Adalo Python API Docs | dltHub

Adalo is a no-code platform for building mobile/web apps; its API provides programmatic access to app collections (data) and push notifications. The REST API base URL is Dynamic per-app API documentation. Common pattern for collection endpoints in Adalo docs: https://api.adalo.com/apps/{app_id}/collections/{collection_id}/records (replace {app_id} and {collection_id} with values shown in your app's API documentation). Note: Adalo exposes per-application API docs (open from the Editor) that include the exact base URL for that app. and all requests require a Bearer token for authentication.

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 Adalo data in under 10 minutes.

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What data can I load from Adalo?

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

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How do I authenticate with the Adalo API?

Adalo uses a single API key per app. All requests must include the Authorization header with a Bearer token (Authorization: Bearer <YOUR_APP_API_KEY>) and Content-Type: application/json.

1. Get your credentials

1. Open your Adalo app in the Adalo editor. 2) In the left nav click Settings > App Access. 3) Click Generate API Key (or copy an existing key). 4) Alternatively, open any Collection in the Database panel, click the three dots next to the collection and select 'API Documentation' to view the key and per-endpoint URLs.

2. Add them to .dlt/secrets.toml

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[sources.adalo_source]
api_key = "your_adalo_app_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 Adalo 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 adalo_pipeline.py

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

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Pipeline adalo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset adalo_data
The duckdb destination used duckdb:/adalo.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 adalo_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 collection_records and collection_record from the Adalo 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 adalo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Dynamic per-app API documentation. Common pattern for collection endpoints in Adalo docs: https://api.adalo.com/apps/{app_id}/collections/{collection_id}/records (replace {app_id} and {collection_id} with values shown in your app's API documentation). Note: Adalo exposes per-application API docs (open from the Editor) that include the exact base URL for that app.",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "collection_records", "endpoint": {"path": "apps/{app_id}/collections/{collection_id}/records"}},
            {"name": "collection_record", "endpoint": {"path": "apps/{app_id}/collections/{collection_id}/records/{record_id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="adalo_pipeline",
        destination="duckdb",
        dataset_name="adalo_data",
    )
    load_info = pipeline.run(adalo_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("adalo_pipeline").dataset()
sessions_df = data.collection_records.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM adalo_data.collection_records LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("adalo_pipeline").dataset()
data.collection_records.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 Adalo 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 the Authorization header is set exactly as: Authorization: Bearer <YOUR_APP_API_KEY>. Regenerate the API key in Settings > App Access if needed.

Rate limiting (429)

Adalo enforces a rate limit of 5 requests per second. If you hit 429 Too Many Requests, back off and retry after a short delay.

Filtering quirks

Adalo supports filterKey and filterValue query parameters only for single-value properties (Number, Text, Boolean, Date). Relationship fields return arrays of IDs and cannot be filtered using these parameters; to filter by a relationship, add a single-value field mirroring the related ID.

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