Textline Python API Docs | dltHub

Textline is a business texting platform that provides a REST API to send/receive messages, manage conversations, and access address book and reporting data. The REST API base URL is https://application.textline.com/ and all requests require an access token (X-TGP-ACCESS-TOKEN or access_token param).

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 Textline data in under 10 minutes.

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What data can I load from Textline?

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

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How do I authenticate with the Textline API?

Obtain an access token by calling the authentication endpoint using your API key plus the agent's email/password (and MFA). Pass the token in the X-TGP-ACCESS-TOKEN header or as access_token parameter on subsequent requests.

1. Get your credentials

1. Log into Textline as an org admin. 2) Go to Settings -> Tools & Integrations -> Developer API (or https://application.textline.com/organization/api_settings). 3) Create/locate an API Key. 4) Use the API key plus an agent email and password (and MFA code) to call the authentication endpoint to retrieve an access token. 5) Store the returned access token for API calls.

2. Add them to .dlt/secrets.toml

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[sources.textline_source]
access_token = "your_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 Textline 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 textline_pipeline.py

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

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Pipeline textline_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset textline_data
The duckdb destination used duckdb:/textline.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 textline_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 conversations and customers from the Textline 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 textline_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://application.textline.com/",
            "auth": {
                "type": "api_key",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "api/conversations.json", "data_selector": "conversations"}},
            {"name": "customers", "endpoint": {"path": "api/customers.json", "data_selector": "customers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="textline_pipeline",
        destination="duckdb",
        dataset_name="textline_data",
    )
    load_info = pipeline.run(textline_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("textline_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM textline_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("textline_pipeline").dataset()
data.conversations.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 Textline 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 401/403, verify you retrieved the access token via the auth endpoint using a valid API key plus agent credentials (include MFA). Ensure you pass the token in the X-TGP-ACCESS-TOKEN header or as access_token parameter.

Rate limits

Textline enforces 200 requests per minute. Exceeding this returns HTTP 429. Implement retries with exponential backoff.

Pagination and reporting API quirks

Non-reporting endpoints use page=0 as the default first page. Reporting API responses include a meta object with next_page and prev_page fields — use those endpoint values to iterate pages for accurate results. Reporting next_page/prev_page values are endpoint URLs without auth included.

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