PentAGI: Autonomous AI Penetration Testing Agent with Multi-Agent Architecture
PentAGI is a fully autonomous AI-powered penetration testing system that combines artificial intelligence with professional security tools to automate complex security assessments. The project has surged to 11.5K stars on GitHub and is trending with over 1,000 stars per day.
The system features a multi-agent architecture with specialized agents for research, development, and infrastructure tasks. All operations run in sandboxed Docker environments with complete isolation. It integrates 20+ professional security tools including nmap, metasploit, and sqlmap, and supports multiple LLM providers including OpenAI, Anthropic, Google AI, and Ollama.
What makes PentAGI distinctive is its knowledge graph system powered by Neo4j via Graphiti, which stores semantic relationships between tools, targets, vulnerabilities, and techniques. This enables the agent to learn from past penetration tests and apply insights to new assessments. The system also features long-term memory through vector embeddings (PostgreSQL + pgvector) and web intelligence via browser-based scraping.
The timing is notable: PentAGI's rise on GitHub coincides with RSAC 2026 this week, where agent-powered security is a dominant theme. For security teams, PentAGI represents a shift from manual pentesting workflows to autonomous, AI-driven assessments.
GitHub: https://github.com/vxcontrol/pentagi
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The system features a multi-agent architecture with specialized agents for research, development, and infrastructure tasks. All operations run in sandboxed Docker environments with complete isolation. It integrates 20+ professional security tools including nmap, metasploit, and sqlmap, and supports multiple LLM providers including OpenAI, Anthropic, Google AI, and Ollama.
What makes PentAGI distinctive is its knowledge graph system powered by Neo4j via Graphiti, which stores semantic relationships between tools, targets, vulnerabilities, and techniques. This enables the agent to learn from past penetration tests and apply insights to new assessments. The system also features long-term memory through vector embeddings (PostgreSQL + pgvector) and web intelligence via browser-based scraping.
The timing is notable: PentAGI's rise on GitHub coincides with RSAC 2026 this week, where agent-powered security is a dominant theme. For security teams, PentAGI represents a shift from manual pentesting workflows to autonomous, AI-driven assessments.
GitHub: https://github.com/vxcontrol/pentagi
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