Describe the decision.
Send the encoded public game context, preceding action history, legal actions, and the acting player’s hole cards.
Give your poker application a decision engine. Turn game context and hole cards into hand-aware action probabilities and conditional bet sizing—with one API call.
Public access is in development. Experience the gameplay in Blitzfold while we build.
For developers building Hold’em bots, simulations, and poker experiences. Bring your game engine; let the API supply the action distribution.
Send the encoded public game context, preceding action history, legal actions, and the acting player’s hole cards.
Receive fold, call, and raise probabilities when facing a bet—or check and bet probabilities otherwise. Sizing weights are conditional on aggression.
Sample an action and, for a bet or raise, a size. Your engine handles legal amounts, stack limits, and execution.
The equity-actions endpoint combines a learned public-context policy with equity-based allocation for the acting hand.
/api/equity_actionsOur server-side SDK is being updated for the new API. The existing SDK requires Node.js 20+; public installation instructions and equity-actions support are coming with the release.
SDK availability & API documentation ↗// Illustrative response · facing a bet
{
"ok": true,
"hand": "AsAh",
"equity": 0.84,
"facingBet": true,
"output": {
"fold": 0.02,
"call": 0.26,
"raise": 0.72
}
}Equity is measured against a uniform heads-up opponent. It is not opponent-specific or multiway equity, and the policy is not a guarantee of optimal or profitable play.
Supply the actor’s candidate-range weights, or use a uniform board-compatible population. The endpoint does not infer or update ranges from action history.
The API returns a distribution rather than selecting an action. Your application owns sampling, game rules, and what happens next.
Public API access is on the way. Play Blitzfold in the meantime.
Building something with poker? Let’s talk about it ↗