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memory-management

ruvnet/claude-flow/.agents/skills/memory-management/SKILL.md

AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.

Skill74k starsChanged 29 days ago

What's in it

  1. Memory Management Skill
  2. Purpose
  3. When to Trigger
  4. When to Skip
  5. Commands
  6. Store Pattern
  7. Semantic Search
  8. Retrieve Entry
  9. List Entries
  10. Delete Entry
  11. Initialize HNSW Index
  12. Memory Stats
  13. Export Memory
  14. Scripts
  15. References
  16. Best Practices
---
name: memory-management
description: >
  AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management.
  Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base.
  Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
---

# Memory Management Skill

## Purpose
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management.

## When to Trigger
- need to store successful patterns
- searching for similar solutions
- semantic lookup of past work
- learning from previous tasks
- sharing knowledge between agents
- building knowledge base

## When to Skip
- no learning needed
- ephemeral one-off tasks
- external data sources available
- read-only exploration

## Commands

### Store Pattern
Store a pattern or knowledge item in memory

```bash
npx @claude-flow/cli memory store --key "[key]" --value "[value]" --namespace patterns
```

**Example:**
```bash
npx @claude-flow/cli memory store --key "auth-jwt-pattern" --value "JWT validation with refresh tokens" --namespace patterns
```

### Semantic Search
Search memory using semantic similarity

```bash
npx @claude-flow/cli memory search --query "[search terms]" --limit 10
```

**Example:**
```bash
npx @claude-flow/cli memory search --query "authentication best practices" --limit 5
```

### Retrieve Entry
Retrieve a specific memory entry by key

```bash
npx @claude-flow/cli memory get --key "[key]" --namespace [namespace]
```

**Example:**
```bash
npx @claude-flow/cli memory get --key "auth-jwt-pattern" --namespace patterns
```

### List Entries
List all entries in a namespace

```bash
npx @claude-flow/cli memory list --namespace [namespace]
```

**Example:**
```bash
npx @claude-flow/cli memory list --namespace patterns --limit 20
```

### Delete Entry
Delete a memory entry

```bash
npx @claude-flow/cli memory delete --key "[key]" --namespace [namespace]
```

### Initialize HNSW Index
Initialize HNSW vector search index

```bash
npx @claude-flow/cli memory init --enable-hnsw
```

### Memory Stats
Show memory usage statistics

```bash
npx @claude-flow/cli memory stats
```

### Export Memory
Export memory to JSON

```bash
npx @claude-flow/cli memory export --output memory-backup.json
```


## Scripts

| Script | Path | Description |
|--------|------|-------------|
| `memory-backup` | `.agents/scripts/memory-backup.sh` | Backup memory to external storage |
| `memory-consolidate` | `.agents/scripts/memory-consolidate.sh` | Consolidate and optimize memory |


## References

| Document | Path | Description |
|----------|------|-------------|
| `HNSW Guide` | `docs/hnsw.md` | HNSW vector search configuration |
| `Memory Schema` | `docs/memory-schema.md` | Memory namespace and schema reference |

## Best Practices
1. Check memory for existing patterns before starting
2. Use hierarchical topology for coordination
3. Store successful patterns after completion
4. Document any new learnings

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