How are people handling long-term memory + replay/debugging for AI agents?

I’ve been building AI agents recently (LangGraph/CrewAI workflows), and I keep running into the same issue: Agent memory in production feels very hacked together. Most systems seem to rely on: * stuffing previous chats into prompts, * vector search over logs, * Redis/session memory, * or manually summarized context. But once workflows get longer or multi-session, problems start showing up: * agents repeat the same mistakes, * context windows become huge, * debugging becomes painful, * and there’s no proper “history” of agent decisions/actions. So I’m exploring building a small developer-focused memory layer for agents. Core idea: * store agent actions/results as “episodes” * semantically retrieve relevant past episodes * automatically link related episodes into a graph * replay/debug agent history similar to Git logs Example: An agent fails a deployment, fixes it later, and future deployment agents can automatically recall that prior fix instead of repeating the same failure. Thinking of: * vector search + graph links * REST/gRPC API * Python/TS SDK * LangGraph/CrewAI integration * replay/debug dashboard Main thing I’m trying to validate: Is this actually a painful enough problem that people would adopt a dedicated memory layer for it? Or are current solutions already good enough? Would appreciate brutally honest feedback from people building production agents/tools.