The AI alignment problem is, at its core, a profoundly philosophical challenge: How can we ensure that artificial intelligence systems—especially powerful new models like Large Language Models (LLMs)—act in accordance with human values, intentions, and ethical principles? This question is not only technical, but also raises fundamental issues about the nature of values, ethics, and agency.
Red Agent is designed as a rigorous experimental platform to explore this philosophical problem in practice. By leveraging the tools of complexity science, an agentic multi-agent approach, and the capabilities of modern LLMs, Red Agent creates a dynamic environment where the boundaries of AI alignment can be systematically tested and analyzed. Through structured debates, adversarial red-teaming, and automated evaluation, this framework enables deep investigation into how and where LLMs succeed—or fail—to align with human ethical criteria.
Red Agent is an experimental framework inspired by the principles of red-teaming—a methodology traditionally used to rigorously test the robustness and boundaries of systems by actively seeking to break or circumvent their criteria. In this context, Red Agent adapts red-teaming to the domain of Large Language Models (LLMs), orchestrating structured, multi-agent debates to systematically challenge and evaluate the ethical, risk, and alignment boundaries of both open local and proprietary AI models.
The primary goal of this project is to deeply investigate the AI alignment problem: how well do LLMs adhere to intended ethical guidelines, and where do they fail? By leveraging dynamic, agentic interactions and a dedicated referee agent, Red Agent provides a platform for measuring, analyzing, and understanding the emergent behaviors and limitations of LLMs—especially in the face of adversarial or boundary-pushing scenarios.
Red Agent enables the instantiation of multiple autonomous agents, each with distinct roles and objectives, to engage in debates on complex topics. These agents may attempt to break, circumvent, or uphold ethical and alignment criteria, simulating real-world adversarial and cooperative dynamics. The framework supports both open-source local models and proprietary models, allowing for comparative analysis of biases, rule-following, and alignment strategies across different LLM implementations.
A specialized referee agent evaluates each debate contribution using a rigorous, criteria-based prompt, ensuring objective and reproducible assessments of agent behavior and model alignment.
- Python 3.10+: Core language for orchestration and logic.
- LangChain: Agent workflow and LLM abstraction.
- Ollama: Local LLM serving for privacy and experimentation.
- Jinja2: Dynamic prompt and output templating.
- Black, Flake8: Code formatting and linting.
- CSV/Logging: Persistent, auditable records of debates and evaluations.
- Red-Teaming Agents: Agents are instantiated with roles that may include adversarial, critical, or defending perspectives, actively probing the boundaries of LLM alignment.
- Role-Driven Reasoning: Prompts and templates enforce role adherence, ensuring diverse and contextually relevant outputs.
- Referee/Evaluator Agent: Applies a structured rubric to assess ethical soundness, risk, and alignment for each contribution.
- Memory and Turn Management: Agents maintain conversational memory and follow configurable turn-taking protocols.
- Prompt Engineering: Systematic use of templates and context injection to steer agent behavior and evaluation.
Red Agent is grounded in complexity science, treating debates as emergent phenomena from the interaction of heterogeneous, adaptive agents. This approach enables the study of collective intelligence, negotiation, and the evolution of consensus or dissent in AI systems.
By orchestrating adversarial and cooperative agent interactions, Red Agent overcomes the limitations of LLMs as mere statistical language generators. It exposes hidden biases, tests the robustness of ethical constraints, and provides dynamic, empirical measurements of alignment—offering a deeper understanding of the AI alignment problem in practice.
- Configure Agents and Debate Parameters: Define agent roles, models (open or proprietary), and debate settings.
- Run the Debate: Launch the debate arena for structured, multi-turn agent interactions.
- Automated Evaluation: The referee agent evaluates each comment post-debate, appending results to a CSV file for analysis.
- Analyze Results: Use the generated logs and evaluation files for research, reporting, or further experimentation.
- Research in AI alignment, adversarial testing, and complexity science.
- Benchmarking and evaluation of LLMs in adversarial, multi-agent contexts.
- Development of robust, auditable AI systems for decision support, negotiation, and ethical reasoning.
This project is released under the MIT License.
For further details, see the source code and documentation within the repository.