Reviews.io Python API Docs | dltHub

Reviews.io is a review collection and publishing platform that provides REST APIs to retrieve and manage product and company reviews, questions, ratings, invitations, and related review data. The REST API base URL is https://api.reviews.io and API access requires an API key (available from the dashboard); the key is sent as a query parameter 'apikey' or via the API‑Key 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 Reviews.io data in under 10 minutes.

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What data can I load from Reviews.io?

Here are some of the endpoints you can load from Reviews.io:

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How do I authenticate with the Reviews.io API?

Obtain an API key from the Reviews.io dashboard (Integrations → API). Include the key either as the query parameter 'apikey' or in the 'API-Key' header as documented.

1. Get your credentials

1. Log into your Reviews.io dashboard.
2. Navigate to Integrations → API (or Developer → API credentials).
3. Create a new API key or copy the existing one displayed in the API credentials section.
4. Store the key securely; include it in requests as the 'apikey' query parameter or the 'API-Key' header.

2. Add them to .dlt/secrets.toml

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[sources.reviews_io_source]
api_key = "your_reviews_io_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 Reviews.io 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 reviews_io_pipeline.py

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

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Pipeline reviews_io_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset reviews_io_data
The duckdb destination used duckdb:/reviews_io.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 reviews_io_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 reviews and merchant_reviews from the Reviews.io 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 reviews_io_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.reviews.io",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "reviews", "endpoint": {"path": "reviews", "data_selector": "reviews"}},
            {"name": "merchant_reviews", "endpoint": {"path": "merchant/reviews", "data_selector": "reviews"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="reviews_io_pipeline",
        destination="duckdb",
        dataset_name="reviews_io_data",
    )
    load_info = pipeline.run(reviews_io_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("reviews_io_pipeline").dataset()
sessions_df = data.reviews.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM reviews_io_data.reviews LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("reviews_io_pipeline").dataset()
data.reviews.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 Reviews.io 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

Ensure the API key from Integrations → API is included. Some endpoints require the key as a query parameter named apikey when fetching combined types (e.g., omitting type on /reviews). 401/403 responses indicate a missing or invalid key.

Rate limiting

The public docs do not specify exact rate limits. If a 429 Too Many Requests response is received, implement exponential back‑off and retry.

Pagination and large result sets

Most list endpoints support pagination via page and limit query parameters. Consult the endpoint reference pages for the exact parameter names. When retrieving /reviews without a type parameter, the API may also fetch third‑party reviews, which requires the apikey parameter.

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