The Agentic Threat Hunting Framework (ATHF) provides a structured approach to threat hunting, enhancing memory and automation. It applies the LOCK pattern—Learn, Observe, Check, Keep—to document investigations, ensuring that past hunts are accessible and usable for both analysts and AI tools. This framework doesn’t replace existing methodologies like PEAK or TaHiTI but complements them by making them AI-ready. With ATHF, teams can maintain a searchable repository of investigations, allowing for more efficient and informed future hunts. ATHF operates on a maturity model with five levels, from ad-hoc hunts in Slack or analyst notes (Level 0) to fully autonomous agents that can monitor, act, and generate hypotheses (Level 4). Most teams typically function at Levels 1 or 2, which involve documented and searchable hunts. The framework is designed for easy installation via pip and can integrate with various SIEM/EDR platforms. New features, such as AI-powered research and hypothesis generation agents, are included in version 0.3.0 and above. Beyond ease of use, ATHF emphasizes the importance of capturing knowledge to prevent loss due to personnel turnover or overlooked notes. By allowing AI assistants to reference collective memory, the framework enhances the threat hunting process. It encourages customization, enabling organizations to adapt ATHF to their specific needs while maintaining the integrity of their hunt data. The article includes detailed installation instructions and command references, making it straightforward for users to get started.