memory-patterns

Memory Patterns Skill

Implement persistent memory patterns for AI agents using ReasoningBank.

Quick Commands

# Store a pattern
npx agentic-flow@alpha memory store "api:auth" "OAuth2 with JWT"

# Retrieve a pattern
npx agentic-flow@alpha memory get "api:auth"

# Search patterns
npx agentic-flow@alpha memory search "authentication"

# List all patterns
npx agentic-flow@alpha memory list --namespace project

Memory Namespaces

Pattern Types

Decision Patterns

# Store decision with context
npx agentic-flow@alpha memory store \
  "decisions:auth-method" \
  '{"choice": "JWT", "reason": "stateless, scalable", "date": "2024-01-01"}'

Code Patterns

# Store reusable code pattern
npx agentic-flow@alpha memory store \
  "patterns:error-handling" \
  "try-catch with custom error classes and logging"

Learning Patterns

# Store learning from successful task
npx agentic-flow@alpha memory store \
  "learnings:react-hooks" \
  "useCallback for event handlers, useMemo for expensive computations"

MCP Tools

// Store memory
mcp__claude-flow__memory_usage({
  action: "store",
  key: "project:architecture",
  value: "microservices with event-driven communication",
  namespace: "project"
})

// Retrieve memory
mcp__claude-flow__memory_usage({
  action: "retrieve",
  key: "project:architecture",
  namespace: "project"
})

// Search memories
mcp__claude-flow__memory_search({
  pattern: "auth*",
  namespace: "project",
  limit: 10
})

ReasoningBank Integration

ReasoningBank provides:

• 150x faster vector search with HNSW
• Reflexion memory for self-improvement
• Skill library for learned capabilities
• Causal graphs for decision tracking

Best Practices

1. Use namespaces: Organize by scope
2. Be specific: Clear, searchable keys
3. Include context: Store decisions with reasoning
4. Clean up: Delete stale patterns
5. Version patterns: Track changes over time