Show HN: Aegis Memory v1.2 – We solved "what's worth remembering" for AI agents (github.com)

Aegis Memory has been launched as an open-source memory engine tailored for multi-agent AI systems, enabling a persistent learning loop for agents. This versatile platform combines features like semantic search, scope-aware access control, and Agentic Context Engineering (ACE) to enhance collaboration among agents, allowing them to share knowledge, vote on strategies, and reflect on past experiences. Notably, Aegis is designed for easy self-hosting, supporting quick setup with Docker or Kubernetes, and empowering users to perform core operations through a robust command-line interface. The significance of Aegis Memory for the AI/ML community lies in its capacity to transform agent interactions from isolated tasks into a cohesive organizational intelligence framework. By implementing advanced features like memory voting, incremental updates, and cross-agent memory sharing, Aegis fosters an environment where agents can learn from mistakes and collaboratively improve their performance. Technical innovations, such as a HNSW index for fast semantic searches and system observability via Prometheus metrics, make Aegis an appealing solution for developers looking to create smarter, self-improving AI systems.