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title AI agent patterns
linkTitle Patterns
description Production-ready patterns for AI agents using Redis
weight 50

This section provides production-ready patterns for building AI agents with Redis. Each pattern combines high-level abstractions with raw Redis commands, enabling both rapid development and deep debugging.

Available patterns

Caching and cost optimization

  • Semantic cache - Cache LLM responses by semantic meaning to reduce API costs by 40-60%

Search and retrieval

  • RAG hybrid search - Retrieval-augmented generation with vector similarity and metadata filtering

Agent coordination

  • Agent message streams - Event-driven agent coordination with persistent message history and at-most-once delivery

Feature serving

  • Feature stores - Serve pre-computed ML features with sub-millisecond latency

Pattern structure

Each pattern follows a consistent format:

  1. The abstraction (developer experience) - High-level libraries like RedisVL and Featureform for rapid development
  2. The raw commands (machine verification) - Exact Redis commands for debugging and validation
  3. Alternative approaches - Redis 8 native features and other implementation options
  4. Production patterns - Real-world considerations including multi-tenancy, memory management, and monitoring
  5. Performance characteristics - Latency benchmarks and scalability guidance
  6. Why this works - Design rationale covering safety, accuracy, efficiency, and flexibility

Key benefits

Dual-layer approach: Each pattern shows both high-level abstractions (for safety and speed) and raw Redis commands (for debugging and verification). This enables rapid development while maintaining full control when needed.

Production-ready: Patterns include memory estimates, performance benchmarks, failure modes, and multi-tenant isolation strategies that are critical for production deployments.

Framework agnostic: Works with LangChain, LangGraph, CrewAI, or custom agent implementations.

Future-proof: Patterns show both current approaches (Redis Search, RedisVL) and Redis 8 native features (Vector Sets) where applicable.

Additional resources

  • [RedisVL documentation]({{< relref "/develop/ai/redisvl" >}})
  • [Featureform documentation]({{< relref "/develop/ai/featureform" >}})
  • [Redis for AI libraries]({{< relref "/integrate/redis-ai-libraries" >}})
  • [Vector database quick start]({{< relref "/develop/get-started/vector-database" >}})
  • [RAG quick start guide]({{< relref "/develop/get-started/rag" >}})