Olark Python API Docs | dltHub

Olark is a live chat platform providing a client-side JavaScript API for embedding and controlling chat widgets and webhook-based delivery of chat transcripts and events. The REST API base URL is (no public REST base URL; integrations are via the client-side JavaScript API and webhooks POSTs to your endpoint) and No public REST bearer/API-token endpoints; integrations use the JS site identifier (olark.identify) for the client-side API and webhooks are configured in the Olark dashboard to POST to your endpoint..

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 Olark data in under 10 minutes.

---

What data can I load from Olark?

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

---

How do I authenticate with the Olark API?

The JavaScript API requires the account site identifier set via olark.identify('YOUR_SITE_ID'). Webhook POSTs are configured in your Olark dashboard and delivered to the URL you provide; Olark does not include an API token in webhook payloads by default (configure your endpoint to validate requests by IP or a shared secret).

1. Get your credentials

1. Sign in to your Olark account at https://www.olark.com/.
2. Visit Setup / Account or the site setup page to find your site identifier (used with olark.identify in the JS API).
3. To enable webhooks: go to Olark dashboard -> Settings -> Integrations -> Webhooks (or https://www.olark.com/settings/integrations/webhooks) and create/configure a webhook endpoint URL; enable Extra Events if needed.

2. Add them to .dlt/secrets.toml

Copy
[sources.olark_source]
(no API token required for Olark REST; for webhook-based ingestion you might store:
webhook_shared_secret = "your_secret_if_you_create_one"
site_id = "your_site_id_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 Olark 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 olark_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline olark_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 visitor_details and webhooks_transcripts from the Olark 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 olark_source(site_id (for JS API). For webhook ingestion no auth param unless you implement a shared secret.=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "(no public REST base URL; integrations are via the client-side JavaScript API and webhooks POSTs to your endpoint)",
            "auth": {
                "type": "none (JS API uses site identifier; webhooks do not use bearer/API key by default)",
                "(none)": site_id (for JS API). For webhook ingestion no auth param unless you implement a shared secret.,
            },
        },
        "resources": [
            {"name": "visitor_details", "endpoint": {"path": "(javascript) api.visitor.getDetails"}},
            {"name": "webhooks_transcripts", "endpoint": {"path": "(dashboard-configured webhook POST) -> your endpoint receives form field 'data' (JSON)", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="olark_pipeline",
        destination="duckdb",
        dataset_name="olark_data",
    )
    load_info = pipeline.run(olark_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("olark_pipeline").dataset()
sessions_df = data.webhooks_transcripts.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM olark_data.webhooks_transcripts LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("olark_pipeline").dataset()
data.webhooks_transcripts.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 Olark 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 and verification

Olark’s JS API uses a site identifier (olark.identify) set in page code; missing/incorrect site_id will prevent the widget from connecting. Webhooks do not include a built-in API token; to verify webhook calls implement one or more of: checking the request origin IPs, adding a query parameter/shared secret in the webhook URL, or returning/expecting a challenge/response in your endpoint logic.

Rate limits and delivery

Olark’s documentation does not publish REST rate limits because there is no public REST GET surface. Webhooks are delivered on conversation completion; implement idempotency on your endpoint (use the conversation id field) to avoid duplicate ingestion issues.

Pagination and data selectors

There are no REST list GET endpoints to paginate. For webhook transcripts the conversation JSON contains the 'items' array (ordered messages) and 'operators' and 'visitor' objects — use conversation.id as the unique key.

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