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.
What's in it
- Memory Management Skill
- Purpose
- When to Trigger
- When to Skip
- Commands
- Store Pattern
- Semantic Search
- Retrieve Entry
- List Entries
- Delete Entry
- Initialize HNSW Index
- Memory Stats
- Export Memory
- Scripts
- References
- 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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