Keeper Python API Docs | dltHub

Keeper Commander Service Mode is a REST API that provides programmatic access to Keeper Commander commands for managing secrets and records. The REST API base URL is https://<host>:<port>/api/v2 and All requests require an API key passed in the 'api-key' header..

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 Keeper data in under 10 minutes.

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What data can I load from Keeper?

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

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How do I authenticate with the Keeper API?

Authentication uses an API key passed in the HTTP header named 'api-key'.

1. Get your credentials

1. Install and configure Keeper Commander locally.
2. Run the service-create command to start the Service Mode instance; this generates a vault record containing the API key.
3. Open the Keeper vault UI, locate the generated record (often named "Commander Service API Key"), and copy the API key value.
4. Store the key securely (e.g., in a secrets.toml file) for use by dlt.

2. Add them to .dlt/secrets.toml

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[sources.keeper_source]
api_key = "<your_api_key>"

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 Keeper 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 keeper_pipeline.py

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

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Pipeline keeper_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset keeper_data
The duckdb destination used duckdb:/keeper.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 keeper_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 status and result from the Keeper 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 keeper_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<host>:<port>/api/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "status", "endpoint": {"path": "api/v2/status/<request_id>"}},
            {"name": "result", "endpoint": {"path": "api/v2/result/<request_id>", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="keeper_pipeline",
        destination="duckdb",
        dataset_name="keeper_data",
    )
    load_info = pipeline.run(keeper_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("keeper_pipeline").dataset()
sessions_df = data.status.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM keeper_data.status LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("keeper_pipeline").dataset()
data.status.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 Keeper 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

Authentication Errors

• 401 Unauthorized – The API key is missing, misspelled, or invalid. Verify that the api-key header is present and contains a valid key generated from the Keeper vault.

Rate Limiting

• 429 Too Many Requests – The service has exceeded its rate limit. Back‑off and retry after a short delay.

Queue Issues

• 503 Service Unavailable – The request queue is full. Reduce the number of concurrent async requests or increase queue capacity in the service configuration.

Not Found

• 404 Not Found – The provided <request_id> does not exist or has already been purged. Ensure the request ID is correct and still active.

Internal Failures

• 500 Internal Server Error – The commanded operation failed on the server side. Review the command syntax and check the service logs for details.

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