Redox Python API Docs | dltHub

Redox is a healthcare integration platform that provides REST APIs (Platform API and FHIR API) to manage Redox organization configuration and exchange healthcare data. The REST API base URL is Platform API: https://api.redoxengine.com/platform (per-endpoint: https://api.redoxengine.com/platform/{endpoint}) FHIR base URL pattern: https://api.redoxengine.com/fhir/R4/{{destination-slug}}/{{environment-type}} and Platform API requires user-level API keys and an access token; FHIR and other APIs use OAuth API keys or other supported auth schemes..

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 Redox data in under 10 minutes.

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What data can I load from Redox?

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

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How do I authenticate with the Redox API?

Platform API requests are authenticated using a user-level API key which you create in the Redox dashboard; you must request and store an access token (OAuth flow) and include it when calling Platform endpoints. FHIR and other data-exchange APIs use OAuth API keys (JWT-based) or other supported schemes listed in the docs.

1. Get your credentials

1. Sign in to the Redox dashboard. 2) Open the user menu (bottom-left) and choose "User API Keys." 3) Click "Create API key", name it and create or supply a public key (generate keys or provide JWKS URL/entry). 4) Save the key pair and copy/download the private key. 5) Follow the "Request and store an access token" instructions (OAuth API key instructions) to exchange your key for an access token to use with Platform API calls.

2. Add them to .dlt/secrets.toml

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[sources.redox_source]
user_api_key = "YOUR_USER_API_KEY"

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 Redox 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 redox_pipeline.py

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

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Pipeline redox_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset redox_data
The duckdb destination used duckdb:/redox.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 redox_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 sources and logs from the Redox 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 redox_source(user_api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Platform API: https://api.redoxengine.com/platform (per-endpoint: https://api.redoxengine.com/platform/{endpoint}) FHIR base URL pattern: https://api.redoxengine.com/fhir/R4/{{destination-slug}}/{{environment-type}}",
            "auth": {
                "type": "bearer",
                "token": user_api_key,
            },
        },
        "resources": [
            {"name": "sources", "endpoint": {"path": "platform/sources", "data_selector": "payload.records"}},
            {"name": "logs", "endpoint": {"path": "platform/logs", "data_selector": "payload.records"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="redox_pipeline",
        destination="duckdb",
        dataset_name="redox_data",
    )
    load_info = pipeline.run(redox_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("redox_pipeline").dataset()
sessions_df = data.sources.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM redox_data.sources LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("redox_pipeline").dataset()
data.sources.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 Redox 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 the access token is missing, expired, or invalid the Platform API returns an error payload under payload.error with fields such as code, title, detail, transient, and optional status (e.g., 401). Ensure you created a user-level API key in the dashboard, generated or provided a public key (JWKS), exchanged credentials for an access token, and include the token when calling https://api.redoxengine.com/platform/{endpoint}.

Pagination and large result sets

Platform API responses that return collections use the wrapper meta.page.links with first, prev, self, next URLs. Use those links to fetch subsequent pages; results are in payload.records. Count metadata (totalRecords, totalPages, pageSize) may be present but should not be relied upon programmatically.

Timeouts and retries

Platform API requests time out after ~1 minute and may return 504 Gateway Timeout. Narrow result windows using createdAfter/createdBefore or updatedAfter/updatedBefore, and retry transient errors when payload.error.transient is true.

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