ReddRadar
Agent skill
redis-memory-backend
Redis backend for conversation state persistence and caching
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/ai-agents-conversational/skills/redis-memory-backend
SKILL.md
Redis Memory Backend Skill
Capabilities
- Configure Redis for conversation state storage
- Implement message history persistence
- Set up Redis caching for LLM responses
- Configure TTL-based memory expiration
- Implement Redis Pub/Sub for real-time updates
- Design efficient key schemas
Target Processes
- conversational-memory-system
- chatbot-design-implementation
Implementation Details
Core Components
- Message Store: RedisChatMessageHistory
- Cache: LLM response caching
- State Store: Conversation state persistence
- Pub/Sub: Real-time updates
Configuration Options
- Redis connection settings
- Key prefix configuration
- TTL settings
- Serialization format
- Cluster configuration
Key Schema Patterns
- session:{session_id}:messages
- cache:llm:{prompt_hash}
- state:{user_id}:{key}
Best Practices
- Use appropriate data structures
- Configure proper TTLs
- Implement connection pooling
- Monitor memory usage
Dependencies
- redis
- langchain-community (RedisChatMessageHistory)
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