agent-memorylisted

**CONSUMES**: User agent description + memory/context requirements + optional existing memory configs, checkpointer setup, or prompt structure **PRODUCES**: Applied memory-architecture judgment rules + compaction strategy selection + checkpointer/time-travel configs + caching breakpoint layout + memory-vs-vector-DB decision # Agent Memory & Context Engineering Capability Pack **Version**: 0.1.0 **Compatibility**: Claude Code (Phase 1); Codex / Cursor / Gemini in Phase 3 **License**: Apache 2.0 --- ## What This Pack Does AI agents build "memory" by appending every conversation turn to a vector store and calling it long-term memory. They expand the context window to fit raw history, paying quadratic attention cost. They summarize blindly at no fixed threshold, drifting and hallucinating. They store temporary events ("user bought coffee March 4") with the same weight as durable preferences ("user prefers black coffee"). They restart multi-step workflows from scratch on a crash because nothing was checkpointed. They place a timestamp at the top of the prompt and silently destroy every cache hit. This pack embeds the judgment rules that memory and context engineers apply automatically — rules from MemGPT/Letta, Mem0, LangGraph, the CoALA framework, and Anthropic's caching documentation. **Pack = memory & context judgment. Your workflow system = process constraints. No overlap.** --- ## Cross-Cutting Rule: Memory System ≠ Vector Database > **A vector database is a statele