Expert-texting Python API Docs | dltHub

Expert-texting is an SMS gateway platform and REST/SOAP API that lets applications send SMS messages, query account balance, check message status, and retrieve unread inbound messages. The REST API base URL is https://www.experttexting.com/exptapi/exptsms.asmx and All requests require USERID, PWD and APIKEY parameters for authentication..

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 Expert-texting data in under 10 minutes.

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What data can I load from Expert-texting?

Here are some of the endpoints you can load from Expert-texting:

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How do I authenticate with the Expert-texting API?

Authentication uses account USERID, PWD (password) and APIKEY passed as request parameters (query string or POST body) to each API method.

1. Get your credentials

1. Sign in to your ExpertTexting account at experttexting.com.
2. Open your Profile / Account Settings.
3. Locate or generate your API Key (APIKEY).
4. Use your account username (USERID), account password (PWD) and the APIKEY when calling API endpoints. If you cannot find it, contact support at integeration@experttexting.com or sms.support@experttexting.com.

2. Add them to .dlt/secrets.toml

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[sources.expert_texting_source]
USERID = "your_username"
PWD = "your_password"
APIKEY = "your_api_key"

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 Expert-texting 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 expert_texting_pipeline.py

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

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Pipeline expert_texting_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset expert_texting_data
The duckdb destination used duckdb:/expert_texting.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 expert_texting_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 send_sms and query_balance from the Expert-texting 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 expert_texting_source(api_key_and_creds=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.experttexting.com/exptapi/exptsms.asmx",
            "auth": {
                "type": "http_basic",
                "APIKEY": api_key_and_creds,
            },
        },
        "resources": [
            {"name": "send_sms", "endpoint": {"path": "exptapi/exptsms.asmx/SendSMS"}},
            {"name": "get_unread_inbox", "endpoint": {"path": "exptapi/exptsms.asmx/getUnreadInbox"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="expert_texting_pipeline",
        destination="duckdb",
        dataset_name="expert_texting_data",
    )
    load_info = pipeline.run(expert_texting_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("expert_texting_pipeline").dataset()
sessions_df = data.send_sms.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM expert_texting_data.send_sms LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("expert_texting_pipeline").dataset()
data.send_sms.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 Expert-texting 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 you receive authentication errors, verify USERID, PWD and APIKEY are correct and supplied as parameters. The PDF states: "Note: Authentication key is required to access any web method; you can get your personalized authentication key from your area at experttexting.com". Contact integeration@experttexting.com or sms.support@experttexting.com if the key is missing.

Format and content errors

The official API guide describes SOAP/XML web methods and expects parameters like USERID, PWD and APIKEY; ensure you send parameters in the correct form (SOAP/XML POST or the documented REST/HTTP querystring for campaign endpoints). Incorrect parameter names or missing fields will result in method-specific errors.

Pagination / record selectors

The published API is SOAP/XML oriented and returns XML nodes (not a JSON list); there is no documented JSON top‑level list key for GET listing endpoints. When using the campaign REST URL (blog example) the response format is not specified; parse according to returned content‑type. For unread inbox, parse the XML response elements for sender, message content and date.

Rate limiting and other errors

No rate limit details are published in the provided docs. If you encounter HTTP 429 or unexpected failures, contact support. Common HTTP errors: 401/403 for auth problems, 400 for bad request/invalid parameters, 500 for server errors.

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