Pickyassist Python API Docs | dltHub

Pickyassist is a messaging gateway platform that exposes a JSON REST API (v2) to send WhatsApp, SMS, call conference and device‑management requests via an Android bridge. The REST API base URL is https://pickyassist.com/app/api/v2 and All requests require a project API token passed in the JSON body.

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 Pickyassist data in under 10 minutes.

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What data can I load from Pickyassist?

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

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How do I authenticate with the Pickyassist API?

Authentication is done with an API token (project token). Include the token key in the JSON request body as "token": "YOUR_API_TOKEN" for all API requests. Picky Assist does not require header‑based auth for v2; token is sent in the request payload.

1. Get your credentials

1. Log in to https://pickyassist.com/app
2. Create/select a Project
3. Open Settings -> API Tokens (or Settings -> API)
4. Generate a new token, optionally name it and whitelist an IP address
5. Copy the token and store securely; use it as the "token" value in request bodies.

2. Add them to .dlt/secrets.toml

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[sources.pickyassist_source]
token = "YOUR_PICKYASSIST_TOKEN"

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 Pickyassist 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 pickyassist_pipeline.py

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

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Pipeline pickyassist_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pickyassist_data
The duckdb destination used duckdb:/pickyassist.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 pickyassist_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 push and conference_report from the Pickyassist 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 pickyassist_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://pickyassist.com/app/api/v2",
            "auth": {
                "type": "api_key",
                "token": token,
            },
        },
        "resources": [
            {"name": "push", "endpoint": {"path": "push", "data_selector": "data"}},
            {"name": "conference_report", "endpoint": {"path": "conference-report"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pickyassist_pipeline",
        destination="duckdb",
        dataset_name="pickyassist_data",
    )
    load_info = pipeline.run(pickyassist_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("pickyassist_pipeline").dataset()
sessions_df = data.push.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pickyassist_data.push LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pickyassist_pipeline").dataset()
data.push.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 Pickyassist 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 status 401 or "Authentication Failed": verify the token value in the JSON body, ensure API access is enabled for the project, IP restrictions (if set) match the caller IP, and the project is active and subscribed. Error codes: 401 => Authentication Failed.

Rate limits

Requests are rate limited to 90 requests per minute per project. Excess requests receive HTTP 429 and are dropped at firewall level; an email may be sent to admin. To scale, create additional projects and distribute load.

Pagination & response shapes

Most endpoints are POST‑only. Some report endpoints (e.g., conference-report) return a top‑level JSON array of records. The standard push response is an object: {"status":100,"push_id":"...","message":"Success"}. When per‑recipient details are returned, they are in the "data" array with fields like msg_id, number, credit.

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