Mastra

Mastra traces every agent run, model call and tool call in GenAI semantic conventions through its OpenTelemetry exporter.

New to Oodle?

Oodle is a managed observability platform for metrics, logs, traces and agent traces. It ingests OpenTelemetry natively, so the snippet on this page is the complete setup.

Each trace shows the full transcript, the token counts and cost of every call, the agent structure (runs, steps and tool calls) and Signals: labels for loops, rate limits, refusals and tool failures, detected as the trace arrives.

Sign up for free to get an instance ID and an API key, or see the Agent Observability overview first.

You will need:

• OODLE_INSTANCE: your Oodle instance ID (ap1, us1)
• OODLE_API_KEY: an Oodle API key (ap1, us1)
• OTLP_ENDPOINT: your OTLP collector domain, shown on the tile

Open the Mastra tile on the ap1, us1 page to get these filled in for you, or let an agent do the setup with /oodle-onboarding set up the llm_observability_mastra integration.

Install

Install the Mastra OTel exporter alongside the OTLP transport:

npm install @mastra/core @mastra/observability @mastra/otel-exporter \
  @opentelemetry/exporter-trace-otlp-proto @ai-sdk/openai zod

Instrument

Add the OTel exporter to the Mastra observability config. Mastra then traces every agent run, model call and tool call in GenAI semantic conventions:

// src/mastra/index.ts
import { Mastra } from '@mastra/core';
import { Agent } from '@mastra/core/agent';
import { createTool } from '@mastra/core/tools';
import { Observability } from '@mastra/observability';
import { OtelExporter } from '@mastra/otel-exporter';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const getWeather = createTool({
  id: 'get_weather',
  description: 'Return the weather for a city.',
  inputSchema: z.object({ city: z.string() }),
  execute: async ({ city }) => `Sunny in ${city}`,
});

const weatherAgent = new Agent({
  id: 'weather-agent',
  name: 'weather-agent',
  instructions: 'Use tools when asked about the weather.',
  model: openai('gpt-4o-mini'),
  tools: { getWeather },
});

export const mastra = new Mastra({
  agents: { weatherAgent },
  observability: new Observability({
    configs: {
      default: {
        serviceName: 'my-llm-app',
        exporters: [
          new OtelExporter({
            provider: {
              custom: {
                endpoint: 'https://<OTLP_ENDPOINT>',
                protocol: 'http/protobuf',
                headers: {
                  'X-API-KEY': '<OODLE_API_KEY>',
                  'X-OODLE-INSTANCE': '<OODLE_INSTANCE>',
                },
              },
            },
          }),
        ],
      },
    },
  }),
});

// Every agent.generate() / agent.stream() call is then traced:
// const result = await mastra.getAgent('weatherAgent').generate(
//   'What is the weather in Paris?',
// );

Environment

Point the application at Oodle:

# Compress the export: prompt payloads are large
export OTEL_EXPORTER_OTLP_COMPRESSION=gzip

Verify

Run your application, then open ap1, us1. Spans carry gen_ai.* attributes: the model, token counts, and the prompt and response content. Click a trace for the Transcript, the waterfall, and the cost breakdown.

If nothing arrives, check that the exporter can reach https://<OTLP_ENDPOINT> and that the instance and key are set: the OTLP gateway answers 401 without them.