Redis 8.8 is here, and now available for anyone to download and use for free. It's got a new general-purpose data structure, a window counter rate limiter, streams message NACKing, and more. Here's what's inside: 🗄️ Redis Array. A brand-new general-purpose data structure created by Redis founder Salvatore Sanfilippo. It's an index-addressable collection of string values that's dynamic, sparse-friendly, and compute-aware. Use it for sliding windows, server-side aggregation (SUM/MIN/MAX), and fast access by index. ARRING gives you 2x the throughput of the classic RPUSH+LTRIM ring buffer pattern. 🪟 Window counter rate limiter. The new INCREX command means no more Lua scripting to build one of the most common Redis patterns. 🛑 Streams NACKing. Stream consumers can now explicitly release pending messages with XNACK, so they're immediately available for other consumers instead of sitting idle. #️⃣ Hash subkey notifications. Subscribe to field-level events like expiration and deletion, not just key-level. 📊 Multiple time series aggregators in one command. Compute MIN, MAX, FIRST, and LAST in a single call. Candlestick charts, made simple. ☕ Explicit JSON float storage (BF16/FP16/FP32/FP64) and a new COUNT aggregator for sorted set unions and intersections. Plus throughput improvements across strings, hashes, streams, sorted sets, and more. Read more here: https://lnkd.in/g4Kgvyvj Download Redis 8.8 here: https://lnkd.in/gYcp3gcv
Redis
Software Development
Mountain View, CA 295,602 followers
The world's fastest data platform.
About us
Redis is the world's fastest data platform. We provide cloud and on-prem solutions for caching, vector search, and more that seamlessly fit into any tech stack. With fast setup and fast support, we make it simple for digital customers to build, scale, and deploy the fast apps our world runs on.
- Website
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http://redis.io
External link for Redis
- Industry
- Software Development
- Company size
- 1,001-5,000 employees
- Headquarters
- Mountain View, CA
- Type
- Privately Held
- Founded
- 2011
- Specialties
- In-Memory Database, NoSQL, Redis, Caching, Key Value Store, real-time transaction processing, Real-Time Analytics, Fast Data Ingest, Microservices, Vector Database, Vector Similarity Search, JSON Database, Search Engine, Real-Time Index and Query, Event Streaming, Time-Series Database, DBaaS, Serverless Database, Online Feature Store, and Active-Active Geo-Distribution
Locations
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700 E. El Camino Real
Suite 250
Mountain View, CA 94041, US
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Bridge House, 4 Borough High Street
London, England SE1 9QQ, GB
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94 Yigal Alon St.
Alon 2 Tower, 32nd Floor
Tel Aviv, Tel Aviv 6789140, IL
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316 West 12th Street, Suite 130
Austin, Texas 78701, US
Employees at Redis
Updates
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Agents 🤝 Ham radio 🤝 Guy Royse—it's a match made in AI heaven. Be sure to watch Guy build Earshot, his transcription agent that captures ham radio chatter as it happens, live this Thursday, June 4 at 9am PT as part of Microsoft's VS Code Live. Links below 👇
I built an agentic thing for an upcoming livestream that I'll be on. While building that agentic thing, I learned a lesson. I already knew the lesson. But I didn't know that I knew the lesson. Pretty sure this is called an epiphany. You probably know it too and might benefit from the epiphany. So, I'm gonna share it and then shamelessly plug the livestream. The thing I built is called Earshot. Earshot consists of three agents: two listeners—one on a microphone, one on my ham radio—that transcribe whatever they hear, decide if it needs any correction, and write it down. And a chatbot that reads what they wrote and lets you, well, chat about it. That's the whole app. Here's the cool bit. These three agents never talk to each other. The listeners don't know the chatbot exists. The chatbot doesn't know the listeners exist. They just read and write the same shared memory. Ain't nobody wired to anybody. If that sounds familiar, it should. It's decoupling. The same decoupling we've done with message queues and shared databases and job tables for decades—producers on one side, consumers on the other, some sort of store in the middle. Neither end holding a reference to the other. I reached for it intuitively without realizing I was implementing the same distributed-systems pattern I've used scores of times in the past. And since this isn't really new, the tradeoffs are the same. No delivery guarantees, no ordering. Things happen when they happen if they happen. Decoupling buys you flexibility at the cost of certainty, same as ever. But that flexibility is so, so nice. It lets you scale and it lets you extend. Since nothing is wired together, you can add more listeners and more types of listeners, more chatbots and more types of chatbots. Potentially a lot more if your shared state can scale with them. In Earshot, I used Redis for the shared state, specifically Redis Agent Memory. It was easy to use and gives me scaling, scaling that my toy agents don't really need. But your agents, which do real work for real users, will totally need it. Probably sooner than you think. I built all of this in VS Code and used GitHub Copilot to assist. In particular, I used Redis' set of agent skills so that Copilot would know how, and more importantly when, to use Redis Agent Memory's APIs. I plan to demo it all on VS Code Live this Thursday, June 4 at 9am PT. Join Reynald Adolphe and me as we take a chatbot that starts with no memory and, one capability at a time, learns to listen to what is in earshot. Livestream: https://lnkd.in/exhNbF-q GitHub repo with the demo: https://lnkd.in/eFT2M9Jk Redis Agent Skills: https://lnkd.in/eHARVia8 Redis Agent Memory & Redis Iris: https://lnkd.in/eN-7M3hj https://redis.io/iris/
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Last week, we launched Redis Iris, our real-time context engine for AI apps. AI agents forget everything when a conversation ends. They can't query structured data without custom code. Redis Iris changes that. We’ve put together a tutorial that shows off two of the core pieces of Redis Iris. Redis Context Retriever turns your entity data into auto-generated MCP tools that any agent can discover and call. Redis Agent Memory gives agents persistent session memory and cross-session long-term memory backed by vector search. Learn how to use both as you build a wealth advisor agent built on Redis Iris: https://lnkd.in/gNpJmNKy
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Redis reposted this
🔍 Just attended a brilliant Redis webinar on Semantic Search & Hybrid Search at Scale and it genuinely shifted how I think about information retrieval in AI systems. Huge thanks to Samuel Agbede and Ricardo Ferreira for breaking down complex concepts in such a practical, engineering-focused way. Here's what stood out to me: 🔤 Full-text search is fast and exact but it fails the moment a user phrases their query differently from what's in the database. Great for keyword lookups, not great for meaning. 🧠 Semantic search solves this by encoding meaning into vector embeddings using a bi-encoder. Instead of matching words, it matches intent. This is what powers modern AI search and it's transformative when you need to retrieve contextually relevant results at scale. ⚡ Hybrid search is where it gets really powerful: combining the precision of keyword search with the contextual depth of semantic search. Best of both worlds. 🔄 The bi-encoder + cross-encoder pipeline was an eye-opener. Bi-encoders retrieve candidates fast; cross-encoders re rank them with deeper pairwise comparison for accuracy. This two-stage architecture is how production RAG pipelines stay both fast AND accurate. 🛠️ And Redis VL (Vector Library) ties this all together offering a developer-friendly layer to run vector similarity search, hybrid queries, and filtering at Redis speed. Why this matters for AI engineering: Whether you're building a RAG pipeline, a recommendation engine, or an AI-powered search feature understanding when to use full-text vs semantic vs hybrid search directly impacts your system's quality. Throwing vectors at every problem isn't the answer. The right retrieval strategy depends on your data, latency constraints, and how your users query. If you haven't explored Redis for AI workloads yet, this webinar is a great starting point. #AIEngineering #Redis #VectorSearch #SemanticSearch #RAG #MachineLearning #LLM #NLP
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Redis reposted this
Happy Friday everyone - you know what time it is, end of the month means a just a few minutes to catch up everything happening at Redis. And yes... I know its longer than two minutes, okay. I could've spent six minutes at least because all of the releases that happened this month. You probably saw Redis Iris (which I covered if you haven't heard about it) got announced but there are some serious sleeper updates that flew under the radar... check it out! https://lnkd.in/gJTcBDRE Huge amount of gratitude to the folks helping us get these videos and blogs out: #Redis Avery Peterson Lana Kotova Sylvia Ogweng Fionce Siow Jonathan Salomon Mirko Ortensi Bosmat Tuvel Aharon Blitzer Noam Stern
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Redis reposted this
Happy Friday everyone - you know what time it is, end of the month means a just a few minutes to catch up everything happening at Redis. And yes... I know its longer than two minutes, okay. I could've spent six minutes at least because all of the releases that happened this month. You probably saw Redis Iris (which I covered if you haven't heard about it) got announced but there are some serious sleeper updates that flew under the radar... check it out! https://lnkd.in/gJTcBDRE Huge amount of gratitude to the folks helping us get these videos and blogs out: #Redis Avery Peterson Lana Kotova Sylvia Ogweng Fionce Siow Jonathan Salomon Mirko Ortensi Bosmat Tuvel Aharon Blitzer Noam Stern
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#MicrosoftBuild 2026 is next week. We’ll be there, showing how teams can use Azure Managed Redis to build faster, smarter agents. Here’s where to find us: ▶️ Our lightning talk “Faster and smarter agents with Redis and Foundry” at noon PT on Tuesday, June 2. A focused live session demoing how Azure Managed Redis and Microsoft Foundry help developers build agents with real-time memory, context, vector search, semantic caching, and RAG patterns. ▶️ An on-demand session “Faster AI Responses with Semantic Caching in Azure Managed Redis.” A deeper walkthrough for developers building copilots, autonomous agents, and large-scale LLM chatbots on how to use Azure Managed Redis with Microsoft Foundry for agent memory, real-time context, vector search, and semantic caching. ▶️ Come see us at our booth. Azure Managed Redis will be part of the shared Azure Data / NoSQL booth experience. We’ll have demos across real-time AI applications, vector search, semantic caching, and integrations with Azure AI Foundry and agent frameworks. It’s also a chance to talk directly with product experts about where Azure Managed Redis fits in production architectures. Read Microsoft’s full “know before you go” post here: https://lnkd.in/gJRCZUCZ We'll see you there: https://lnkd.in/eKgQTn4q
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Redis reposted this
Redis shipped Iris last week — a context engine for AI agents that packages memory, caching, and data retrieval as managed infrastructure. That caught my attention, so I spent the weekend building a travel agent on it to see how it holds up in practice. My takeaway: the hard part of production agents usually isn’t the model. It’s the plumbing around it — session memory, long-term memory, current data, retrieval, and avoiding repeated LLM calls for the same work. Iris is essentially a bet that more of this should live in infrastructure, not scattered across app code. I wrote up what worked, what felt promising, and what I had to work around. Not an endorsement — just a hands-on read. https://lnkd.in/gsfebMRA #AIAgents #Redis #LangGraph
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We made the Redpoint InfraRed 100, a list that recognizes the companies that are building the AI infrastructure of today and tomorrow through reliability, scalability, security, and innovation. Context orchestration at scale is the most important problem to be solved in the agentic era. We’ve built Redis to deliver the right context, with the right meaning, fast enough for agents to act on. Proud to be building alongside the other teams and companies who raised the bar. Check out the full report: https://lnkd.in/gVRDNS6Q
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Redis reposted this
Redis has just announced Iris, their architecture for AI agent retrieval that takes an interesting approach to the context layer problem. Rather than pre-compiling answers into static artifacts, Iris focuses on keeping a fast, navigable copy of your operational data that agents can query in real time. In this video, I walk through the Redis Iris stack, what each component does, and how its runtime approach compares to build-time alternatives like Pinecone's recently announced Nexus offering. What's covered: 🔹 Redis' four core requirements for agent retrieval at scale: navigability, speed, freshness, and self-improvement 🔹 Redis Data Integration (RDI) and how change data capture keeps an operational copy of data in sync 🔹 Redis Context Retriever: defining entities, fields, relationships, and tools (find, get, search, filter) exposed via MCP or CLI with row-level access control 🔹 Redis Agent Memory: short-term session memory with custom TTL, plus long-term memory for preferences, learned patterns, and promoted session data 🔹 LangCache for semantic response caching, including similarity thresholds, search strategies, and the risks of stale or out-of-context cache hits 🔹 Redis Search across vector, structured, and unstructured data in a single index 🔹 Redis Flex, SSD-based storage tier for more cost-effective scaling beyond pure in-memory 🔹 A direct comparison of Redis Iris (runtime, fresh-on-demand) vs Pinecone Nexus (build-time, pre-compiled knowledge artifacts) and when each architecture fits best. Full video in the comments.
