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---
name: search-memory
description: "Search your knowledge base when past decisions, preferences, or procedures would improve the response. Covers memories from every AI tool you use."
---
# Search Memory
## When to Search
**Strong signals:**
- References prior work: "the approach we used", "like last time"
- Resumes a named feature, project, or migration
- Review, regression, release, docs-alignment, or connector-behavior task
- Debugging resembles a past fix or known root cause
- Asks for rationale: "why did we choose X?"
- Recurring theme discussed in earlier sessions
**Contextual signals:**
- Complex debugging (may match past root causes)
- Architecture discussion (choices may be documented)
- Domain-specific question (conventions likely stored)
- User mentions a timeframe: "last week", "back in January"
**Skip when:**
- Fundamentally new topic with no prior context
- Generic syntax or language questions
- User explicitly requests a fresh perspective
## Retrieval Routing
### 1. Search memories (distilled knowledge)
```bash
nmem --json m search "3-7 word semantic query"
```
If the runtime already knows the active project or agent lane, add `--space "<space name>"`.
### 2. Search threads (past conversations)
When the user asks about a prior session, discussion, or exact exchange:
```bash
nmem --json t search "query" --limit 5
```
### 3. Progressive thread inspection
If a thread looks relevant, load it incrementally:
```bash
nmem --json t show <thread_id> --limit 8 --offset 0 --content-limit 1200
```
Increase `--offset` only when more messages are actually needed.
For continuation-heavy engineering work, search near the start of the task rather than waiting for an explicit recall request.
## Key Flags
| Flag | Purpose |
|------|---------|
| `--mode deep` | Conceptual or weak first-pass results |
| `-l label` | Filter by label (multiple uses AND logic) |
| `-n limit` | Limit number of results (default: 10) |
| `--importance MIN` | Minimum importance score (0.0-1.0) |
| `--time RANGE` | Time filter: today, week, month, year |
## Examples
```bash
# Semantic search with importance filter
nmem --json m search "database optimization" --importance 0.7
# Filter by labels
nmem --json m search "React patterns" -l frontend -l react
# Recent memories only
nmem --json m search "deployment fix" --time week -n 5
# Deep mode for conceptual queries
nmem --json m search "auth architecture rationale" --mode deep
```
## Interpreting Results
**Scores:** 0.6-1.0 direct match. 0.3-0.6 related. Below 0.3, skip.
**Found:** Synthesize and cite when helpful.
**None:** State clearly. Suggest distilling if the current discussion is valuable.
## When NOT to Search
Do not search for every message. Search when there is a reasonable expectation that prior knowledge exists and would improve the response. One well-targeted search is better than three speculative ones.
## Links
- [Documentation](https://mem.nowledge.co/docs/integrations/pi)
- [Troubleshooting](https://mem.nowledge.co/docs/troubleshooting)