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The SDK ships with a token economy module designed to estimate cost and expose telemetry for MCP operations.

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Estimators

createTokenEstimator() initializes the best available estimator. By default, it loads the o200k_base BPE encoding scheme, which is the industry standard for modern OpenAI models (GPT-4o, o1, o3-mini) and provides a highly accurate baseline for Anthropic and Google models. createSyncTokenEstimator() provides an immediate heuristic fallback (chars/4) when asynchronous loading of the heavy BPE tokenizer is not possible. 
import {
  createTokenEstimator,
  createSyncTokenEstimator,
  HeuristicTokenEstimator,
  RealTokenEstimator
} from "@nekzus/liop";

// Automatically uses the o200k_base exact BPE tokenizer
const asyncEstimator = await createTokenEstimator();
const syncEstimator = createSyncTokenEstimator();

const estimated = asyncEstimator.countTokens("hello liop");

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TokenTelemetryEngine

TokenTelemetryEngine is a singleton collector for operation-level metrics:
  • Input/output token estimates
  • Operation type (tools_list, tool_call, resource_read, etc.)
  • Duration metadata
  • Session-level aggregates
import { TokenTelemetryEngine } from "@nekzus/liop";

const telemetry = TokenTelemetryEngine.getInstance();
telemetry.record({
  type: "tool_call",
  method: "tools/call",
  estimatedInputTokens: 120,
  estimatedOutputTokens: 64
});

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LiopOTelBridge

LiopOTelBridge maps token telemetry into OpenTelemetry gen_ai.* semantics so external observability backends can ingest LIOP token data seamlessly. The bridge automatically binds to your global MeterProvider and emits the following metrics:
  • gen_ai.client.token.usage (Histogram)
  • gen_ai.client.operation.duration (Histogram)
Use this module when you need production cost visibility across local and mesh-routed MCP workloads.

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LiopMeshStatus Integration

The LIOP Agent exposes a local diagnostic tool called LiopMeshStatus that consumes the TokenTelemetryEngine internally to produce a rich, human-readable status block via formatStatusBlock(). When an LLM calls this tool, it receives a structured report containing:
  • Session ID and uptime
  • Estimator model (o200k_base or heuristic)
  • Operation breakdown — each MCP method called, with input/output token estimates
  • Cumulative totals — total tokens consumed across the session
  • Cost projection — estimated cost based on configurable per-token pricing
The token economy telemetry is intentionally visible in the LiopMeshStatus output. This transparency enables B2B clients to audit consumption patterns and allows AI agents to autonomously manage their context window budgets without requiring external monitoring infrastructure.