k6 Python API Docs | dltHub

k6 is a performance testing tool that exposes a local REST API to control and inspect running tests and metrics. The REST API base URL is http://localhost:6565/v1 and No authentication required for the local k6 REST API (binds to localhost by default)..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading k6 data in under 10 minutes.

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What data can I load from k6?

Here are some of the endpoints you can load from k6:

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How do I authenticate with the k6 API?

The built-in k6 REST API is bound to localhost (default: localhost:6565) and does not require authentication; requests are typical HTTP JSON requests with Content-Type: application/json header.

1. Get your credentials

No credentials required. To enable/modify the REST API address, start k6 with the --address flag (e.g. k6 run --address :6565 script.js).

2. Add them to .dlt/secrets.toml

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[sources.k6_performance_testing_source]
# no secrets needed for local k6 REST API

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the k6 API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python k6_performance_testing_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline k6_performance_testing_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset k6_performance_testing_data
The duckdb destination used duckdb:/k6_performance_testing.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline k6_performance_testing_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads metrics and status from the k6 API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def k6_performance_testing_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:6565/v1",
            "auth": {
                "type": "none",
                "": ,
            },
        },
        "resources": [
            {"name": "metrics", "endpoint": {"path": "metrics", "data_selector": "data"}},
            {"name": "status", "endpoint": {"path": "status", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="k6_performance_testing_pipeline",
        destination="duckdb",
        dataset_name="k6_performance_testing_data",
    )
    load_info = pipeline.run(k6_performance_testing_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("k6_performance_testing_pipeline").dataset()
sessions_df = data.metrics.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM k6_performance_testing_data.metrics LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("k6_performance_testing_pipeline").dataset()
data.metrics.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load k6 data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Local server unreachable

If you get connection refused, ensure the k6 process is running and the REST API address is set (default: localhost:6565). Start k6 with --address to bind to a different interface.

No authentication but network exposure

The local REST API is not authenticated; avoid binding it to public interfaces. Do not run the API on 0.0.0.0 in untrusted environments.

Unexpected response structure

Responses follow a JSON:API‑like structure with a top‑level data key (either an array for list endpoints or an object for single resources). Inspect the data key for the records/objects.

Rate limiting and errors

The local k6 REST API does not document rate limits. Common errors are 404 for unknown resource IDs and 400 for malformed PATCH/PUT bodies; check Content-Type: application/json and the documented request body structure.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack