YNAB Python API Docs | dltHub

YNAB is a budgeting platform that exposes a REST API to read and manage budgets, accounts, transactions, payees, categories, months and related entities. The REST API base URL is https://api.ynab.com/v1 and All requests require a Bearer token (Personal Access Token or OAuth access 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 pip install "dlt[workspace]" and start loading YNAB data in under 10 minutes.

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What data can I load from YNAB?

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

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How do I authenticate with the YNAB API?

Authenticate by sending an HTTP header: Authorization: Bearer YOUR_TOKEN. Use either a Personal Access Token (for single-user scripts) or an OAuth access token for third‑party apps.

1. Get your credentials

1. Sign in to your YNAB account at app.ynab.com.
2. Open Account Settings → Developer Settings (or visit Developer Settings in your user account).
3. Create a Personal Access Token; copy and store it securely. For third‑party apps, register an OAuth app to obtain client_id/client_secret and follow the OAuth flow to get an access token.

2. Add them to .dlt/secrets.toml

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[sources.ynab_source]
api_key = "your_personal_access_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 YNAB 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 ynab_pipeline.py

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

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Pipeline ynab_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ynab_data
The duckdb destination used duckdb:/ynab.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 ynab_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 budgets and transactions from the YNAB 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 ynab_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.ynab.com/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "budgets", "endpoint": {"path": "budgets", "data_selector": "data.budgets"}},
            {"name": "transactions", "endpoint": {"path": "plans/{plan_id}/transactions", "data_selector": "data.transactions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ynab_pipeline",
        destination="duckdb",
        dataset_name="ynab_data",
    )
    load_info = pipeline.run(ynab_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("ynab_pipeline").dataset()
sessions_df = data.transactions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ynab_data.transactions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ynab_pipeline").dataset()
data.transactions.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 YNAB 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 that the Authorization header is present and contains a valid Personal Access Token or OAuth access token: Authorization: Bearer YOUR_TOKEN. Personal Access Tokens are generated in Developer Settings; OAuth tokens expire and must be refreshed per standard OAuth flows.

Rate limits and 429 responses

The API may return 429 Too Many Requests when rate limits are exceeded. Implement exponential backoff and retry. Check response headers for any rate‑limit guidance; respect the recommended backoff period.

Pagination / server knowledge

Many list endpoints accept the query parameter last_knowledge_of_server (integer) to return only entities changed since that value. Responses include server_knowledge inside the data object (e.g. data.server_knowledge) to be used for incremental syncs. For date‑based filtering of transactions, use the since_date parameter.

Common HTTP errors

• 200: Success. Response body is structured under the top‑level data key (e.g. data.transactions).
• 400: Bad Request – malformed parameters.
• 401: Unauthorized – invalid or missing token.
• 404: Not Found – invalid plan_id or resource ID.
• 429: Too Many Requests – rate limit hit. Error responses include an error object in the response body under the top‑level error key.

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