Pendo Engage Python API Docs | dltHub

Pendo Engage is a REST API that allows programmatic access to Pendo subscription data (pages, features, guides, visitors/accounts, events) and supports aggregation queries for analytics. The REST API base URL is https://app.pendo.io and All requests require an API integration key sent 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 Pendo Engage data in under 10 minutes.

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What data can I load from Pendo Engage?

Here are some of the endpoints you can load from Pendo Engage:

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How do I authenticate with the Pendo Engage API?

Pendo uses an integration API key. Include the key in every request using the header x-pendo-integration-key. Some endpoints (Aggregation API) also accept the same header. All request/response bodies are JSON.

1. Get your credentials

1. Log in to Pendo as an Admin. 2) Navigate to Settings > Integrations > Integration Keys (or open https://app.pendo.io/admin/integrationkeys). 3) Click Add Integration Key, give it a description and optionally enable write access. 4) Create and securely store the generated key (it is shown only once).

2. Add them to .dlt/secrets.toml

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[sources.pendo_engage_source]
integration_key = "your_integration_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 Pendo Engage 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 pendo_engage_pipeline.py

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

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Pipeline pendo_engage_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pendo_engage_data
The duckdb destination used duckdb:/pendo_engage.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 pendo_engage_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 aggregation and guides from the Pendo Engage 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 pendo_engage_source(integration_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.pendo.io",
            "auth": {
                "type": "api_key",
                "integration_key": integration_key,
            },
        },
        "resources": [
            {"name": "aggregation", "endpoint": {"path": "api/v1/aggregation", "data_selector": "results"}},
            {"name": "guides", "endpoint": {"path": "api/v1/aggregation", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pendo_engage_pipeline",
        destination="duckdb",
        dataset_name="pendo_engage_data",
    )
    load_info = pipeline.run(pendo_engage_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("pendo_engage_pipeline").dataset()
sessions_df = data.aggregation.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pendo_engage_data.aggregation LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pendo_engage_pipeline").dataset()
data.aggregation.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 Pendo Engage 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 you are sending the integration key in the x-pendo-integration-key header and that the key exists and has appropriate permissions. Integration keys are shown only once — create a new key if lost.

Rate limiting and 429 Too Many Requests

The API will return 429 when requests are made too rapidly. Implement exponential backoff and retries. For server errors (5xx) retry after a short delay.

Aggregation size/timeout limits and pagination

The Aggregation API is not a bulk export. Large queries may time out or be size‑limited; break queries into smaller time ranges or partitions. Aggregation responses return results arrays; time‑series aggregations may return multiple result sets (one per time bucket). Follow the aggregation request model to request specific time ranges.

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