> ## Documentation Index
> Fetch the complete documentation index at: https://axiom.co/docs/llms.txt
> Use this file to discover all available pages before exploring further.

<AgentInstructions>

## Submitting Feedback

If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback:

POST https://axiom.co/docs/feedback

```json
{
  "path": "/ai-engineering/evaluate/run-evaluations",
  "feedback": "Description of the issue"
}
```

Only submit feedback when you have something specific and actionable to report.

</AgentInstructions>

# Run offline evaluations

> Learn how to run offline evaluations using the Axiom CLI and interpret the results.

This page covers running offline evaluations with the Axiom AI SDK CLI. The CLI provides commands for running offline evaluations locally or in CI/CD pipelines.

<Info>
  Online evaluations run inline in your app code and don't use the CLI. For more information, see [Online evaluations](/ai-engineering/evaluate/online-evaluations/write-run-evaluations).
</Info>

## Run offline evaluations

The simplest way to run offline evaluations is to execute all of them in your project:

```bash theme={null}
axiom eval
```

You can also target specific evaluations by name, file path, or glob pattern:

```bash theme={null}
# By evaluation name
axiom eval spam-classification

# By file path
axiom eval src/evals/spam-classification.eval.ts

# By glob pattern
axiom eval "**/*spam*.eval.ts"
```

To see which evaluations are available without running them:

```bash theme={null}
axiom eval --list
```

## Common options

For quick local testing without sending traces to Axiom, use debug mode:

```bash theme={null}
axiom eval --debug
```

To compare results against a previous evaluation, view both runs in the Axiom Console where you can analyze differences in scores, latency, and cost.

## Run experiments with flags

Flags let you test different configurations without changing code. Override flag values directly in the command:

```bash theme={null}
# Single flag
axiom eval --flag.ticketClassification.model=gpt-4o

# Multiple flags
axiom eval \
  --flag.ticketClassification.model=gpt-4o \
  --flag.ticketClassification.temperature=0.3
```

For complex experiments, load flag overrides from a JSON file:

```bash theme={null}
axiom eval --flags-config=experiments/gpt4.json
```

## Understand evaluation output

When you run an evaluation, the CLI shows progress, scores, and a link to view detailed results in the Axiom Console:

```
✓ spam-classification (4/4 passed)
  ✓ Test case 1: spam detection
  ✓ Test case 2: legitimate question

Scorers:
  category-match: 100% (4/4)
  high-confidence: 75% (3/4)

Results:
  Total: 4 test cases
  Passed: 4 (100%)
  Duration: 3.2s
  Cost: $0.0024

View full report:
https://app.axiom.co/your-org/ai-engineering/evaluations?runId=ABC123
```

Click the link to view results in the Console, compare runs, and analyze performance.

## What's next?

To learn how to view and analyze evaluation results, see [Analyze results](/ai-engineering/evaluate/analyze-results).