Evernote Python API Docs | dltHub

Evernote is a note‑taking and organization service that provides a REST API for accessing notes, notebooks, tags, and other user data. The REST API base URL is https://www.evernote.com and All requests require an OAuth token or a developer token for authentication..

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 Evernote data in under 10 minutes.

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What data can I load from Evernote?

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

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How do I authenticate with the Evernote API?

Authentication is performed via OAuth 1.0a or by sending the developer token (the string beginning with "S=") as a Bearer token in the Authorization header.

1. Get your credentials

1. Open https://www.evernote.com/api/DeveloperToken.action in a web browser.
2. Click "Create a developer token".
3. When prompted, agree to the terms and click "Generate".
4. Copy the generated token string (it starts with "S=") and store it securely.
5. (Optional) To revoke, return to the same page and click "Revoke your developer token".

For OAuth:

1. Visit https://dev.evernote.com/doc/ and request an API key (consumer key and secret).
2. Implement the OAuth 1.0a flow using those credentials to obtain an access token.
3. The returned response includes "edamNoteStoreUrl" and "edamWebApiUrlPrefix" for subsequent calls.

2. Add them to .dlt/secrets.toml

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[sources.evernote_source]
api_key = "S=your_developer_token_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 Evernote 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 evernote_pipeline.py

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

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Pipeline evernote_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset evernote_data
The duckdb destination used duckdb:/evernote.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 evernote_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 list_notebooks and find_notes from the Evernote 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 evernote_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.evernote.com",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "list_notebooks", "endpoint": {"path": "noteStore/listNotebooks", "data_selector": "notebooks"}},
            {"name": "find_notes", "endpoint": {"path": "noteStore/findNotes", "data_selector": "notes"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="evernote_pipeline",
        destination="duckdb",
        dataset_name="evernote_data",
    )
    load_info = pipeline.run(evernote_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("evernote_pipeline").dataset()
sessions_df = data.find_notes.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM evernote_data.find_notes LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("evernote_pipeline").dataset()
data.find_notes.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 Evernote 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

• Error 401 – Returned when the developer token or OAuth token is missing, malformed, or expired. Re‑generate the token via the developer token page or refresh the OAuth access token.

Rate limits

• Evernote enforces request limits per second and per day. Exceeding these limits results in HTTP 429 responses with a message indicating the limit has been reached. Implement exponential back‑off and respect the Retry-After header.

Pagination / sync errors

• The getFilteredSyncChunk endpoint returns chunkHighUSN. If chunkHighUSN is lower than expected, you may need to request the next chunk using the afterUSN parameter. Errors such as EDAMUserException or EDAMSystemException are mapped to HTTP 4xx/5xx with errorCode and parameter fields in the JSON response.

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