Qualifire AI Open-Sources Rogue: An Finish-to-Finish Agentic AI Testing Framework Designed to Consider the Efficiency, Compliance, and Reliability of AI Brokers

Agentic techniques are stochastic, context-dependent, and policy-bounded. Typical QA—unit exams, static prompts, or scalar “LLM-as-a-judge” scores—fails to show multi-turn vulnerabilities and gives weak audit trails. Developer groups want protocol-accurate conversations, specific coverage checks, and machine-readable proof that may gate releases with confidence.

Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI brokers over the Agent-to-Agent (A2A) protocol. Rogue converts enterprise insurance policies into executable eventualities, drives multi-turn interactions in opposition to a goal agent, and outputs deterministic stories appropriate for CI/CD and compliance critiques.

Fast Begin

Conditions

• uvx – If not put in, comply with uv set up information
• Python 3.10+
• An API key for an LLM supplier (e.g., OpenAI, Google, Anthropic).

Set up

Choice 1: Fast Set up (Advisable)

Use our automated set up script to stand up and operating shortly:

# TUI
uvx rogue-ai
# Net UI
uvx rogue-ai ui
# CLI / CI/CD
uvx rogue-ai cli

Choice 2: Guide Set up

(a) Clone the repository:

git clone https://github.com/qualifire-dev/rogue.git
cd rogue

(b) Set up dependencies:

In case you are utilizing uv:

Or, in case you are utilizing pip:

(c) OPTIONALLY: Arrange your surroundings variables: Create a .env file within the root listing and add your API keys. Rogue makes use of LiteLLM, so you may set keys for varied suppliers.

OPENAI_API_KEY="sk-..."
ANTHROPIC_API_KEY="sk-..."
GOOGLE_API_KEY="..."

Operating Rogue

Rogue operates on a client-server structure the place the core analysis logic runs in a backend server, and varied purchasers connect with it for various interfaces.

Default Habits

Whenever you run uvx rogue-ai with none mode specified, it:

1. Begins the Rogue server within the background
2. Launches the TUI (Terminal Consumer Interface) shopper

Obtainable Modes

• Default (Server + TUI): uvx rogue-ai – Begins server in background + TUI shopper
• Server: uvx rogue-ai server – Runs solely the backend server
• TUI: uvx rogue-ai tui – Runs solely the TUI shopper (requires server operating)
• Net UI: uvx rogue-ai ui – Runs solely the Gradio net interface shopper (requires server operating)
• CLI: uvx rogue-ai cli – Runs non-interactive command-line analysis (requires server operating, superb for CI/CD)

Mode Arguments

Server Mode

uvx rogue-ai server [OPTIONS]

Choices:

• –host HOST – Host to run the server on (default: 127.0.0.1 or HOST env var)
• –port PORT – Port to run the server on (default: 8000 or PORT env var)
• –debug – Allow debug logging

TUI Mode

uvx rogue-ai tui [OPTIONS]
Net UI Mode
uvx rogue-ai ui [OPTIONS]

Choices:

• –rogue-server-url URL – Rogue server URL (default: http://localhost:8000)
• –port PORT – Port to run the UI on
• –workdir WORKDIR – Working listing (default: ./.rogue)
• –debug – Allow debug logging

Instance: Testing the T-Shirt Retailer Agent

This repository features a easy instance agent that sells T-shirts. You should use it to see Rogue in motion.

Set up instance dependencies:

In case you are utilizing uv:

or, in case you are utilizing pip:

pip set up -e .[examples]

(a) Begin the instance agent server in a separate terminal:

In case you are utilizing uv:

uv run examples/tshirt_store_agent

If not:

python examples/tshirt_store_agent

This can begin the agent on http://localhost:10001.

(b) Configure Rogue within the UI to level to the instance agent:

• Agent URL: http://localhost:10001
• Authentication: no-auth

(c) Run the analysis and watch Rogue check the T-Shirt agent’s insurance policies!

You should use both the TUI (uvx rogue-ai) or Net UI (uvx rogue-ai ui) mode.

The place Rogue Suits: Sensible Use Circumstances

• Security & Compliance Hardening: Validate PII/PHI dealing with, refusal habits, secret-leak prevention, and regulated-domain insurance policies with transcript-anchored proof.
• E-Commerce & Assist Brokers: Implement OTP-gated reductions, refund guidelines, SLA-aware escalation, and tool-use correctness (order lookup, ticketing) below adversarial and failure situations.
• Developer/DevOps Brokers: Assess code-mod and CLI copilots for workspace confinement, rollback semantics, rate-limit/backoff habits, and unsafe command prevention.
• Multi-Agent Methods: Confirm planner↔executor contracts, functionality negotiation, and schema conformance over A2A; consider interoperability throughout heterogeneous frameworks.
• Regression & Drift Monitoring: Nightly suites in opposition to new mannequin variations or immediate adjustments; detect behavioral drift and implement policy-critical go standards earlier than launch.

What Precisely Is Rogue—and Why Ought to Agent Dev Groups Care?

Rogue is an end-to-end testing framework designed to judge the efficiency, compliance, and reliability of AI brokers. Rogue synthesizes enterprise context and threat into structured exams with clear goals, techniques and success standards. The EvaluatorAgent runs protocol appropriate conversations in quick single flip or deep multi flip adversarial modes. Convey your personal mannequin, or let Rogue use Qualifire’s bespoke SLM judges to drive the exams. Streaming observability and deterministic artifacts: dwell transcripts,go/fail verdicts, rationales tied to transcript spans, timing and mannequin/model lineage.

Underneath the Hood: How Rogue Is Constructed

Rogue operates on a client-server structure:

• Rogue Server: Accommodates the core analysis logic
• Consumer Interfaces: A number of interfaces that connect with the server:
  • TUI (Terminal UI): Fashionable terminal interface constructed with Go and Bubble Tea
  • Net UI: Gradio-based net interface
  • CLI: Command-line interface for automated analysis and CI/CD

This structure permits for versatile deployment and utilization patterns, the place the server can run independently and a number of purchasers can connect with it concurrently.

Abstract

Rogue helps developer groups check agent habits the best way it truly runs in manufacturing. It turns written insurance policies into concrete eventualities, workouts these eventualities over A2A, and data what occurred with transcripts you may audit. The result’s a transparent, repeatable sign you should utilize in CI/CD to catch coverage breaks and regressions earlier than they ship.

Because of the Qualifire staff for the thought management/ Sources for this text. Qualifire staff has supported this content material/article.