Skip to main content

ADR-003: Memory-Centric Architecture

Status​

Accepted

Context​

The MCP ADR Analysis Server requires persistent storage and retrieval of architectural knowledge, analysis results, and learning experiences. Based on the project analysis and memories, the system implements a sophisticated memory system with knowledge graphs, entity storage, and intelligent querying capabilities. This architecture needs to support both short-term caching and long-term knowledge retention.

Decision​

We will implement a memory-centric architecture using JSON-based storage with knowledge graph capabilities, entity relationship management, and intelligent memory retrieval systems.

Key components:

  • Knowledge Graph Storage: Structured storage of architectural decisions and relationships
  • Entity Management: Persistent storage of analysis entities with metadata
  • Intelligent Querying: Context-aware memory retrieval with relevance scoring
  • Cache Infrastructure: Multi-layer caching for performance optimization
  • Memory Integration: Seamless integration across all MCP tools
  • Snapshot System: Historical tracking of architectural evolution

Consequences​

Positive:

  • Persistent knowledge retention across analysis sessions
  • Intelligent context-aware retrieval of relevant past experiences
  • Knowledge graph enables relationship discovery and pattern recognition
  • Performance optimization through multi-layer caching
  • Historical tracking enables architectural evolution analysis
  • Seamless integration provides consistent memory access across tools

Negative:

  • Increased storage requirements for comprehensive memory retention
  • Complexity in managing entity relationships and knowledge graph consistency
  • Potential performance impact from memory operations during analysis
  • Need for sophisticated cache invalidation and consistency management
  • Risk of memory corruption affecting analysis quality
  • Dependency on file system reliability for persistent storage