Scapy Python API Docs | dltHub

Scapy is a Python-based interactive packet manipulation library and program that allows for crafting, sending, sniffing, dissecting, and manipulating network packets. The REST API base URL is https://scapy.net/ and all requests require a Bearer token.

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 Scapy data in under 10 minutes.

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What data can I load from Scapy?

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

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How do I authenticate with the Scapy API?

The authentication mechanism uses a Bearer token, which is passed in the request. In a dlt configuration, this is typically managed through a secrets management file or environment variable.

1. Get your credentials

Scapy is a Python network packet manipulation library and does not have a native REST API. However, if you are using the dlt (Data Load Tool) Scapy verified source to load data, API credentials are managed via your dlt environment. You should define your credentials in a .dlt/secrets.toml file. If an access token is required by the specific data source you are bridging, you can obtain it from the provider's specific dashboard (e.g., the service you are sending packet data to). Within dlt, these are referenced securely using dlt.secrets.value.

2. Add them to .dlt/secrets.toml

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[sources.scapy_source]
access_token = "your_actual_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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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:

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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 Scapy 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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uv run python scapy_pipeline.py

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

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Pipeline scapy_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset scapy_data
The duckdb destination used duckdb:/scapy.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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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.

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Python pipeline example

This example loads dns and sniff from the Scapy 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 scapy_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://scapy.net/",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "dns", "endpoint": {"path": "dns"}},
            {"name": "sniff", "endpoint": {"path": "sniff"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="scapy_pipeline",
        destination="duckdb",
        dataset_name="scapy_data",
    )
    load_info = pipeline.run(scapy_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("scapy_pipeline").dataset()
sessions_df = data.dns.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM scapy_data.dns LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("scapy_pipeline").dataset()
data.dns.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 Scapy 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 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.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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