Jasmin SMS Gateway Python API Docs | dltHub

Build a Jasmin SMS Gateway-to-database pipeline in Python using dlt with AI harness support for Claude Code, Cursor, and Codex.

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Jasmin SMS Gateway is a modular open-source SMS gateway that provides a RESTful API for sending SMS messages, checking balance, and managing routing. The REST API base URL is http://127.0.0.1:1401/ and all requests to /secure/ endpoints require Basic Authentication via 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 add "dlt[hub]" and start loading Jasmin SMS Gateway data in under 10 minutes.


What data can I load from Jasmin SMS Gateway?

Here are some of the endpoints you can load from Jasmin SMS Gateway:

ResourceEndpointMethodData selectorDescription
ping/pingGETA simple check to ensure this is a Jasmin API.
balance/secure/balanceGETGet user account’s balance and quota.
rate/secure/rateGETCheck a route and its rate.
send/secure/sendPOSTSend a single message to one destination address.
send_batch/secure/sendbatchPOSTSend multiple messages to one or more destination addresses.

How do I authenticate with the Jasmin SMS Gateway API?

The API uses Basic Authentication where credentials are transmitted in the 'Authorization' header as a base64-encoded 'username

' string.

1. Get your credentials

Jasmin SMS Gateway does not use API keys in the traditional sense. Instead, it utilizes standard Jasmin user account credentials (username and password). To obtain these, you must access the Jasmin Management CLI (jCli) via telnet (typically at 127.0.0.1

) and create a user account with sufficient credit. The default credentials for the jCli admin console are often jcliadmin/jclipwd, though these should be updated for security. Once the user account is created, you use that username and password for authentication.

2. Add them to .dlt/secrets.toml

[sources.jasmin_sms_gateway_source] jasmin_username = "your_username" jasmin_password = "your_password"

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.


How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

uv init uv add "dlt[hub]"

1. Install the dlt AI harness:

uv run dlthub 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:

uv run dlthub ai toolkit install rest-api-pipeline

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 Jasmin SMS Gateway 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:

uv run python jasmin_sms_gateway_pipeline.py

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

Pipeline jasmin_sms_gateway_pipeline load step completed in 0.26 seconds 1 load package(s) were loaded to destination duckdb and into dataset jasmin_sms_gateway_data The duckdb destination used duckdb:/jasmin_sms_gateway.duckdb location to store data Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.


Python pipeline example

This example loads /secure/send and /secure/sendbatch from the Jasmin SMS Gateway API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def jasmin_sms_gateway_source(auth=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "http://127.0.0.1:1401/", "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": auth}, }, "resources": [ {"name": "balance", "endpoint": {"path": "secure/balance"}}, {"name": "rate", "endpoint": {"path": "secure/rate"}} ], } yield from rest_api_resources(config) def get_data() -> None: pipeline = dlt.pipeline( pipeline_name="jasmin_sms_gateway_pipeline", destination="duckdb", dataset_name="jasmin_sms_gateway_data", ) load_info = pipeline.run(jasmin_sms_gateway_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.


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):

import dlt data = dlt.pipeline("jasmin_sms_gateway_pipeline").dataset() sessions_df = data.balance.df() print(sessions_df.head())

SQL (DuckDB example):

SELECT * FROM jasmin_sms_gateway_data.balance LIMIT 10;

In a marimo or Jupyter notebook:

import dlt data = dlt.pipeline("jasmin_sms_gateway_pipeline").dataset() data.balance.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.


What destinations can I load Jasmin SMS Gateway data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

DestinationExample value
DuckDB (local, default)"duckdb"
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.


Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

  • data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
  • dlthub-platform — Deploy, schedule, and monitor your pipeline in production.
uv run dlthub ai toolkit install data-exploration uv run dlthub ai toolkit install dlthub-platform

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