terrizoaguimor/celiums-memory

Celiums-memory is an extremely early-stage project (2 days old, 2 stars) attempting to solve the 'memory' problem for AI agents using a neuroscience-inspired approach rather than just simple vector retrieval. While the conceptual framing of using 14 modules and 10 equations suggests a sophisticated cognitive architecture (similar to ACT-R or SOAR for the LLM era), it lacks the developer traction or peer-reviewed validation necessary to be considered defensible. It enters a hyper-competitive 'Agent Memory' market dominated by well-funded projects like MemGPT and Zep, as well as native platform capabilities like OpenAI's 'Memory' feature and LangChain's diverse memory modules. The primary moat would be the specific efficacy of its 'emotional' and 'neuro-grounded' equations, but without a community or significant benchmarks, it currently functions as a personal research experiment. Frontier labs are likely to supersede this by building more robust, transformer-native memory systems that don't rely on hard-coded cognitive modules.