Healthie Python API Docs | dltHub

Healthie is an API-first healthcare platform exposing patient, scheduling, clinical, billing, and practice management data via a GraphQL-based REST endpoint. The REST API base URL is https://api.gethealthie.com and all requests require authentication via API keys / tokens using an Authorization Bearer token 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 Healthie data in under 10 minutes.

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What data can I load from Healthie?

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

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How do I authenticate with the Healthie API?

Healthie uses bearer-style API tokens. Include Authorization: Bearer on API requests and any provider/account identifiers as required by the GraphQL schema.

1. Get your credentials

1. Sign in to your Healthie account as an admin or developer user.
2. Navigate to the Developer/API or API Keys section in the Healthie dashboard (API Keys listed in docs reference as apiKeys).
3. Create a new API key or view an existing key; copy the token value.
4. Use the token in the Authorization header: Authorization: Bearer <your_token>

2. Add them to .dlt/secrets.toml

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[sources.healthie_source]
api_key = "your_healthie_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 Healthie 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 healthie_pipeline.py

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

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Pipeline healthie_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset healthie_data
The duckdb destination used duckdb:/healthie.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 healthie_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 documents from the Healthie 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 healthie_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.gethealthie.com",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "GraphQL (POST to /graphql or the documented API host)", "data_selector": "data.users"}},
            {"name": "documents", "endpoint": {"path": "GraphQL", "data_selector": "data.documents"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM healthie_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("healthie_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 Healthie 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 401/403 responses, verify your Bearer token is valid, not expired, and has necessary scopes. Ensure Authorization: Bearer header is present.

Pagination and counts

Many Healthie collections are paginated (e.g., documents, users, appointments) and have companion fields like documentsCount or usersCount to obtain totals. Use cursor or pagination arguments in GraphQL queries as documented.

Rate limits and errors

The docs reference typical API errors returned by the GraphQL endpoint. Implement exponential backoff for 429 responses and check error messages in the GraphQL errors array.

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