---
id: dreadnode
version: "2.0.38"
license: unclear
license_treatment: unclear
maintenance: active
---

# dreadnode — Dreadnode SDK

License: unclear · Maintenance: active · Downloads: 142.4K/mo

## What it is and what it does

Dreadnode is a Python SDK for building and testing AI security agents—systems that reason over multiple steps, call tools, and produce observable traces of their execution. It provides decorators to define agents with tools and hooks, run them against datasets with composable scorers, and trace all steps via OpenTelemetry. The package also includes a suite of AI red teaming attacks (TAP, GOAT, Crescendo, AutoDAN-Turbo, ReNeLLM) designed to probe LLMs for safety and security failure modes by systematically generating adversarial prompts.

The SDK integrates with HuggingFace for dataset loading, supports deployment via FastAPI or Ray, and includes a TUI for interactive development and platform sync. It depends on a large ecosystem: boto3 and AWS SDKs for cloud services, litellm for LLM routing, fastapi for serving, optuna for hyperparameter tuning, and data libraries like pandas and numpy. Most workflows require external LLM API keys (OpenAI, Anthropic, AWS Bedrock) and will incur costs proportional to token usage.

Use it for:

- Build a multi-step security analysis agent that searches vulnerability databases and reports findings with automatic tracing.
- Run systematic red teaming attacks against an LLM to find jailbreaks or unsafe outputs using TAP or Crescendo.
- Evaluate a penetration-testing agent against a dataset of web applications with custom scorers measuring thoroughness and safety.
- Deploy an AI agent as a FastAPI service with built-in observability and real-time scoring hooks.
- Experiment with language-adapted red teaming by translating attack prompts or framing them in benign contexts.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Dreadnode is an SDK for building, testing, and evaluating AI security agents with built-in tools for red teaming, observability, and systematic evaluation against datasets.

Yes, with conditions. Install if you are building or testing AI security agents and need a unified framework for agent definition, evaluation, and red teaming. The active maintenance, low install friction, and comprehensive feature set (agents, evaluations, red teaming, tracing) make it a solid choice for security-focused AI workflows. However, verify the license before use in proprietary projects, budget for LLM API costs, and confirm that the red teaming attacks meet your research or production requirements. The large dependency footprint may be a concern in resource-constrained deployments.

## Install

pip install dreadnode
uv add dreadnode
poetry add dreadnode

## Installing dreadnode

Before you install: Low install friction with a pure-Python wheel. Active maintenance (latest release 22 days old). Requires Python 3.11 or later. The 44 runtime dependencies include heavy stacks (boto3, fastapi, datasets, litellm, optuna) which will pull in substantial transitive dependencies.

License in practice: License treatment is unclear—no SPDX identifier or raw license text is available in the metadata. Verify the actual license before use in proprietary or copyleft-sensitive projects.

Quickstart:

pip install dreadnode

import dreadnode as dn

@dn.tool
def search_database(query: str) -> list[str]:
    return ["CVE-2024-1234"]

@dn.agent(model="openai/gpt-4o", tools=[search_database])
def security_analyst():
    """You are a security analyst."""

trajectory = await security_analyst.run("Analyze vulnerabilities")
print(f"Steps: {len(trajectory.steps)}")

Requires Python 3.11 or later. Most use cases require API keys for LLM providers (OpenAI, Anthropic, AWS Bedrock) configured separately.

Verify before relying:

- Whether the package's red teaming attacks (TAP, GOAT, Crescendo, AutoDAN-Turbo, ReNeLLM) are production-ready or research prototypes.
- Cost and rate-limit implications of running evaluations against large datasets with multiple LLM calls per example.
- Whether the local TUI runtime and platform sync work reliably without the Dreadnode platform backend.
- Performance characteristics when running agents with the full 44-dependency stack on resource-constrained environments.

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 142.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ai security agent framework, red teaming tools for llms, ai agent evaluation platform, adversarial testing for ai, agent tracing and observability, ai safety evaluation, multi-step reasoning agents, ai-security, red-teaming, agent-framework

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