> ## Documentation Index > Fetch the complete documentation index at: https://docs.galtea.ai/llms.txt > Use this file to discover all available pages before exploring further. # Evaluation > An evaluation that assesses traces from a session using a metric ## What is an Evaluation? An evaluation in Galtea represents the assessment of traces from a [session](/concepts/product/version/session) using the evaluation criteria of a [metric](/concepts/metric). Evaluations are now directly linked to sessions, allowing for comprehensive evaluation of full sessions containing multiple traces for multi-turn dialogues. Evaluations do not call your AI product — they score outputs that have already been generated. Run your product first to produce outputs, then trigger the evaluation to score them. You can create evaluations from the [Galtea dashboard](https://platform.galtea.ai/) (using [Endpoint Connections](/concepts/product/endpoint-connection)) or programmatically using the [Galtea SDK](/sdk/api/evaluation/service). ## Evaluation Lifecycle Evaluations follow a specific lifecycle: [Trigger an evaluation](/sdk/tutorials/run-dataset-based-evaluations). It will appear in the session's details page with the status "pending" Galtea's evaluation system processes the evaluation using the evaluation criteria of the selected [metric](/concepts/metric) Once processed, the status changes to "completed" and the [results](/concepts/product/version/session/evaluation#evaluation-properties) are available ## SDK Integration The Galtea SDK allows you to create, view, and manage evaluations programmatically. This is particularly useful for organizations that want to automate their evaluation process or integrate it into their CI/CD pipeline. Manage evaluations using the Python SDK Learn how to set up GitHub Actions to automatically evaluate new versions ## Evaluation Properties ### Result Properties Once an evaluation completes, the following fields are available: The current status of the evaluation. Possible values: * **Pending**: The evaluation has been created but not yet processed. * **Pending Human**: The evaluation is waiting for a human annotator to review and score it. This status is used for metrics with `source: "human_evaluation"`. * **Success**: The evaluation was processed successfully. * **Failed**: The evaluation encountered an error during processing. Check the `canRetry` field to determine if this evaluation can be retried. * **Skipped**: The evaluation could not produce a score. The error message names the cause. Common causes are: * Metric validation failed, for example a required parameter is missing. * The organization ran out of credits. * The metric could not read the actual output, for example plain text where [JSON Field Match](/concepts/metric/json-field-match) expects a JSON object. * The judge model was unavailable. Check the `canRetry` field to see whether the evaluation is eligible for a retry. A retry re-scores the output already stored on the session. When the cause is that output, send a new inference result instead. * **Outdated**: The evaluation was produced by a [Monitor](/concepts/product/monitor), and the session then reopened and gained a new turn. The score describes an older version of the conversation, so it no longer counts as current: charts and averages leave it out, and the monitor scores the session again once it closes. The row keeps its score and its reason, so you can still read what it found. The score assigned to the output by the metric's evaluation criteria. **Example**: 0.85 The explanation of the score assigned to the output by the [metric's](/concepts/metric) evaluation criteria. The error message if the evaluation failed during processing. The number of retry attempts made for this evaluation. Starts at 0 and increments each time the evaluation is retried. Used to track retry history and enforce retry limits. Indicates whether the evaluation is eligible for the retry operation. When `false`, it cannot be retried at all. When `true`, it can, but a retry does not always help: `canRetry` is a permission, not a promise. A retry re-scores the output already stored on the session, so it succeeds for a temporary cause (an evaluation processing error, insufficient credits, a judge model that was unavailable) and skips again when the cause is the stored output itself. Defaults to `false`. The ID of the human evaluator assigned to this evaluation, if applicable. The human-provided annotation score, separate from the AI-generated score. When a human annotates an AI-evaluated metric, this field stores their score without overwriting the original AI score. The human-provided annotation reason, separate from the AI-generated reason. When a human annotates an AI-evaluated metric, this field stores their reasoning without overwriting the original AI reason. The timestamp when the human evaluator started reviewing this evaluation, if applicable. The timestamp when the human evaluator submitted their evaluation or annotation. ## Related How Galtea's concepts connect — diagram + per-entity quick reference. A dataset of test cases for evaluating product performance Each challenge in a dataset for evaluating product performance A full conversation between a user and an AI system. Ways to evaluate and score product performance