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# source: VoltAgent__voltagent/website/evaluation-docs/overview.md type: docs
---
title: Overview
sidebar_position: 1
slug: /
---
VoltAgent Evaluations give you a consistent way to measure how well your agents perform across offline test sets, nightly regressions, and CI gates. The same experiment definition can run locally, in automation, or from a dashboard, while VoltOps captures detailed telemetry and scoring breakdowns.
This page explains the big picture: what makes up an evaluation, how runs flow from your code to VoltOps, and where to look next when you’re ready to dive in.
## Why run evaluations?
- **Catch regressions before they ship.** Re-run the same dataset whenever a prompt or model changes and spot drops in accuracy or quality.
- **Standardise measurement.** A single experiment can mix multiple scorers (exact match, semantic similarity, moderation, LLM judges, …) and compute pass thresholds automatically.
- **Close the loop with VoltOps.** Every offline run shows up alongside your live traffic, with the same tags, filters, and search you already use to debug agents.
- **Scriptable & repeatable.** Experiments are plain TypeScript modules, so you can version them, share them, and run them in any Node.js environment.
## Core building blocks
| Concept | Responsibility |
| ---------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| **Experiment definition** (`createExperiment`) | Describes the run: dataset source, how to invoke your agent/runner, which scorers to apply, and any pass criteria. |
| **Runner** | A function that receives one dataset item and returns the agent output (plus optional metadata / traces). |
| **Dataset** | The evaluation inputs. Can be inline JSON, generated on the fly, or pulled from VoltOps. |
| **Scorers** | Re-usable scoring functions (exact match, embeddings, LLM graders, moderation, etc.) that produce scores, thresholds, and metadata. |
| **Aggregator** | Computes summary stats (mean score, pass rate, threshold compliance) and overall pass/fail. |
| **VoltOps integration** | Optional client that automatically creates a run, streams item results, and finalises the summary so you can inspect everything in the Console. |
## How it fits with the rest of VoltAgent
- **Agents & workflows.** Experiments call into your agent code (e.g. the same functions your API or workflow uses). It’s your runner’s responsibility to invoke the agent with the right inputs.
- **VoltOps telemetry.** Runs appear alongside live traces. You get the same search, tagging, and drill-down experience for offline and online evaluation.
- **Scorer registry.** `@voltagent/scorers` ships ready-made graders (string similarity, number comparison, LLM judges). You can also write custom scorers or wrap external services.
- **Datasets.** Reuse the same dataset whether you run from CLI, CI, or VoltOps’s UI. The framework supports inline, generated, and VoltOps-hosted sources.
## Offline Evaluations
Offline evaluations run against a fixed dataset, return deterministic scores, and are ideal for regression testing, CI gates, and comparing branches.
### End-to-End Flow
1. **Define the experiment.** Import `createExperiment`, choose a dataset, and set up your runner.
```ts
// experiments/support-nightly.experiment.ts
import { createExperiment } from "@voltagent/evals";
export default createExperiment({
id: "support-nightly",
dataset: { name: "support-nightly" },
runner: async ({ item }) => ({ output: item.input }),
});
```
2. **Resolve the dataset.** Inline arrays run as-is; named datasets pull from VoltOps or a registry.
```ts
dataset: {
items: [
{ id: "greeting", input: "hello", expected: "hello" },
{ id: "farewell", input: "goodbye", expected: "goodbye" },
],
};
// or
dataset: { name: "support-nightly" };
```
3. **Execute the runner.** Call your agent and collect outputs/metadata.
```ts
runner: async ({ item }) => {
const reply = await supportAgent.generateText(item.input);
return { output: reply.text, metadata: { tokens: reply.usage?.total_tokens } };
};
```
4. **Score the result.** Combine scorers with thresholds or LLM judges.
```ts
import { scorers } from "@voltagent/scorers";
scorers: [scorers.exactMatch, scorers.embeddingSimilarity({ expectedMin: 0.8 })];
```
5. **Aggregate and apply pass criteria.**
```ts
passCriteria: { type: "passRate", min: 0.95 };
```
### Running offline evals
You can execute the experiment either from the CLI or directly in Node.js:
```bash title="CLI"
npm run volt eval run \
-- --experiment ./src/experiments/support-nightly.experiment.ts \
--concurrency 4