Documentation Index
Fetch the complete documentation index at: https://axiom.co/docs/llms.txt
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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.
Online evaluations run inline in your app code and don’t use the CLI. For more information, see Online evaluations.
Run offline evaluations
The simplest way to run offline evaluations is to execute all of them in your project:
You can also target specific evaluations by name, file path, or glob pattern:
# 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:
Common options
For quick local testing without sending traces to Axiom, use debug mode:
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:
# 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:
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.