Logto Python API Docs | dltHub

Logto is a cloud identity platform that provides authentication, authorization, and a Management API for programmatic administration of users, applications, organizations, roles, resources and connectors. The REST API base URL is https://{tenant_id}.logto.app/api and all requests require a Bearer access token obtained via the Machine-to-Machine (client_credentials) flow.

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 Logto data in under 10 minutes.

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What data can I load from Logto?

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

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How do I authenticate with the Logto API?

Obtain an M2M access token from the tenant OIDC token endpoint using grant_type=client_credentials and Basic auth with AppID:AppSecret; include Authorization: Bearer <access_token> with each Management API request.

1. Get your credentials

1. Open Logto Console > Applications and create a Machine-to-Machine application.
2. Note the App ID and generate/copy the App Secret from the app detail page.
3. Assign the M2M app roles that include the Logto Management API permissions (or the pre‑configured Management API role).
4. Request an access token at https://{tenant_id}.logto.app/oidc/token using HTTP Basic auth (base64 AppID:AppSecret) with form body grant_type=client_credentials, resource=https://{tenant_id}.logto.app/api, scope=all.

2. Add them to .dlt/secrets.toml

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[sources.logto_management_api_source]
client_id = "your_app_id"
client_secret = "your_app_secret"

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 Logto 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 logto_management_api_pipeline.py

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

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Pipeline logto_management_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset logto_management_api_data
The duckdb destination used duckdb:/logto_management_api.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 logto_management_api_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 users and applications from the Logto 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 logto_management_api_source(management_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{tenant_id}.logto.app/api",
            "auth": {
                "type": "bearer",
                "token": management_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "api/users"}},
            {"name": "applications", "endpoint": {"path": "api/applications"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="logto_management_api_pipeline",
        destination="duckdb",
        dataset_name="logto_management_api_data",
    )
    load_info = pipeline.run(logto_management_api_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("logto_management_api_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM logto_management_api_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("logto_management_api_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.

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What destinations can I load Logto 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 the access token is valid, not expired, and that you requested the token using the tenant OIDC token endpoint (https://{tenant_id}.logto.app/oidc/token for Cloud). Ensure the M2M app has been assigned Management API permissions and that Authorization: Bearer header is present.

Rate limits

Logto Cloud applies tenant-level runtime rate limits. Watch for 429 responses; respect Link and Total-Number headers for pagination and implement retries with backoff.

Pagination

Large list endpoints are paginated. Use Link response headers (rel="next", "prev", etc.) and Total-Number header to iterate pages. Optional query params: page (default 1) and page_size (default 20).

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