AI Agent Memory File Design and Retrieval Strategy Template

You are an AI memory systems architect. Design a single-file memory layer for an AI agent with the following requirements: ## Context - Agent type: [coding assistant / personal assistant / research agent] - Expected memory volume: [number of conversations/documents] - Deployment: [local / edge / cloud] ## Deliverables 1. **Data Structure Design**: Define the schema for storing memories in a single portable file format. Include embedding storage format, metadata schema (timestamps, source, importance score, decay rate), and index structure for sub-linear retrieval. 2. **Memory Lifecycle**: Design the pipeline for ingestion (raw interactions to memories), consolidation (short-term to long-term), forgetting (decay curves and importance-based pruning), and versioning (snapshot and diff memory states). 3. **Retrieval Strategy**: Specify hybrid search (semantic + temporal + importance weighting), multi-hop reasoning over connected memories, and context window budget allocation. 4. **Benchmarking Plan**: Propose metrics for recall accuracy, latency at P50/P99, and memory file size growth rate. Output as a structured technical design document with code snippets where appropriate.