PersistIQ Python API Docs | dltHub

PersistIQ is an outbound sales automation and customer discovery platform with a REST API for managing leads, campaigns, users and related resources. The REST API base URL is https://api.persistiq.com and all requests require an API key in a request 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 PersistIQ data in under 10 minutes.

---

What data can I load from PersistIQ?

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

---

How do I authenticate with the PersistIQ API?

PersistIQ uses a single API key for authentication. Include the API key in requests using the X-API-KEY header (or the provider dashboard-specified header) for all API calls.

1. Get your credentials

1. Sign in to your PersistIQ account at https://app.persistiq.com or https://persistiq.com/app/. 2) Click your profile (bottom-left) -> Settings and Billing -> Integrations. 3) Locate the PersistIQ REST API Key and copy it. 4) Use this key as the X-API-KEY header value in API requests.

2. Add them to .dlt/secrets.toml

Copy
[sources.persistiq_source]
api_key = "your_persistiq_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 PersistIQ 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 persistiq_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline persistiq_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 leads and campaigns from the PersistIQ 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 persistiq_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.persistiq.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "leads", "endpoint": {"path": "leads", "data_selector": "leads"}},
            {"name": "campaigns", "endpoint": {"path": "campaigns", "data_selector": "campaigns"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="persistiq_pipeline",
        destination="duckdb",
        dataset_name="persistiq_data",
    )
    load_info = pipeline.run(persistiq_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("persistiq_pipeline").dataset()
sessions_df = data.leads.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM persistiq_data.leads LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("persistiq_pipeline").dataset()
data.leads.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 PersistIQ 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

Authentication failures

If you receive 401/403 responses, verify that your API key is correct and sent in the X-API-KEY header. Ensure the key copied from Settings -> Integrations is active.

Pagination

List endpoints return paginated results. Use the API's page and per_page query parameters (where available) or the provided next/prev links in the response. Iterate pages until no more records.

Rate limiting

If you receive 429 responses, back off and retry after the period specified in Retry-After header. Implement exponential backoff to avoid throttling.

Resource not found / invalid IDs

Endpoints that reference an ID (e.g., /leads/{id}) return 404 for missing resources. Verify the ID exists in the dashboard URL before calling.

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