drchinca/cemaf

CEMAF enters an extremely crowded market for LLM orchestration frameworks with zero quantitative signal (0 stars, 1 fork, 0 velocity). While it aims to solve critical problems like context engineering and dynamic DAGs, these are core features of well-funded, highly adopted projects like LangGraph (LangChain), AutoGen (Microsoft), and CrewAI. The project lacks a unique technical moat or 'unfair advantage'—such as a proprietary dataset or a breakthrough optimization—that would prevent users from choosing industry-standard alternatives. Given the age of 100+ days without traction, it is likely a personal exploration or a niche tool for the author rather than a viable competitor to existing ecosystem players. OpenAI's 'Swarm' and similar native implementations from frontier labs pose a direct existential threat to lightweight orchestration wrappers like this.