marcossangomes/SmartLLM-Router

Zero-star, zero-fork repository with no observable adoption or community traction. The project describes a common optimization pattern in LLM applications: routing requests based on capability/cost tradeoffs and caching semantically similar queries. This is a well-understood problem space with multiple existing solutions (LangChain routing chains, Llamaindex routing, various in-house implementations at scale). The technical approach combines standard techniques (semantic similarity via embeddings, cache invalidation, cost metrics tracking) without apparent novel methodology. Frontier labs have strong incentives to compete here directly: OpenAI's routing logic, Anthropic's model selection, and Google's capability are all built into platform features or can be trivially added as wrapper logic. The lack of any adoption signal (0 stars after 8+ years) indicates this never gained traction, likely because the problem is better solved as middleware within existing orchestration frameworks or as platform features. Implementation appears to be prototype-stage based on age and lack of community signals. This is defensible only as internal tooling; as a public project, it offers no moat against either established competitors or frontier lab capability additions.