Memory Types
5 Types of Context-Aware Memory
"Context that persists, decisions that compound, insights that accumulate"
3-Layer Context System
Memory is automatically loaded and injected at three levels to ensure agents always have the context they need.
Layer 1: Keywords
Auto-load context with triggers like "my research", "연구 진행"
User says "my research" → Project Context automatically loadedLayer 2: Task Tool
Auto-injects context to agents via Task tool delegation
Agent receives Project Context in system prompt automaticallyLayer 3: CLI
Explicit memory commands via CLI
/diverga:memory context → View current memory stateFive Memory Types
Each memory type serves a specific purpose in maintaining research continuity and context.
Project Context
Research question, paradigm, methodology, and theoretical framework
Storage:
.research/project-state.yamlExamples:
- ◆Research question and objectives
- ◆Selected paradigm (positivist, interpretivist, etc.)
- ◆Chosen theoretical framework with T-Score
- ◆Research design approach
Session Memory
Current session state, active tasks, and in-progress work
Storage:
.research/sessions/session-{id}.yamlExamples:
- ◆Active agent tasks and status
- ◆Current analysis stage
- ◆In-progress calculations
- ◆Session-specific variables
Decision Log
All human checkpoint decisions with timestamps and rationale
Storage:
.research/decision-log.yamlExamples:
- ◆Checkpoint approvals/rejections
- ◆Selected alternatives with T-Scores
- ◆Decision timestamps
- ◆Rationale and justifications
Research Notes
Accumulated insights, findings, and observations
Storage:
.research/notes/Examples:
- ◆Key findings from literature
- ◆Pattern observations
- ◆Methodological insights
- ◆Future research directions
Tool Preferences
Visualization styles, output formats, and tool configurations
Storage:
.research/preferences.yamlExamples:
- ◆Preferred visualization style
- ◆Output format (APA, Chicago, etc.)
- ◆Language preferences (EN/KO)
- ◆Agent behavior settings
Project Structure
Memory is organized in a .research/ directory within your project:
.research/
├── baselines/
│ ├── baseline-001.yaml
│ └── baseline-002.yaml
├── changes/
│ ├── change-001.yaml
│ └── change-002.yaml
├── sessions/
│ ├── session-20250205-143022.yaml
│ └── session-20250205-153145.yaml
├── notes/
│ ├── literature-notes.md
│ └── methodological-insights.md
├── project-state.yaml
├── decision-log.yaml
├── preferences.yaml
└── checkpoints.yamlAll memory files are version-controlled and can be committed to git for team collaboration.
Context Keywords (Auto-Trigger)
When you use these keywords, relevant memory is automatically loaded:
Project Reference
Keywords:
Loads:
Session Continuity
Keywords:
Loads:
Decision Reference
Keywords:
Loads:
Insight Recall
Keywords:
Loads:
Why Memory Matters
Context-aware memory provides critical advantages for research workflows:
No Repetition
Never re-explain your research context—it's always available to agents
Consistent Decisions
Past decisions inform future choices, ensuring methodological consistency
Audit Trail
Complete decision history for your methodology section
Team Collaboration
Version-controlled memory enables team members to stay synchronized
Session Resilience
Resume work seamlessly after interruptions or context window limits
Memory Commands
Explicit CLI commands to interact with memory:
/diverga:memory contextView current loaded memory state
/diverga:memory refreshReload memory from files
/diverga:memory clearClear session memory (keeps project memory)
/diverga:memory exportExport memory as markdown report
Ready to Experience Context-Aware Memory?
Start your research with memory that persists across sessions and agents.