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Show HN: Remoroo. trying to fix memory in long-running coding agents
I built Remoroo because most coding agents fall apart once the work stops being a short edit-and-run loop.<p>A real engineering experiment can run for hours. Along the way, the agent reads files, runs commands, checks logs, compares metrics, tries ideas that fail, and needs to remember what already happened. Once context starts slipping, it forgets the goal, loses track of the baseline, and retries bad ideas.<p>Remoroo is my attempt to solve that problem.<p>You point it at a repo and give it a measurable goal. It runs locally, tries changes, executes experiments, measures the result, keeps what helps, and throws away what does not.<p>A big part of the system is memory. Long runs generate far more context than a model can hold, so I built a demand-paging memory system inspired by OS virtual memory to keep the run coherent over time.<p>There is a technical writeup here: <a href="https://www.remoroo.com/blog/how-remoroo-works" rel="nofollow">https://www.remoroo.com/blog/how-remoroo-works</a><p>Would love feedback from people working on long-running agents, training loops, eval harnesses, or similar workflows.
Remoroo addresses a critical bottleneck in AI agent autonomy—long-term memory and context management—using a sophisticated OS-inspired technical approach. While the product innovation is high and the market for autonomous engineering is massive, the project is in its earliest stages with minimal traction and faces intense competition from well-funded incumbents.