AI Agent 记忆增强编码会话设计模板

You are an expert in AI agent memory architectures, specifically for coding assistants. Help me design a memory-enhanced coding session system. ## My Setup - **Coding agent**: [e.g., Claude Code, Codex, Cursor] - **Project type**: [e.g., large monorepo, microservices, full-stack app] - **Team or solo**: [solo developer / team of N] - **Session frequency**: [e.g., daily multi-hour sessions] ## Design the Following ### 1. Memory Layer Architecture Design a 3-tier memory system: - **Working Memory**: What the agent needs in the current session (active files, recent errors, current task) - **Session Memory**: Compressed summary of each coding session (decisions made, patterns learned, blockers hit) - **Project Memory**: Long-term knowledge base (architecture decisions, coding conventions, known gotchas, dependency notes) For each tier, specify: - Storage format (e.g., structured JSON, markdown, embeddings) - Retention policy (what to keep, what to prune, when) - Injection strategy (how/when to load into context window) ### 2. Memory File Templates Provide concrete file templates for: - `SESSION_LOG.md` — auto-generated session summary - `PROJECT_MEMORY.md` — curated long-term knowledge - `LESSONS_LEARNED.md` — patterns and anti-patterns discovered - `CONTEXT_INJECTION.md` — what to prepend to each new session ### 3. Compression Strategy - How to summarize a 2-hour coding session into <500 tokens - What information is critical to preserve vs. safe to discard - How to detect and merge duplicate/conflicting memories ### 4. Retrieval & Context Engineering - How to select relevant memories for a new task - Token budget allocation: how much context for memory vs. code vs. instructions - Semantic search vs. recency-based vs. task-type matching ### 5. Implementation Plan - Step-by-step setup with the chosen coding agent - Automation hooks (pre-session load, post-session save) - Metrics to track memory system effectiveness Be practical and implementation-ready. Include example content for each template.