Distill Memory
Save proactively when the conversation produces a durable fact, preference, decision, plan, procedure, learning, event, or important context. Do not wait to be asked.
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:
If an existing memory already captures the same decision, workflow, or preference and the new information refines it, update that memory instead of creating a duplicate:
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:
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 in PATH: pip install nmem-cli, or on Arch Linux yay -S nmem-cli / paru -S nmem-cli
For remote servers: run nmem config client set url https://... and nmem config client set api-key ... once on this machine.
Run nmem status to check server connection.
