Distill Memory

When to Suggest (Moment Detection)

Breakthrough: Extended debugging resolves, user relief ("Finally!", "Aha!"), root cause found

Decision: Compared options, chose with rationale, trade-off resolved

Research: Investigated multiple approaches, conclusion reached, optimal path determined

Twist: Unexpected cause-effect, counterintuitive solution, assumption challenged

Lesson: "Next time do X", preventive measure, pattern recognized

Skip: Routine fixes, work in progress, simple Q&A, generic info

Memory Quality

Good (atomic + actionable):

• "React hooks cleanup must return function. Caused leaks."
• "PostgreSQL over MongoDB: ACID needed for transactions."

Poor: Vague "Fixed bugs", conversation transcript

Tool Usage

Use nmem CLI to create memories:

nmem m add "Insight + context for future use" \
  -t "Searchable title (50-60 chars)" \
  -i 0.8

Content: Outcome/insight focus, include "why", enough context

Importance: 0.8-1.0 major | 0.5-0.7 useful | 0.3-0.4 minor

Note: For programmatic use, add --json flag to get JSON response

Examples:

# High-value insight
nmem m add "React hooks cleanup must return function. Caused memory leaks in event listeners." \
  -t "React Hooks Cleanup Pattern" \
  -i 0.9

# Decision with context
nmem m add "Chose PostgreSQL over MongoDB for ACID compliance and complex queries" \
  -t "Database: PostgreSQL" \
  -i 0.9

Suggestion

Timing: After resolution/decision, when user pauses

Pattern: "This [type] seems valuable - [essence]. Distill into memory?"

Frequency: 1-3 per session typical, quality over quantity

Troubleshooting

If nmem is not available:

Option 1 (Recommended): Use uvx

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Run nmem (no installation needed)
uvx nmem --version

Option 2: Install with pip

pip install nmem
nmem --version