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Observability Overview

Mastra provides comprehensive observability features designed specifically for AI applications. Monitor LLM operations, trace agent decisions, and debug complex workflows with specialized tools that understand AI-specific patterns.

Key Features

Structured Logging

Debug applications with contextual logging:

  • Context propagation: Automatic correlation with traces
  • Configurable levels: Filter by severity in development and production

AI Tracing

Specialized tracing for AI operations that captures:

  • LLM interactions: Token usage, latency, prompts, and completions
  • Agent execution: Decision paths, tool calls, and memory operations
  • Workflow steps: Branching logic, parallel execution, and step outputs
  • Automatic instrumentation: Zero-configuration tracing with decorators

OTEL Tracing

Traditional distributed tracing with OpenTelemetry:

  • Standard OTLP protocol: Compatible with existing observability infrastructure
  • HTTP and database instrumentation: Automatic spans for common operations
  • Provider integrations: Datadog, New Relic, Jaeger, and other OTLP collectors
  • Distributed context: W3C Trace Context propagation

Quick Start

Configure Observability in your Mastra instance:

src/mastra/index.ts
import { Mastra } from "@mastra/core";
import { PinoLogger } from "@mastra/core";
import { LibSqlStorage } from "@mastra/libsql";

export const mastra = new Mastra({
// ... other config
logger: new PinoLogger(),
observability: {
default: { enabled: true }, // Enables AI Tracing
},
storage: new LibSQLStore({
url: "file:./mastra.db", // Storage is required for tracing
}),
telemetry: {
enabled: true, // Enables OTEL Tracing
},
});

With this basic setup, you will see Traces and Logs in both Studio and in Mastra Cloud.

We also support various external tracing providers like Langfuse, Braintrust, and any OpenTelemetry-compatible platform (Datadog, New Relic, SigNoz, etc.). See more about this in the AI Tracing documentation.

What's Next?

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