Advanced Start Follow the Ax story through selected runnable examples. java quick-start advanced-start website/content-src/templates/advanced-start.md quick-start Advanced Start

Advanced Start

Advanced Start is built from runnable Java examples. The story below follows the same source files that appear under Examples, so code changes start in src/examples/java/.

Java Typed Generation

Start with a typed contract: the model receives named inputs and Ax parses named outputs.

Runs a small typed generation program against OpenAI.

Java
import dev.axllm.ax.*;
import java.nio.file.*;
import java.util.*;

public final class BasicGenerationExample {
  static String apiKey() {
    String apiKey = System.getenv("OPENAI_API_KEY");
    if (apiKey == null || apiKey.isBlank()) apiKey = System.getenv("OPENAI_APIKEY");
    if (apiKey == null || apiKey.isBlank()) {
      throw new IllegalStateException("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.");
    }
    return apiKey;
  }

  static OpenAICompatibleClient client() {
    return new OpenAICompatibleClient(
        Map.of("api_key", apiKey(), "model", System.getenv().getOrDefault("AX_OPENAI_MODEL", "gpt-5.4-mini"), "model_config", Map.of("temperature", 0.0)));
  }

  public static void main(String[] args) throws Exception {
    AxGen program = Ax.ax("question:string -> answer:string");
    Map<String, Object> output = program.forward(client(), Map.of("question", "In one sentence, explain Ax as a language-agnostic LLM programming library."));
    System.out.println(Json.stringify(output));
  }
}

Java Grounded Support Agent

Move to an agent when the model needs a runtime loop and a final typed answer.

Answers a support question grounded in a handbook that is kept out of the model prompt via contextFields.

Java
import dev.axllm.ax.*;
import dev.axllm.ax.runtime.quickjs.*;
import java.util.*;

public final class BasicAgentExample {
  static String apiKey() {
    String apiKey = System.getenv("OPENAI_API_KEY");
    if (apiKey == null || apiKey.isBlank()) apiKey = System.getenv("OPENAI_APIKEY");
    if (apiKey == null || apiKey.isBlank()) {
      throw new IllegalStateException("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.");
    }
    return apiKey;
  }

  static OpenAICompatibleClient client() {
    return new OpenAICompatibleClient(Map.of(
        "api_key", apiKey(),
        "model", System.getenv().getOrDefault("AX_OPENAI_MODEL", "gpt-5.4-mini"),
        "model_config", Map.of("temperature", 0.0)));
  }

  // The handbook can be arbitrarily large. Listing it in `contextFields` keeps it
  // in the agent's runtime so it never inflates the model prompt -- the agent reads
  // it through code, not through tokens. That is the whole point of an Ax agent
  // over a plain gen() call: the source material stays out of the context window.
  static final String HANDBOOK = String.join("\n",
      "# Acme Cloud -- Support Handbook",
      "",
      "## Billing",
      "- Invoices are issued on the 1st of each month and are due net-15.",
      "- Plan downgrades take effect at the END of the current billing cycle, not immediately.",
      "- Refunds are issued to the original payment method within 5 business days.",
      "",
      "## Access",
      "- Seats can be added by any workspace Owner under Settings -> Members.",
      "- SSO (SAML) is available on Enterprise; SCIM provisioning is Owner-only.",
      "",
      "## Incidents",
      "- Status and uptime are published at status.acme.example.",
      "- Sev-1 incidents page the on-call within 5 minutes; updates post every 30 minutes.",
      "",
      "## Data",
      "- Exports are available in CSV and JSON from Settings -> Data.",
      "- Deleted workspaces are recoverable for 30 days, then permanently purged.");

  public static void main(String[] args) throws Exception {
    AxAgent assistant = Ax.agent(
        "question:string, handbook:string -> answer:string, citations:string[] \"Handbook sections the answer relies on\"",
        // Keep the handbook in the runtime, out of the prompt.
        Map.of("contextFields", List.of("handbook"), "runtime", Map.of("language", "JavaScript")));

    try (AxQuickJsCodeRuntime runtime = new AxQuickJsCodeRuntime()) {
      Map<String, Object> result = assistant.forward(
          client(),
          Map.of(
              "question", "A customer downgraded their plan today. When does it take effect, and can they get a refund for the current cycle?",
              "handbook", HANDBOOK),
          Map.of("runtime", runtime, "max_actor_steps", 12));

      System.out.println(Json.pretty(result));
    }
  }
}

Java Sequential Flow

Use a flow when the application should own the order of multi-step work.

Runs a two-step Ax flow against OpenAI.

Java
import dev.axllm.ax.*;
import java.nio.file.*;
import java.util.*;

public final class SequentialFlowExample {
  static String apiKey() {
    String apiKey = System.getenv("OPENAI_API_KEY");
    if (apiKey == null || apiKey.isBlank()) apiKey = System.getenv("OPENAI_APIKEY");
    if (apiKey == null || apiKey.isBlank()) {
      throw new IllegalStateException("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.");
    }
    return apiKey;
  }

  static OpenAICompatibleClient client() {
    return new OpenAICompatibleClient(
        Map.of("api_key", apiKey(), "model", System.getenv().getOrDefault("AX_OPENAI_MODEL", "gpt-5.4-mini"), "model_config", Map.of("temperature", 0.0)));
  }

  public static void main(String[] args) throws Exception {
    AxGen step = Ax.ax("documentText:string -> summaryText:string");
    AxFlow program =
        Ax.flow(Map.of("id", "examples.sequentialFlow"))
            .execute("step", step)
            .map("note", state -> Map.of("note", "Mapped flow state after the provider-backed step."))
            .returns(Map.of("step", "step", "note", "note"));
    Map<String, Object> output = program.forward(client(), Map.of("documentText", "Ax gives developers signatures, provider clients, agents, flows, tracing, and optimization."));
    System.out.println(Json.stringify(output));
  }
}

Java Text To Speech

Add audio when the same provider-backed contract should accept or produce speech.

Generates speech audio through OpenAI.

Java
import dev.axllm.ax.*;
import java.nio.file.*;
import java.util.*;
import java.util.Base64;

public final class SpeechAudioExample {
  static String apiKey() {
    String apiKey = System.getenv("OPENAI_API_KEY");
    if (apiKey == null || apiKey.isBlank()) apiKey = System.getenv("OPENAI_APIKEY");
    if (apiKey == null || apiKey.isBlank()) {
      throw new IllegalStateException("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.");
    }
    return apiKey;
  }

  static OpenAIResponsesClient client() {
    return new OpenAIResponsesClient(
        Map.of("api_key", apiKey(), "model", System.getenv().getOrDefault("AX_OPENAI_MODEL", "gpt-5.4-mini"), "model_config", Map.of("temperature", 0.0)));
  }

  public static void main(String[] args) throws Exception {
    OpenAIResponsesClient audio = client();
    Map<String, Object> speech = audio.speak(Map.of("text", "Ax turns LLM prompts into typed programs.", "voice", "alloy", "format", "mp3"));
    System.out.println(Json.stringify(Map.of("format", speech.get("format"), "audioBytesBase64", String.valueOf(speech.get("audio")).length())));
  }
}

Java AxGen Optimization

Close the loop by measuring examples and applying optimizer artifacts to the program.

Runs a baseline OpenAI prediction and applies an optimizer artifact.

Java
import dev.axllm.ax.*;
import java.nio.file.*;
import java.util.*;

public final class AxgenOptimizationExample {
  static String apiKey() {
    String apiKey = System.getenv("OPENAI_API_KEY");
    if (apiKey == null || apiKey.isBlank()) apiKey = System.getenv("OPENAI_APIKEY");
    if (apiKey == null || apiKey.isBlank()) {
      throw new IllegalStateException("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example.");
    }
    return apiKey;
  }

  static OpenAICompatibleClient client() {
    return new OpenAICompatibleClient(
        Map.of("api_key", apiKey(), "model", System.getenv().getOrDefault("AX_OPENAI_MODEL", "gpt-5.4-mini"), "model_config", Map.of("temperature", 0.0)));
  }

  static final class ExampleOptimizer implements OptimizerEngine {
    public String name() { return "example"; }
    public String version() { return "1"; }
    public Map<String, Object> optimize(Map<String, Object> request) {
      return Map.of("componentMap", Map.of("priority::instruction", "Classify operational risk. Use high for production-impacting urgency."), "metadata", Map.of("source", "axgen"));
    }
  }

  public static void main(String[] args) throws Exception {
    AxGen program = new AxGen(Ax.s("emailText:string -> priority:class \"high, normal, low\", rationale:string"), Map.of("id", "priority", "instruction", "Classify the email priority."));
    Map<String, Object> baseline = program.forward(client(), Map.of("emailText", "Production checkout is failing for enterprise customers."));
    Map<String, Object> artifact = program.optimizeWith(new ExampleOptimizer(), List.of(Map.of("emailText", "URGENT: checkout is down", "priority", "high")), Map.of("apply", false));
    program.applyOptimization(Json.stringify(artifact));
    Map<String, Object> after = program.forward(client(), Map.of("emailText", "Production checkout is failing for enterprise customers."));
    System.out.println(Json.stringify(Map.of("baseline", baseline, "after", after)));
  }
}
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