One of the most frustrating parts of using AI coding agents isn’t the quality of the models—it’s losing context. I’d spend hours working with an agent, only to hit a context limit, restart a session, switch to another agent, or recover from a crash. Even when the code was saved, the reasoning behind it was gone. Decisions, constraints, discoveries, and next steps had to be reconstructed over and over again. That experience led me to build HammaDev. Instead of trying to save entire conversations, HammaDev focuses on preserving the information that actually matters: verified decisions, project constraints, risks, discoveries, and the next action. It integrates with trusted lifecycle events where supported, provides exact session recovery for Codex, and keeps everything local to your machine—no accounts, no cloud backend, and no vendor lock-in. The project changed quite a bit during development. It started as a simple way to save context, but quickly evolved into a continuity layer that understands session boundaries, detects repository changes, and prepares the next agent with bounded, explainable project memory instead of dumping transcripts. This is still an early beta, and there are plenty of things I want to improve. I’d genuinely love your feedback—whether it’s about the concept, the implementation, or which AI coding agents you’d like to see supported next. Thanks for checking out HammaDev! 🚀

HammaDev

Project memory that survives AI coding agent context loss

Project memory that survives AI coding agent context loss