Highlight Python API Docs | dltHub

Highlight is a session replay and frontend observability platform that records user sessions, errors, network requests, and custom events to help debug and analyze user behavior. The REST API base URL is `` and Requests are authorized using a projectId supplied during client initialization..

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 Highlight data in under 10 minutes.

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What data can I load from Highlight?

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

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How do I authenticate with the Highlight API?

The client SDK sends data using the provided projectId; when a custom backendUrl is set, the SDK posts to that URL without requiring additional HTTP headers.

1. Get your credentials

1. Log in to your Highlight account at https://app.highlight.io.
2. Navigate to the Setup page.
3. Copy the Project ID shown on the page.
4. Use this value as the credential for the API integration.

2. Add them to .dlt/secrets.toml

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[sources.highlight_source]
project_id = "your_project_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.

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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 Highlight 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 highlight_pipeline.py

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

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Pipeline highlight_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset highlight_data
The duckdb destination used duckdb:/highlight.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 highlight_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 sessions and errors from the Highlight 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 highlight_source(project_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "api_key",
                "api_key": project_id,
            },
        },
        "resources": [
            {"name": "sessions", "endpoint": {"path": "sessions"}},
            {"name": "errors", "endpoint": {"path": "errors"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="highlight_pipeline",
        destination="duckdb",
        dataset_name="highlight_data",
    )
    load_info = pipeline.run(highlight_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("highlight_pipeline").dataset()
sessions_df = data.sessions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM highlight_data.sessions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("highlight_pipeline").dataset()
data.sessions.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 Highlight 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

Missing or Invalid Project ID

The SDK will not send any data if projectId is omitted or incorrect. Ensure the value copied from the Highlight setup page is used.

CORS and Network Recording Errors

When inlining images or stylesheets, browsers may block requests due to CORS policies. Configure appropriate CORS headers on any custom backendUrl.

Privacy Configuration Errors

If privacy settings are too strict, recorded data may be redacted causing incomplete session logs. Review the privacy options in the SDK configuration.

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