Butler Labs Python API Docs | dltHub

Butler Labs is an OCR and document‑extraction API platform that provides pretrained models (invoice, receipt, ID card, W2, etc.) to extract structured fields from uploaded documents. The REST API base URL is https://app.butlerlabs.ai/ and All requests require an API key (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 Butler Labs data in under 10 minutes.

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What data can I load from Butler Labs?

Here are some of the endpoints you can load from Butler Labs:

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How do I authenticate with the Butler Labs API?

Authentication uses a single API key provided by Butler Labs. Include the key in requests as a Bearer token in the Authorization header: Authorization: Bearer <API_KEY>. The official Python SDK also accepts the API key when constructing Client(api_key).

1. Get your credentials

1. Sign in to your Butler Labs account at https://app.butlerlabs.ai/ or visit the docs at https://docs.butlerlabs.ai.
2. Open the dashboard or the 'API keys' / 'Integrations' section (see "Getting Started" / "Uploading documents to the REST API" in docs) and create or copy an API key.
3. Store the key securely; use it as the Bearer token in requests or pass it to the Python SDK: Client(api_key).

2. Add them to .dlt/secrets.toml

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[sources.butler_labs_source]
api_key = "your_api_key_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 Butler Labs 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 butler_labs_pipeline.py

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

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Pipeline butler_labs_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset butler_labs_data
The duckdb destination used duckdb:/butler_labs.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 butler_labs_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 models and extraction_results from the Butler Labs 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 butler_labs_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.butlerlabs.ai/",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "models", "data_selector": "data"}},
            {"name": "extraction_results", "endpoint": {"path": "extract/{extraction_id}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="butler_labs_pipeline",
        destination="duckdb",
        dataset_name="butler_labs_data",
    )
    load_info = pipeline.run(butler_labs_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("butler_labs_pipeline").dataset()
sessions_df = data.extraction_results.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM butler_labs_data.extraction_results LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("butler_labs_pipeline").dataset()
data.extraction_results.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 Butler Labs 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 or 403 Forbidden, verify your API key is correct and included as: Authorization: Bearer <API_KEY>. Ensure the key has not been revoked in the dashboard.

Rate limiting

If the API returns 429 Too Many Requests, back off and retry with exponential backoff. Check account plan limits in the Butler Labs dashboard.

Missing or delayed extraction results

Extraction is asynchronous for large documents. Poll the GET /extract/{id} endpoint until the status in the response indicates completion, and handle transient 202/204/503 responses by retrying.

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