# Tracing

Tracing provides specialized monitoring and debugging for the AI-related operations in your application. When enabled, Mastra automatically creates traces for agent runs, LLM generations, tool calls, and workflow steps with AI-specific context and metadata.

Unlike traditional application tracing, Tracing focuses specifically on understanding your AI pipeline — capturing token usage, model parameters, tool execution details, and conversation flows. This makes it easier to debug issues, optimize performance, and understand how your AI systems behave in production.

## How it works

Traces are created by:

- **Configure exporters** → send trace data to observability platforms
- **Set sampling strategies** → control which traces are collected
- **Run agents and workflows** → Mastra auto-instruments them with Tracing

## Configuration

### Basic Config

```ts
import { Mastra } from '@mastra/core'
import {
  Observability,
  DefaultExporter,
  CloudExporter,
  SensitiveDataFilter,
} from '@mastra/observability'

export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      default: {
        serviceName: 'mastra',
        exporters: [
          new DefaultExporter(), // Persists traces to storage for Studio
          new CloudExporter(), // Sends traces to Mastra Cloud (if MASTRA_CLOUD_ACCESS_TOKEN is set)
        ],
        spanOutputProcessors: [
          new SensitiveDataFilter(), // Redacts sensitive data like passwords, tokens, keys
        ],
      },
    },
  }),
  storage: new LibSQLStore({
    id: 'mastra-storage',
    url: 'file:./mastra.db', // Storage is required for tracing
  }),
})
```

This configuration includes:

- **Service Name**: `"mastra"` - identifies your service in traces

- **Sampling**: `"always"` by default (100% of traces)

- **Exporters**:

  - `DefaultExporter` - Persists traces to your configured storage for Studio
  - `CloudExporter` - Sends traces to Mastra Cloud (requires `MASTRA_CLOUD_ACCESS_TOKEN`)

- **Span Output Processors**: `SensitiveDataFilter` - Redacts sensitive fields

## Exporters

Exporters determine where your trace data is sent and how it's stored. They integrate with your existing observability stack, support data residency requirements, and can be optimized for cost and performance. You can use multiple exporters simultaneously to send the same trace data to different destinations — for example, storing detailed traces locally for debugging while sending sampled data to a cloud provider for production monitoring.

### Internal Exporters

Mastra provides two built-in exporters:

- **[Default](https://mastra.ai/docs/observability/tracing/exporters/default)** - Persists traces to local storage for viewing in Studio
- **[Cloud](https://mastra.ai/docs/observability/tracing/exporters/cloud)** - Sends traces to Mastra Cloud for production monitoring and collaboration

### External Exporters

In addition to the internal exporters, Mastra supports integration with popular observability platforms. These exporters allow you to leverage your existing monitoring infrastructure and take advantage of platform-specific features like alerting, dashboards, and correlation with other application metrics.

- **[Arize](https://mastra.ai/docs/observability/tracing/exporters/arize)** - Exports traces to Arize Phoenix or Arize AX using OpenInference semantic conventions
- **[Braintrust](https://mastra.ai/docs/observability/tracing/exporters/braintrust)** - Exports traces to Braintrust's eval and observability platform
- **[Datadog](https://mastra.ai/docs/observability/tracing/exporters/datadog)** - Sends traces to Datadog APM via OTLP for full-stack observability with AI tracing
- **[Laminar](https://mastra.ai/docs/observability/tracing/exporters/laminar)** - Sends traces to Laminar via OTLP/HTTP (protobuf) with Laminar-native span attributes + scorer support
- **[Langfuse](https://mastra.ai/docs/observability/tracing/exporters/langfuse)** - Sends traces to the Langfuse open-source LLM engineering platform
- **[LangSmith](https://mastra.ai/docs/observability/tracing/exporters/langsmith)** - Pushes traces into LangSmith's observability and evaluation toolkit
- **[PostHog](https://mastra.ai/docs/observability/tracing/exporters/posthog)** - Sends traces to PostHog for AI analytics and product insights
- **[Sentry](https://mastra.ai/docs/observability/tracing/exporters/sentry)** - Sends traces to Sentry for AI tracing and monitoring using OpenTelemetry semantic conventions
- **[OpenTelemetry](https://mastra.ai/docs/observability/tracing/exporters/otel)** - Deliver traces to any OpenTelemetry-compatible observability system
  - Supports: Dash0, MLflow, New Relic, SigNoz, Traceloop, Zipkin, and others!

## Bridges

Bridges provide bidirectional integration with external tracing systems. Unlike exporters that send trace data to external platforms, bridges create native spans in external systems and inherit context from them. This enables Mastra operations to participate in existing distributed traces.

- **[OpenTelemetry Bridge](https://mastra.ai/docs/observability/tracing/bridges/otel)** - Integrate with existing OpenTelemetry infrastructure

### Bridges vs Exporters

| Feature                                  | Bridges                      | Exporters                 |
| ---------------------------------------- | ---------------------------- | ------------------------- |
| Creates native spans in external systems | Yes                          | No                        |
| Inherits context from external systems   | Yes                          | No                        |
| Sends data to backends                   | Via external SDK             | Directly                  |
| Use case                                 | Existing distributed tracing | Standalone Mastra tracing |

You can use both together — a bridge for context propagation and exporters to send traces to additional destinations.

## Sampling strategies

Sampling allows you to control which traces are collected, helping you balance between observability needs and resource costs. In production environments with high traffic, collecting every trace can be expensive and unnecessary. Sampling strategies let you capture a representative subset of traces while ensuring you don't miss critical information about errors or important operations.

Mastra supports four sampling strategies:

### Always Sample

Collects 100% of traces. Best for development, debugging, or low-traffic scenarios where you need complete visibility.

```ts
sampling: {
  type: 'always'
}
```

### Never Sample

Disables tracing entirely. Useful for specific environments where tracing adds no value or when you need to temporarily disable tracing without removing configuration.

```ts
sampling: {
  type: 'never'
}
```

### Ratio-Based Sampling

Randomly samples a percentage of traces. Ideal for production environments where you want statistical insights without the cost of full tracing. The probability value ranges from 0 (no traces) to 1 (all traces).

```ts
sampling: {
  type: 'ratio',
  probability: 0.1  // Sample 10% of traces
}
```

### Custom Sampling

Implements your own sampling logic based on request context, metadata, or business rules. Perfect for complex scenarios like sampling based on user tier, request type, or error conditions.

```ts
sampling: {
  type: 'custom',
  sampler: (options) => {
    // Sample premium users at higher rate
    if (options?.metadata?.userTier === 'premium') {
      return Math.random() < 0.5; // 50% sampling
    }

    // Default 1% sampling for others
    return Math.random() < 0.01;
  }
}
```

### Complete Example

```ts
export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      '10_percent': {
        serviceName: 'my-service',
        // Sample 10% of traces
        sampling: {
          type: 'ratio',
          probability: 0.1,
        },
        exporters: [new DefaultExporter()],
      },
    },
  }),
})
```

## Multi-config setup

Complex applications often require different tracing configurations for different scenarios. You might want detailed traces with full sampling during development, sampled traces sent to external providers in production, and specialized configurations for specific features or customer segments. The `configSelector` function enables dynamic configuration selection at runtime, allowing you to route traces based on request context, environment variables, feature flags, or any custom logic.

This approach is particularly valuable when:

- Running A/B tests with different observability requirements
- Providing enhanced debugging for specific customers or support cases
- Gradually rolling out new tracing providers without affecting existing monitoring
- Optimizing costs by using different sampling rates for different request types
- Maintaining separate trace streams for compliance or data residency requirements

> **Info:** Note that only a single config can be used for a specific execution. But a single config can send data to multiple exporters simultaneously.

### Dynamic Configuration Selection

Use `configSelector` to choose the appropriate tracing configuration based on request context:

```ts
export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      langfuse: {
        serviceName: 'langfuse-service',
        exporters: [langfuseExporter],
      },
      braintrust: {
        serviceName: 'braintrust-service',
        exporters: [braintrustExporter],
      },
      debug: {
        serviceName: 'debug-service',
        sampling: { type: 'always' },
        exporters: [new DefaultExporter()],
      },
    },
    configSelector: (context, availableTracers) => {
      // Use debug config for support requests
      if (context.requestContext?.get('supportMode')) {
        return 'debug'
      }

      // Route specific customers to different providers
      const customerId = context.requestContext?.get('customerId')
      if (customerId && premiumCustomers.includes(customerId)) {
        return 'braintrust'
      }

      // Route specific requests to langfuse
      if (context.requestContext?.get('useExternalTracing')) {
        return 'langfuse'
      }

      throw new Error('no config found')
    },
  }),
})
```

### Environment-Based Configuration

A common pattern is to select configurations based on deployment environment:

```ts
export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      development: {
        serviceName: 'my-service-dev',
        sampling: { type: 'always' },
        exporters: [new DefaultExporter()],
      },
      staging: {
        serviceName: 'my-service-staging',
        sampling: { type: 'ratio', probability: 0.5 },
        exporters: [langfuseExporter],
      },
      production: {
        serviceName: 'my-service-prod',
        sampling: { type: 'ratio', probability: 0.01 },
        exporters: [cloudExporter, langfuseExporter],
      },
    },
    configSelector: (context, availableTracers) => {
      const env = process.env.NODE_ENV || 'development'
      return env
    },
  }),
})
```

### Common Configuration Patterns & Troubleshooting

#### Maintaining Studio and Cloud Access

When adding external exporters, include `DefaultExporter` and `CloudExporter` to maintain access to Studio and Mastra Cloud:

```ts
import {
  Observability,
  DefaultExporter,
  CloudExporter,
  SensitiveDataFilter,
} from '@mastra/observability'
import { ArizeExporter } from '@mastra/arize'

export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      production: {
        serviceName: 'my-service',
        exporters: [
          new ArizeExporter({
            endpoint: process.env.PHOENIX_ENDPOINT,
            apiKey: process.env.PHOENIX_API_KEY,
          }),
          new DefaultExporter(), // Keep Studio access
          new CloudExporter(), // Keep Cloud access
        ],
        spanOutputProcessors: [new SensitiveDataFilter()],
      },
    },
  }),
})
```

This configuration sends traces to all three destinations simultaneously:

- **Arize Phoenix/AX** for external observability
- **DefaultExporter** for Studio
- **CloudExporter** for Mastra Cloud dashboard

> **Info:** Remember: A single trace can be sent to multiple exporters. You don't need separate configs for each exporter unless you want different sampling rates or processors.

## Adding custom metadata

Custom metadata allows you to attach additional context to your traces, making it easier to debug issues and understand system behavior in production. Metadata can include business logic details, performance metrics, user context, or any information that helps you understand what happened during execution.

You can add metadata to any span using the tracing context:

```ts
execute: async (inputData, context) => {
  const startTime = Date.now()
  const response = await fetch(inputData.endpoint)

  // Add custom metadata to the current span
  context?.tracingContext.currentSpan?.update({
    metadata: {
      apiStatusCode: response.status,
      endpoint: inputData.endpoint,
      responseTimeMs: Date.now() - startTime,
      userTier: inputData.userTier,
      region: process.env.AWS_REGION,
    },
  })

  return await response.json()
}
```

Metadata set here will be shown in all configured exporters.

### Automatic Metadata from `RequestContext`

Instead of manually adding metadata to each span, you can configure Mastra to automatically extract values from RequestContext and attach them as metadata to all spans in a trace. This is useful for consistently tracking user identifiers, environment information, feature flags, or any request-scoped data across your entire trace.

#### Configuration-Level Extraction

Define which RequestContext keys to extract in your tracing configuration. These keys will be automatically included as metadata for all spans created with this configuration:

```ts
export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      default: {
        serviceName: 'my-service',
        requestContextKeys: ['userId', 'environment', 'tenantId'],
        exporters: [new DefaultExporter()],
      },
    },
  }),
})
```

Now when you execute agents or workflows with a RequestContext, these values are automatically extracted:

```ts
const requestContext = new RequestContext()
requestContext.set('userId', 'user-123')
requestContext.set('environment', 'production')
requestContext.set('tenantId', 'tenant-456')

// All spans in this trace automatically get userId, environment, and tenantId metadata
const result = await agent.generate('Hello', {
  requestContext,
})
```

#### Per-Request Additions

You can add trace-specific keys using `tracingOptions.requestContextKeys`. These are merged with the configuration-level keys:

```ts
const requestContext = new RequestContext()
requestContext.set('userId', 'user-123')
requestContext.set('environment', 'production')
requestContext.set('experimentId', 'exp-789')

const result = await agent.generate('Hello', {
  requestContext,
  tracingOptions: {
    requestContextKeys: ['experimentId'], // Adds to configured keys
  },
})

// All spans now have: userId, environment, AND experimentId
```

#### Nested Value Extraction

Use dot notation to extract nested values from RequestContext:

```ts
export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      default: {
        requestContextKeys: ['user.id', 'session.data.experimentId'],
        exporters: [new DefaultExporter()],
      },
    },
  }),
})

const requestContext = new RequestContext()
requestContext.set('user', { id: 'user-456', name: 'John Doe' })
requestContext.set('session', { data: { experimentId: 'exp-999' } })

// Metadata will include: { user: { id: 'user-456' }, session: { data: { experimentId: 'exp-999' } } }
```

#### How it works

1. **TraceState Computation**: At the start of a trace (root span creation), Mastra computes which keys to extract by merging configuration-level and per-request keys
2. **Automatic Extraction**: Root spans (agent runs, workflow executions) automatically extract metadata from RequestContext
3. **Child Span Extraction**: Child spans can also extract metadata if you pass `requestContext` when creating them
4. **Metadata Precedence**: Explicit metadata passed to span options always takes precedence over extracted metadata

### Adding Tags to Traces

Tags are string labels that help you categorize and filter traces. Unlike metadata (which contains structured key-value data), tags are plain strings designed for quick filtering and organization.

Use `tracingOptions.tags` to add tags when executing agents or workflows:

```ts
// With agents
const result = await agent.generate('Hello', {
  tracingOptions: {
    tags: ['production', 'experiment-v2', 'user-request'],
  },
})

// With workflows
const run = await mastra.getWorkflow('myWorkflow').createRun()
const result = await run.start({
  inputData: { data: 'process this' },
  tracingOptions: {
    tags: ['batch-processing', 'priority-high'],
  },
})
```

#### How Tags Work

- **Root span only**: Tags are applied only to the root span of a trace (the agent run or workflow run span)

- **Widely supported**: Tags are supported by most exporters for filtering and searching traces:

  - **Braintrust** - Native `tags` field
  - **Langfuse** - Native `tags` field on traces
  - **ArizeExporter** - `tag.tags` OpenInference attribute
  - **OtelExporter** - `mastra.tags` span attribute
  - **OtelBridge** - `mastra.tags` span attribute

- **Combinable with metadata**: You can use both `tags` and `metadata` in the same `tracingOptions`

```ts
const result = await agent.generate([{ role: 'user', content: 'Analyze this' }], {
  tracingOptions: {
    tags: ['production', 'analytics'],
    metadata: { userId: 'user-123', experimentId: 'exp-456' },
  },
})
```

#### Common Tag Patterns

- **Environment**: `"production"`, `"staging"`, `"development"`
- **Feature flags**: `"feature-x-enabled"`, `"beta-user"`
- **Request types**: `"user-request"`, `"batch-job"`, `"scheduled-task"`
- **Priority levels**: `"priority-high"`, `"priority-low"`
- **Experiments**: `"experiment-v1"`, `"control-group"`, `"treatment-a"`

### Hiding Sensitive Input/Output

When processing sensitive data, you may want to prevent input and output values from being logged to your observability platforms. Use `hideInput` and `hideOutput` in `tracingOptions` to exclude this data from all spans in a trace:

```ts
// Hide input data (e.g., user credentials, PII)
const result = await agent.generate([{ role: 'user', content: 'Process this sensitive data' }], {
  tracingOptions: {
    hideInput: true, // Input will be hidden from all spans
  },
})

// Hide output data (e.g., generated secrets, confidential results)
const result = await agent.generate([{ role: 'user', content: 'Generate API keys' }], {
  tracingOptions: {
    hideOutput: true, // Output will be hidden from all spans
  },
})

// Hide both input and output
const result = await agent.generate([{ role: 'user', content: 'Handle confidential request' }], {
  tracingOptions: {
    hideInput: true,
    hideOutput: true,
  },
})
```

#### How it works

- **Trace-wide effect**: When set on the root span, these options apply to all child spans in the trace (tool calls, model generations, etc.)
- **Export-time filtering**: The data is still available internally during execution but is excluded when spans are exported to observability platforms
- **Combinable with other options**: You can use `hideInput`/`hideOutput` alongside `tags`, `metadata`, and other `tracingOptions`

```ts
const result = await agent.generate([{ role: 'user', content: 'Sensitive operation' }], {
  tracingOptions: {
    hideInput: true,
    hideOutput: true,
    tags: ['sensitive-operation', 'pii-handling'],
    metadata: { operationType: 'credential-processing' },
  },
})
```

> **Tip:** For more granular control over sensitive data, consider using the [Sensitive Data Filter](https://mastra.ai/docs/observability/tracing/processors/sensitive-data-filter) processor, which can redact specific fields (like passwords, tokens, and keys) while preserving the rest of the input/output.

#### Child Spans and Metadata Extraction

When creating child spans within tools or workflow steps, you can pass the `requestContext` parameter to enable metadata extraction:

```ts
execute: async (inputData, context) => {
  // Create child span WITH requestContext - gets metadata extraction
  const dbSpan = context?.tracingContext.currentSpan?.createChildSpan({
    type: 'generic',
    name: 'database-query',
    requestContext: context?.requestContext, // Pass to enable metadata extraction
  })

  const results = await db.query('SELECT * FROM users')
  dbSpan?.end({ output: results })

  // Or create child span WITHOUT requestContext - no metadata extraction
  const cacheSpan = context?.tracingContext.currentSpan?.createChildSpan({
    type: 'generic',
    name: 'cache-check',
    // No requestContext - won't extract metadata
  })

  return results
}
```

This gives you fine-grained control over which child spans include RequestContext metadata. Root spans (agent/workflow executions) always extract metadata automatically, while child spans only extract when you explicitly pass `requestContext`.

## Creating child spans

Child spans allow you to track fine-grained operations within your workflow steps or tools. They provide visibility into sub-operations like database queries, API calls, file operations, or complex calculations. This hierarchical structure helps you identify performance bottlenecks and understand the exact sequence of operations.

Create child spans inside a tool call or workflow step to track specific operations:

```ts
execute: async (inputData, context) => {
  // Create another child span for the main database operation
  const querySpan = context?.tracingContext.currentSpan?.createChildSpan({
    type: 'generic',
    name: 'database-query',
    input: { query: inputData.query },
    metadata: { database: 'production' },
  })

  try {
    const results = await db.query(inputData.query)
    querySpan?.end({
      output: results.data,
      metadata: {
        rowsReturned: results.length,
        queryTimeMs: results.executionTime,
        cacheHit: results.fromCache,
      },
    })
    return results
  } catch (error) {
    querySpan?.error({
      error,
      metadata: { retryable: isRetryableError(error) },
    })
    throw error
  }
}
```

Child spans automatically inherit the trace context from their parent, maintaining the relationship hierarchy in your observability platform.

## Span formatting

Mastra provides two ways to transform span data before it reaches your observability platform: **span processors** and **custom span formatters**. Both allow you to modify, filter, or enrich trace data, but they operate at different levels and serve different purposes.

| Feature             | Span Processors                 | Custom Span Formatters                         |
| ------------------- | ------------------------------- | ---------------------------------------------- |
| Configuration level | Observability config            | Per-exporter                                   |
| Operates on         | Internal `Span` object          | Exported `ExportedSpan` data                   |
| Applies to          | All exporters                   | Single exporter                                |
| Async support       | No                              | Yes                                            |
| Use case            | Security, filtering, enrichment | Platform-specific formatting, async enrichment |

Use **span processors** for synchronous transformations that should apply to all exporters (like redacting sensitive data). Use **custom span formatters** when different exporters need different representations of the same data (like plain text for one platform and structured data for another), or when you need to perform asynchronous operations like fetching data from external APIs.

### Span Processors

Span processors transform, filter, or enrich trace data before it's exported. They act as a pipeline between span creation and export, enabling you to modify spans for security, compliance, or debugging purposes. Processors run once and affect all exporters.

#### Built-in Processors

- [Sensitive Data Filter](https://mastra.ai/docs/observability/tracing/processors/sensitive-data-filter) redacts sensitive information. It's enabled in the default observability config.

#### Creating Custom Processors

You can create custom span processors by implementing the `SpanOutputProcessor` interface. Here's a basic example that converts all input text in spans to lowercase:

```ts
import type { SpanOutputProcessor, AnySpan } from '@mastra/observability'

export class LowercaseInputProcessor implements SpanOutputProcessor {
  name = 'lowercase-processor'

  process(span: AnySpan): AnySpan {
    span.input = `${span.input}`.toLowerCase()
    return span
  }

  async shutdown(): Promise<void> {
    // Cleanup if needed
  }
}

// Use the custom processor
export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      development: {
        spanOutputProcessors: [new LowercaseInputProcessor(), new SensitiveDataFilter()],
        exporters: [new DefaultExporter()],
      },
    },
  }),
})
```

Processors are executed in the order they're defined, allowing you to chain multiple transformations. Common use cases include:

- Redacting sensitive data (passwords, tokens, API keys)
- Adding environment-specific metadata
- Filtering out spans based on criteria
- Normalizing data formats
- Enriching spans with business context

## Span filtering

Span filtering lets you reduce noise and per-span costs before data reaches your observability platform. Configure it per observability instance, so different exporters or environments can keep different levels of detail.

- Use `excludeSpanTypes` to drop entire categories of spans with minimal configuration.
- Use `spanFilter` when you need custom logic based on the exported span data.

The following example demonstrates how to combine both options in a single config:

```ts
import { Mastra } from '@mastra/core'
import { SpanType } from '@mastra/core/observability'
import { Observability, DefaultExporter } from '@mastra/observability'
import { LangfuseExporter } from '@mastra/langfuse'

export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      default: {
        serviceName: 'my-app',
        exporters: [new DefaultExporter(), new LangfuseExporter()],
        excludeSpanTypes: [SpanType.MODEL_CHUNK, SpanType.MODEL_STEP],
        spanFilter: span => {
          if (span.type === SpanType.TOOL_CALL && span.attributes?.success) {
            return false
          }

          return true
        },
      },
    },
  }),
})
```

Filtering happens at export time in this order:

1. Internal spans are dropped unless `includeInternalSpans` is `true`.
2. `excludeSpanTypes` removes matching span types.
3. `spanOutputProcessors` transform the remaining spans.
4. `spanFilter` decides whether to keep the final exported span.

If `spanFilter` throws, Mastra keeps the span and logs the error to avoid silent data loss. For the full span type list and more examples, see the [Span filtering reference](https://mastra.ai/reference/observability/tracing/span-filtering).

### Custom Span Formatters

Custom span formatters transform how spans appear in specific observability platforms. Unlike span processors, formatters are configured per-exporter, allowing different formatting for different destinations. Formatters support both synchronous and asynchronous operations.

#### Use cases

- **Extract plain text from AI SDK messages** - Convert structured message arrays to readable text
- **Transform input/output formats** - Customize how data displays in specific platforms
- **Platform-specific field mapping** - Add or remove fields based on platform requirements
- **Async data enrichment** - Fetch additional context from external APIs or databases

#### Configuration

Add a `customSpanFormatter` to any exporter configuration:

```ts
import { BraintrustExporter } from '@mastra/braintrust'
import { LangfuseExporter } from '@mastra/langfuse'
import { SpanType } from '@mastra/core/observability'
import type { CustomSpanFormatter } from '@mastra/core/observability'

// Formatter that extracts plain text from AI messages
const plainTextFormatter: CustomSpanFormatter = span => {
  if (span.type === SpanType.AGENT_RUN && Array.isArray(span.input)) {
    const userMessage = span.input.find(m => m.role === 'user')
    return {
      ...span,
      input: userMessage?.content ?? span.input,
    }
  }
  return span
}

export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      default: {
        serviceName: 'my-service',
        exporters: [
          // Braintrust gets plain text formatting
          new BraintrustExporter({
            customSpanFormatter: plainTextFormatter,
          }),
          // Langfuse keeps the original structured format
          new LangfuseExporter(),
        ],
      },
    },
  }),
})
```

#### Chaining Multiple Formatters

Use `chainFormatters` to combine multiple formatters. Chains support both sync and async formatters:

```ts
import { chainFormatters } from '@mastra/observability'

const inputFormatter: CustomSpanFormatter = span => ({
  ...span,
  input: extractPlainText(span.input),
})

const outputFormatter: CustomSpanFormatter = span => ({
  ...span,
  output: extractPlainText(span.output),
})

const exporter = new BraintrustExporter({
  customSpanFormatter: chainFormatters([inputFormatter, outputFormatter]),
})
```

#### Async Formatters

Custom span formatters support asynchronous operations, enabling use cases like fetching data from external APIs or databases to enrich your spans:

```ts
import type { CustomSpanFormatter } from '@mastra/core/observability'

// Async formatter that enriches spans with user data
const userEnrichmentFormatter: CustomSpanFormatter = async span => {
  const userId = span.metadata?.userId
  if (!userId) return span

  // Fetch user data from your API or database
  const userData = await fetchUserData(userId)

  return {
    ...span,
    metadata: {
      ...span.metadata,
      userName: userData.name,
      userEmail: userData.email,
      department: userData.department,
    },
  }
}

// Async formatter that looks up additional context
const contextEnrichmentFormatter: CustomSpanFormatter = async span => {
  if (span.type !== SpanType.AGENT_RUN) return span

  // Fetch experiment configuration
  const experimentConfig = await getExperimentConfig(span.metadata?.experimentId)

  return {
    ...span,
    metadata: {
      ...span.metadata,
      experimentVariant: experimentConfig?.variant,
      experimentGroup: experimentConfig?.group,
    },
  }
}

// Use async formatters with an exporter
const exporter = new BraintrustExporter({
  customSpanFormatter: userEnrichmentFormatter,
})

// Or chain sync and async formatters together
const exporter = new LangfuseExporter({
  customSpanFormatter: chainFormatters([
    plainTextFormatter, // sync
    userEnrichmentFormatter, // async
    contextEnrichmentFormatter, // async
  ]),
})
```

> **Note:** Async formatters add latency to span export. Keep async operations fast (under 100ms) to avoid slowing down your application. Consider using caching for frequently accessed data.

## Serialization options

Serialization options control how span data (input, output, and attributes) is truncated before export. This is useful when working with large payloads, deeply nested objects, or when you need to optimize trace storage.

### Configuration

Add `serializationOptions` to your observability configuration:

```ts
export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      default: {
        serviceName: 'my-service',
        serializationOptions: {
          maxStringLength: 2048, // Maximum length for string values (default: 1024)
          maxDepth: 10, // Maximum depth for nested objects (default: 6)
          maxArrayLength: 100, // Maximum number of items in arrays (default: 50)
          maxObjectKeys: 75, // Maximum number of keys in objects (default: 50)
        },
        exporters: [new DefaultExporter()],
      },
    },
  }),
})
```

### Available Options

| Option            | Default | Description                                                      |
| ----------------- | ------- | ---------------------------------------------------------------- |
| `maxStringLength` | 1024    | Maximum length for string values. Longer strings are truncated.  |
| `maxDepth`        | 6       | Maximum depth for nested objects. Deeper levels are omitted.     |
| `maxArrayLength`  | 50      | Maximum number of items in arrays. Additional items are omitted. |
| `maxObjectKeys`   | 50      | Maximum number of keys in objects. Additional keys are omitted.  |

### Use cases

**Increasing limits for debugging**: If your agents or tools work with large documents, API responses, or data structures, increase these limits to capture more context in your traces:

```ts
serializationOptions: {
  maxStringLength: 8192,  // Capture longer text content
  maxDepth: 12,           // Handle deeply nested JSON responses
  maxArrayLength: 200,    // Keep more items from large lists
}
```

**Reducing trace size for production**: Lower these values to reduce storage costs and improve performance when you don't need full payload visibility:

```ts
serializationOptions: {
  maxStringLength: 256,   // Truncate strings aggressively
  maxDepth: 3,            // Shallow object representation
  maxArrayLength: 10,     // Keep only first few items
  maxObjectKeys: 20,      // Limit object keys
}
```

All options are optional — if not specified, they fall back to the defaults shown above.

## Retrieving trace IDs

When you execute agents or workflows with tracing enabled, the response includes a `traceId` that you can use to look up the full trace in your observability platform. This is useful for debugging, customer support, or correlating traces with other events in your system.

### Agent Trace IDs

Both `generate` and `stream` methods return the trace ID in their response:

```ts
// Using generate
const result = await agent.generate('Hello')

console.log('Trace ID:', result.traceId)

// Using stream
const streamResult = await agent.stream('Tell me a story')

console.log('Trace ID:', streamResult.traceId)
```

### Workflow Trace IDs

Workflow executions also return trace IDs:

```ts
// Create a workflow run
const run = await mastra.getWorkflow('myWorkflow').createRun()

// Start the workflow
const result = await run.start({
  inputData: { data: 'process this' },
})

console.log('Trace ID:', result.traceId)

// Or stream the workflow
const { stream, getWorkflowState } = run.stream({
  inputData: { data: 'process this' },
})

// Get the final state which includes the trace ID
const finalState = await getWorkflowState()
console.log('Trace ID:', finalState.traceId)
```

### Using Trace IDs

Once you have a trace ID, you can:

1. **Look up traces in Studio**: Navigate to the traces view and search by ID
2. **Query traces in external platforms**: Use the ID in Langfuse, Braintrust, MLflow, or your observability platform
3. **Correlate with logs**: Include the trace ID in your application logs for cross-referencing
4. **Share for debugging**: Provide trace IDs to support teams or developers for investigation

The trace ID is only available when tracing is enabled. If tracing is disabled or sampling excludes the request, `traceId` will be `undefined`.

## Integrating with external tracing systems

When running Mastra agents or workflows within applications that have existing distributed tracing (OpenTelemetry, Datadog, etc.), you can connect Mastra traces to your parent trace context. This creates a unified view of your entire request flow, making it easier to understand how Mastra operations fit into the broader system.

### Passing External Trace IDs

Use the `tracingOptions` parameter to specify the trace context from your parent system:

```ts
// Get trace context from your existing tracing system
const parentTraceId = getCurrentTraceId() // Your tracing system
const parentSpanId = getCurrentSpanId() // Your tracing system

// Execute Mastra operations as part of the parent trace
const result = await agent.generate('Analyze this data', {
  tracingOptions: {
    traceId: parentTraceId,
    parentSpanId: parentSpanId,
  },
})

// The Mastra trace will now appear as a child in your distributed trace
```

### OpenTelemetry Integration

Integration with OpenTelemetry allows Mastra traces to appear seamlessly in your existing observability platform:

```ts
import { trace } from '@opentelemetry/api'

// Get the current OpenTelemetry span
const currentSpan = trace.getActiveSpan()
const spanContext = currentSpan?.spanContext()

if (spanContext) {
  const result = await agent.generate(userMessage, {
    tracingOptions: {
      traceId: spanContext.traceId,
      parentSpanId: spanContext.spanId,
    },
  })
}
```

### Workflow Integration

Workflows support the same pattern for trace propagation:

```ts
const workflow = mastra.getWorkflow('data-pipeline')
const run = await workflow.createRun()

const result = await run.start({
  inputData: { data: '...' },
  tracingOptions: {
    traceId: externalTraceId,
    parentSpanId: externalSpanId,
  },
})
```

### ID Format Requirements

Mastra validates trace and span IDs to ensure compatibility:

- **Trace IDs**: 1-32 hexadecimal characters (OpenTelemetry uses 32)
- **Span IDs**: 1-16 hexadecimal characters (OpenTelemetry uses 16)

Invalid IDs are handled gracefully — Mastra logs an error and continues:

- Invalid trace ID → generates a new trace ID
- Invalid parent span ID → ignores the parent relationship

This ensures tracing never crashes your application, even with malformed input.

### Example: Express Middleware

Here's a complete example showing trace propagation in an Express application:

```ts
import { trace } from '@opentelemetry/api'
import express from 'express'

const app = express()

app.post('/api/analyze', async (req, res) => {
  // Get current OpenTelemetry context
  const currentSpan = trace.getActiveSpan()
  const spanContext = currentSpan?.spanContext()

  const result = await agent.generate(req.body.message, {
    tracingOptions: spanContext
      ? {
          traceId: spanContext.traceId,
          parentSpanId: spanContext.spanId,
        }
      : undefined,
  })

  res.json(result)
})
```

This creates a single distributed trace that includes both the HTTP request handling and the Mastra agent execution, viewable in your observability platform of choice.

## Flushing traces in serverless environments

In serverless environments like Vercel's fluid compute, AWS Lambda, or Cloudflare Workers, runtime instances can be reused across multiple requests. The `flush()` method allows you to ensure all buffered spans are exported before the runtime terminates, without shutting down the exporter (which would prevent future exports).

> **Storage requirements:** Serverless environments have ephemeral filesystems. Use external storage instead of local file storage (`file:./mastra.db`). See the [Vercel deployment guide](https://mastra.ai/guides/deployment/vercel) for a complete setup example.

### Using `flush()`

Call `flush()` on the observability instance to flush all exporters:

```ts
// Get the observability instance from Mastra
const observability = mastra.getObservability()

// Flush all buffered spans to all exporters
await observability.flush()
```

### When to Use `flush()`

Use `flush()` in these scenarios:

- **End of serverless function execution**: Ensure spans are exported before the runtime is paused or terminated
- **Before long-running operations**: Flush accumulated spans before a potentially slow operation
- **Periodic flushing**: In long-running processes, periodically flush to ensure timely data availability

```ts
// Example: Vercel serverless function
export async function POST(req: Request) {
  const result = await agent.generate([{ role: 'user', content: await req.text() }])

  // Ensure spans are exported before function completes
  const observability = mastra.getObservability()
  await observability.flush()

  return Response.json(result)
}
```

### `flush()` vs `shutdown()`

| Method       | Behavior                                      | Use Case                                   |
| ------------ | --------------------------------------------- | ------------------------------------------ |
| `flush()`    | Exports buffered spans, keeps exporter active | Serverless environments, periodic flushing |
| `shutdown()` | Exports buffered spans, releases resources    | Application shutdown, graceful termination |

Use `flush()` when you need to ensure data is exported but want to keep the exporter ready for future requests. Use `shutdown()` only when the application is terminating.

## What gets traced

Mastra automatically creates spans for:

### Agent Operations

- **Agent runs** - Complete execution with instructions and tools
- **LLM calls** - Model interactions with tokens and parameters
- **Tool executions** - Function calls with inputs and outputs
- **Memory operations** - Thread and semantic recall

### Workflow Operations

- **Workflow runs** - Full execution from start to finish
- **Individual steps** - Step processing with inputs/outputs
- **Control flow** - Conditionals, loops, parallel execution
- **Wait operations** - Delays and event waiting

## See also

### Reference Documentation

- [Configuration API](https://mastra.ai/reference/observability/tracing/configuration) - ObservabilityConfig details
- [Tracing Classes](https://mastra.ai/reference/observability/tracing/instances) - Core classes and methods
- [Span Interfaces](https://mastra.ai/reference/observability/tracing/spans) - Span types and lifecycle
- [Type Definitions](https://mastra.ai/reference/observability/tracing/interfaces) - Complete interface reference
- [Span filtering](https://mastra.ai/reference/observability/tracing/span-filtering) - Filtering behavior, span types, and examples

### Exporters

- [DefaultExporter](https://mastra.ai/reference/observability/tracing/exporters/default-exporter) - Storage persistence
- [CloudExporter](https://mastra.ai/reference/observability/tracing/exporters/cloud-exporter) - Mastra Cloud integration
- [ConsoleExporter](https://mastra.ai/reference/observability/tracing/exporters/console-exporter) - Debug output
- [Arize](https://mastra.ai/reference/observability/tracing/exporters/arize) - Arize Phoenix and Arize AX integration
- [Braintrust](https://mastra.ai/reference/observability/tracing/exporters/braintrust) - Braintrust integration
- [Langfuse](https://mastra.ai/reference/observability/tracing/exporters/langfuse) - Langfuse integration
- [MLflow](https://mastra.ai/docs/observability/tracing/exporters/otel) - MLflow OTLP endpoint setup
- [OpenTelemetry](https://mastra.ai/reference/observability/tracing/exporters/otel) - OTEL-compatible platforms

### Bridges

- [OpenTelemetry Bridge](https://mastra.ai/reference/observability/tracing/bridges/otel) - OTEL context integration

### Processors

- [Sensitive Data Filter](https://mastra.ai/docs/observability/tracing/processors/sensitive-data-filter) - Data redaction