artemMprokhorov/hippograph-pro

Hippograph-pro combines established techniques (graph databases, spreading activation networks from cognitive science, emotional tagging) in a personal AI memory context. The 25-star count, 3 forks, 46-day age, and zero velocity indicate early-stage adoption with minimal community engagement. The README promises self-hosted operation and zero LLM cost, which are commoditizing features rather than novel capabilities. Spreading activation is a well-studied algorithm from cognitive psychology (Quillian, 1968; Collins & Loftus, 1975), and graph-based memory systems are now standard in RAG pipelines. The 'emotional weighting' component adds a personalization layer but lacks technical depth signals in the description. Implementation appears to be prototype-grade: no evidence of production deployment, active community testing, or integration ecosystem. Defensibility is low because (1) similar functionality exists in established RAG frameworks (LlamaIndex, LangChain memory modules), (2) the core graph + activation algorithm is not proprietary, and (3) a frontier lab could trivially add local graph-based memory retrieval as a feature module. Frontier risk is medium because OpenAI/Anthropic are investing in efficient retrieval and local models, and this directly addresses cost reduction—but they would likely build this as a platform feature rather than adopt this specific implementation. The lack of momentum (0 velocity over 46 days) and small fork count suggest the project has stalled post-launch.