> Discover all available pages from the documentation index: https://mastra.ai/llms.txt

# Datadog

[Datadog](https://datadoghq.com/) is a complete monitoring platform with dedicated LLM Observability features. Mastra supports Datadog through an exporter for sending completed traces and a bridge for integrating with `dd-trace` context in real time.

## Exporter

The Datadog exporter sends your traces to Datadog's LLM Observability product, providing insights into model performance and token usage, plus conversation flows.

### Installation

**npm**:

```bash
npm install @mastra/datadog@latest
```

**pnpm**:

```bash
pnpm add @mastra/datadog@latest
```

**Yarn**:

```bash
yarn add @mastra/datadog@latest
```

**Bun**:

```bash
bun add @mastra/datadog@latest
```

### Configuration

#### Prerequisites

1. **Datadog Account**: Sign up at [datadoghq.com](https://datadoghq.com/) with LLM Observability enabled
2. **API Key**: Get your API key from Datadog Organization Settings → API Keys
3. **Environment Variables**: Set your credentials

```bash
DD_API_KEY=your-datadog-api-key
DD_LLMOBS_ML_APP=my-llm-app
DD_SITE=datadoghq.com  # Optional: defaults to datadoghq.com
DD_ENV=production      # Optional: environment name
```

#### Zero-Config Setup

With environment variables set, use the exporter with no configuration:

```typescript
import { Mastra } from '@mastra/core'
import { Observability } from '@mastra/observability'
import { DatadogExporter } from '@mastra/datadog'

export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      datadog: {
        serviceName: 'my-service',
        exporters: [new DatadogExporter()],
      },
    },
  }),
})
```

#### Explicit Configuration

You can also pass credentials directly (takes precedence over environment variables):

```typescript
import { Mastra } from '@mastra/core'
import { Observability } from '@mastra/observability'
import { DatadogExporter } from '@mastra/datadog'

export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      datadog: {
        serviceName: 'my-service',
        exporters: [
          new DatadogExporter({
            mlApp: process.env.DD_LLMOBS_ML_APP!,
            apiKey: process.env.DD_API_KEY!,
          }),
        ],
      },
    },
  }),
})
```

### Configuration options

#### Complete Configuration

```typescript
new DatadogExporter({
  // Required settings
  mlApp: process.env.DD_LLMOBS_ML_APP!, // Groups traces under this ML app name
  apiKey: process.env.DD_API_KEY!, // Required for agentless mode (default)

  // Optional settings
  site: 'datadoghq.com', // Datadog site (datadoghq.eu, us3.datadoghq.com, etc.)
  service: 'my-service', // Service name (defaults to mlApp)
  env: 'production', // Environment name
  agentless: true, // true = direct HTTPS, false = local Datadog Agent

  // Advanced settings
  integrationsEnabled: false, // Enable dd-trace auto-instrumentation

  // Diagnostic logging
  logLevel: 'info', // debug | info | warn | error
})
```

#### With Local Datadog Agent

If you have a Datadog Agent running locally, you can route traces through it instead of direct HTTPS:

```typescript
new DatadogExporter({
  mlApp: process.env.DD_LLMOBS_ML_APP!,
  agentless: false, // Use local Datadog Agent
  env: 'production',
})
```

Note: When using agent mode, the API key is read from the local agent's configuration.

### Span type mapping

Mastra span types are automatically mapped to Datadog LLM Observability span kinds:

| Mastra SpanType      | Datadog Kind |
| -------------------- | ------------ |
| `AGENT_RUN`          | `agent`      |
| `MODEL_GENERATION`   | `workflow`   |
| `MODEL_STEP`         | `llm`        |
| `TOOL_CALL`          | `tool`       |
| `MCP_TOOL_CALL`      | `tool`       |
| `WORKFLOW_RUN`       | `workflow`   |
| Other workflow types | `task`       |
| `GENERIC`            | `task`       |

Other/future Mastra span types will default to 'task' when mapped unless specified.

### Application Performance Monitoring

The sections above cover Mastra's [LLM Observability](https://docs.datadoghq.com/llm_observability/) integration. To trace your Mastra HTTP server routes (request latency, error tracking, service maps), use `dd-trace` directly for Datadog Application Performance Monitoring (APM).

#### APM prerequisites

1. **Datadog Agent**: Install a [Datadog Agent](https://docs.datadoghq.com/agent/) on the same host or accessible via network. The agent receives traces from `dd-trace` on `localhost:8126` and forwards them to Datadog. Follow the [agent installation guide](https://docs.datadoghq.com/agent/) to set it up.

2. **dd-trace package**: Install the tracing library in your project:

   **npm**:

   ```bash
   npm install dd-trace
   ```

   **pnpm**:

   ```bash
   pnpm add dd-trace
   ```

   **Yarn**:

   ```bash
   yarn add dd-trace
   ```

   **Bun**:

   ```bash
   bun add dd-trace
   ```

> **Note:** APM traces always route through the Datadog Agent. This is different from LLM Observability, which supports agentless mode (direct HTTPS to Datadog).

#### APM only

Import and initialize `dd-trace` at the top of your entry file, before any other imports:

```typescript
import tracer from 'dd-trace'

tracer.init({
  service: process.env.DD_SERVICE || 'my-mastra-app',
  env: process.env.DD_ENV || 'production',
  version: process.env.DD_VERSION,
})

import { Mastra } from '@mastra/core'

export const mastra = new Mastra({
  bundler: {
    externals: [
      'dd-trace',
      '@datadog/native-metrics',
      '@datadog/native-appsec',
      '@datadog/native-iast-taint-tracking',
      '@datadog/pprof',
    ],
  },
})
```

Set the tracer metadata environment variables:

```bash
DD_SERVICE=my-mastra-app
DD_ENV=production
DD_VERSION=1.0.0
```

`dd-trace` auto-instruments popular HTTP frameworks, including those supported by Mastra's [server adapters](https://mastra.ai/docs/server/server-adapters). Inbound requests, outbound HTTP calls, and database queries appear as APM traces in Datadog.

#### APM and LLM Observability

Import and initialize `dd-trace` before creating the Mastra instance. The `DatadogExporter` detects the existing tracer and skips re-initialization, adding LLM Observability on top of your APM setup:

```typescript
import tracer from 'dd-trace'

tracer.init({
  service: process.env.DD_SERVICE || 'my-mastra-app',
  env: process.env.DD_ENV || 'production',
  version: process.env.DD_VERSION,
})

import { Mastra } from '@mastra/core'
import { Observability } from '@mastra/observability'
import { DatadogExporter } from '@mastra/datadog'

export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      datadog: {
        serviceName: 'my-mastra-app',
        exporters: [
          new DatadogExporter({
            mlApp: process.env.DD_LLMOBS_ML_APP!,
            apiKey: process.env.DD_API_KEY!,
          }),
        ],
      },
    },
  }),
  bundler: {
    externals: [
      'dd-trace',
      '@datadog/native-metrics',
      '@datadog/native-appsec',
      '@datadog/native-iast-taint-tracking',
      '@datadog/pprof',
    ],
  },
})
```

```bash
DD_SERVICE=my-mastra-app
DD_ENV=production
DD_VERSION=1.0.0
DD_API_KEY=your-datadog-api-key
DD_LLMOBS_ML_APP=my-llm-app
```

Server routes appear as APM traces and LLM calls appear as LLM Observability spans, all under the same service in Datadog.

> **Note:** Import and initialize `dd-trace` before all other modules. This allows its auto-instrumentation to patch HTTP, database, and framework libraries at load time.

### Troubleshooting

#### Native module ABI mismatch

If you see errors like:

```text
Error: No native build was found for runtime=node abi=137 platform=linuxglibc arch=x64
```

This indicates a Node.js version compatibility issue with `dd-trace`'s native modules. These native modules are **optional** and provide performance monitoring features.

**Solutions:**

1. **Use Node.js 22.x**: Native modules have the best compatibility with Node.js 22.x.

2. **Ignore native module warnings**: The native modules (`@datadog/native-metrics`, `@datadog/native-appsec`, etc.) are optional. If they fail to load, core tracing functionality still works.

#### Bundler externals configuration

When using bundlers like esbuild, webpack, or the Mastra CLI bundler, you may need to mark `dd-trace` and its dependencies as external:

```typescript
export const mastra = new Mastra({
  bundler: {
    externals: [
      'dd-trace',
      '@datadog/native-metrics',
      '@datadog/native-appsec',
      '@datadog/native-iast-taint-tracking',
      '@datadog/pprof',
    ],
  },
})
```

### Related

- [Tracing overview](https://mastra.ai/docs/observability/tracing/overview)
- [Datadog LLM Observability documentation](https://docs.datadoghq.com/llm_observability/)
- [Datadog APM documentation](https://docs.datadoghq.com/tracing/)

## Bridge

> **Warning:** The Datadog Bridge is currently **experimental**. APIs and configuration options may change in future releases.

The Datadog Bridge enables bidirectional integration between Mastra's tracing system and Datadog. Unlike exporters that send trace data after execution completes, the bridge creates native `dd-trace` spans in real time so that auto-instrumented APM operations (HTTP calls, database queries, etc.) inside your tools and processors are correctly nested under their parent Mastra spans.

### When to use the bridge

Use the `DatadogBridge` when you:

- Use `dd-trace` auto-instrumentation in your application (HTTP servers, database clients, etc.)
- Want APM service calls made by tools, MCP tools, or output processors to appear under their parent Mastra span instead of the request handler
- Need both APM traces and LLM Observability data to share a consistent trace topology
- Are building a distributed system where Datadog trace context must propagate across services

### How it works

The `DatadogBridge` participates in two parts of the `dd-trace` pipeline:

**APM context propagation (real time):**

- Creates a `dd-trace` APM span via `tracer.startSpan()` when each Mastra span is created
- Activates the APM span in `dd-trace`'s scope via `tracer.scope().activate()` during execution
- Auto-instrumented operations inside the active scope are parented to the correct Mastra span
- Inherits the active `dd-trace` context (e.g., an incoming request span) when no explicit Mastra parent exists

**LLM Observability emission (on span end):**

- Emits annotations (model info, token usage, input/output, errors) through `dd-trace`'s LLM Observability pipeline
- Maintains parent-child relationships in Datadog LLM Observability using nested `llmobs.trace()` calls
- Reuses the same data shape and span-kind mapping as the [Datadog Exporter](#span-type-mapping)

### Trace and log correlation

Without the bridge, the Datadog Exporter only creates LLM Observability spans after a trace completes. During execution, no `dd-trace` span is active in scope, so any HTTP or database call made by a tool falls back to whatever `dd-trace` span is active at the time, typically the incoming request handler. The result is that service calls from MCP tools or output processors appear as children of the request span instead of the agent or processor span that actually made them.

The bridge fixes this by creating real `dd-trace` spans up front, so the scope is correct when auto-instrumentation runs.

### Installation

**npm**:

```bash
npm install @mastra/datadog dd-trace
```

**pnpm**:

```bash
pnpm add @mastra/datadog dd-trace
```

**Yarn**:

```bash
yarn add @mastra/datadog dd-trace
```

**Bun**:

```bash
bun add @mastra/datadog dd-trace
```

The bridge requires `dd-trace` to be installed and a local Datadog Agent (or compatible OTLP receiver) to receive APM data. See the [APM prerequisites](#apm-prerequisites) for agent setup details.

### Configuration

Using the `DatadogBridge` requires two steps:

1. Initialize `dd-trace` so its auto-instrumentation patches HTTP, database, and framework libraries
2. Add the `DatadogBridge` to your Mastra observability config

#### Step 1: Initialize dd-trace

`dd-trace` must be initialized before any other imports so its auto-instrumentation can patch libraries at load time. The bridge will detect an already-initialized tracer and reuse it.

```typescript
import tracer from 'dd-trace'

tracer.init({
  service: process.env.DD_SERVICE || 'my-mastra-app',
  env: process.env.DD_ENV || 'production',
  version: process.env.DD_VERSION,
})

import { Mastra } from '@mastra/core'
import { Observability } from '@mastra/observability'
import { DatadogBridge } from '@mastra/datadog'

// ...
```

> **Note:** Import and initialize `dd-trace` at the very top of your application's entry file, before any other imports.

#### Step 2: Mastra Configuration

Add the `DatadogBridge` to your Mastra observability config:

```typescript
export const mastra = new Mastra({
  observability: new Observability({
    configs: {
      default: {
        serviceName: 'my-mastra-app',
        bridge: new DatadogBridge({
          mlApp: process.env.DD_LLMOBS_ML_APP!,
        }),
      },
    },
  }),
  bundler: {
    externals: [
      'dd-trace',
      '@datadog/native-metrics',
      '@datadog/native-appsec',
      '@datadog/native-iast-taint-tracking',
      '@datadog/pprof',
    ],
  },
})
```

```bash
DD_SERVICE=my-mastra-app
DD_ENV=production
DD_VERSION=1.0.0
DD_LLMOBS_ML_APP=my-llm-app
```

When `dd-trace` is initialized, it routes APM data to your local Datadog Agent on `localhost:8126`. The bridge enables LLM Observability on top of the same tracer, so both sets of data appear under the same service in Datadog.

No Mastra exporters are required when using the bridge. Both APM and LLM Observability data flow through `dd-trace`. You can still add Mastra exporters if you want to send traces to additional destinations.

### Agent vs. agentless mode

The bridge defaults to **agent mode** (`agentless: false`). This assumes a local Datadog Agent is running on `localhost:8126` to receive both APM and LLM Observability data. This is the typical setup when using `dd-trace` auto-instrumentation, since APM data always routes through the agent.

If you don't have a local Datadog Agent and only need LLM Observability data (no APM auto-instrumentation), you can enable agentless mode to send data directly to Datadog. In this case, you must provide an API key.

```typescript
new DatadogBridge({
  mlApp: process.env.DD_LLMOBS_ML_APP!,
  apiKey: process.env.DD_API_KEY!,
  agentless: true,
})
```

> **Note:** For most bridge users, agent mode is the right choice. APM data can't be sent in agentless mode, so enabling agentless splits LLM Observability traffic away from APM traffic. If you want LLM Observability only without an agent, use the [Datadog Exporter](#exporter) instead.

### Trace hierarchy

With the `DatadogBridge`, your traces maintain proper hierarchy across `dd-trace` and Mastra boundaries. Service calls made by tools and processors appear under the correct Mastra span:

```text
HTTP POST /api/chat (from web framework instrumentation)
└── agent.orchestrator (from Mastra via DatadogBridge)
    ├── chat gpt-5.4 (LLM call)
    ├── tool.execute search (tool execution)
    │   └── HTTP GET api.example.com (auto-instrumented from inside the tool)
    └── processor.guardrail (output processor)
        └── HTTP POST guardrail-service/check (auto-instrumented from inside the processor)
```

In Datadog, the APM trace shows this full topology, and the LLM Observability product shows the agent and LLM-specific spans with their inputs, outputs, and token metrics.

### Span type mapping

The bridge uses the same span-kind mapping as the Datadog Exporter for LLM Observability. See [span type mapping](#span-type-mapping) in the exporter section.

### Using tags

Tags help you categorize and filter traces in Datadog. Add tags when executing agents or workflows:

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

Tags formatted as `key:value` (e.g., `instance_name:career-scout-api`) are split into structured tag entries. Tags without a colon are set with a `true` value.

### Promoting context keys to flat tags

Use `requestContextKeys` to promote specific keys from the request context or span attributes into flat, indexable LLM Observability tags. This makes them filterable in the Datadog UI:

```typescript
new DatadogBridge({
  mlApp: process.env.DD_LLMOBS_ML_APP!,
  requestContextKeys: ['tenantId', 'agentId'],
})
```

Promoted keys are removed from `annotations.metadata` and added as flat tags on each LLM Observability span.

### Troubleshooting

If APM spans aren't connecting to Mastra spans as expected:

- Verify `dd-trace` is initialized **before** any other imports (it patches libraries at load time)
- Verify a local Datadog Agent is running and reachable at `localhost:8126`
- Ensure the `DatadogBridge` is set as `bridge` (not as an entry in `exporters`) in your observability config
- Confirm you haven't also added the `DatadogExporter` to `exporters`: using both will double-emit LLM Observability data

For native-module compatibility issues with `dd-trace` and bundler externals, see the [Datadog exporter troubleshooting](#troubleshooting) section.

### Related

- [Tracing overview](https://mastra.ai/docs/observability/tracing/overview)
- [DatadogBridge reference](https://mastra.ai/reference/observability/tracing/bridges/datadog)
- [Datadog APM documentation](https://docs.datadoghq.com/tracing/)
- [Datadog LLM Observability documentation](https://docs.datadoghq.com/llm_observability/)