$ coming soon
Build a real AI agent with Redis
A free, hands-on video series. We use Redis for everything an agent needs, context, memory, guardrails, routing, retrieval, and caching, and build a real one end to end. You bring the LLM.
// what we build
1. Context surfaces
Give the agent exactly the right data at the right moment, served from Redis.
2. Agent memory
Short and long-term memory, so the agent remembers who it is talking to and what happened.
3. Semantic guardrails
Block off-topic or unsafe turns by meaning, not by brittle keyword lists.
4. Semantic router
Send each request to the right tool or flow based on intent.
5. RAG
Ground answers in your own data with retrieval-augmented generation.
6. Semantic cache
Skip the model when a semantically similar question was already answered.
Model-agnostic on purpose: you pick the LLM. The Redis patterns (context, memory, guardrails, routing, RAG, cache) are the point, and they work with any model.