search-memory▌
nowledge-co/community · updated Apr 8, 2026
Search your personal knowledge base to surface relevant past insights, decisions, and solutions.
- ›Proactively searches durable knowledge and conversation history when context suggests prior work would improve the response
- ›Distinguishes between memory searches ( nmem m search ) for stored breakthroughs and thread searches ( nmem t search ) for exact session history
- ›Recognizes trigger patterns: user references to prior fixes, resumed features, debugging similarities, requests for ration
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:
# 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
Use thread search when the user is really asking about a prior conversation, previous session, or exact discussion:
nmem --json t search "query" --limit 5
If a memory result includes source_thread or thread search finds the likely conversation, inspect it progressively instead of loading the whole thread at once:
nmem --json t show <thread_id> --limit 8 --offset 0 --content-limit 1200
Increase --offset only when more messages are actually needed.
Scores: 0.6-1.0 direct | 0.3-0.6 related | <0.3 skip
Examples:
# 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 in PATH: pip install nmem-cli
For remote servers: create ~/.nowledge-mem/config.json with {"apiUrl": "...", "apiKey": "..."}.
Run /status to check server connection.
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★42 reviews- ★★★★★Olivia Nasser· Dec 20, 2024
Keeps context tight: search-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Pratham Ware· Dec 16, 2024
Registry listing for search-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Kwame Liu· Dec 16, 2024
I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Noah Jackson· Nov 11, 2024
I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sakshi Patil· Nov 7, 2024
search-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Carlos Garcia· Nov 7, 2024
Keeps context tight: search-memory is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chaitanya Patil· Oct 26, 2024
I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Carlos Haddad· Oct 26, 2024
Registry listing for search-memory matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Noah Desai· Oct 2, 2024
search-memory reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Anaya Anderson· Sep 21, 2024
I recommend search-memory for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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