Java / Kotlin SDK

Install

<!-- Maven -->
<dependency>
  <groupId>com.notilens</groupId>
  <artifactId>notilens</artifactId>
  <version>0.4.1</version>
</dependency>

// Gradle (Kotlin DSL)
implementation("com.notilens:notilens:0.4.1")

Initialize

import com.notilens.NotiLens;

// Java
NotiLens nl = NotiLens.init("my-app", "YOUR_TOKEN", "YOUR_SECRET");

// Or use environment variables
NotiLens nl = NotiLens.init("my-app");

// Kotlin
val nl = NotiLens.init("my-app", "YOUR_TOKEN", "YOUR_SECRET")

Task Events

Real-world uses: wrap an AI research agent (start → progress → complete/fail), a nightly invoice import (loop per batch → complete), or a user-triggered export (start → output_generated → complete).

NotiLens.Run run = nl.task("email");
run.queue();
run.start();

run.progress("Fetching records...");
run.loop("Processing batch 2");
run.retry();
run.pause("Rate limited");
run.resume("Resuming");
run.wait("Awaiting tool");
run.error("Non-fatal error");

run.complete("All done");
run.fail("Unrecoverable");
run.timeout("Exceeded 30s");
run.cancel("User cancelled");
run.terminate("OOM");

Metrics

Real-world uses: track LLM token spend across a multi-step run. Monitor records in a nightly ETL — NotiLens alerts if count drops vs baseline. Watch API error rate across runs.

run.metric("tokens", 350);
run.metric("tokens", 210);   // now 560
run.metric("cost", 0.0012);
run.metric("model", "gpt-4");

run.resetMetrics("tokens");
run.resetMetrics();

Complete Working Example

import com.notilens.NotiLens;

NotiLens nl      = NotiLens.init("summarizer", "TOKEN", "SECRET");
NotiLens.Run run = nl.task("report");
run.start();

try {
    run.progress("Fetching PDF");
    Result result = llm.complete(prompt);
    run.metric("tokens", result.getUsage().getTotalTokens());
    run.metric("cost",   result.getUsage().getCost());
    run.outputGenerated("Summary ready");
    run.complete("Done");
} catch (Exception e) {
    run.fail(e.getMessage());
}