🤖 Mengram: Human-Like Memory System for AI Agents

The direction here is quite interesting: building a human-like memory system for AI agents. It goes beyond basic semantic memory by introducing episodic and procedural memory types, emphasizing that agents can use experience-driven procedures to learn from past failures. The hard part is not attaching a vector database to an LLM, but designing a cognitive architecture that allows an agent to distinguish between "what I know" (semantic), "what I have experienced" (episodic), and "how to do things" (procedural). The project provides Python and JS SDKs and integrates with LangChain, CrewAI, and MCP servers. This is more like an exploration into the underlying logic of next-generation agents. Compared to simple RAG setups, categorizing memory types could offer distinct advantages when handling long-term, complex tasks. If you are interested in agent cognitive architectures, this early-stage project is worth watching.