---

name: Search Memory description: Search memory store when past insights would improve response. Recognize when user's stored breakthroughs, decisions, or solutions are relevant. Search proactively based on context, not just explicit requests.

Search Memory

When to Search (Autonomous Recognition)

Strong signals:

• Continuity: Current topic connects to prior work
• Pattern match: Problem resembles past solved issue
• Decision context: "Why/how we chose X" implies documented rationale
• Recurring theme: Topic discussed in past sessions
• Implicit recall: "that approach", "like before"

Contextual signals:

• Complex debugging (may match past root causes)
• Architecture discussion (choices may be documented)
• Domain-specific question (conventions likely stored)

Skip when:

• Fundamentally new topic
• Generic syntax questions
• Fresh perspective explicitly requested

Tool Usage

Use nmem CLI with --json flag for programmatic search:

Copy# Basic search
nmem --json m search "3-7 core concepts"

# With filters
nmem --json m search "API design" --importance 0.8

# With labels (multiple labels use AND logic)
nmem --json m search "authentication" -l backend -l security

# With time filter
nmem --json m search "meeting notes" -t week

Query: Extract semantic core, preserve terminology, multi-language aware

Filters:

• --importance MIN: Minimum importance score (0.0-1.0)
• -l, --label LABEL: Filter by label (can specify multiple)
• -t, --time RANGE: Time filter (today, week, month, year)
• -n NUM: Limit number of results (default: 10)

JSON Response: Parse memories array, check score field for relevance

Scores: 0.6-1.0 direct | 0.3-0.6 related | <0.3 skip

Examples:

Copy# Search with importance filter
nmem --json m search "database optimization" --importance 0.7

# Search with multiple labels
nmem --json m search "React patterns" -l frontend -l react

# Search recent memories
nmem --json m search "bug fix" -t week -n 5

Response

Found: Synthesize, cite when helpful None: State clearly, suggest distilling if current discussion valuable

Troubleshooting

If nmem is not available:

Option 1 (Recommended): Use uvx

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

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

Option 2: Install with pip

Copypip install nmem-cli
nmem --version