p-funk/fegis

Fegis addresses a real problem in the agentic workflow—giving agents memory of their own actions—but it does so using a pattern that has become a commodity. The project has very low traction (22 stars) and appears stagnant (0.0 velocity over a year). Since its inception, the ecosystem has moved toward standardized function calling (OpenAI, Anthropic) and robust agent frameworks like LangChain, CrewAI, and LlamaIndex, all of which offer more sophisticated 'Tool-as-a-Service' or 'Memory' abstractions. The specific mechanism of logging tool outputs to a vector DB (Qdrant) is a standard architectural pattern rather than a proprietary moat. Frontier labs are effectively absorbing this functionality; for example, OpenAI's Assistants API handles state and retrieval natively. Consequently, the project faces high displacement risk from both dominant frameworks and model providers who are moving vertically up the stack into agent management.