# Redis Blog Latest blog posts from Redis This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more DevFeed: [Announcing Redis 8.10: Compact Hash, JSONPath extensions, performance improvements, & more]() Original publisher: [Read original article]() Author: Bosmat Tuvel Published: 2026-09-14T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Redis](), [Data structures](), [Data Management](), [Open Source]() Tags: [data-management](), [improvements](), [memory](), [new-features](), [operations](), [performance](), [redis](), [streams](), [tech](), [time-series]() ### AI overview Redis 8.10 in Redis Open Source introduces compact hashes, incremental backup and restore, JSONPath syntax extensions, more flexible Stream consumption, new Set cardinality operations, atomic movement of multiple List elements, and enhanced Time Series capabilities. The release also improves memory efficiency, throughput, and operational reliability at scale. ### Source excerpt Redis 8.10 in Redis Open Source is now available, delivering improvements that make Redis more memory efficient, expressive, and easier to operate at scale. Highlights include compact hashes with up to 50% lower memory usage and 2x higher hash loadin... ## Put Redis data and engineering guidance to work in ChatGPT Work DevFeed: [Put Redis data and engineering guidance to work in ChatGPT Work]() Original publisher: [Read original article]() Author: Olga Lopaci Published: 2026-09-11T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Developer Tools](), [AI Chat](), [AI Bots]() Tags: [agent-skills](), [chatgpt](), [codex](), [engineering](), [observability](), [rag](), [redis](), [review](), [security](), [skills](), [tech](), [work]() ### AI overview Redis launched a development plugin for ChatGPT Work and Codex that supplies current Redis guidance for writing, reviewing, and troubleshooting code. It also describes connecting Redis data to ChatGPT Work's Data agent for plain-language exploration and investigation. ### Source excerpt Redis has launched a development plugin that brings current Redis engineering guidance into ChatGPT Work and Codex. It helps teams write, review, and troubleshoot Redis code without switching between documentation and development tools. Alongside Ope... ## Delivering Real-Time Personalization with Databricks and Redis DevFeed: [Delivering Real-Time Personalization with Databricks and Redis]() Original publisher: [Read original article]() Author: Philip Laussermair, Anant Pingle Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [data]() Tags: [analytics](), [apache-flink](), [batch](), [data](), [databricks](), [e-commerce](), [performance](), [personalization](), [real-time](), [redis](), [spark](), [streaming](), [tech]() ### AI overview The article explains how Databricks Real-Time Mode and Redis support low-latency personalization by continuously processing event streams and serving fresh results quickly. ### Source excerpt Why real-time matters A customer is browsing an e-commerce site. They search for running shoes, open a product, read reviews, and add an item to the cart. Every one of those actions is a signal about what they want right now. If the homepage they lan... ## Security Advisory: CVE-2026-81934 DevFeed: [Security Advisory: CVE-2026-81934]() Original publisher: [Read original article]() Author: Riaz Lakhani Published: 2026-08-28T00:00:00Z Content type: news Language: en Sources: [Redis Blog]() Topics: [vulnerability](), [releases]() Tags: [authentication](), [cloud](), [redis](), [releases](), [security](), [tech](), [tls](), [vulnerability]() ### AI overview Redis remediated CVE-2026-81934, a high-severity use-after-free flaw in TLS pending-data processing that could allow authenticated attackers to execute remote code under specific conditions. The advisory lists fixed Redis releases and recommends upgrading, restricting network access, and enforcing least-privilege authentication controls. ### Source excerpt Update 9/1/2026: Following further review, the public CVE record for CVE-2026-81934 was updated to reflect a revised CVSS score of 7.5 (High). What happened? Redis identified and remediated a use-after-free vulnerability in TLS pending-data processi... ## Efficient Bulk Hash Insertion with Redis 8.10's HIMPORT DevFeed: [Efficient Bulk Hash Insertion with Redis 8.10's HIMPORT]() Original publisher: [Read original article]() Author: David Maier Published: 2026-08-24T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [Redis](), [Ruby](), [Code](), [Command-line interface]() Tags: [article](), [cli](), [code](), [make](), [redis](), [ruby](), [tech]() ### AI overview This article explains how to use Redis 8.10's HIMPORT command with redis-rb to perform more efficient bulk hash insertion in Ruby. It covers connection setup, prepared field sets, automatic preparation, reconnection behavior, the demo CLI, and pipelined imports. ### Source excerpt I'm not a Ruby developer, and my code samples might make that clear. However, there are two reasons I'm using Ruby for the examples in this article: A new release of redis-rb. Its support for a new Redis command: HIMPORT. Getting started Establish... ## ReAct agents explained: concepts & practical uses DevFeed: [ReAct agents explained: concepts & practical uses]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [Artificial Intelligence](), [ai-coding](), [LangChain](), [Iris](), [Chain-of-thought](), [coding](), [Error Propagation]() Tags: [agent](), [agents](), [ai](), [ai-coding](), [coding](), [langchain](), [reasoning](), [tech-de](), [tool]() ### AI overview A practical guide to ReAct agents: AI systems that alternate between reasoning, tool use, and feedback in a loop. It explains the pattern, compares it with chain-of-thought approaches, and discusses production concerns such as latency, cost, hallucination, and error propagation, with examples involving LangChain, LangGraph, and Redis Iris. ### Source excerpt If you've watched an AI coding assistant hunt down a bug, run a test, read the failure, and adapt its next fix, you've watched Reasoning and Acting (ReAct)-like behavior at work. ReAct is a common pattern in production agent systems today. It's simple... ## Metadata filtering: boost search precision as data grows DevFeed: [Metadata filtering: boost search precision as data grows]() Original publisher: [Read original article]() Author: Simran Regmi Published: 2026-08-17T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI search](), [data](), [datasets](), [Retrieval Augmented Generation (RAG)](), [Software as a service](), [Authorization](), [Benchmark]() Tags: [benchmark](), [data](), [datasets](), [rag](), [retrieval](), [saas](), [search](), [tech-de](), [vector]() ### AI overview Metadata filtering improves vector search precision by applying structured constraints such as price, status, tenant, and date to semantically similar results. The article explains pre-filtering and post-filtering, filtering at scale, hybrid queries, and the role of unified engines in production search, RAG, e-commerce, academic search, and multi-tenant SaaS. It cites a benchmark where filtering increased accuracy from 0.12 to 0.61. ### Source excerpt Vector search is great at finding things that are semantically similar, but similarity isn't the same as correctness. A pure vector query doesn't know that a product is out of stock, that a document belongs to a different customer, or that a policy wa... ## Choosing & integrating LLM APIs: a practical guide DevFeed: [Choosing & integrating LLM APIs: a practical guide]() Original publisher: [Read original article]() Author: Cedric Turner Published: 2026-08-17T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Large Language Model](), [API](), [SDKs](), [Latency](), [Caching](), [Inference](), [Streaming](), [Embeddings](), [multimodal]() Tags: [apis](), [caching](), [code](), [engineering](), [inference](), [latency](), [llm](), [multimodal](), [sdks](), [streaming](), [tech-de]() ### AI overview A practical guide to choosing and integrating LLM APIs, covering provider capabilities, pricing, latency, quality evaluation, and the caching, retry, and memory layers needed for reliable production applications. ### Source excerpt Making your first LLM API call is easy: with most provider SDKs, it's about five lines of code. Keeping that call fast, affordable, and reliable once real users show up is where the actual engineering happens: costs can compound as conversations grow,... ## Vector search in production: index trade-offs, failure modes & what to watch DevFeed: [Vector search in production: index trade-offs, failure modes & what to watch]() Original publisher: [Read original article]() Author: Cedric Turner Published: 2026-08-14T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI search]() Tags: [algorithms](), [data](), [database](), [embeddings](), [production](), [search](), [tech-de](), [vector]() ### AI overview A practical guide to vector search in production, explaining embeddings, nearest-neighbor retrieval, index accuracy-speed trade-offs, and when keyword search is preferable. ### Source excerpt Vector search runs on a simple idea: turn data into coordinates, and treat similarity as distance. An embedding model maps each sentence, image, or document to a point in a few hundred dimensions of space, where items with related meaning land near ea... ## Vector search database: news & 2026 guide DevFeed: [Vector search database: news & 2026 guide]() Original publisher: [Read original article]() Author: Simran Regmi Published: 2026-08-13T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Database](), [AI search](), [Embeddings](), [ann](), [Large Language Model](), [Algorithms]() Tags: [ann](), [database](), [embeddings](), [guide](), [latency](), [llm](), [search](), [tech-de](), [vector]() ### AI overview This guide explains how vector search databases store embeddings and retrieve semantically similar items for LLM-powered applications. It covers vector spaces, dimensionality, similarity measures, traditional indexes, the curse of dimensionality, and approximate nearest neighbor search, including HNSW. ### Source excerpt If you've built anything on top of an LLM in the past couple of years, you may have hit the wall many builders hit: the model writes fluently but has no view into your data. A vector search database helps close that gap. It stores vector embeddings an... ## Fresh context: change data capture, not batch ETL DevFeed: [Fresh context: change data capture, not batch ETL]() Original publisher: [Read original article]() Author: Simran Regmi Published: 2026-08-12T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [event driven](), [data](), [Database](), [PostgreSQL]() Tags: [agent](), [applications](), [batch](), [canada](), [customer](), [data](), [database](), [event](), [event-driven](), [events](), [guide](), [postgresql](), [systems](), [tech-de]() ### AI overview This guide explains how change data capture (CDC) keeps agent context current by forwarding database inserts, updates, and deletes as events. It contrasts CDC with nightly batch ETL, which can leave agents using stale policies or pricing, and describes log-based, trigger-based, and polling approaches. ### Source excerpt In many systems, the reason an agent quotes yesterday's data isn't the model. It's the pipeline behind it: a nightly ETL job that refreshed the agent's context hours ago. Change data capture (CDC) can shrink that staleness window from hours to seconds... ## Agent memory as a moat: how context compounds DevFeed: [Agent memory as a moat: how context compounds]() Original publisher: [Read original article]() Author: Cedric Turner Published: 2026-08-12T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [AI Bots](), [AI Chat]() Tags: [agent](), [inference](), [llm](), [memory](), [procedural](), [rag](), [retrieval](), [tech-de](), [workflow]() ### AI overview The article explains how persistent memory lets otherwise stateless LLM-based agents retain and reuse context across interactions. It distinguishes short- and long-term memory, outlines semantic, episodic, and procedural memory, and positions RAG as a starting point for building learning systems. ### Source excerpt Base LLM inference is stateless. The model doesn't remember your last conversation, your users' preferences, or the mistake your agent made ten minutes ago. Unless the app supplies persisted context, everything gets discarded after each request. That ... ## Vector embeddings & language: how models turn words into geometry DevFeed: [Vector embeddings & language: how models turn words into geometry]() Original publisher: [Read original article]() Author: Simran Regmi Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [Embeddings](), [Artificial Intelligence](), [Redis]() Tags: [ai](), [embeddings](), [models](), [retrieval](), [search](), [tech-de](), [vector]() ### AI overview A guide to vector embeddings and how language models represent text as points in high-dimensional space. It explains semantic similarity, distributional context, ambiguity, and why language is harder to encode than pixels. ### Source excerpt A user types "refund policy" into your search box, but the doc they need is titled "returns and reimbursements." Keyword matching scores it near zero even though it's exactly what the user asked for. Vector embeddings help address this mismatch by rep... ## Reciprocal rank fusion: why combining search results is harder than it looks DevFeed: [Reciprocal rank fusion: why combining search results is harder than it looks]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-09T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI search](), [Redis](), [real-time]() Tags: [guide](), [redis](), [retrieval](), [search](), [tech-de](), [vector]() ### AI overview A guide to reciprocal rank fusion (RRF), a method for combining keyword and vector search rankings without directly adding their raw scores. It explains the formula, the role of the constant k, why agreement between retrievers matters, and RRF's unsupervised nature. ### Source excerpt You run a keyword search and get back a ranked list with Best Matching 25 (BM25) scores. You run a vector search over the same documents and get a second list with cosine similarities. You want to merge them into a single ranking that surfaces the mos... ## Inference latency: what it measures & why it varies DevFeed: [Inference latency: what it measures & why it varies]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-06T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Inference Performance](), [Low-Latency Inference](), [Language models]() Tags: [agent](), [benchmarks](), [gpu](), [inference](), [latency](), [llm](), [memory](), [metrics](), [model](), [production](), [redis](), [retrieval](), [tech-de]() ### AI overview A guide to LLM inference latency, distinguishing first-token time, per-token time, total response time, and multi-step agent run time. It contrasts latency with throughput and explains how prompt prefill, token decoding, queueing, and tail latency affect user experience. ### Source excerpt Ask an engineer what their LLM app's inference latency is, and the honest answer is "which one?" The time to the first visible token, the time to the finished response, and the time an agent spends across a chain of calls are three different numbers. ... ## How Redis brings persistent memory to Snowflake Cortex Agents DevFeed: [How Redis brings persistent memory to Snowflake Cortex Agents]() Original publisher: [Read original article]() Author: Mike Moss Published: 2026-08-06T00:00:00Z Content type: release Language: en Sources: [Redis Blog]() Topics: [Redis](), [Artificial Intelligence](), [AI Development]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [applications](), [memory](), [redis](), [tech]() ### AI overview Redis Agent Memory is now available on the Snowflake Marketplace for Snowflake Cortex AI agents. It provides persistent long-term memory across conversations, allowing agents to retain business context, preferences, events, and reusable knowledge instead of losing relevant context when a session ends. ### Source excerpt AI agents can reason and act, but without memory, every interaction starts from zero. Intelligent short-term memory and persistent context across conversations are what turns a capable model into a truly useful agent. It should remember the useful det... ## Top vector database alternatives for RAG pipelines DevFeed: [Top vector database alternatives for RAG pipelines]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-05T00:00:00Z Content type: comparison Language: en Sources: [Redis Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [AI search]() Tags: [cache](), [database](), [postgresql](), [rag](), [redis](), [tech-de](), [vector]() ### AI overview A comparison of vector-search architectures for RAG pipelines, contrasting Redis as a unified real-time platform with dedicated vector databases and PostgreSQL with pgvector. It focuses on infrastructure overhead, coordination, scaling costs, caching, session management, and operational tradeoffs. ### Source excerpt You're building an AI app: maybe a RAG system, an agent with memory, or a chatbot with semantic caching. You need vector search, and you're weighing your options. One is a unified real-time platform like Redis, which runs vector search alongside cachi... ## When does the A2A protocol actually matter? DevFeed: [When does the A2A protocol actually matter?]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [Protocol (disambiguation)](), [Model Context Protocol](), [Remote Procedure Call (RPC)](), [Security](), [JSON](), [Authentication](), [Web]() Tags: [authentication](), [http](), [json](), [mcp](), [model-context-protocol](), [rpc](), [security](), [server](), [tech-de]() ### AI overview A2A is an open standard for communication between independently deployed agents, particularly when they are owned by different teams or vendors. The article presents an ownership test for deciding whether A2A is needed, explains its security model and core primitives, and contrasts it with MCP. ### Source excerpt If you're building multi-agent systems, someone has probably asked whether you're "doing A2A yet," with the implication that you should be. When teams actually reach for it, most can't say why they need A2A over MCP. A more useful question: do your ag... ## Semantic memory search for AI agents DevFeed: [Semantic memory search for AI agents]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [AI Agent](), [Redis](), [Embeddings](), [Language models]() Tags: [ai](), [ai-agents](), [data](), [embeddings](), [redis](), [tech-de]() ### AI overview Semantic memory search gives AI agents durable recall by storing facts outside the language model and retrieving them by meaning. The article explains how vector embeddings and similarity search supply relevant context, with Redis Iris combining memory, live data, and retrieval for fast agent context. ### Source excerpt Your AI agent handles a long onboarding conversation. The next day, it asks the same user for their name. That's not a bug. A language model keeps no memory of earlier calls, so without an external memory layer, each request starts fresh and the agent... ## Multi-agent observability: why one trace isn't enough DevFeed: [Multi-agent observability: why one trace isn't enough]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-03T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [ai observability](), [telemetry]() Tags: [agent-observability](), [ai-agent](), [llm](), [memory](), [observability](), [tech-de](), [tools](), [tracing]() ### AI overview The article explains why observing multi-agent AI systems requires correlating delegation, tool calls, shared memory, and inter-agent messages into a causal account. It contrasts this with single-agent tracing and highlights runtime decisions and fragmented work as key challenges. ### Source excerpt A single AI agent is usually easy to trace. One loop, one context window, one trace--you can read it top to bottom, spot the bad prompt or the failed tool call, and fix it. Multi-agent systems are different. Agents, shared memory, and external tools sp... ## Connect AI agents to data sources with Redis DevFeed: [Connect AI agents to data sources with Redis]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-08-03T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI Agent](), [Redis](), [data](), [Model Context Protocol](), [Retrieval Augmented Generation (RAG)](), [API](), [Embeddings](), [JSON Schema](), [Large Language Model]() Tags: [agents](), [ai](), [ai-agents](), [api](), [data](), [embeddings](), [integration](), [json](), [llm](), [model-context-protocol](), [rag](), [redis](), [retrieval](), [tech-de]() ### AI overview This guide explains how to connect AI agents to production data sources and provide runtime context. It covers RAG, tool and function calling, Model Context Protocol, and custom API connectors, with Redis Iris presented as a real-time context engine for production workloads. ### Source excerpt An AI agent that can't reach your data is just a chatbot with opinions. Without runtime context, a model only knows its training data and whatever sits in the current prompt. So your app has to feed it production-specific facts at runtime: your produc... ## Context engineering for AI: what it is & how to build it DevFeed: [Context engineering for AI: what it is & how to build it]() Original publisher: [Read original article]() Author: Simba Khadder Published: 2026-07-29T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI Chat](), [AI Bots]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [apps](), [crm](), [engineering](), [history](), [how-to](), [inference](), [infrastructure](), [latency](), [llm](), [model](), [policy](), [prompt-engineering](), [reasoning](), [support](), [systems](), [tech-de](), [tool]() ### AI overview A guide to context engineering for AI agents: selecting and managing the instructions, history, retrieved documents, tool outputs, and state sent to an LLM during inference. It explains how deliberate context management helps avoid hallucinations, excessive context, latency, and degraded reasoning in multi-step agent workflows. ### Source excerpt Your support agent confidently tells a customer they qualify for a refund under a 60-day return policy. Your actual policy is 30 days. The agent hallucinated the longer window, and the easy reaction is to blame the model. But the model never saw your ... ## Token-budget-aware LLM reasoning: cut costs in 2026 DevFeed: [Token-budget-aware LLM reasoning: cut costs in 2026]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-07-28T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Large Language Model](), [LLMs](), [Chain-of-thought](), [Caching](), [Routing (disambiguation)](), [Benchmark](), [Claude](), [OpenAI]() Tags: [caching](), [chain-of-thought](), [claude](), [cost](), [evaluation](), [llm](), [models](), [openai](), [reasoning](), [routing](), [tech-de](), [techniques](), [tokens]() ### AI overview This guide explains token-budget-aware LLM reasoning, a technique for matching a model's reasoning-token budget to problem complexity. It covers the cost of reasoning and output tokens, prompt-level methods such as chain-of-thought and Chain of Draft, and architectural approaches including caching, routing, and memory. ### Source excerpt Reasoning models think before they answer, and those reasoning tokens are usually part of what you pay for. They're billed as output tokens, the expensive kind, and a single request can generate a few hundred of them depending on the problem. If your ... ## The 4 Failure Modes of Agent Context in Production DevFeed: [The 4 Failure Modes of Agent Context in Production]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [data](), [data-architecture](), [Orchestration](), [real-time](), [systems]() Tags: [agent](), [agentic](), [agentic-ai](), [ai](), [ai-agent](), [amazon-simple-storage-service-s3](), [data-management](), [gartner](), [orchestration](), [real-time](), [tech-de]() ### AI overview The article examines four infrastructure failure modes that can undermine production AI agents: fragmented context, opacity, speed degradation, and non-accumulation. It explains how incomplete or stale information drawn from enterprise systems can produce confident but incorrect answers, and presents a real-time context layer as part of the solution. ### Source excerpt A production AI agent depends heavily on the context layer that tells it what to know at the moment it acts. It can pass every staging test, answer questions, call the right tools, and demo beautifully, then hit production and confidently offer a re... [Next page]()