Resource-guru Python API Docs | dltHub

Resource Guru is a scheduling and resource management SaaS that provides a REST API to manage accounts, bookings, resources, projects, clients, users, reports and webhooks. The REST API base URL is https://api.resourceguruapp.com and all requests require a Bearer access token (OAuth2) — HTTP Basic allowed for testing only..

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 Resource-guru data in under 10 minutes.

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What data can I load from Resource-guru?

Here are some of the endpoints you can load from Resource-guru:

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How do I authenticate with the Resource-guru API?

Resource Guru uses OAuth2 (authorization code / PKCE / password grant for private apps) to obtain access tokens which must be supplied as an Authorization: Bearer <access_token> header. HTTP Basic auth (username:password) is accepted for exploration only and is rate-limited.

1. Get your credentials

1. Go to the Developer Center: https://app.resourceguruapp.com/developers 2) Click "Register New App" and complete the form (name, redirect URI, credential type). 3) On success you receive a client_id and client_secret (confidential apps) or use PKCE for public apps. 4) Use the OAuth2 flows: GET https://api.resourceguruapp.com/oauth/authorize to get authorization code, then POST https://api.resourceguruapp.com/oauth/token to exchange code for an access token. For quick/private use you can use the password grant to POST /oauth/token with username/password and grant_type=password.

2. Add them to .dlt/secrets.toml

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[sources.resource_guru_source]
access_token = "your_oauth2_access_token_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 Resource-guru 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 resource_guru_pipeline.py

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

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Pipeline resource_guru_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset resource_guru_data
The duckdb destination used duckdb:/resource_guru.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 resource_guru_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 bookings and resources from the Resource-guru 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 resource_guru_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.resourceguruapp.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "bookings", "endpoint": {"path": "v1/{account}/bookings", "data_selector": "bookings"}},
            {"name": "resources", "endpoint": {"path": "v1/{account}/resources", "data_selector": "resources"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="resource_guru_pipeline",
        destination="duckdb",
        dataset_name="resource_guru_data",
    )
    load_info = pipeline.run(resource_guru_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("resource_guru_pipeline").dataset()
sessions_df = data.bookings.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM resource_guru_data.bookings LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("resource_guru_pipeline").dataset()
data.bookings.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 Resource-guru 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, verify your OAuth2 access token is valid and included as Authorization: Bearer . If using HTTP Basic for exploration note Basic auth is rate-limited and may be disabled.

Rate limits (429)

Registered apps are rate limited (examples in docs: 200 requests/min or ~33 requests/10s depending on doc version). On 429 check Retry-After and X-RateLimit-Remaining/X-RateLimit-Limit headers; back off and retry after Retry-After seconds.

Pagination and limits

List endpoints support limit and offset query parameters (limit default 50, use limit=0 to remove limit). Use offset to page results.

Common validation errors (4xx)

400/422 returned for bad requests or validation errors; 403 when insufficient permissions; 404 when entity not found. Error bodies often include an "errors" array with objects containing name, date, code and message.

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