Phoneburner Python API Docs | dltHub

PhoneBurner is a cloud-based sales dialer and contact management platform exposing a REST API for managing contacts, dial sessions/calls, members, and related resources. The REST API base URL is https://www.phoneburner.com/rest/1 and all requests requiring authentication use an OAuth2 Bearer access token in the Authorization 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 Phoneburner data in under 10 minutes.

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What data can I load from Phoneburner?

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

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How do I authenticate with the Phoneburner API?

Authentication uses OAuth 2.0 access tokens (Bearer tokens) sent in the Authorization header. Example header: Authorization: Bearer <access_token>. OAuth is recommended for production; some routes may allow alternate auth methods but protected endpoints require this header.

1. Get your credentials

1. Register an application with PhoneBurner by following Getting Started / Get a Developer Account in the developer portal.
2. Implement the OAuth2 authorization flow: redirect users to PhoneBurner's authorization endpoint to obtain an authorization code.
3. Exchange the authorization code for an access_token.
4. Use the access_token as the Bearer token in the Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.phoneburner_source]
access_token = "your_oauth_access_token_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 Phoneburner 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 phoneburner_pipeline.py

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

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Pipeline phoneburner_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset phoneburner_data
The duckdb destination used duckdb:/phoneburner.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 phoneburner_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 contacts and dialsession from the Phoneburner 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 phoneburner_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.phoneburner.com/rest/1",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "contacts.contacts"}},
            {"name": "dialsession", "endpoint": {"path": "dialsession", "data_selector": "dialsessions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="phoneburner_pipeline",
        destination="duckdb",
        dataset_name="phoneburner_data",
    )
    load_info = pipeline.run(phoneburner_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("phoneburner_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM phoneburner_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("phoneburner_pipeline").dataset()
data.contacts.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 Phoneburner 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 the Authorization header is missing or invalid the API returns 401/403 with JSON errors. Example: {"http_status":401,"status":"error","debug_codes":[{"code":40101,"reason":"Login Failure: Authorization Header required"}]}. Ensure Authorization: Bearer <access_token> is present and token is valid.

Pagination limits and parameters

Most list endpoints are paginated (default page_size 25). Parameters: page, page_size (max 100). Use these to iterate; responses include page, page_size, total_pages and total_results.

Date‑range limits (dialsession usage)

The /dialsession/usage route requires date_start and date_end and enforces a maximum 90‑day range; exceeding it returns a 403 error.

Rate limiting and errors

The API uses standard HTTP status codes. Client and server errors return JSON arrays of error objects. Implement retries/back‑off for 4xx/5xx responses. Specific debug_codes are returned for auth and other failures.

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