> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/getsentry/cli/llms.txt
> Use this file to discover all available pages before exploring further.

# sentry issue explain

> Get AI-powered root cause analysis for a Sentry issue using Seer AI

# sentry issue explain

Get a root cause analysis for a Sentry issue using Seer AI. This command analyzes the issue and provides identified root causes, reproduction steps, and relevant code locations.

## Usage

```bash theme={null}
sentry issue explain <issue-id>
```

## Issue ID Formats

The `explain` command accepts the same issue identifier formats as `view`:

```bash theme={null}
# Explicit org + ID
sentry issue explain sentry/EXTENSION-7

# Project + suffix  
sentry issue explain cli-G

# Short ID (searches across orgs)
sentry issue explain CLI-G

# Suffix only (requires DSN context)
sentry issue explain G

# Numeric ID
sentry issue explain 123456789
```

## Parameters

<ParamField path="issue-id" type="string" required>
  Issue identifier in any supported format.
</ParamField>

## Flags

<ParamField path="--force" type="boolean" default="false">
  Force a fresh analysis even if one already exists. Useful when issue context has changed.

  **Example:**

  ```bash theme={null}
  sentry issue explain 123456789 --force
  ```
</ParamField>

<ParamField path="--json" type="boolean" default="false">
  Output results as JSON array of root causes.

  **Example:**

  ```bash theme={null}
  sentry issue explain EXTENSION-7 --json
  ```
</ParamField>

## Examples

### Basic Usage

```bash theme={null}
# Analyze issue by numeric ID
sentry issue explain 123456789

# Analyze by short ID
sentry issue explain EXTENSION-7

# With explicit org
sentry issue explain sentry/CLI-G
```

### Force Re-analysis

```bash theme={null}
# Trigger fresh analysis
sentry issue explain 123456789 --force
```

### JSON Output

```bash theme={null}
# Export to JSON
sentry issue explain EXTENSION-7 --json > root-causes.json

# Process with jq
sentry issue explain CLI-G --json | jq '.[0].description'
```

## Sample Output

### Human-Readable Format

```
Analyzing issue... (this may take a few minutes)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Root Cause Analysis
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Root Cause #0:

The error occurs because the `processData` function attempts to call `.map()` 
on a variable that is undefined. This happens when the API response does not 
include the expected `users` array in the payload.

Reproduction Steps:
  1. Navigate to /dashboard in the application
  2. Ensure the external API endpoint returns a response without the 'users' field
  3. The application attempts to render the user list
  4. TypeError is thrown when calling users.map()

Relevant Code:
  File: src/components/UserList.js
  Line: 42
  Code: const userElements = users.map(user => renderUser(user));

Tip: To create a plan, run: sentry issue plan 123456789
```

### JSON Format

```json theme={null}
[
  {
    "id": 0,
    "title": "Undefined variable access in processData",
    "description": "The error occurs because the `processData` function attempts to call `.map()` on a variable that is undefined. This happens when the API response does not include the expected `users` array in the payload.",
    "reproduction": [
      "Navigate to /dashboard in the application",
      "Ensure the external API endpoint returns a response without the 'users' field",
      "The application attempts to render the user list",
      "TypeError is thrown when calling users.map()"
    ],
    "code_context": {
      "file": "src/components/UserList.js",
      "line": 42,
      "snippet": "const userElements = users.map(user => renderUser(user));"
    }
  }
]
```

## How It Works

1. **Trigger Analysis**: The command sends the issue to Seer AI for analysis
2. **Poll Status**: The CLI polls the analysis status every few seconds
3. **Extract Root Causes**: Once complete, root causes are extracted from the analysis steps
4. **Display Results**: Formatted output shows the identified causes with context

The analysis may take several minutes for complex issues. Progress is shown via a spinner (hidden in `--json` mode).

## Requirements

<Warning>
  **Seer AI must be enabled for your organization.** If Seer is not available, you'll see an error with a link to enable it in your organization settings.
</Warning>

### Prerequisites

* Seer AI enabled in organization settings
* Available Seer AI budget/quota
* Organization has AI features enabled

### Common Errors

**Seer not enabled:**

```
Seer AI is not enabled for this organization.
Enable it at: https://sentry.io/settings/your-org/processing/ai-tools/
```

**No budget:**

```
Seer AI analysis quota exceeded. Check usage at:
https://sentry.io/settings/your-org/processing/ai-tools/
```

**AI disabled in settings:**

```
AI features are disabled for this organization.
```

## Notes

* The first analysis of an issue takes longer as Seer examines code context, stack traces, and event patterns.
* Subsequent runs reuse cached analysis unless `--force` is specified.
* If multiple root causes are found, each is numbered (0, 1, 2, ...) for reference in `sentry issue plan --cause N`.
* Organization context is required to access Seer AI. The command automatically resolves org from the issue ID.

## See Also

* [sentry issue plan](/commands/issue/plan) - Generate a solution plan from root causes
* [sentry issue view](/commands/issue/view) - View issue details
* [sentry issue list](/commands/issue/list) - List issues from projects
* [AI Debugging Guide](/guides/ai-debugging) - Complete guide to using Seer AI