# Instrument AI Agents | Sentry for Next.js

With [Sentry AI Agent Monitoring](https://docs.sentry.io/product/insights/ai/agents/dashboard.md), you can monitor and debug your AI systems with full-stack context. You'll be able to track key insights like token usage, latency, tool usage, and error rates. AI Agent Monitoring data will be fully connected to your other Sentry data like logs, errors, and traces.

As a prerequisite to setting up AI Agent Monitoring with JavaScript, you'll need to first [set up tracing](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing.md). Once this is done, the JavaScript SDK will automatically instrument AI agents created with supported libraries. If that doesn't fit your use case, you can use custom instrumentation described below.

## [Automatic Instrumentation](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#automatic-instrumentation)

The JavaScript SDK supports automatic instrumentation for some AI libraries. We recommend adding their integrations to your Sentry configuration to automatically capture spans for AI agents.

* [Vercel AI SDK](https://docs.sentry.io/platforms/javascript/guides/nextjs/configuration/integrations/vercelai.md)
* [OpenAI](https://docs.sentry.io/platforms/javascript/guides/nextjs/configuration/integrations/openai.md)
* [Anthropic](https://docs.sentry.io/platforms/javascript/guides/nextjs/configuration/integrations/anthropic.md)
* [Google Gen AI SDK](https://docs.sentry.io/platforms/javascript/guides/nextjs/configuration/integrations/google-genai.md)
* [LangChain](https://docs.sentry.io/platforms/javascript/guides/nextjs/configuration/integrations/langchain.md)
* [LangGraph](https://docs.sentry.io/platforms/javascript/guides/nextjs/configuration/integrations/langgraph.md)

## [Manual Instrumentation](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#manual-instrumentation)

If you're using a library that Sentry does not automatically instrument, you can manually instrument your code to capture spans. For your AI agents data to show up in the Sentry [AI Agents Insights](https://sentry.io/orgredirect/organizations/:orgslug/insights/ai/agents/), two spans must be created and have well-defined names and data attributes. See below.

## [Spans](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#spans)

### [AI Request Span](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#ai-request-span)

This span represents a request to a LLM model or service that generates a response based on the input prompt.

AI Request span attributes

* The span `op` MUST be `"gen_ai.{gen_ai.operation.name}"`. (e.g. `"gen_ai.request"`)
* The span `name` SHOULD be `{gen_ai.operation.name} {gen_ai.request.model}"`. (e.g. `"chat o3-mini"`)
* All [Common Span Attributes](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#common-span-attributes) SHOULD be set (all `required` common attributes MUST be set).

Additional attributes on the span:

| Data Attribute                         | Type   | Requirement Level | Description                                                                          | Example                                                                                                           |
| -------------------------------------- | ------ | ----------------- | ------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------- |
| `gen_ai.request.available_tools`       | string | optional          | List of objects describing the available tools. **\[0]**                             | `"[{\"name\": \"random_number\", \"description\": \"...\"}, {\"name\": \"query_db\", \"description\": \"...\"}]"` |
| `gen_ai.request.frequency_penalty`     | float  | optional          | Model configuration parameter.                                                       | `0.5`                                                                                                             |
| `gen_ai.request.max_tokens`            | int    | optional          | Model configuration parameter.                                                       | `500`                                                                                                             |
| `gen_ai.request.messages`              | string | optional          | List of objects describing the messages (prompts) sent to the LLM **\[0]**, **\[1]** | `"[{\"role\": \"system\", \"content\": [{...}]}, {\"role\": \"system\", \"content\": [{...}]}]"`                  |
| `gen_ai.request.presence_penalty`      | float  | optional          | Model configuration parameter.                                                       | `0.5`                                                                                                             |
| `gen_ai.request.temperature`           | float  | optional          | Model configuration parameter.                                                       | `0.1`                                                                                                             |
| `gen_ai.request.top_p`                 | float  | optional          | Model configuration parameter.                                                       | `0.7`                                                                                                             |
| `gen_ai.response.tool_calls`           | string | optional          | The tool calls in the model's response. **\[0]**                                     | `"[{\"name\": \"random_number\", \"type\": \"function_call\", \"arguments\": \"...\"}]"`                          |
| `gen_ai.response.text`                 | string | optional          | The text representation of the model's responses. **\[0]**                           | `"[\"The weather in Paris is rainy\", \"The weather in London is sunny\"]"`                                       |
| `gen_ai.usage.input_tokens.cached`     | int    | optional          | The number of cached tokens used in the AI input (prompt)                            | `50`                                                                                                              |
| `gen_ai.usage.input_tokens`            | int    | optional          | The number of tokens used in the AI input (prompt).                                  | `10`                                                                                                              |
| `gen_ai.usage.output_tokens.reasoning` | int    | optional          | The number of tokens used for reasoning.                                             | `30`                                                                                                              |
| `gen_ai.usage.output_tokens`           | int    | optional          | The number of tokens used in the AI response.                                        | `100`                                                                                                             |
| `gen_ai.usage.total_tokens`            | int    | optional          | The total number of tokens used to process the prompt. (input and output)            | `190`                                                                                                             |

* **\[0]:** Span attributes only allow primitive data types. This means you need to use a stringified version of a list of dictionaries. Do NOT set `[{"foo": "bar"}]` but rather the string `"[{\"foo\": \"bar\"}]"`.
* **\[1]:** Each message item uses the format `{role:"", content:""}`. The `role` can be `"user"`, `"assistant"`, or `"system"`. The `content` can be either a string or a list of dictionaries.

#### [Example AI Request Span](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#example-ai-request-span)

```javascript
const messages = [{ role: "user", content: "Tell me a joke" }];

await Sentry.startSpan(
  {
    op: "gen_ai.request",
    name: "request o3-mini",
    attributes: {
      "gen_ai.request.model": "o3-mini",
      "gen_ai.request.messages": JSON.stringify(messages),
    },
  },
  async (span) => {
    // Call your LLM here
    const result = await client.chat.completions.create({
      model: "o3-mini",
      messages,
    });

    span.setAttribute(
      "gen_ai.response.text",
      JSON.stringify([result.choices[0].message.content]),
    );
    // Set token usage
    span.setAttribute(
      "gen_ai.usage.input_tokens",
      result.usage.prompt_tokens,
    );
    span.setAttribute(
      "gen_ai.usage.output_tokens",
      result.usage.completion_tokens,
    );
  },
);
```

### [Invoke Agent Span](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#invoke-agent-span)

This span represents the execution of an AI agent, capturing the full lifecycle from receiving a task to producing a final response.

Invoke Agent span attributes

Describes AI agent invocation.

* The spans `op` MUST be `"gen_ai.invoke_agent"`.
* The span `name` SHOULD be `"invoke_agent {gen_ai.agent.name}"`.
* The `gen_ai.operation.name` attribute MUST be `"invoke_agent"`.
* The `gen_ai.agent.name` attribute SHOULD be set to the agent's name. (e.g. `"Weather Agent"`)
* All [Common Span Attributes](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#common-span-attributes) SHOULD be set (all `required` common attributes MUST be set).

Additional attributes on the span:

| Data Attribute                         | Type   | Requirement Level | Description                                                                          | Example                                                                                                           |
| -------------------------------------- | ------ | ----------------- | ------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------- |
| `gen_ai.request.available_tools`       | string | optional          | List of objects describing the available tools. **\[0]**                             | `"[{\"name\": \"random_number\", \"description\": \"...\"}, {\"name\": \"query_db\", \"description\": \"...\"}]"` |
| `gen_ai.request.frequency_penalty`     | float  | optional          | Model configuration parameter.                                                       | `0.5`                                                                                                             |
| `gen_ai.request.max_tokens`            | int    | optional          | Model configuration parameter.                                                       | `500`                                                                                                             |
| `gen_ai.request.messages`              | string | optional          | List of objects describing the messages (prompts) sent to the LLM **\[0]**, **\[1]** | `"[{\"role\": \"system\", \"content\": [{...}]}, {\"role\": \"system\", \"content\": [{...}]}]"`                  |
| `gen_ai.request.presence_penalty`      | float  | optional          | Model configuration parameter.                                                       | `0.5`                                                                                                             |
| `gen_ai.request.temperature`           | float  | optional          | Model configuration parameter.                                                       | `0.1`                                                                                                             |
| `gen_ai.request.top_p`                 | float  | optional          | Model configuration parameter.                                                       | `0.7`                                                                                                             |
| `gen_ai.response.tool_calls`           | string | optional          | The tool calls in the model’s response. **\[0]**                                     | `"[{\"name\": \"random_number\", \"type\": \"function_call\", \"arguments\": \"...\"}]"`                          |
| `gen_ai.response.text`                 | string | optional          | The text representation of the model’s responses. **\[0]**                           | `"[\"The weather in Paris is rainy\", \"The weather in London is sunny\"]"`                                       |
| `gen_ai.usage.input_tokens.cached`     | int    | optional          | The number of cached tokens used in the AI input (prompt)                            | `50`                                                                                                              |
| `gen_ai.usage.input_tokens`            | int    | optional          | The number of tokens used in the AI input (prompt).                                  | `10`                                                                                                              |
| `gen_ai.usage.output_tokens.reasoning` | int    | optional          | The number of tokens used for reasoning.                                             | `30`                                                                                                              |
| `gen_ai.usage.output_tokens`           | int    | optional          | The number of tokens used in the AI response.                                        | `100`                                                                                                             |
| `gen_ai.usage.total_tokens`            | int    | optional          | The total number of tokens used to process the prompt. (input and output)            | `190`                                                                                                             |

* **\[0]:** Span attributes only allow primitive data types (like `int`, `float`, `boolean`, `string`). This means you need to use a stringified version of a list of dictionaries. Do NOT set `[{"foo": "bar"}]` but rather the string `"[{\"foo\": \"bar\"}]"`.
* **\[1]:** Each message item uses the format `{role:"", content:""}`. The `role` can be `"user"`, `"assistant"`, or `"system"`. The `content` can be either a string or a list of dictionaries.

#### [Example Invoke Agent Span](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#example-invoke-agent-span)

```javascript
await Sentry.startSpan(
  {
    op: "gen_ai.invoke_agent",
    name: "invoke_agent Weather Agent",
    attributes: {
      "gen_ai.request.model": "o3-mini",
      "gen_ai.agent.name": "Weather Agent",
    },
  },
  async (span) => {
    // Run the agent
    const result = await myAgent.run();

    span.setAttribute(
      "gen_ai.response.text",
      JSON.stringify([result.output]),
    );
    // Set token usage
    span.setAttribute(
      "gen_ai.usage.input_tokens",
      result.usage.inputTokens,
    );
    span.setAttribute(
      "gen_ai.usage.output_tokens",
      result.usage.outputTokens,
    );
  },
);
```

### [Execute Tool Span](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#execute-tool-span)

This span represents the execution of a tool or function that was requested by an AI model, including the input arguments and resulting output.

Execute Tool span attributes

Describes a tool execution.

* The span `op` MUST be `"gen_ai.execute_tool"`.
* The span `name` SHOULD be `"execute_tool {gen_ai.tool.name}"`. (e.g. `"execute_tool query_database"`)
* The `gen_ai.tool.name` attribute SHOULD be set to the name of the tool. (e.g. `"query_database"`)
* All [Common Span Attributes](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#common-span-attributes) SHOULD be set (all `required` common attributes MUST be set).

Additional attributes on the span:

| Data Attribute            | Type   | Requirement Level | Description                                          | Example                                    |
| ------------------------- | ------ | ----------------- | ---------------------------------------------------- | ------------------------------------------ |
| `gen_ai.tool.description` | string | optional          | Description of the tool executed.                    | `"Tool returning a random number"`         |
| `gen_ai.tool.input`       | string | optional          | Input that was given to the executed tool as string. | `"{\"max\":10}"`                           |
| `gen_ai.tool.name`        | string | optional          | Name of the tool executed.                           | `"random_number"`                          |
| `gen_ai.tool.output`      | string | optional          | The output from the tool.                            | `"7"`                                      |
| `gen_ai.tool.type`        | string | optional          | The type of the tools.                               | `"function"`; `"extension"`; `"datastore"` |

#### [Example Execute Tool Span](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#example-execute-tool-span)

```javascript
await Sentry.startSpan(
  {
    op: "gen_ai.execute_tool",
    name: "execute_tool get_weather",
    attributes: {
      "gen_ai.tool.name": "get_weather",
      "gen_ai.tool.input": JSON.stringify({ location: "Paris" }),
    },
  },
  async (span) => {
    // Call the tool
    const result = await getWeather({ location: "Paris" });

    span.setAttribute("gen_ai.tool.output", JSON.stringify(result));
  },
);
```

### [Handoff Span](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#handoff-span)

This span marks the transition of control from one agent to another, typically when the current agent determines another agent is better suited to handle the task.

Handoff span attributes

A span that describes the handoff from one agent to another.

* The spans `op` MUST be `"gen_ai.handoff"`.
* The spans `name` SHOULD be `"handoff from {from_agent} to {to_agent}"`.
* All [Common Span Attributes](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#common-span-attributes) SHOULD be set.

#### [Example Handoff Span](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#example-handoff-span)

```javascript
await Sentry.startSpan(
  {
    op: "gen_ai.handoff",
    name: "handoff from Weather Agent to Travel Agent",
  },
  () => {}, // Handoff span just marks the transition
);

await Sentry.startSpan(
  { op: "gen_ai.invoke_agent", name: "invoke_agent Travel Agent" },
  async () => {
    // Run the target agent here
  },
);
```

## [Common Span Attributes](https://docs.sentry.io/platforms/javascript/guides/nextjs/tracing/instrumentation/ai-agents-module.md#common-span-attributes)

Some attributes are common to all AI Agents spans:

| Data Attribute          | Type   | Requirement Level | Description                                          | Example           |
| ----------------------- | ------ | ----------------- | ---------------------------------------------------- | ----------------- |
| `gen_ai.request.model`  | string | required          | The name of the AI model a request is being made to. | `"o3-mini"`       |
| `gen_ai.operation.name` | string | optional          | The name of the operation being performed.           | `"summarize"`     |
| `gen_ai.agent.name`     | string | optional          | The name of the agent this span belongs to.          | `"Weather Agent"` |