MongoDB Python API Docs | dltHub

MongoDB Atlas Data API provides HTTPS access to MongoDB collections for CRUD and aggregation operations without requiring native database drivers. The REST API base URL is https://data.mongodb-api.com/app/{app-id}/endpoint/data/v1 and requests require an 'api-key' header or a Bearer token in the 'Authorization' 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 add "dlt[hub]" and start loading MongoDB data in under 10 minutes.

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What data can I load from MongoDB?

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

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How do I authenticate with the MongoDB API?

The Data API is authenticated using an API key provided in the 'api-key' header, or alternatively, by including a Bearer token in the 'Authorization' header.

1. Get your credentials

To obtain API credentials in the MongoDB Atlas dashboard: 1. Navigate to your Organization or Project dashboard. 2. Under the 'Access Manager' or 'Users and Teams' section (depending on your UI version), select 'API Keys'. 3. Click 'Add API Key'. 4. Provide a description and select the appropriate organization or project roles. 5. Click 'Next'. 6. Copy and save your Public Key and Private Key immediately. The Private Key will not be visible again after this step. Ensure you add your current IP address to the API Key's access list if required by your security policy. Note: MongoDB recommends using Service Accounts (OAuth 2.0) as a more secure alternative to legacy API keys.

2. Add them to .dlt/secrets.toml

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[sources.mongodb_source]
# Note: Use of legacy API Keys is discouraged in favor of Service Account credentials
mongodb_public_key = "your_public_key_here"
mongodb_private_key = "your_private_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.

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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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 MongoDB 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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uv run python mongodb_pipeline.py

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

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Pipeline mongodb_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mongodb_data
The duckdb destination used duckdb:/mongodb.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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uv run dlthub 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 /orgs and /groups (or projects) from the MongoDB 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 mongodb_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://data.mongodb-api.com/app/{app-id}/endpoint/data/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "find_documents", "endpoint": {"path": "action/find", "data_selector": "documents"}},
            {"name": "aggregate_documents", "endpoint": {"path": "action/aggregate", "data_selector": "documents"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mongodb_pipeline",
        destination="duckdb",
        dataset_name="mongodb_data",
    )
    load_info = pipeline.run(mongodb_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("mongodb_pipeline").dataset()
sessions_df = data.find_documents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mongodb_data.find_documents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mongodb_pipeline").dataset()
data.find_documents.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 MongoDB 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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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