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

Tracing

Specialized tracing for AI operations that captures:

• Model 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

Quick Start

Configure Observability in your Mastra instance:

src/mastra/index.ts

import { Mastra } from "@mastra/core";
import { PinoLogger } from "@mastra/loggers";
import { LibSqlStorage } from "@mastra/libsql";
import { Observability } from "@mastra/observability";

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

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 MLflow, Langfuse, Braintrust, and any OpenTelemetry-compatible platform (Datadog, New Relic, SigNoz, etc.). See more about this in the Tracing documentation.

What's Next?

• Set up Tracing: Configure tracing for your application
• Configure Logging: Add structured logging
• API Reference: Detailed configuration options