Karbon Python API Docs | dltHub

Karbon is a cloud‑based practice management platform for accounting firms offering a REST API. The REST API base URL is https://api.karbonhq.com and All requests require an AccessKey header plus a Bearer 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 Karbon data in under 10 minutes.

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What data can I load from Karbon?

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

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How do I authenticate with the Karbon API?

Karbon requires two headers on each request: an AccessKey header containing a JWT‑like key and an Authorization header with a Bearer token.

1. Get your credentials

1. Sign in to your Karbon account. 2) Open the Developer / API settings page (see Karbon Developer Center or Help article). 3) Create or copy your AccessKey and generate an API token (Bearer). 4) Store both values securely; supply AccessKey and token as headers for API calls.

2. Add them to .dlt/secrets.toml

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[sources.karbon_source]
access_key = "your_access_key_here"
 token = "your_bearer_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 Karbon 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 karbon_pipeline.py

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

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Pipeline karbon_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset karbon_data
The duckdb destination used duckdb:/karbon.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 karbon_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 users and contacts from the Karbon 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 karbon_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.karbonhq.com",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "v3/Users"}},
            {"name": "contacts", "endpoint": {"path": "v3/Contacts"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="karbon_pipeline",
        destination="duckdb",
        dataset_name="karbon_data",
    )
    load_info = pipeline.run(karbon_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("karbon_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM karbon_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("karbon_pipeline").dataset()
data.users.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 Karbon 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 requests return 401 Unauthorized, verify both AccessKey and Authorization Bearer token headers are present and not expired. Ensure token is copied exactly and the AccessKey is the org/application AccessKey from Karbon Developer settings.

Pagination and filtering

Karbon v3 supports OData‑style query parameters (e.g. $filter). Use standard paging query params returned in responses; if responses are truncated, use OData $top/$skip or provided next links per endpoint.

Rate limits and errors

If you receive 429 Too Many Requests, back off and retry after the window. Review response body for error details per Karbon API reference.

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