Pennylane Python API Docs | dltHub

Pennylane is a cloud accounting and financial management platform exposing a REST API to manage companies, invoices, customers, suppliers, ledger entries and related accounting data. The REST API base URL is https://app.pennylane.com/api/external/v2 and All requests require a Bearer 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 Pennylane data in under 10 minutes.

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What data can I load from Pennylane?

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

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How do I authenticate with the Pennylane API?

The API accepts a Bearer token in the Authorization header. Example header: Authorization: Bearer <YOUR_TOKEN>.

1. Get your credentials

1. Sign in to your Pennylane account. 2) For Companies: go to Account Settings or API Tokens and generate a Company API token. For Firms: go to the Firm account settings and generate a Firm API token. For Integration Partners: request OAuth2 access via Pennylane Partnerships or follow the OAuth flow documented by Pennylane. 3) Copy the generated token and store securely.

2. Add them to .dlt/secrets.toml

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[sources.pennylane_finance_source]
api_key = "your_pennylane_api_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 Pennylane 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 pennylane_finance_pipeline.py

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

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Pipeline pennylane_finance_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pennylane_finance_data
The duckdb destination used duckdb:/pennylane_finance.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 pennylane_finance_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 customers and invoices from the Pennylane 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 pennylane_finance_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.pennylane.com/api/external/v2",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "companies/{company_id}/customers", "data_selector": "items"}},
            {"name": "invoices", "endpoint": {"path": "companies/{company_id}/customer_invoices", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pennylane_finance_pipeline",
        destination="duckdb",
        dataset_name="pennylane_finance_data",
    )
    load_info = pipeline.run(pennylane_finance_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("pennylane_finance_pipeline").dataset()
sessions_df = data.customers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pennylane_finance_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pennylane_finance_pipeline").dataset()
data.customers.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 Pennylane 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, check the Authorization header: Authorization: Bearer . Ensure the token is valid and has correct scope (Company vs Firm vs OAuth scopes). 403 Forbidden indicates insufficient scopes (request the appropriate API scopes for ledger, file_attachments, etc.).

Rate limits

Pennylane enforces rate limits (example: typically 4 requests/sec for most endpoints; customer invoices may be limited to 2 requests/sec). Implement retry/backoff and respect X-RateLimit headers if present.

Pagination and migration notes

List endpoints return paginated results in an object with an "items" key containing the records array. Pennylane is migrating to cursor-based pagination: responses include has_more and next_cursor fields; older offset fields (current_page, total_pages) may be present during migration. Use next_cursor for cursor pagination when available. Some ledger endpoints changed pagination and filtering behavior in 2026 -- follow the 2026 migration guide and include the X-Use-2026-API-Changes header during testing if instructed.

Common 404 error format

When a resource is not found the API returns a 404 with a JSON body like: { "status": 404, "error": "Couldn't find with 'id'=123" }

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