Descope Python API Docs | dltHub

Build a Descope-to-database pipeline in Python using dlt with AI Workbench support for Claude Code, Cursor, and Codex.

Last updated: Mar 11, 2026

Descope is a user authentication and management platform providing REST APIs to authenticate users (OTP, magic link, social, TOTP), manage users and test users, and administer projects. The REST API base URL is https://api.descope.com and All requests require a Bearer token; management calls require ProjectID:ManagementKey..

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 Descope data in under 10 minutes.

What data can I load from Descope?

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

Resource	Endpoint	Method	Data selector	Description
users	/management/users/load	GET	(object) user	Load user by UID or login ID (response is an object with user key)
users_search	/management/users/search	POST	users	Search users — response contains users array
test_users_generate_otp	/management/users/test-users/generate-otp	POST		Generate OTP for test user (management auth)
test_users	/management/users/test-users	POST		Create or manage test users (management auth)
users_get_login_history	/management/users/get-login-history	POST	loginHistory	Get a user's login history
users_get_provider_token	/management/users/get-provider-token	GET		Get provider token for a user
Note: Documentation groups many management endpoints; GET endpoints are limited. For exact data selectors: users responses usually return a top-level object with keys such as "user" (single user) or "users" (list). Some endpoints return arrays under custom keys like "loginHistory".

How do I authenticate with the Descope API?

Most management and REST endpoints require a Bearer token in the Authorization header. Management operations use a management key combined with your Project ID in the format : as the bearer token. For some sign-in/signup endpoints you supply the Project ID alone as the Bearer token; for signed-in user actions include Refresh JWT appended to the Project ID as :.

1. Get your credentials

Sign in to Descope console. 2) Navigate to Company > Management Keys. 3) Create/generate a Management Key for your project. 4) Use the Management Key combined with your Project ID in Authorization: Bearer ProjectID:ManagementKey.

2. Add them to .dlt/secrets.toml


[sources.descope_management_users_source]
project_id = "your_project_id_here"
management_key = "your_management_key_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.

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:


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

1. Install the dlt AI Workbench:


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:


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 Descope 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:


python descope_management_users_pipeline.py

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


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

Inspect your pipeline and data:


dlt pipeline descope_management_users_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.

Python pipeline example

This example loads users and test_users from the Descope API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:


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

@dlt.source
def descope_management_users_source(management_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.descope.com",
            "auth": {
                "type": "bearer",
                "token": management_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "management/users/load", "data_selector": "user"}},
            {"name": "test_users", "endpoint": {"path": "management/users/test-users"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="descope_management_users_pipeline",
        destination="duckdb",
        dataset_name="descope_management_users_data",
    )
    load_info = pipeline.run(descope_management_users_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.

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):


import dlt

data = dlt.pipeline("descope_management_users_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):


SELECT * FROM descope_management_users_data.users LIMIT 10;

In a marimo or Jupyter notebook:


import dlt

data = dlt.pipeline("descope_management_users_pipeline").dataset()
data.users.df().head()

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

What destinations can I load Descope data to?

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

Destination	Example value
DuckDB (local, default)	`"duckdb"`
PostgreSQL	`"postgres"`
BigQuery	`"bigquery"`
Snowflake	`"snowflake"`
Redshift	`"redshift"`
Databricks	`"databricks"`
Filesystem (S3, GCS, Azure)	`"filesystem"`

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

Troubleshooting

Authentication failures

If Authorization is missing or incorrect you will get 401 responses. For management APIs ensure the header uses the Project ID and Management Key in the format: Authorization: Bearer ProjectID:ManagementKey.

Rate limits

Requests are rate-limited; when limit is exceeded responses include Retry-After header and an error body indicating rate limiting.

Pagination and selectors

Search/list endpoints may return paginated results; check the response for common keys such as "users" (array) and pagination tokens/metadata. Use the exact top-level key (e.g., "users") as the dlt data selector.

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

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.


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

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