Aptrinsic Python API Docs | dltHub

Aptrinsic is Gainsight PX, a product‑analytics and in‑app engagement platform exposing a REST API to access users, accounts, events, engagements, articles and related telemetry. The REST API base URL is https://api.aptrinsic.com/v1 and All requests require an API key passed in the X-APTRINSIC-API-KEY 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 Aptrinsic data in under 10 minutes.

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What data can I load from Aptrinsic?

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

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How do I authenticate with the Aptrinsic API?

API Key authentication: generate an API key in the Gainsight PX Admin UI and include it in every request header as X-APTRINSIC-API-KEY: {apiKey}. Use HTTPS and set Content-Type: application/json where appropriate.

1. Get your credentials

1. Log into Gainsight PX (Aptrinsic) and go to Administration > REST API. 2) Click New API Key. 3) Enter Name, Description and set Permissions (Read, Write, Production Launch as needed). 4) Click Generate and copy the API key. 5) Use this key for X-APTRINSIC-API-KEY in requests.

2. Add them to .dlt/secrets.toml

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[sources.aptrinsic_source]
api_key = "your_aptrinsic_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 Aptrinsic 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 aptrinsic_pipeline.py

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

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Pipeline aptrinsic_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset aptrinsic_data
The duckdb destination used duckdb:/aptrinsic.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 aptrinsic_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 users and survey_responses from the Aptrinsic 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 aptrinsic_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.aptrinsic.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "results"}},
            {"name": "survey_responses", "endpoint": {"path": "survey/responses", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="aptrinsic_pipeline",
        destination="duckdb",
        dataset_name="aptrinsic_data",
    )
    load_info = pipeline.run(aptrinsic_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("aptrinsic_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM aptrinsic_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("aptrinsic_pipeline").dataset()
data.users.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 Aptrinsic 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 or 403 Access Denied, verify the X-APTRINSIC-API-KEY header contains a valid API key with appropriate permissions (Read for GET calls). Regenerate the key in Administration > REST API if needed.

Rate limits

Public REST APIs rate limit: ~200 requests/sec and ~1,000,000 requests/day. If you hit 429 Rate limit exceeded, implement exponential backoff and reduce concurrency.

Pagination and scrolling

List endpoints default pageSize varies (often 200). You can set pageSize up to the documented max (examples: max 500 for articles, max 1000 for users/accounts). Use pageNumber (zero‑based) or scrollId for cursor‑style pagination: include scrollId returned in the response to fetch subsequent pages. Continue scrolling until returned results length is less than the requested pageSize.

Date‑range limits for historical events

Historical event endpoints enforce a maximum date range of 190 days; requests exceeding that return 400 Bad Request with message “Exceeded date range190days”. Split large ranges into multiple calls.

Common API errors (summary): 400 Bad Request (invalid params, exceeded date range), 401 Unauthorized (invalid API key), 403 Forbidden (insufficient permissions), 404 Not Found (resource not found), 429 Rate limit exceeded, 500+ Server errors.

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