FreshBooks Python API Docs | dltHub

FreshBooks is an online accounting platform and REST API that lets developers programmatically access and manage accounting data (clients, invoices, expenses, projects, time entries, payments, etc.). The REST API base URL is https://api.freshbooks.com and all requests require an OAuth2 Bearer access token.

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 FreshBooks data in under 10 minutes.

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What data can I load from FreshBooks?

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

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How do I authenticate with the FreshBooks API?

FreshBooks uses OAuth 2.0 (authorization code grant). Obtain client_id and client_secret from the FreshBooks developer dashboard, perform the authorization grant to receive a code, then exchange it at POST https://api.freshbooks.com/auth/oauth/token for access_token and refresh_token. Include header Authorization: Bearer <access_token> and Content-Type: application/json for API calls.

1. Get your credentials

1. Log in to FreshBooks and go to the Developer page (my.freshbooks.com/#/developer). 2) Create a new application (provide name and redirect URI) and select required scopes. 3) Save the app and copy the Client ID and Client Secret shown on the application settings page. 4) Direct users to the authorization URL produced using your client_id and redirect_uri to obtain an authorization code. 5) Exchange the code for tokens by POST to https://api.freshbooks.com/auth/oauth/token with grant_type=authorization_code, client_id, client_secret, code, and redirect_uri. 6) Store access_token and refresh_token; refresh with grant_type=refresh_token when needed.

2. Add them to .dlt/secrets.toml

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[sources.freshbooks_source]
client_id = "your_client_id"
client_secret = "your_client_secret"

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 FreshBooks 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 freshbooks_pipeline.py

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

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Pipeline freshbooks_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset freshbooks_data
The duckdb destination used duckdb:/freshbooks.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 freshbooks_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 clients and invoices from the FreshBooks 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 freshbooks_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.freshbooks.com",
            "auth": {
                "type": "oauth2",
                "access_token": client_secret,
            },
        },
        "resources": [
            {"name": "clients", "endpoint": {"path": "accounting/account/{account_id}/users/clients", "data_selector": "response.result.clients"}},
            {"name": "invoices", "endpoint": {"path": "accounting/account/{account_id}/invoices/invoices", "data_selector": "response.result.invoices"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="freshbooks_pipeline",
        destination="duckdb",
        dataset_name="freshbooks_data",
    )
    load_info = pipeline.run(freshbooks_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("freshbooks_pipeline").dataset()
sessions_df = data.clients.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM freshbooks_data.clients LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("freshbooks_pipeline").dataset()
data.clients.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 FreshBooks 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 or 403 responses: verify the Authorization header is "Authorization: Bearer <access_token>" and that the token is not expired. Exchange refresh_token at POST https://api.freshbooks.com/auth/oauth/token with grant_type=refresh_token to get a new access_token. Ensure your app's scopes include the resource being requested.

Pagination and missing records

List endpoints return pagination metadata under response.result (page, pages, per_page or size, total). Use query parameters per_page and page. FreshBooks silently caps per_page to 100.

Rate limits and retries

FreshBooks recommends implementing retries and exponential backoff for transient failures and rate limits (responses with 429). Design deduplication for idempotency when retrying.

Resource-not-found and validation errors

404 responses can include API error objects with message and errorCode (e.g., errorCode 1012 for "Client not found"). Validation errors return an error object with fields: message, errorCode, field, object, value.

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