SAVVY Security Python API Docs | dltHub

SAVVY Security is a security platform exposing a GraphQL API to query playbooks, activity logs and lists. The REST API base URL is https://graphql-gateway.savvy.security/graphql and all requests require a Bearer token for authentication.

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 SAVVY Security data in under 10 minutes.

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What data can I load from SAVVY Security?

Here are some of the endpoints you can load from SAVVY Security:

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How do I authenticate with the SAVVY Security API?

The API uses Bearer token authentication. Include header 'Authorization: Bearer ' on every request and send GraphQL queries as JSON POST to the /graphql endpoint with content-type: application/json.

1. Get your credentials

1. Log into your SAVVY Security account. 2) Navigate to the API or Integrations section (API token). 3) Create or copy an API token. 4) Store the token securely and provide it in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.savvy_security_source]
token = "your_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 SAVVY Security 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 savvy_security_pipeline.py

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

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Pipeline savvy_security_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset savvy_security_data
The duckdb destination used duckdb:/savvy_security.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 savvy_security_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 playbooks and activity_logs from the SAVVY Security 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 savvy_security_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graphql-gateway.savvy.security/graphql",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "playbooks", "endpoint": {"path": "graphql", "data_selector": "data.playbooks"}},
            {"name": "activity_logs", "endpoint": {"path": "graphql", "data_selector": "data.activityLogs"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="savvy_security_pipeline",
        destination="duckdb",
        dataset_name="savvy_security_data",
    )
    load_info = pipeline.run(savvy_security_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("savvy_security_pipeline").dataset()
sessions_df = data.playbooks.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM savvy_security_data.playbooks LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("savvy_security_pipeline").dataset()
data.playbooks.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 SAVVY Security 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 receive 401 Unauthorized, check that the Authorization header is present and the token is valid. Tokens are provided from the SAVVY dashboard; expired or revoked tokens produce 401.

Permission errors

A 403 Forbidden indicates your token lacks permission for the requested resource. Contact an administrator to grant API scopes or use a token with broader permissions.

Malformed queries and 400 responses

GraphQL syntax errors or invalid field names return 400 Bad Request with details in the 'errors' array of the response. Inspect the 'errors' object to correct the query.

Rate limiting

If you receive 429 Too Many Requests, back off and retry after the indicated Retry-After header. Implement exponential backoff.

GraphQL data selector note

GraphQL responses wrap requested fields under the top-level 'data' object. For lists, the exact key under 'data' corresponds to the field name used in your query (e.g., data.playbooks). Always inspect the response 'data' object to determine the correct selector.

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