@juspay/neurolink/dist/core/analytics.js

The pipe layer of an AI nervous system: one interface connecting provider neurons to your application, across three inference types — generate, stream and decide. `decide` returns typed, calibrated judgements from a non-generative model (~400ms, ~$0.00002

/**
 * NeuroLink Analytics System
 *
 * Provides lightweight analytics tracking for AI provider usage,
 * including tokens, costs, performance metrics, and custom context.
 */
import { logger } from "../utils/logger.js";
import { extractTokenUsage as extractTokenUsageUtil } from "../utils/tokenUtils.js";
import { calculateCost, hasPricing } from "../utils/pricing.js";
/**
 * Create analytics data structure from AI response
 */
export function createAnalytics(provider, model, result, responseTime, context) {
    const functionTag = "createAnalytics";
    try {
        // Extract token usage from different result formats
        const tokens = extractTokenUsage(result);
        // Estimate cost based on provider and tokens
        const cost = estimateCost(provider, model, tokens);
        // Turn-lifecycle telemetry from native agentic loops (Vertex
        // Gemini/Claude): stopReason / rawFinishReason / stepsUsed ride the
        // provider result; toolCallCount and elapsedMs derive from it.
        const turnResult = result;
        const toolCallCount = Array.isArray(turnResult.toolExecutions)
            ? turnResult.toolExecutions.length
            : Array.isArray(turnResult.toolsUsed)
                ? turnResult.toolsUsed.length
                : undefined;
        const analytics = {
            provider,
            model,
            tokenUsage: tokens,
            cost,
            requestDuration: responseTime,
            context: context,
            timestamp: new Date().toISOString(),
            ...(typeof turnResult.stepsUsed === "number" && {
                stepsUsed: turnResult.stepsUsed,
            }),
            ...(toolCallCount !== undefined && { toolCallCount }),
            ...(typeof turnResult.stopReason === "string" && {
                stopReason: turnResult.stopReason,
            }),
            ...(typeof turnResult.responseTime === "number" && {
                elapsedMs: turnResult.responseTime,
            }),
            ...(typeof turnResult.rawFinishReason === "string" && {
                rawFinishReason: turnResult.rawFinishReason,
            }),
        };
        logger.debug(`[${functionTag}] Analytics created`, {
            provider,
            model,
            tokens: tokens.total,
            responseTime,
            cost,
        });
        return analytics;
    }
    catch (error) {
        logger.error(`[${functionTag}] Failed to create analytics`, { error });
        // Return minimal analytics on error
        return {
            provider,
            model,
            tokenUsage: { input: 0, output: 0, total: 0 },
            requestDuration: responseTime,
            context: context,
            timestamp: new Date().toISOString(),
        };
    }
}
/**
 * Extract token usage from various AI result formats
 * Delegates to centralized tokenUtils for consistent extraction across providers
 */
function extractTokenUsage(result) {
    // Use centralized token extraction utility
    // The utility handles nested usage objects, multiple provider formats,
    // cache tokens, reasoning tokens, and cache savings calculation
    // Cast result to allow extractTokenUsageUtil to handle type normalization
    return extractTokenUsageUtil(result.usage);
}
/**
 * Estimate cost based on provider, model, and token usage.
 * Uses known per-model rates, including cache tiers. An unpriced model has
 * unknown cost; another model's provider default cannot estimate its bill.
 */
function estimateCost(provider, model, tokens) {
    try {
        // Try the per-model pricing table first (includes cache token rates)
        if (hasPricing(provider, model)) {
            return calculateCost(provider, model, tokens);
        }
        return undefined;
    }
    catch (error) {
        logger.debug("Cost estimation failed", { provider, model, error });
        return undefined;
    }
}