Infobip Python API Docs | dltHub

Infobip is a global cloud communications platform that offers a wide range of communication channels and tools for businesses to connect with their customers. The REST API base URL is https://{your_base_url}.api.infobip.com and All API requests require authentication through the Authorization header, supporting API keys, IBSSO tokens, or OAuth 2.0..

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 Infobip data in under 10 minutes.

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What data can I load from Infobip?

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

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How do I authenticate with the Infobip API?

Authentication is performed by including an Authorization header in all API requests. Supported methods include API Key (Authorization: App {API_KEY}), IBSSO tokens (Authorization: IBSSO {TOKEN}), and OAuth 2.0 (Authorization: Bearer {ACCESS_TOKEN}).

1. Get your credentials

To obtain your API credentials, log in to the Infobip API Resource hub. Your personal base URL and other necessary credentials will be displayed on all pages within the hub.

2. Add them to .dlt/secrets.toml

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[sources.infobip_source]
api_key = "your_api_key_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 Infobip 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 infobip_pipeline.py

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

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Pipeline infobip_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset infobip_data
The duckdb destination used duckdb:/infobip.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 infobip_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 outbound_sms_messages and inbound_sms_messages from the Infobip 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 infobip_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your_base_url}.api.infobip.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "outbound_sms_messages", "endpoint": {"path": "sms/2/messages", "data_selector": "messages"}},
            {"name": "inbound_sms_messages", "endpoint": {"path": "sms/2/inbound/messages", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="infobip_pipeline",
        destination="duckdb",
        dataset_name="infobip_data",
    )
    load_info = pipeline.run(infobip_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("infobip_pipeline").dataset()
sessions_df = data.outbound_sms_messages.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM infobip_data.outbound_sms_messages LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("infobip_pipeline").dataset()
data.outbound_sms_messages.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 Infobip 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 Errors

If you encounter a 401 Unauthorized error, it indicates invalid login details. Ensure your API key, IBSSO token, or OAuth 2.0 access token is correct and properly included in the Authorization header.

Example 401 Unauthorized response:

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{
  "requestError": {
    "serviceException": {
      "messageId": "UNAUTHORIZED",
      "text": "Invalid login details"
    }
  }
}

General API Errors

Infobip APIs return standard HTTP status codes for errors. Common errors include:

• 400 Bad Request: The request was malformed.
• 403 Forbidden: You do not have permission to access the resource.
• 404 Not Found: The requested resource does not exist.
• 405 Method Not Allowed: The HTTP method used is not supported for the resource.
• 409 Conflict: A conflict occurred, often due to duplicate data.
• 429 Too Many Requests: Rate limiting has been applied. Check for Retry-After headers if available.

For detailed error information, refer to the serviceException object in the response body.

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