Mode Python API Docs | dltHub

Mode is a collaborative data analysis platform that provides a REST API for programmatic access to reports, runs, and data sources. The REST API base URL is https://app.mode.com/api and All requests require basic authentication using an API token and secret..

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 Mode data in under 10 minutes.

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What data can I load from Mode?

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

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How do I authenticate with the Mode API?

Provide the API token as the username and the secret as the password in the HTTP Basic Authorization header (base64‑encoded "token:secret").

1. Get your credentials

1. Log in to your Mode workspace.
2. Click on your profile avatar in the top‑right corner and select Account Settings.
3. Navigate to API Tokens in the sidebar.
4. Click Generate New Token, give it a name, and copy the displayed token and secret.
5. Store the token and secret securely; you will use the token as the username and the secret as the password for Basic authentication.

2. Add them to .dlt/secrets.toml

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[sources.mode_analytics_source]
username = "your_api_token_here"
password = "your_api_secret_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 Mode 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 mode_analytics_pipeline.py

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

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Pipeline mode_analytics_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mode_analytics_data
The duckdb destination used duckdb:/mode_analytics.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 mode_analytics_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 runs and data_sources from the Mode 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 mode_analytics_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.mode.com/api",
            "auth": {
                "type": "http_basic",
                "username": api_token,
            },
        },
        "resources": [
            {"name": "runs", "endpoint": {"path": "{account}/reports/{report}/runs", "data_selector": "runs"}},
            {"name": "data_sources", "endpoint": {"path": "{workspace}/data_sources", "data_selector": "data_sources"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mode_analytics_pipeline",
        destination="duckdb",
        dataset_name="mode_analytics_data",
    )
    load_info = pipeline.run(mode_analytics_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("mode_analytics_pipeline").dataset()
sessions_df = data.runs.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mode_analytics_data.runs LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mode_analytics_pipeline").dataset()
data.runs.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 Mode 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 a 401 Unauthorized response, verify that the token and secret are correct and that they are being sent using HTTP Basic authentication (Authorization header with base64‑encoded token:secret).

Rate limiting

Mode enforces roughly 40 requests per 10 seconds. When the limit is exceeded the API returns 429 Too Many Requests and includes the headers X-RateLimit-Limit and X-RateLimit-Remaining. Slow down your request rate or implement exponential back‑off.

Pagination

Many list endpoints support pagination via the page and per_page query parameters. Check the response for a next_page URL or pagination metadata; continue requesting subsequent pages until no next_page is provided.

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