@countly/ai-sdk-google-genai

Countly AI observability adapter for the Google GenAI TypeScript SDK.

Part of the Countly AI SDK — provider-agnostic LLM observability for every AI stack.

Install

npm install @countly/ai-sdk-google-genai

@countly/ai-sdk-core is pulled in automatically.

Peer dependency

@google/genai >= 1.0.0

Quick Start

import { GoogleGenAI } from "@google/genai";
import { observeGoogleGenAI } from "@countly/ai-sdk-google-genai";
import { AsyncLocalStorage } from "node:async_hooks";

const userStore = new AsyncLocalStorage<{ userId: string }>();

app.use((req, res, next) => {
  userStore.run({ userId: req.user.id }, next);
});

const client = observeGoogleGenAI(new GoogleGenAI({ apiKey: "..." }), {
  appKey: "YOUR_APP_KEY",
  url: "https://your-countly-server.com",
  getDeviceId: () => userStore.getStore()?.userId,
});

const response = await client.models.generateContent({
  model: "gemini-2.5-pro",
  contents: "Explain quantum computing",
});

Streaming

const stream = await client.models.generateContentStream({
  model: "gemini-2.5-flash",
  contents: "Hello",
});

for await (const chunk of stream) {
  process.stdout.write(chunk.text || "");
}

What's captured

• Token usage (promptTokenCount, candidatesTokenCount, totalTokenCount)
• Reasoning tokens (thoughtsTokenCount for thinking models)
• Cached content tokens (cachedContentTokenCount)
• Cost (computed from model pricing)
• Latency (total + TTFT for streaming)
• Finish reason normalized to stop | length | content_filter | error | other (from STOP, MAX_TOKENS, SAFETY, RECITATION, etc.)
• Function call tool extraction
• Error tracking with categorization
• APM traces, per-user aggregation

Caller-supplied prompt_id

By default every tracked call is stamped with an auto-generated prompt_id. If your app already has an identifier for the interaction (a chat message id, a request id, a trace id), supply it with getPromptId and the adapter uses it verbatim for the [CLY]_llm_interaction event instead of generating one. This lets you correlate Countly analytics with your own logs and store feedback against an id you already control:

import { AsyncLocalStorage } from "node:async_hooks";

const requestStore = new AsyncLocalStorage<{ promptId: string }>();

const ai = observeGoogleGenAI(new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY }), {
  appKey: "YOUR_APP_KEY",
  url: "https://your-countly-server.com",
  // Return your own id; return undefined to fall back to the generated one.
  getPromptId: () => requestStore.getStore()?.promptId,
});

getPromptId is called once per generateContent / generateContentStream call, before the request runs. When it is absent or returns undefined, the adapter falls back to the generated prompt_id (identical behavior to not setting it). The resolved id is exactly what surfaces through the onPrompt callback below, so caller-supplied ids flow straight into feedback correlation.

Feedback

User feedback (thumbs up/down, ratings, comments) is not auto-collected — wire it from your UI. Capture the prompt_id of each tracked interaction via the onPrompt callback, then record feedback against it with createFeedbackTracker (re-exported from this package, so no extra install is needed):

import { GoogleGenAI } from "@google/genai";
import { observeGoogleGenAI, createFeedbackTracker, type PromptInfo } from "@countly/ai-sdk-google-genai";

const countly = { appKey: "YOUR_APP_KEY", url: "https://your-countly-server.com" };

let lastPrompt: PromptInfo | undefined;
const ai = observeGoogleGenAI(new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY }), {
  ...countly,
  onPrompt: (info) => { lastPrompt = info; }, // fires after every tracked call
});

const feedback = createFeedbackTracker(countly, { sdk_adapter: "google-genai" });

const response = await ai.models.generateContent({
  model: "gemini-2.0-flash",
  contents: "Explain quantum computing",
});

// ...later, when the user rates the answer:
feedback.track({
  prompt_id: lastPrompt!.prompt_id,
  rating: "thumbs_up", // or "thumbs_down", or any custom string
  score: 0.9, // optional 0-1 numeric score
  category: "helpful", // optional: hallucination, irrelevant, harmful, ...
  comment: "Great answer", // optional free-form text
  deviceId: user.id, // attribute to the same user as the interaction
});

Each track() call emits a [CLY]_llm_interaction_feedback event whose prompt_id links back to the [CLY]_llm_interaction event — powering prompt → feedback funnels and per-model satisfaction breakdowns in Countly. In a real app, store prompt_id alongside the rendered message (or return it to your client) and read it back when the user rates the answer. Feedback is batched like interaction events; call feedback.flush() to send immediately, or feedback.shutdown() on process exit.

Full documentation

See the Countly AI SDK repository for the unified data model, observability levels, cost calculation, privacy controls, and Countly plugin integration (Drill, Funnels, Cohorts, APM, Crash Analytics).

License

MIT