Dimension Labs LiveChat Python API Docs | dltHub

Dimension Labs LiveChat is an online customer service software that provides live support, web analytics, and online marketing capabilities. The REST API base URL is https://api.livechatinc.com/ and All requests require HTTP Basic Auth with your LiveChat login as username and API key as password, or an OAuth 2.0 Bearer token..

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 Dimension Labs LiveChat data in under 10 minutes.

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What data can I load from Dimension Labs LiveChat?

Here are some of the endpoints you can load from Dimension Labs LiveChat:

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How do I authenticate with the Dimension Labs LiveChat API?

Authentication is performed via HTTP Basic Auth where the username is your LiveChat login email and the password is the API key. Include the header X-API-VERSION: 2 (or the version you need) on every request. For OAuth, add Authorization: Bearer <token>.

1. Get your credentials

1. Log in to your LiveChat account.
2. Navigate to Settings → API.
3. Click Generate new API key (or Create OAuth app for token‑based access).
4. Copy the generated API key or client credentials and store them securely.
5. For OAuth, follow the developer portal instructions to obtain an access token.

2. Add them to .dlt/secrets.toml

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[sources.dimension_labs_livechat_source]
api_key = "your_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 Dimension Labs LiveChat 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 dimension_labs_livechat_pipeline.py

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

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Pipeline dimension_labs_livechat_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dimension_labs_livechat_data
The duckdb destination used duckdb:/dimension_labs_livechat.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 dimension_labs_livechat_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 agents and chats from the Dimension Labs LiveChat 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 dimension_labs_livechat_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.livechatinc.com/",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "agents", "endpoint": {"path": "agents", "data_selector": "agents"}},
            {"name": "chats", "endpoint": {"path": "chats", "data_selector": "chats"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dimension_labs_livechat_pipeline",
        destination="duckdb",
        dataset_name="dimension_labs_livechat_data",
    )
    load_info = pipeline.run(dimension_labs_livechat_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("dimension_labs_livechat_pipeline").dataset()
sessions_df = data.chats.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dimension_labs_livechat_data.chats LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dimension_labs_livechat_pipeline").dataset()
data.chats.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 Dimension Labs LiveChat 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

• 401 Unauthorized – Usually indicates missing or invalid credentials. Verify that the API key (or OAuth token) is correct and that the X-API-VERSION header is present.

Rate limiting

• 429 Too Many Requests – The API is being called too frequently. Implement exponential back‑off and respect the Retry-After header if provided.

General errors

• 400 Bad Request – The request format is invalid. Check required parameters and JSON payload.
• 404 Not Found – The requested endpoint or resource does not exist. Confirm the endpoint path.
• 500 Internal Server Error – An unexpected server error. Retry after a short delay or contact LiveChat support.

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