Inlets Python API Docs | dltHub

Inlets is a tool for creating secure tunnels over restrictive networks. inletsctl automates the setup of inlets servers on various cloud platforms. It provisions cloud VMs and manages tunnels for local services. The REST API base URL is https://{clientRouter.domain}/v1 and API is secured via TLS/Ingress and controlled by the inlets‑uplink license; no bearer token required..

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 Inlets data in under 10 minutes.

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What data can I load from Inlets?

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

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How do I authenticate with the Inlets API?

The API is accessed over HTTPS (TLS) behind the client‑router Ingress; authentication is implicitly handled by the installed license secret and the Ingress TLS configuration.

1. Get your credentials

1. Deploy inlets‑uplink via Helm and set clientRouter.domain to your domain. 2) Ensure cert‑manager/Ingress is configured and a TLS certificate is issued for that domain. 3) Enable the REST API by setting clientApi.enabled=true in your values.yaml. 4) Create the inlets‑uplink license secret in the inlets namespace (e.g., kubectl create secret generic -n inlets inlets-uplink-license --from-file license=$HOME/.inlets/LICENSE_UPLINK).

2. Add them to .dlt/secrets.toml

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[sources.inletsctl_source]
clientRouter_domain = "uplink.example.com"
clientApi_enabled = true
inlets_uplink_license = "YOUR_UPLINK_LICENSE"

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 Inlets 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 inletsctl_pipeline.py

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

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Pipeline inletsctl_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset inletsctl_data
The duckdb destination used duckdb:/inletsctl.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 inletsctl_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 tunnels and namespaces from the Inlets 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 inletsctl_source(clientRouter_domain=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{clientRouter.domain}/v1",
            "auth": {
                "type": "http_tls",
                "tls_host": clientRouter_domain,
            },
        },
        "resources": [
            {"name": "tunnels", "endpoint": {"path": "v1/tunnels", "data_selector": "items"}},
            {"name": "namespaces", "endpoint": {"path": "v1/namespaces", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="inletsctl_pipeline",
        destination="duckdb",
        dataset_name="inletsctl_data",
    )
    load_info = pipeline.run(inletsctl_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("inletsctl_pipeline").dataset()
sessions_df = data.tunnels.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM inletsctl_data.tunnels LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("inletsctl_pipeline").dataset()
data.tunnels.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 Inlets 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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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