SDK Overview

The SDK is the Python surface for Dreadnode. Use it when you want agent workflows, datasets, evaluations, and improvement loops that live in code instead of only in the app or CLI.

For installation and account setup, start with /getting-started/installation and /getting-started/authentication. The pages here assume you are already working inside a Python environment and want to automate Dreadnode programmatically.

Mental model

Think of the SDK as a stack of building blocks:

Layer	What it does	Start here
packages / capabilities	reusable artifacts and agent bundles you load or publish	Packages & Capabilities
Generator	raw model calls with no tool loop or memory	Generators
Agent	multi-step reasoning loop with tools, hooks, and trajectories	Agents
Tool	external actions an agent can call	Tools
transforms	input rewriting and prompt adaptation	Transforms
Scorer	reusable numeric metric or pass/fail gate	Scorers
Dataset / LocalDataset	persistent input sets for analysis and benchmarking	Data
Evaluation	repeatable benchmark run over a dataset	Evaluations
AIRT	prebuilt attack studies and grouped red-team assessments	AIRT
studies / samplers	the iterative search loop behind optimization and AIRT	Studies & Samplers
tracing	execution telemetry, spans, and artifacts	Tracing
optimization / training	local and hosted improvement workflows once your datasets and metrics are stable	Optimization and Training

SDK vs CLI

Use the SDK when you want:

• workflows checked into a repo
• reusable Python abstractions
• notebook, script, or CI execution
• custom scorers, hooks, or tools composed in code

Use the CLI when you want:

• login and profile management
• package publishing and registry operations
• hosted job submission from a shell
• quick inspection without writing Python

The two surfaces are complementary. A common pattern is to build and test locally in Python, then use the CLI for package publishing or job submission.

import dreadnode as dn

dn.configure(
    server="https://app.dreadnode.io",
    api_key="dn_...",
    organization="acme",
    workspace="research",
)

A common workflow

Most teams end up following this sequence:

1. Load or publish the capability, dataset, model, or environment you need.
2. Build an Agent and the Tool objects it needs.
3. Attach Scorer objects that express quality, safety, or policy checks.
4. Run an Evaluation or AIRT workflow and inspect the result plus traces.
5. Move to optimization or training only after the task, dataset, and scoring logic are stable.

If you need the shortest path to “something running,” start with Agents, then come back for Scorers, Data, and Evaluations.

Start with the right page

Packages & Capabilities

Agents

Tools

Transforms

Scorers

Data

Evaluations

AIRT

Studies & Samplers

Tracing

Optimization

Training

Examples