How Uber Serves over 150 Million Reads per Second from Integrated Cache In this article, we break down how Uber built this system, the challenges they faced, and the innovative solutions they developed.

Read the full breakdown from @bytebytego.skystack.xyz about how engineered CacheFront using Redis: blog.bytebytego.com/p/how-uber-s...

Their secret? Treating cache as a first-class part of the data system, not an afterthought. By integrating Redis directly into the write path, Uber tackled one of the hardest problems in distributed systems: cache invalidation at massive scale.

Uber’s CacheFront serves 150M+ reads per second with 99.9%+ cache hit rates, and does it while maintaining strong consistency guarantees.

GitHub - redis-developer/podcast-chatbot-with-agent-memory-azure-demo: Demo showing how to use Redis Agent Memory Server to build context-aware AI chatbots with persistent conversation memory on Azure... Demo showing how to use Redis Agent Memory Server to build context-aware AI chatbots with persistent conversation memory on Azure. - redis-developer/podcast-chatbot-with-agent-memory-azure-demo

Meet "Podbot," an AI chatbot @guy.dev built using Azure OpenAI to recommend podcasts based on your preferences. It uses Redis Memory Server to manage memory, so it remembers what you like across conversations.

Build your own Podbot here (and feel free to open a PR): github.com/redis-develo...

Downloads | Redis Developers love Redis. Unlock the full potential of the Redis database with Redis Enterprise and start building blazing fast apps.

Download Redis 8.4 here: redis.io/downloads/

Redis 8.4 is the fastest and most powerful Redis yet Redis 8.4 adds improved vector search, unmatched performance at scale, and atomic operations

⚛️ Atomic Slot Migration (ASM): Enables easier, more reliable cluster scaling.

💬 Stream enhancements: Allow clients to process new and pending messages in a single step.

🔑 Atomic key operations: Provide safer, script-free key updates and expirations.

Read more here: redis.io/blog/redis-8...

New features include:

🔎 Hybrid search: Combines full-text and vector results in one query, for more accurate AI, RAG, and semantic search.

📈 Performance improvements: Multi-threaded I/O, improved memory handling + smarter JSON storage delivers 30% higher throughput, 91% lower memory use.

Redis 8.4, the fastest, simplest, and most powerful Redis yet—now GA in Redis Open Source

Redis Open Source 8.4 is now GA. It’s faster, more scalable, and now has hybrid search. It’s the most advanced version of Redis yet, continuing our mission to make Redis faster, simpler, and more powerful.

Semantic Caching for AI Agents Speed up and reduce the costs of your AI agents by implementing semantic caching that reuses responses based on meaning rather than exact text.

Start building AI agents that respond faster and cost less to run.

Enroll here: hubs.la/Q03V034V0

In this course, you'll learn how to build a semantic cache that reuses responses based on meaning, not exact text matches, as well as:
• Measure cache performance
• Enhance accuracy with cross-encoders & LLM validation
• Build caching into an AI agent that's faster + more cost-effective over time

AI agents often make redundant API calls for questions that mean the same thing. Semantic caching helps your agents recognize when different queries share the same meaning, reducing costs and speeding up responses.

We worked with @deeplearningai.bsky.social to create a new course called "Semantic Caching for AI Agents." Redis experts will help you build a semantic cache that makes AI agents faster and more cost-effective by recognizing when different questions mean the same thing.

Deploying Redis Agent Memory Server on EC2 with Terraform Learn how to deploy the Redis Agent Memory Server with Terraform

Redis Agent Memory Server is our OSS tool for managing memory for agents and AI apps. @riferrei.com walks you through deploying Redis Agent Memory Server on Amazon EC2 using Terraform, creating a scalable, production-ready setup to power AI apps with intelligent memory. medium.com/@riferrei/de...

Why You Need Semantic Routing in Your LangGraph Toolkit: A Beginner’s Guide Not every question needs a 175-billion-parameter LLM. Semantic routing helps decide which model, tool, or path should handle each query

Semantic routing improves predictability and efficiency in AI systems. RedisVL classifies user queries without LLM calls, reducing token usage and latency. Combined with LangGraph, it creates a deterministic flow for routing, execution, and decision logic. Read how here: medium.com/@bhavana0405...

🧵 1/ LLMs are stateless.

They don’t remember past interactions.
Every prompt is treated as an isolated request.

To build actual agents, we need memory.

Here’s how to do it using Spring AI and Redis — short-term + long-term memory, fully integrated. (@redis.io)

Is Your Vector Database Really Fast? YouTube video by Redis

In my latest video, I discuss the 6️⃣ technical aspects that *actually* determine vector database speed—from hardware vectorization to quantization tradeoffs to chunking strategies.

There is no vendor pitch, just technical analysis. 😉

🎥 Watch it now:

www.youtube.com/watch?v=3ZOt...

Do you want to learn how to develop searches using @redis.io that doesn't look like the situation below?

I will be doing a live demo about this on May 14, 2025 at 9:00 am PT. You will get to see how data is handled and processed using #Golang and #Redis.

redis.io/events/moder...

1/🤔 Have you ever heard of the Count-min Sketch data structure?

We just added full support for it in Redis OM Spring — and it fits right in alongside other probabilistic data structures like Bloom and Cuckoo filters.

Quick rundown 👇 cc @redis.io

@redis.io is proudly sponsoring @devoxx.uk! Visit our booth to chat about vector databases, semantic caching, AI integration, or even UFOs! 🛸

Short-Term Memory with LangGraph YouTube video by Redis

In this hands-on tutorial, I show how to design an AI agent using #LangGraph that stores thread-scope memory (conversational memory) using the native check-pointer created by @redis.io.

➡️ Check it out:

www.youtube.com/watch?v=k3FU...

1/4 Every time I present the same talk, it gets better. Sometimes because I spot things to improve. Sometimes because of great feedback from the audience.

Check it out 🧵

cc @redis.io #javabubble

Good news, everyone.

Redis is open source again: antirez.com/news/151

Get started here, today: redis.io/docs/latest/...

▶️ New libraries including Redis vector library for GenAI use cases, open source client libraries, mapping libraries and more
▶️ Full compatibility with Redis Insight and Redis for VS Code, visual tools that let you explore data, design, develop, and optimize your apps

➡️ Redis Query Engine for advanced search
➡️ Significant performance improvements: an 87% reduction in command latency, 2x more ops per second throughput via multithreading, 35% less memory use for replication, 16x more query processing power with horizontal & vertical scaling

There's a ton of new stuff in Redis 8, including:
➡️ The addition of the AGPLv3 open source license
➡️ Redis Stack included – no separate download
➡️ New data structures, including vector sets, JSON, time series, and probabilistic data structures like Bloom and Cuckoo filters

It's a big day for us – Redis 8 is GA today. It's fast, loaded with new features, comes with the tools in Redis Stack included – and it's open source. Read more here: redis.io/blog/redis-8...

Semantic Search with Spring Boot & Redis TL;DR: You’re building a semantic search app using Spring Boot and Redis. Instead of matching exact...

Just dropped a new guide on how to build semantic search using Spring Boot and Redis! Instead of matching exact words, we use semantic search to understand what the user means.

dev.to/raphaeldelio...

cc @redis.io @jeffquesado.ulivre.dev @carlosenog.dev @lobaorn.bsky.social #javabubble

What is semantic search? YouTube video by Redis

Semantic search is searching for meaning instead of exact keyword match. It's possible with Vector Similarity Search, a technique that combines Vector Databases, such as Redis, with Embedding Models, such as Hugging Face Transformers.

Enjoy the video!

www.youtube.com/watch?v=o3XN... cc @redis.io