# Manage datasets
This topic covers creating, retrieving, listing, updating, and deleting datasets.
## Create dataset
The `CreateDataset` API creates a new evaluation dataset. This is an asynchronous operation (HTTP 202) — the dataset transitions from `CREATING` to `ACTIVE` once ingestion completes.
**Required parameters:** `datasetName` (alphanumeric and underscores only, `^[a-zA-Z][a-zA-Z0-9_]{0,47}$`), `schemaType`, and `source` (inline examples or S3 URI).
**Optional parameters:** `description`, `kmsKeyArn` (customer managed encryption key, immutable after creation — see [Dataset encryption](datasets-encryption.md)), `tags`.
The following examples show how to create a dataset:
**Example**
1.
```
# Add a dataset to your project
agentcore add dataset --name my_eval_dataset \
--schema-type AGENTCORE_EVALUATION_PREDEFINED_V1
# Edit the generated JSONL file with your scenarios
# File location: agentcore/datasets/my_eval_dataset.jsonl
# Deploy to create the dataset in your AWS account
agentcore deploy
```
This creates a local JSONL file and registers the dataset in your project configuration. Run `agentcore deploy` to create the dataset resource and sync examples to the service.
**Note**
Run this from inside an AgentCore project directory (created with `agentcore create`).
1.
```
from bedrock_agentcore.evaluation import DatasetClient
client = DatasetClient(region_name="us-west-2")
# Create with inline examples (polls until ACTIVE)
ds = client.create_dataset_and_wait(
datasetName="customer_support_scenarios",
schemaType="AGENTCORE_EVALUATION_PREDEFINED_V1",
source={
"inlineExamples": {
"examples": [
{
"scenario_id": "TC-01",
"turns": [{"input": "What is my balance?", "expected_response": "Your balance is $50."}],
"assertions": ["Response includes a dollar amount"],
}
]
}
},
)
print(f"Dataset ID: {ds['datasetId']}, Status: {ds['status']}")
# Create with S3 source
ds = client.create_dataset_and_wait(
datasetName="my_s3_dataset",
schemaType="AGENTCORE_EVALUATION_PREDEFINED_V1",
source={"s3Source": {"s3Uri": "s3://my-bucket/scenarios.jsonl"}},
)
```
**Note**
For S3 ingestion, each line in the JSONL file must include an `exampleId` field. The S3 bucket must be accessible using the caller’s credentials.
1.
```
import boto3
import time
client = boto3.client('bedrock-agentcore-control')
response = client.create_dataset(
datasetName='customer_support_scenarios',
schemaType='AGENTCORE_EVALUATION_PREDEFINED_V1',
source={
'inlineExamples': {
'examples': [
{
'scenario_id': 'TC-01',
'turns': [{'input': 'What is my balance?', 'expected_response': 'Your balance is $50.'}],
'assertions': ['Response includes a dollar amount'],
}
]
}
}
)
dataset_id = response['datasetId']
# Create with S3 source
response = client.create_dataset(
datasetName='my_s3_dataset',
schemaType='AGENTCORE_EVALUATION_PREDEFINED_V1',
source={
's3Source': {'s3Uri': 's3://my-bucket/scenarios.jsonl'}
}
)
# Poll until ACTIVE
while True:
ds = client.get_dataset(datasetId=dataset_id)
if ds['status'] in ('ACTIVE', 'CREATE_FAILED'):
break
time.sleep(2)
```
1.
```
# Create with inline examples
aws bedrock-agentcore-control create-dataset \
--dataset-name "customer_support_scenarios" \
--schema-type AGENTCORE_EVALUATION_PREDEFINED_V1 \
--source '{"inlineExamples": {"examples": [{"scenario_id": "TC-01", "turns": [{"input": "What is my balance?", "expected_response": "Your balance is $50."}], "assertions": ["Response includes a dollar amount"]}]}}'
# Create with S3 source
aws bedrock-agentcore-control create-dataset \
--dataset-name "my_s3_dataset" \
--schema-type AGENTCORE_EVALUATION_PREDEFINED_V1 \
--source '{"s3Source": {"s3Uri": "s3://my-bucket/scenarios.jsonl"}}'
# Poll until ACTIVE
aws bedrock-agentcore-control get-dataset \
--dataset-id my-dataset-id
```
## Get dataset
The `GetDataset` API retrieves dataset metadata, status, example count, and a presigned download URL for the dataset content. By default reads the Draft; specify `datasetVersion` for a published version.
The `downloadUrl` is a presigned S3 URL for the full `dataset.jsonl` file. You can download it with a plain HTTP GET request without authentication headers.
The following examples show how to get a dataset:
**Example**
1.
```
# Show dataset deployment status and metadata
agentcore status --type dataset
# Download dataset content to your local JSONL file (default: Draft)
agentcore dataset download --name my_eval_dataset
# Download a specific published version
agentcore dataset download --name my_eval_dataset --version 1
```
1.
```
from bedrock_agentcore.evaluation import DatasetClient
client = DatasetClient(region_name="us-west-2")
# Get dataset (default: Draft)
ds = client.get_dataset(datasetId="my-dataset-id")
print(f"Status: {ds['status']}, Examples: {ds['exampleCount']}")
print(f"Download URL: {ds['downloadUrl']}")
# Get a specific published version
ds_v1 = client.get_dataset(datasetId="my-dataset-id", datasetVersion="1")
```
1.
```
import boto3
client = boto3.client('bedrock-agentcore-control')
response = client.get_dataset(datasetId='my-dataset-id')
print(f"Status: {response['status']}, Examples: {response['exampleCount']}")
# Download the dataset content via presigned URL
if 'downloadUrl' in response:
import requests
data = requests.get(response['downloadUrl'])
print(data.text)
# Get a specific published version
response = client.get_dataset(datasetId='my-dataset-id', datasetVersion='1')
```
1.
```
# Get dataset (default: Draft)
aws bedrock-agentcore-control get-dataset \
--dataset-id my-dataset-id
# Get a specific published version
aws bedrock-agentcore-control get-dataset \
--dataset-id my-dataset-id \
--dataset-version 1
```
## List datasets
The `ListDatasets` API returns a paginated list of datasets in your account and Region.
The following examples show how to list datasets:
**Example**
1.
```
agentcore status --type dataset
```
1.
```
from bedrock_agentcore.evaluation import DatasetClient
client = DatasetClient(region_name="us-west-2")
response = client.list_datasets()
for dataset in response["datasets"]:
print(f" {dataset['datasetName']} ({dataset['status']})")
```
1.
```
import boto3
client = boto3.client('bedrock-agentcore-control')
response = client.list_datasets()
for dataset in response['datasets']:
print(f" {dataset['datasetName']} ({dataset['status']})")
```
1.
```
aws bedrock-agentcore-control list-datasets
```
## Update dataset
The `UpdateDataset` API updates dataset metadata. This is a synchronous operation (HTTP 200). Only `description` and `tags` can be updated. `datasetName`, `schemaType`, and `kmsKeyArn` are immutable after creation.
The dataset must be in `ACTIVE`, `UPDATE_FAILED`, or `CREATE_FAILED` status.
The following examples show how to update dataset metadata:
**Example**
1. To update a dataset with the AgentCore CLI, edit the dataset configuration in your `agentcore.json` file directly, then redeploy:
```
agentcore deploy
```
Open `agentcore.json`, find the dataset in the `datasets` array, modify its `description`, then run `agentcore deploy`. Changes take effect after deployment.
**Note**
Run this from inside an AgentCore project directory (created with `agentcore create`).
1.
```
from bedrock_agentcore.evaluation import DatasetClient
client = DatasetClient(region_name="us-west-2")
client.update_dataset(datasetId="my-dataset-id", description="Updated description")
```
1.
```
import boto3
client = boto3.client('bedrock-agentcore-control')
client.update_dataset(datasetId='my-dataset-id', description='Updated description')
```
1.
```
aws bedrock-agentcore-control update-dataset \
--dataset-id my-dataset-id \
--description "Updated description"
```
## Delete dataset
The `DeleteDataset` API deletes a dataset. This is an asynchronous operation (HTTP 202).
+ **Full delete** (omit `datasetVersion`): Deletes all versions, Draft, and the dataset record.
+ **Version-specific delete** (specify `datasetVersion` as an integer): Deletes only that published version.
The dataset must be in `ACTIVE`, `CREATE_FAILED`, `UPDATE_FAILED`, or `DELETE_FAILED` status.
**Note**
Only integer version numbers are accepted for version-specific deletion.
The following examples show how to delete a dataset:
**Example**
1.
```
# Delete a specific published version
agentcore dataset remove-version 1 --name my_eval_dataset
# Delete entire dataset
agentcore remove dataset --name my_eval_dataset
agentcore deploy
```
1.
```
from bedrock_agentcore.evaluation import DatasetClient
client = DatasetClient(region_name="us-west-2")
# Delete a specific published version
client.delete_dataset_and_wait(datasetId="my-dataset-id", datasetVersion="1")
# Delete entire dataset (polls until complete)
client.delete_dataset_and_wait(datasetId="my-dataset-id")
```
1.
```
import boto3
client = boto3.client('bedrock-agentcore-control')
# Delete a specific published version
client.delete_dataset(datasetId='my-dataset-id', datasetVersion='1')
# Delete entire dataset
client.delete_dataset(datasetId='my-dataset-id')
```
1.
```
# Delete a specific published version
aws bedrock-agentcore-control delete-dataset \
--dataset-id my-dataset-id \
--dataset-version 1
# Delete entire dataset
aws bedrock-agentcore-control delete-dataset \
--dataset-id my-dataset-id
```