Redis

Redis is our primary in-memory data store for agent session management, response caching, and high-performance data sharing between agent services. It's essential for building responsive and cost-effective agentic systems.

Why Redis is fundamental for AI agents:

• Session Management: Store agent conversation state and context across interactions
• Response Caching: Cache LLM responses to reduce API costs and improve latency
• Message Queuing: Coordinate work between different agent services
• Rate Limiting: Implement request rate limiting for agent API endpoints
• Real-time Data: Share real-time data between agents and business systems

Agent-specific use cases:

• Conversation Memory: Persistent storage for agent conversation history
• Semantic Cache: Fast lookup layer for semantically cached LLM responses
• Agent State: Store agent workflow state and progress information
• Event Streaming: Publish/subscribe for agent event notifications
• Lock Management: Distributed locking for agent resource coordination

Performance benefits:

• Sub-millisecond Latency: Faster than database queries for agent state lookup
• High Throughput: Handle thousands of concurrent agent interactions
• Memory Efficiency: Optimized data structures for agent-specific workloads
• Persistence Options: Configurable durability for different data types
• Clustering: Scale horizontally for high-volume agent deployments

Data structures for agents:

• Strings: Simple key-value storage for agent configuration and flags
• Hashes: Store agent profiles and user context information
• Lists: Queue agent tasks and maintain conversation history
• Sets: Manage agent capabilities and user permissions
• Sorted Sets: Prioritize agent tasks and implement leaderboards
• Streams: Event sourcing for agent interactions and audit trails

Integration with our platform:

• Kubernetes: Deployed as StatefulSet with persistent volumes
• External Secrets: Secure management of Redis authentication
• Monitoring: Integrated with Prometheus for performance metrics
• Backup: Automated snapshots and point-in-time recovery
• Service Mesh: Secure communication via Istio sidecar

Configuration for AI workloads:

• Memory Optimization: Tuned for agent session and cache data patterns
• Eviction Policies: LRU eviction for cache data, no eviction for sessions
• Persistence: RDB snapshots for cache recovery, AOF for critical agent state
• Replication: Master-replica setup for high availability
• Partitioning: Cluster mode for scaling beyond single-instance limits

Best practices:

• Use appropriate TTLs for different types of agent data
• Implement consistent key naming conventions for agent namespaces
• Monitor memory usage and plan for data growth
• Use pipelining for bulk operations with agent data
• Implement proper error handling for Redis connectivity issues