Manages long-term research context and decision history through automated checkpoints and multi-layer persistence.
The Diverga Memory System is a specialized tool for Claude Code designed to solve context loss in complex, multi-session research projects. It utilizes a sophisticated 3-layer architecture—combining natural language triggers, task-based injection, and CLI commands—to ensure project state, research questions, and methodology remain persistent. By enforcing a structured checkpoint system and maintaining an immutable, versioned decision audit trail, it allows researchers to maintain continuity, justify methodological choices, and recover essential context even when hitting AI context window limits.
主要功能
013-Layer Context System providing automatic state recovery via keywords or agent calls
02Automated Checkpoint System with REQUIRED, RECOMMENDED, and OPTIONAL enforcement levels
03Immutable Decision Audit Trail with versioning and amendment tracking for research transparency
04MCP-powered Priority Context for resilience against context window compression and recovery
05Standardized Research Workflow support covering Foundation, Design, Planning, and Execution stages
060 GitHub stars
使用场景
01Managing complex academic or technical research projects that span multiple days or weeks
02Maintaining a verifiable audit trail of architectural or methodological decisions for team review
03Quickly resuming deep-work sessions with automated summaries of 'where we left off'
