Snyk Python API Docs | dltHub

Snyk is a security platform that finds, prioritizes and fixes vulnerabilities in open-source dependencies and containerized applications via a REST API. The REST API base URL is https://api.snyk.io/rest/ (region-specific alternatives: https://api.us.snyk.io/rest/, https://api.eu.snyk.io/rest/ — use the URL for your region) and all requests require an API token supplied in the Authorization header as "token <API_TOKEN>" (Bearer-style token header named 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 pip install "dlt[workspace]" and start loading Snyk data in under 10 minutes.

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What data can I load from Snyk?

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

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How do I authenticate with the Snyk API?

The REST API uses a personal API token. Send the token in the Authorization header as: Authorization: token <API_TOKEN>. Also set Content-Type: application/vnd.api+json for requests with bodies and include the version query parameter (e.g. ?version=2024-10-15).

1. Get your credentials

1. Log in to app.snyk.io. 2) Open your Account settings (General). 3) Copy your personal API Token from the Account page. 4) Find your Organization ID in Organization Settings > General to use in org-scoped endpoints.

2. Add them to .dlt/secrets.toml

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[sources.snyk_npm_packages_source]
api_token = "your_snyk_api_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 Snyk 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 snyk_npm_packages_pipeline.py

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

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Pipeline snyk_npm_packages_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset snyk_npm_packages_data
The duckdb destination used duckdb:/snyk_npm_packages.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 snyk_npm_packages_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 projects and packages_issues from the Snyk 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 snyk_npm_packages_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.snyk.io/rest/ (region-specific alternatives: https://api.us.snyk.io/rest/, https://api.eu.snyk.io/rest/ — use the URL for your region)",
            "auth": {
                "type": "api_key",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "orgs/{orgId}/projects", "data_selector": "data"}},
            {"name": "packages_issues", "endpoint": {"path": "orgs/{orgId}/packages/{purl}/issues", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="snyk_npm_packages_pipeline",
        destination="duckdb",
        dataset_name="snyk_npm_packages_data",
    )
    load_info = pipeline.run(snyk_npm_packages_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("snyk_npm_packages_pipeline").dataset()
sessions_df = data.packages_issues.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM snyk_npm_packages_data.packages_issues LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("snyk_npm_packages_pipeline").dataset()
data.packages_issues.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 Snyk 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 Authorization header is missing or invalid the API returns 401/403 errors; ensure you send Authorization: token <API_TOKEN> and that the token is valid and scoped to the organization. Obtain a valid token via Account > API Token.

Rate limits

Snyk applies rate limits (global documented: 1620 requests/minute per API key; some endpoints e.g. package issues may have lower limits documented on the endpoint page such as 180/minute). Exceeding limits returns HTTP 429. Implement retries with backoff and honor Retry-After where provided.

Pagination

Most collection endpoints return JSON:API responses with top-level "data" array and a "links" object containing prev/next/self. Snyk uses cursor-based pagination parameters such as starting_after and ending_before (and some endpoints accept limit/offset). Use the returned links.next or the pagination tokens to iterate.

Common error format

Errors conform to JSON:API and return a top-level "errors" array; entries include status, detail and optionally source.parameter. Example: { "errors": [ { "status": "400", "detail": "Client request did not conform to OpenAPI specification" } ] }

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