2026 Wave
- Open-Source Frameworks
- Hand Evaluators
- Open-Source Solvers
- Commercial Solvers
- Training Platforms
- AI Poker Bots
- LLM Poker Projects
- Benchmarks & Evaluation
- Educational Resources
- Notable Commentary & Analysis
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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.
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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).
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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.
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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.
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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.
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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).
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DecisionHoldem — Open-source HUNL AI combining blueprint strategy and real-time safe depth-limited solving. GitHub.
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PokerGPT — End-to-end lightweight solver for multi-player Texas Hold'em via fine-tuning a pre-trained LLM (OPT-1.3B) with RLHF on real game logs. Demonstrates that small language models can learn poker decision-making without expensive CFR computation (Huang et al., 2024).
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SpinGPT — First LLM specifically trained for Spin & Go poker tournaments. Two-stage training: supervised fine-tuning on 320,000 expert decisions + reinforcement learning on 270,000 solver-generated hands. Achieves 78% solver agreement and 13.4 BB/100 vs Slumbot in heads-up play. Accepted at ACG 2025, published in LNCS (Maugin & Cazenave, 2025).
The explosion of general-purpose LLMs created massive public interest in whether ChatGPT, Claude, and Grok could play poker. Short answer: they can't — not at a competitive level. But the experiments were fascinating.
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Kaggle AI Poker Showdown (February 2026) — Google DeepMind and Kaggle organized the first major LLM-vs-LLM poker exhibition. Ten flagship models (GPT-5.2, GPT-o3, GPT-5 mini, Grok-4, Grok 4.1 Fast Reasoning, Gemini 3 Pro, Gemini 3 Flash, Claude Opus 4.5, Claude Sonnet 4.5, DeepSeek-V3.2) played 900,000+ hands of heads-up NLHE. GPT-5.2 was the most profitable overall; o3 won the exhibition bracket. All models played without solvers or CFR — pure text-pattern decisions. Doug Polk, Nick Schulman, and Liv Boeree provided commentary.
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PokerBattle.ai (October-November 2025) — Five-day LLM cash game marathon organized by Max Pavlov. Nine models played Texas Hold'em around the clock. OpenAI o3 won with $36,691 profit over 3,799 hands. Meta LLAMA 4 performed worst, busting its entire $100K bankroll. Demonstrated the same fundamental LLM weaknesses: excessive aggression, poor bluffing comprehension, inability to fold.
The consensus from Kaggle, PokerBattle.ai, and professional analysis is clear:
- LLMs do not understand poker mathematics. They generate plausible-sounding text about poker but cannot reliably calculate equity, pot odds, or EV.
- LLMs hallucinate hand strength. They confuse suits, misread boards, and misidentify winning hands — even when they answer correctly in isolation.
- LLMs cannot adapt to opponents. No cross-hand memory, no range construction, no exploitative adjustments.
- Specialized poker AI (CFR-based) solved this years ago. Libratus (2017) and Pluribus (2019) achieved superhuman play through game theory, not language modeling. LLMs represent a step backward for poker AI, not forward.
SpinGPT is the notable exception — but it requires solver-generated training data and works only in a severely constrained format (3-player Spin & Go with short stacks).
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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.
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RLCard — Toolkit for RL in card games; supports Limit/No-Limit Hold'em, Leduc, Blackjack, Mahjong, UNO, and more. Website.
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PokerRL — Multi-agent deep RL framework implementing NFSP, Deep CFR, Single Deep CFR, and RPG; supports distributed computing via Ray.
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PyPokerEngine — Lightweight Python poker engine for AI development with an Emulator class for RL and a browser-based GUI.
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PokerKit — Comprehensive Python library supporting Texas Hold'em, Omaha, Stud, Razz, and custom game variants (University of Toronto). Paper.
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clubs — Python poker engine with OpenAI Gym interface; supports arbitrary community card game configurations (~714K hand evaluations/sec).
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neuron_poker — Texas Hold'em OpenAI Gym environment with Keras-RL and a C++ equity module (~500x faster than Python).
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deepcfr-texas-no-limit-holdem-6-players — Deep CFR implementation for 6-player NLHE with progressive training phases (random opponents → self-play → mixed pools), GRU-based opponent modeling, and a PyQt5 GUI. 80+ stars (2024-2025).
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PokerHandEvaluator — High-performance C/C++ evaluator using a perfect hash algorithm; supports 5-7 card hands and Omaha (PLO4/5/6). Python bindings available.
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treys — Python 3 hand evaluator using bit arithmetic and lookup tables; evaluates 5/6/7 card hands (~250K evaluations/sec).
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deuces — Original pure-Python hand evaluation library, written for the MIT Pokerbots Competition.
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TexasSolver — Free open-source Texas Hold'em GTO solver with a GUI (Windows/macOS/Linux); performance comparable to PioSOLVER. Website.
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postflop-solver — Rust-based postflop GTO solver using Discounted CFR. Also available as a web app and desktop app.
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slumbot2019 — C++ CFR implementations (CFR+, MCCFR, Targeted CFR) by the creator of Slumbot, a multi-year ACPC champion.
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ReBeL — Meta's official open-source implementation of the ReBeL algorithm for imperfect-information games.
| Name | Website | Description |
|---|---|---|
| PioSOLVER | piosolver.com | Industry-standard NLHE postflop solver |
| GTO+ | gtoplus.com | Fast, memory-efficient NLHE solver with multi-way support |
| MonkerSolver | monkerware.com | Industry standard for PLO and multi-way spots |
| Deepsolver | deepsolver.com | Cloud-based solver, solves any spot on-demand |
| HRC | holdemresources.net | Tournament (ICM) preflop analysis |
| Name | Website | Description |
|---|---|---|
| GTO Wizard | gtowizard.com | AI-powered solver + trainer with on-demand solving. In 2025, partnered with GGPoker, WPN, and WPT Global for anti-RTA detection — tracking real-time solver use during play |
| PokerSnowie | pokersnowie.com | Neural network poker coach with play-against-AI mode |
- 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.
A growing number of projects attempt to use general-purpose LLMs for poker. None are competitive with CFR-based solvers, but they serve as research tools and benchmarks for LLM reasoning capabilities.
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poker_LLM — AI-powered Texas Hold'em framework where LLMs (OpenAI, Claude, DeepSeek, QWen) act as players. Includes a Vue 3 replay visualization system and AI self-reflection on decisions. Python + Vue 3 (2024-2025).
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HarperJonesGPT/PokerGPT — PokerStars screen-reading bot using GPT-4 API for decision-making via OCR + prompt engineering. Not fine-tuned — pure API calls. A hobby project, not competitive.
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poker-eval — TypeScript framework for evaluating AI agent performance in simulated NLHE cash games. Uses standard poker KPIs (BB/100, EV, VPIP). Supports Vercel AI SDK, OpenAI, Mastra, LlamaIndex, and Langchain. Includes a leaderboard of LLM performance (2024).
Current leaderboard (1000 hands vs 2x GPT-4o baseline):
Model BB/100 mistral-large-latest +11.26 gpt-4o -14.78 claude-3-5-sonnet -19.95 gpt-4o-mini -45.09 gemini-1.5-pro -166.85
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EV and Equity: Why Bots Don't Care About Luck — Expected value and equity in AI decision-making.
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Pot Odds and Implied Odds in 5 Minutes — How bots compute pot odds in real-time.
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GTO Strategy for Poker Bots — Combining GTO-base protection with exploitative play.
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Variance and Sample Size — Why short-term results are deceiving and what the distance threshold means.
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PokerBotAI in 5 Minutes — Quick start guide to AI poker bot platforms.
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Create a Poker Bot Using Python — Tutorial: building a poker bot with Python and CFR.
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Pluribus: The AI That Changed Poker — How Facebook's Pluribus beat six human pros at once.
Coverage and analysis of the LLM-poker intersection from in 6AD0 dustry voices:
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Nate Silver — "ChatGPT is shockingly bad at poker" — Silver Bulletin deep dive: GPT-o3 fails to simulate an 8-player NLHE hand, miscalculates pots, and misidentifies winning hands on a QQ445 board.
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Pokerfuse — "Why the AI Poker Challenge Is a Red Herring — Albeit a Funny One" — Industry analysis arguing that LLM poker tournaments are entertainment, not progress in poker AI.
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Poker.org — "Hyper-aggressive OpenAI bots reign supreme" — Kaggle Game Arena recap with Polk, Schulman, and Boeree commentary.
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GTO Wizard — "Towards a Safer Poker Ecosystem" — GTO Wizard's public announcement of cooperation with poker rooms to detect real-time solver use. Partnership with GGPoker led to 31 immediate account bans (March 2025).
Contributions welcome! Please submit a pull request or open an issue to suggest additions.
