Diigo Python API Docs | dltHub

Diigo API is a platform that allows you to build applications that interact with the Diigo service. The REST API base URL is https://secure.diigo.com/api/v2/ and All requests require HTTP Basic authentication, including a base64 encoded username and password in the Authorization request header, and an API key as an HTTP query parameter..

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 Diigo data in under 10 minutes.

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What data can I load from Diigo?

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

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How do I authenticate with the Diigo API?

The Diigo API uses HTTP Basic authentication, which requires a base64 encoded username and password to be included in the Authorization request header. Additionally, an API key must be provided as an HTTP query parameter in each request.

1. Get your credentials

The documentation states that an API key is required for the Diigo API and must be included in each API request as an HTTP query parameter. However, specific step-by-step instructions for obtaining this API key from a dashboard are not provided in the available documentation.

2. Add them to .dlt/secrets.toml

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[sources.diigo_source]
api_key = "your_api_key_here"
username = "your_username_here"
password = "your_password_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 Diigo 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 diigo_pipeline.py

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

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Pipeline diigo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset diigo_data
The duckdb destination used duckdb:/diigo.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 diigo_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 bookmarks from the Diigo 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 diigo_source(api_key, username, password=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://secure.diigo.com/api/v2/",
            "auth": {
                "type": "http_basic",
                "api_key, username, password": api_key, username, password,
            },
        },
        "resources": [
            {"name": "bookmarks", "endpoint": {"path": "bookmarks"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="diigo_pipeline",
        destination="duckdb",
        dataset_name="diigo_data",
    )
    load_info = pipeline.run(diigo_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("diigo_pipeline").dataset()
sessions_df = data.bookmarks.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM diigo_data.bookmarks LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("diigo_pipeline").dataset()
data.bookmarks.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 Diigo 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

Common API Errors

• 400 Bad Request: This status code indicates that the server cannot process the request due to a client error (e.g., malformed request syntax, invalid request message framing, or deceptive request routing).
• 401 Not Authorized: This error occurs when the request lacks valid authentication credentials for the target resource. Ensure your HTTP Basic authentication (username and password) and API key are correctly provided.
• 403 Forbidden: This status code means the server understood the request but refuses to authorize it. This could be due to insufficient permissions for the authenticated user.
• 404 Not Found: The server cannot find the requested resource. This typically means the URL is incorrect or the resource does not exist.
• 500 Internal Server Error, 502 Bad Gateway, 503 Service Unavailable: These are server-side errors indicating a problem with the Diigo API servers. These usually require no action from the client and should be retried later.

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