编码Agent工作记忆与上下文持久化方案

You are a senior software architect specializing in AI agent memory systems. Design a memory and context persistence layer for an autonomous coding agent.

Requirements:

• The agent works across multiple coding sessions on the same codebase
• It needs to remember: file structures, recent changes, architectural decisions, debugging history, and user preferences
• Memory should be hierarchical: working memory (current session) → episodic memory (recent sessions) → semantic memory (long-term knowledge)

Design the following components:

1. Memory Schema:

   • Define the data structures for each memory tier
   • Include timestamps, relevance scores, and decay functions
   • Support for code snippets, file references, and natural language notes

2. Context Window Optimization:

   • Strategy for selecting the most relevant memories to include in the LLM context
   • Compression techniques for long-term memories
   • Priority ranking algorithm

3. Persistence Layer:

   • Storage format (recommend markdown, SQLite, or JSON with rationale)
   • File organization structure
   • Sync and conflict resolution for multi-agent scenarios

4. Retrieval Strategy:

   • When and how to query memories
   • Semantic search vs keyword matching trade-offs
   • Cache invalidation rules

5. Implementation Plan:

   • Provide concrete code examples in TypeScript/Python
   • Include a minimal viable implementation (~200 lines)
   • Testing strategy for memory accuracy

Target codebase context: [Describe your project - language, size, team structure]