NeuVector Python API Docs | dltHub

NeuVector is a container runtime and Kubernetes runtime security platform (network, vulnerability, runtime protection, admission control) with a full REST API for automation. The REST API base URL is https://<NEUVECTOR_CONTROLLER_HOST>:10443 and all requests require a session token or API token supplied via the X-Auth-Token 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 pip install "dlt[workspace]" and start loading NeuVector data in under 10 minutes.

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What data can I load from NeuVector?

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

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How do I authenticate with the NeuVector API?

NeuVector supports username/password session authentication and API tokens (API keys). POST /v1/auth (username/password) returns a token; created API tokens or session tokens must be sent in the X-Auth-Token header for subsequent requests. Username/password sessions should be deleted via DELETE /v1/auth when finished to avoid hitting per-user concurrent session limits.

1. Get your credentials

1. In NeuVector Console go to Settings -> Users, API Keys & Roles. 2) Create an API Key (copy secret once). OR 1) POST username/password to https://:10443/v1/auth to create a session token; copy the token from the response. 3) Use the token value in the X-Auth-Token HTTP header for all API calls. 4) If using username/password session, DELETE /v1/auth when finished to free session slots.

2. Add them to .dlt/secrets.toml

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[sources.neuvector_automation_source]
# place inside [sources.neuvector_source]
auth_token = "your_neuvector_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 NeuVector 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 neuvector_automation_pipeline.py

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

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Pipeline neuvector_automation_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset neuvector_automation_data
The duckdb destination used duckdb:/neuvector_automation.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 neuvector_automation_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 workload and log/event from the NeuVector 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 neuvector_automation_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<NEUVECTOR_CONTROLLER_HOST>:10443",
            "auth": {
                "type": "api_key",
                "token": auth_token,
            },
        },
        "resources": [
            {"name": "workload", "endpoint": {"path": "v2/workload", "data_selector": "workloads"}},
            {"name": "log_event", "endpoint": {"path": "v1/log/event", "data_selector": "events"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="neuvector_automation_pipeline",
        destination="duckdb",
        dataset_name="neuvector_automation_data",
    )
    load_info = pipeline.run(neuvector_automation_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("neuvector_automation_pipeline").dataset()
sessions_df = data.workload.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM neuvector_automation_data.workload LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("neuvector_automation_pipeline").dataset()
data.workload.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 NeuVector 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 get 401/403, verify you are sending the token in the X-Auth-Token header. For username/password sessions, ensure you have not exceeded the per-user session limit (32 concurrent sessions) — create/delete sessions or use API tokens instead.

Session limits and token lifecycle

Username/password sessions are limited (typical max 32 concurrent sessions per user). If you create session tokens via POST /v1/auth, DELETE /v1/auth when finished to free a slot. Token-based API keys do not have the same concurrent-session limit but do expire per configured TTL.

Common API errors

• 401 Unauthorized: missing/invalid X-Auth-Token or expired session.
• 403 Forbidden: token lacks required RBAC permission for the endpoint.
• 409/400: invalid request payload for POST/PATCH endpoints.
• Empty responses: some endpoints return a top-level object containing the list (e.g., {"workloads": [...]}, {"events": [...]}) — inspect the data selector in the response and use that key when mapping records.

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