Stately Agent: Build agents as state machines

The logic layer for AI agents.

Build agents as state machines, with explicit control flow you can inspect, test, visualize, and run anywhere.

Stately Agent adds model requests and decisions to XState. The state machine defines what the agent can do. Your application chooses the model, runs the requests, and stores the state.

Any agent workflow or loop can be modeled as a state machine. Model calls and tools run as effects inside it. The model proposes an event. The machine decides whether it is allowed and what happens next.

Stately Agent 2 is in alpha. APIs may change before the stable release.

Documentation · Examples · XState

pnpm add @statelyai/agent@alpha xstate@alpha zod ai @ai-sdk/openai

Node 22.18 or newer is required.

This agent reviews refund requests. The model may propose an automatic refund, but the state machine owns the $100 limit.

import { openai } from "@ai-sdk/openai";
import { defineModels, runAgent } from "@statelyai/agent/ai-sdk";
import { setupAgent } from "@statelyai/agent";
import { z } from "zod";

const models = defineModels({
  fast: openai("gpt-5.4-mini"),
});

const agent = setupAgent({
  models,
  context: z.object({
    request: z.string(),
    amount: z.number(),
  }),
  input: z.object({
    request: z.string(),
    amount: z.number(),
  }),
  output: z.object({
    outcome: z.enum(["refunded", "review"]),
  }),
  events: {
    AUTO_REFUND: {},
    REVIEW: z.object({ reason: z.string() }),
  },
});

const refundMachine = agent.createMachine({
  context: ({ input }) => input,
  initial: "deciding",
  states: {
    deciding: {
      invoke: {
        src: "agent.decide",
        input: ({ context }) => ({
          model: "fast",
          system: "Choose AUTO_REFUND for eligible requests. Otherwise choose REVIEW.",
          prompt: `${context.request}\nAmount: $${context.amount}`,
          allowedEvents: ["AUTO_REFUND", "REVIEW"],
        }),
      },
      on: {
        AUTO_REFUND: ({ context }) => (context.amount <= 100 ? { target: "refunded" } : undefined),
        REVIEW: { target: "review" },
      },
    },
    refunded: {
      type: "final",
      output: () => ({ outcome: "refunded" }),
    },
    review: {
      type: "final",
      output: () => ({ outcome: "review" }),
    },
  },
});

const result = await runAgent(refundMachine, {
  input: {
    request: "I was charged twice for the same order.",
    amount: 75,
  },
});

if (result.status === "done") {
  console.log(result.output);
}

When the machine reaches refunded

, the result is:

{ outcome: 'refunded' }

The model chooses between the events allowed in deciding

. The AUTO_REFUND

transition only works when the amount is at most $100. If the model chooses it for a larger amount, the guard rejects the choice and the decision is tried again.

The example has one model decision and two final outcomes. Real machines can add approval states, retries, parallel work, child agents, and long-running waits without changing how the control flow is represented.

Machines own control flow. States, events, transitions, and guards define what can happen.Models make bounded decisions.agent.decide

asks a model to choose one of the events accepted by the current state.Requests are typed. Inputs, outputs, context, and events use Standard Schema. Zod works out of the box.Your code runs the model.runAgent

accepts executor functions. The example uses the Vercel AI SDK adapter, but the machine does not depend on a provider.Snapshots can be stored. An agent can stop for human input, save its XState snapshot, and resume later in another process.Machines can be checked without model calls. Lint their structure, simulate scripted decisions, and explore paths without an API key.Agents are XState machines. Guards, actors, parallel states, inspection, testing, and visualization work as usual.

Twenty Questionsshows a model choosing legal events in a loop.Go Fishpits a model against a human while the machine enforces hidden-information game rules.Human in the loops, stores a snapshot, and resumes after review.Ticket triagereturns structured data from a model request.JSON agentruns a machine defined as data.

See all examples.