> ## Documentation Index
> Fetch the complete documentation index at: https://nekzus-32.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Economy and Telemetry

> Measure token consumption and export observability metrics

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

## 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.

```typescript theme={null}
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");
```

## 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

```typescript theme={null}
import { TokenTelemetryEngine } from "@nekzus/liop";

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

## 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.

## 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

<Tip>
  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.
</Tip>