This is a submission for the Hermes Agent Challenge
Most AI agents have a memory problem they don't admit to. Every session ends, the context resets, and tomorrow you're explaining your codebase, your preferences, and your constraints from scratch again. Hermes Agent by Nous Research is the first open-source agent that structurally solves this - not through a configurable memory feature, but through a closed learning loop baked into the agent runtime itself.
Why Every Other Agent Forgets
The standard agentic loop is three steps: receive task, plan and execute, return result. State resets. The next task starts blank.
Most frameworks tried to patch this with long-term memory bolted on after the fact - a vector database that stores embeddings of past conversations. The problem is that vector retrieval answers the question "what did we talk about that was similar to this?" It doesn't answer "how did I actually solve this class of problem last time, and what were the exact steps?" Those are different questions, and conflating them is why most "memory-enabled" agents still feel stateless in practice.
