| name | search-memory |
| description | Search past decisions, procedures, learnings, or context relevant to the current task. Trigger when work connects to prior decisions, a debugging pattern resembles a past issue, the user asks about rationale, or uses recall language like "that approach" or "like before". |
Find what the user already knows. Search their memories and past conversations for decisions, procedures, and context that make the current task sharper.
For continuation-style engineering work, search near the start of the task. Do not wait for the user to literally say "search memory".
When to use
Strong signals (search when):
- The user references previous work, a prior fix, or an earlier decision
- The task resumes a named feature, bug, refactor, incident, or subsystem
- The task is a review, regression, release, docs-alignment, or connector-behavior question
- A debugging pattern resembles something solved earlier
- The user asks for rationale, preferences, procedures, or recurring workflow details
- The user uses implicit recall language: "that approach", "like before", "the pattern we used"
Contextual signals (consider searching when):
- Complex debugging where prior context would narrow the search space
- Architecture discussion that may intersect with past decisions
- Domain-specific conventions the user has established before
- The current result is ambiguous and past context would make the answer sharper
When NOT to search:
- Fundamentally new topic with no prior history
- Generic syntax or API questions answerable from documentation
- User explicitly asks for a fresh perspective without prior context
Retrieval routing
If this session already exposes the Nowledge Mem MCP server, prefer:
memory_search for durable knowledge (decisions, insights, procedures).thread_search when the user is asking about a prior conversation or exact session history.thread_fetch_messages for progressive inspection of the matching thread.
Otherwise:
- Start with
nmem --json m search "query" for durable knowledge (decisions, insights, procedures).
- Use
nmem --json t search "query" --limit 5 when the user is asking about a prior conversation or exact session history.
- If a result includes
source_thread, inspect it progressively with nmem --json t show <thread_id> --limit 8 --offset 0 --content-limit 1200.
Prefer the smallest retrieval that answers the question. Do not over-fetch.
If the runtime already knows the active project or agent lane, add --space "<space name>" to these commands.
Deep mode
If results are weak or the need is conceptual/historical, try deeper matching:
nmem --json m search "query" --mode deep
Knowledge tree routing
When the user needs to browse across multiple object types, inspect nearby context, or asks for a file/tree/vault-like view, use the Knowledge Filesystem instead of only flat search.
Prefer MCP mem_fs when available:
capabilities
recall "session token strategy" --in /memories -k 5
find /memories --label decisions --since 2026-01-01
grep "JWT rotation" /memories
grep -E "JWT|token" /threads
cat /memories/by-id/<id>.memory.md
Otherwise use:
nmem fs capabilities --json
nmem fs recall "session token strategy" --in /memories -k 5
nmem fs ls /wiki
nmem fs cat /wiki/topics/<topic>.topic.md
Use capabilities before assuming roots or future verbs. Use recall for fuzzy phrasing, find for metadata constraints, grep for exact strings, grep -E for explicit regex, then stat or cat the returned paths. KFS paths are Mem identifiers, not local OS files; mount and SQL/Cypher are later phases.
Filters
Add filters only when the task clearly implies them:
- By label:
-l "label-name"
- By importance:
--importance 0.7
- By date range:
--event-from 2026-01-01 / --event-to 2026-03-01
- By source:
-s codex
- Limit results:
-n 10
Summarize only the strongest matches and clearly say when nothing relevant was found.
Links
