Livevox Python API Docs | dltHub

LiveVox is a cloud contact center platform providing REST APIs to manage accounts, campaigns, contacts, sessions, queues, and reporting. The REST API base URL is https://api.livevox.com/{apiCategory}/{apiResource} and All requests require an LV-Session header (Session ID) obtained from the Login call; an LV-Access token is required for the Login request..

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 Livevox data in under 10 minutes.

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What data can I load from Livevox?

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

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How do I authenticate with the Livevox API?

Authentication uses an access token (LV-Access) supplied in the Login request header; Login returns a Session ID which must be passed as LV-Session in all subsequent requests. Include Content-Type: application/json and optionally Accept: application/json.

1. Get your credentials

1. Contact your LiveVox Account team to request an Access Token for API use. 2) In the LiveVox/SmartReach portal, create or identify a user with appropriate roles/permissions for API access. 3) Use the Access Token as LV-Access in the Login request along with Client Name, Username, and Password to obtain the LV-Session value. 4) Store LV-Access and LV-Session securely.

2. Add them to .dlt/secrets.toml

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[sources.livevox_source]
lv_access = "your_lv_access_token"

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 Livevox 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 livevox_pipeline.py

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

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Pipeline livevox_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset livevox_data
The duckdb destination used duckdb:/livevox.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 livevox_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 contacts from the Livevox 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 livevox_source(lv_access=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.livevox.com/{apiCategory}/{apiResource}",
            "auth": {
                "type": "api_key",
                "lv_access": lv_access,
            },
        },
        "resources": [
            {"name": "sessions", "endpoint": {"path": "session/login", "data_selector": "sessionId"}},
            {"name": "contacts", "endpoint": {"path": "contact/contacts/{acct#}"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM livevox_data.sessions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("livevox_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 Livevox 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 LV-Access is missing or invalid the API returns 401 Unauthorized. Ensure LV-Access header is present on the Login request; include LV-Session on all other requests. Session IDs expire after 2 hours of inactivity.

Pagination requirements

List endpoints that support pagination require count and offset query parameters (mandatory for list queries). Use count to limit records returned and offset for the zero-based starting index.

Common HTTP errors

The API uses standard HTTP status codes. Common responses: 400 Bad Request (missing/invalid params), 401 Unauthorized (invalid LV-Access or LV-Session), 403 Forbidden (insufficient role permissions), 404 Not Found, 500 Internal Server Error, 503 Service Unavailable. Error responses may also include a JSON body with "code" and "message" fields.

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