Raptor is an AI-powered security agent framework that transforms Claude Code into a tool for offensive and defensive security operations.
The tool addresses the need to apply AI reasoning to security tasks by enabling developers to configure Claude as an autonomous agent capable of adversarial thinking. It works by allowing users to define rules, create sub-agents, and build skills that orchestrate security tool usage. This modular approach lets teams teach the agent domain-specific security knowledge and coordinate multiple tools toward attack or defense objectives without manually scripting each interaction.
Raptor suits security teams and researchers who want to automate security workflows while maintaining control over the agent's behavior through explicit configuration. It is particularly valuable for organizations conducting security research, red team operations, or defensive assessments where an AI agent can reason about attack chains and tool combinations. The framework is designed for teams comfortable with Python and willing to invest in defining rules and skills for their specific security context.
The project shows active development with regular commits addressing core functionality and bug fixes. Work spans multiple areas including agent orchestration, skill definition, and integration with external security tools. The codebase receives ongoing refinement to improve how sub-agents coordinate and how rules govern agent behavior. Development activity indicates sustained attention to both feature expansion and stability of the agent framework itself.
