Start with real decisions.
Hand histories provide examples of actions and bet sizes in context. They are the training evidence behind the models; API access does not include raw hand-history downloads.
Build poker experiences around how real players actually play. Build bots, practice environments, and analysis tools with population-based poker models trained on real hand histories. Bring human tendencies into your product with context-aware action probabilities and conditional bet-sizing distributions.
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Explore the demonstrations. Play BlitzFold ↗ · Explore a hand ↗
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Access a reusable model of population behavior: the tendencies, sizing patterns, and imperfect decisions that make human poker different from an optimal strategy.
Hand histories provide examples of actions and bet sizes in context. They are the training evidence behind the models; API access does not include raw hand-history downloads.
Models estimate action frequencies and conditional sizing tendencies from the board, betting history, position, pot, and stacks. Responses are model estimates, rather than direct counts of matching hands in a database.
Use the outputs in your own game, simulation, or analysis interface. Your application supplies game rules, manages state, and turns predictions into an experience.
These are applications you can develop using the model outputs.
Drive opponents with estimated population decisions and sizing patterns. Give simulations behavior grounded in observed play.
Build repeatable situations against modeled opponents. Add your own lessons, feedback, and progression.
Show how predicted action mixes change across situations. Put recorded decisions beside population estimates for context.
Test hypotheses against modeled populations, including exploitative strategy research. You supply the experiment and evaluation; the model does not promise optimal strategy or profitable play.
The public model estimates the overall action mix for a situation. The equity gate assigns hand-dependent probabilities while numerically targeting those learned aggregate frequencies across the weighted candidate hands. Postflop allocation also uses estimated improvement potential.
Matching the aggregate mix does not prove hidden-card ranges are correct or guarantee realism throughout a simulated hand. Range assumptions, missing private cards, and model coverage matter. Your integration carries actor ranges forward after the API’s sampled action.
Read the methodology and assumptions ↗The actions endpoint combines learned game-context decisions with hand strength and draw potential. Bets and raises can represent value, bluffs, or semi-bluffs.
/api/actionsOur server-side SDK is being updated for the new API. The existing SDK requires Node.js 20+; public installation instructions and actions support are coming with the release.
SDK availability & API documentation ↗// Illustrative response · facing a bet
{
"ok": true,
"hand": "AsAh",
"equity": 0.84,
"chosenAction": "raise",
"output": {
"fold": 0.02,
"call": 0.26,
"raise": 0.72
}
}The current released action models were trained on 50,000 hands from PokerStars USD $0.10/$0.25 regular no-limit Hold’em cash games, using a five/six-player cohort. The research archive contains over six million hands; that archive size is not the current model’s training count.
Optional tight-passive, tight-aggressive, loose-passive, and loose-aggressive presets condition the action and sizing model on historical style features. They require a compatible configured style model. These are descriptive behavior models, not validated personalities for every situation.
Accepting a game state or hand history from another stake does not select a separately trained stake model. Broader populations, formats, and our own analysis products are future directions, requiring further training and validation.
A playable poker experience using HoldemAPI. See one way population models can become opponents in a game.
Play BlitzFold ↗Paste or upload a PokerStars USD cash hand, replay its decisions, and explore public-state population action predictions and conditional sizing. Hole cards are shown for reference; they do not condition these predictions.
Explore a hand ↗Choose your monthly allowance. Create an account, pick your plan, and manage your API keys in one place.
For prototypes, personal projects, and your first integration.
1,000 requests per month
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After the trial, your selected plan automatically renews at $30 USD/month until canceled. Cancel before the trial ends to avoid the first charge.
Choose your plan after signup. Cancel renewal anytime.
More room for active development and growing applications.
10,000 requests per month
7-day free trial. Card required. One trial per eligible account.
After the trial, your selected plan automatically renews at $90 USD/month until canceled. Cancel before the trial ends to avoid the first charge.
Choose your plan after signup. Cancel renewal anytime.
Monthly allowances renew with your billing period. No automatic overage charges.
Create an account, choose a plan, and start building with population models.
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