ConfigCat Python API Docs | dltHub

ConfigCat is a feature flag and configuration management platform that provides APIs to manage products, configs, feature flags, environments, SDK keys and related resources. The REST API base URL is https://api.configcat.com and All management API requests require HTTP Basic authentication using a Management API token as the username (password blank)..

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 ConfigCat data in under 10 minutes.

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What data can I load from ConfigCat?

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

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How do I authenticate with the ConfigCat API?

The Public Management API uses HTTP Basic authentication where the Management API token is supplied as the username and the password is empty. Include an Authorization header of the form Authorization: Basic <base64(token:)>.

1. Get your credentials

1. Sign into https://app.configcat.com.
2. Open the organization or product settings and go to “Integrations” or “API Keys/SDK Keys”.
3. Locate the Management API token (also called Management SDK Key), create a new one or reveal an existing token.
4. Copy the token and store it securely; it will be used as the username in HTTP Basic authentication.

2. Add them to .dlt/secrets.toml

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[sources.config_cat_source]
management_token = "your_management_token_here"

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 ConfigCat 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 config_cat_pipeline.py

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

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Pipeline config_cat_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset config_cat_data
The duckdb destination used duckdb:/config_cat.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 config_cat_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 configs and settings from the ConfigCat 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 config_cat_source(management_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.configcat.com",
            "auth": {
                "type": "http_basic",
                "management_token": management_token,
            },
        },
        "resources": [
            {"name": "configs", "endpoint": {"path": "v1/configs", "data_selector": "configs"}},
            {"name": "settings", "endpoint": {"path": "v1/configs/{configId}/settings", "data_selector": "settings"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="config_cat_pipeline",
        destination="duckdb",
        dataset_name="config_cat_data",
    )
    load_info = pipeline.run(config_cat_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("config_cat_pipeline").dataset()
sessions_df = data.configs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM config_cat_data.configs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("config_cat_pipeline").dataset()
data.configs.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 ConfigCat 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 failures

If you receive 401 Unauthorized, verify that you are using HTTP Basic authentication with the Management token as the username and an empty password. The header must be Authorization: Basic <base64(management_token:)>.

Rate limits (429 Too Many Requests)

A 429 response indicates you have exceeded the allowed request rate. Back off for a short period and retry later.

Bad requests and not found (400 / 404)

400 Bad Request means the request syntax or parameters are invalid. 404 Not Found signals that a referenced productId, configId, or settingId does not exist.

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