Planyo Python API Docs | dltHub

Planyo is an API that allows for advanced functions and automation of the Planyo reservation system, such as importing resource data and making automated reservations. The REST API base URL is https://www.planyo.com/rest/ and All requests require an API key for authentication, which is passed as a parameter..

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 Planyo data in under 10 minutes.

---

What data can I load from Planyo?

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

---

How do I authenticate with the Planyo API?

Authentication requires an API key to be passed as a parameter in all requests. Stronger security can optionally be achieved using a hash key and timestamp, and all communication must use SSL.

1. Get your credentials

To obtain your API key, log in to your Planyo account and click the 'API Key' button.

2. Add them to .dlt/secrets.toml

Copy
[sources.planyo_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Planyo 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:

Copy
python planyo_pipeline.py

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

Copy
Pipeline planyo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset planyo_data
The duckdb destination used duckdb:/planyo.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline planyo_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.

---

Python pipeline example

This example loads list_resources and get_resource_info from the Planyo API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def planyo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.planyo.com/rest/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "list_resources", "endpoint": {"path": "?method=list_resources", "data_selector": "data"}},
            {"name": "get_resource_info", "endpoint": {"path": "?method=get_resource_info", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="planyo_pipeline",
        destination="duckdb",
        dataset_name="planyo_data",
    )
    load_info = pipeline.run(planyo_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.

---

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):

Copy
import dlt

data = dlt.pipeline("planyo_pipeline").dataset()
sessions_df = data.list_resources.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM planyo_data.list_resources LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("planyo_pipeline").dataset()
data.list_resources.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Planyo 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.

---

Troubleshooting

API Error Codes

The Planyo API returns specific response_code values in case of failure, which can help in troubleshooting:

• 1: Authentication error (e.g., invalid API key) or invalid method.
• 3: Invalid input data provided in the request.
• 4: Other error originating from the method being called.
• 5: Call rejected due to API overuse (rate limit).
• 6: A fatal error occurred on the server side.

When encountering these codes, verify your API key, ensure correct input parameters, and check for rate limit adherence.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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