P1: Consolidate memory implementations into unified interface

[galic1987]

Source

ChatGPT architecture review feedback

Problem

Multiple memory-like implementations with overlapping responsibilities:

• src/cognitive/episodic.rs — episodic memory (past sessions, learnings)
• src/memory/ — vector memory, RAG
• src/session/cache.rs — tool result caching with TTL
• src/session/local_first.rs — response caching, offline support
• src/analysis/vector_store.rs — vector store for code search

This matters because the "local-first, durable, self-improving workshop" story depends on memory being coherent, not fragmented across 5 subsystems.

Proposal

1. Define one MemoryStore trait with clear semantics:

   • store(key, value, metadata)
   • retrieve(query, top_k) (semantic search)
   • get(key) (exact lookup)
   • invalidate(key) / expire()

2. Provide multiple backends behind the trait:

   • JsonMemory — simple JSON file store (current episodic)
   • VectorMemory — HNSW-backed semantic search (current vector_store)
   • CacheMemory — TTL-based with LRU eviction (current cache.rs)

3. Document retention and deletion policies clearly

4. Single entry point: agent.memory.store() / agent.memory.search()

Relevant Code

• src/cognitive/episodic.rs
• src/memory/
• src/session/cache.rs
• src/session/local_first.rs
• src/analysis/vector_store.rs

Priority

P1 — architectural clarity