Run an experiment

eval run is the core command that ties the evaluation loop together: it takes an evaluation dataset and one or more evaluators, then submits an experiment against a deployed Runtime or TEA source evaluation target. The dataset, evaluators, and target can all be specified by ID or name, and field mapping is automatic by default. Prepare a dataset, evaluators, and an available target on the same backend; submit evaluator versions first on TEA. The first example previews the request. --dry-run still resolves resources over the network, but does not submit an experiment or auto-publish a dataset draft

Removing --dry-run invokes the target and judge models, may incur usage charges, and sends cases and outputs to the evaluation platform. Without a committed dataset version, submission can also publish the current draft automatically

agentkit eval run \
  --dataset qa-set \
  --evaluator relevance \
  --evaluator-version 0.0.1 \
  --target my-agent \
  --dry-run

The CLI first resolves the evaluation backend for the current account. On TEA, every --evaluator must have a corresponding --evaluator-version; on Coze, the command continues to use the evaluator’s current version. To inspect the current backend, run eval backend.

eval run

Flag / Argument	Description	Default
`--dataset <id\|name>`	Evaluation set ID or exact name.	Required
`--dataset-version <id\|name>`	TEA dataset version ID or version string; when omitted, the CLI uses an existing committed version and auto-publishes a draft version when needed.	—
`--evaluator <id\|name>`	Evaluator ID or exact name; repeatable.	Required
`--evaluator-version <id\|name>`	TEA evaluator version ID or version string; must be provided once for each `--evaluator`.	—
`--target <runtime name\|id>`	Deployed Runtime or TEA source evaluation target to evaluate.	Required
`--target-type <n>`	TEA source evaluation target type.	`101`
`--target-version <version>`	TEA source evaluation target version; when omitted, the CLI uses the latest version from the version list.	Latest version
`--name <name>`	Experiment name.	`<dataset>-<timestamp>`
`--description <text>`	Experiment description.	—
`--concurrency <n>`	Number of cases run concurrently.	`5`
`--map <spec>`	Override field mapping, repeatable; syntax below.	Automatic
`--dry-run`	Print the request that would be submitted without starting an experiment.	`false`
`-p, --project <name>`	Coze project name; ignored on TEA.	`default`
`--json`	Output raw JSON.	`false`

agentkit eval run \
  --dataset qa-set \
  --dataset-version 0.0.1 \
  --evaluator relevance \
  --evaluator-version 0.0.1 \
  --target my-agent \
  --target-version <target-version> \
  --concurrency 8

Automatic Field Mapping

An experiment has three layers of data to align: dataset fields, target input and output, and evaluator inputs. eval run connects them automatically by convention:

The target Runtime typically uses user_input as its input field and actual_output as its output field; the dataset’s primary input field, such as input, is passed to the target’s user_input.
The evaluator field that represents the model answer, such as output, is supplied by the target output actual_output; the remaining fields, such as input and reference_output, are taken from the dataset by matching name.

`--map` Syntax

The TEA backend uses prefixed arrow syntax:

Form	Meaning
`evaluator.<field> <- dataset.<field>`	Evaluator input comes from a dataset field.
`evaluator.<field> <- target.<field>`	Evaluator input comes from a target output field.
`target.<field> <- dataset.<field>`	Target input comes from a dataset field.

agentkit eval run --dataset qa-set --evaluator relevance --evaluator-version 0.0.1 --target my-agent \
  --map "evaluator.output <- target.actual_output" \
  --map "target.user_input <- dataset.question"

The Coze backend uses equals syntax:

Form	Meaning
`<evaluatorField>=<datasetField>`	Evaluator input comes from a dataset field.
`<evaluatorField>=target:<targetOutput>`	Evaluator input comes from a target output field.
`target:<targetInput>=<datasetField>`	Target input comes from a dataset field.

Check With Dry Run First

It is recommended to add --dry-run before your first run, to confirm the resolved dataset version, evaluator versions, target version, and field mapping:

agentkit eval run \
  --dataset qa-set \
  --evaluator relevance \
  --evaluator-version 0.0.1 \
  --target my-agent \
  --dry-run

An experiment requires a committed dataset version. When --dataset-version is omitted and the dataset only has an uncommitted draft, eval run auto-publishes a version before submitting. When --dataset-version is provided, that version is used.

Once submitted successfully it returns an experiment ID; on TEA it also returns a run ID. Track it with eval experiment show:

agentkit eval run --dataset qa-set --evaluator relevance --evaluator-version 0.0.1 --target my-agent --json
# → { "experimentId": "75901...", "runId": "75902...", "name": "qa-set-<timestamp>" }

Getting Started

AI-Friendly

Command reference

eval run

Automatic Field Mapping

`--map` Syntax

Check With Dry Run First

​eval run

​Automatic Field Mapping

​--map Syntax

​Check With Dry Run First

eval run

Automatic Field Mapping

`--map` Syntax

Check With Dry Run First