# Amazon OpenSearch Service AWS managed service for deploying, operating, and scaling OpenSearch clusters for search and analytics workloads. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack DevFeed: [Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack]() Original publisher: [Read original article]() Author: Jonas Kim Published: 2026-09-14T16:55:58Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [Graphs](), [Amazon Bedrock](), [Amazon OpenSearch Service](), [Amazon Web Services]() Tags: [amazon-bedrock](), [amazon-opensearch-service](), [aws](), [knowledge-graph](), [open-source](), [rag](), [retrieval](), [search](), [vector-search]() ### AI overview This article presents a unified knowledge-graph RAG stack that brings Microsoft GraphRAG and HKUDS LightRAG together on Amazon Bedrock, Amazon Neptune, and Amazon OpenSearch Service. The shared stack supports common ingestion, indexing, caching, and multilingual handling while allowing the retrieval methodology to be selected per query. ### Source excerpt Picture a compliance analyst staring at a few thousand contracts, amendments, and internal memos, trying to answer one question that sounds straightforward: "Which of our obligations are exposed if this one milestone slips?" The answer isn't written in any single document. It's stitched across three -- a master agreement that ties a payment to a [...] ## A visual guide to troubleshooting search performance using Query Insights dashboards DevFeed: [A visual guide to troubleshooting search performance using Query Insights dashboards]() Original publisher: [Read original article]() Author: Chenyang Ji Published: 2026-09-04T20:11:52Z Content type: tutorial Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [dashboards](), [Monitoring](), [Latency](), [cpu]() Tags: [blog](), [configuration](), [cpu](), [dashboards](), [distribution](), [how-to](), [index](), [latency](), [memory](), [monitoring](), [navigation](), [opensearch](), [performance](), [real-time](), [technical](), [troubleshooting](), [view](), [visualization]() ### AI overview A visual guide to using OpenSearch Query Insights dashboards to investigate slow searches and unexpected resource usage. It covers the Live Queries and Top N Queries views, interactive visualizations, configuration, and metrics such as latency, CPU, and memory. ### Source excerpt OpenSearch Query Insights dashboards provide interactive visualizations for monitoring live queries, analyzing top N query performance, and viewing individual query details. This post explores each visualization and shows how to use visualizations to troubleshoot search performance issues. The post A visual guide to troubleshooting search performance using Query Insights dashboards appeared first on OpenSearch. ## Online index migration and shard scaling in OpenSearch with the AOSC plugin DevFeed: [Online index migration and shard scaling in OpenSearch with the AOSC plugin]() Original publisher: [Read original article]() Author: Arpit Singla Published: 2026-08-18T21:56:28Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [migration](), [Open Source](), [Routing (disambiguation)]() Tags: [blog](), [mapping](), [migration](), [open-source](), [opensearch](), [pipeline](), [plugin](), [production](), [reconciliation](), [routing](), [schema](), [technical](), [workflow]() ### AI overview This article introduces Automatic Online Schema Change (AOSC), an open-source OpenSearch plugin for migrating live indexes to pre-created targets with different mappings, settings, shard counts, or document shapes. It backfills existing documents, replays operations made during migration, and switches an alias after a short write block, while documenting its scaling behavior and limitations. ### Source excerpt Learn how the open-source AOSC plugin migrates live OpenSearch indexes--changing mappings, settings, or shard counts--without losing writes or requiring downtime. The post Online index migration and shard scaling in OpenSearch with the AOSC plugin appeared first on OpenSearch. ## AWS Weekly Roundup: EC2 application status checks, IAM role manager, OpenAI Daybreak on Bedrock, and more (August 17, 2026) DevFeed: [AWS Weekly Roundup: EC2 application status checks, IAM role manager, OpenAI Daybreak on Bedrock, and more (August 17, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-17T16:02:36Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Web Services](), [Amazon EC2](), [AWS IAM](), [Amazon OpenSearch Service](), [Open Source](), [Amazon Bedrock](), [Linux](), [OpenAI](), [MCP]() Tags: [amazon-bedrock](), [amazon-ec2](), [amazon-elasticache](), [amazon-opensearch-service](), [amazon-sagemaker-jumpstart](), [aws](), [aws-certificate-manager](), [aws-client-vpn](), [aws-identity-and-access-management-iam](), [bedrock](), [iam](), [linux](), [mcp](), [news](), [open-source](), [openai](), [oracle-database-aws](), [week-in-review]() ### AI overview AWS Weekly Roundup highlights EC2 application status checks, automatic IAM role setup, OpenAI Daybreak on Amazon Bedrock, and updates involving OpenSearch, Valkey, and open source community events. ### Source excerpt Last week, AWS contributors joined the OpenSearch and Valkey communities at Open Source Summit Korea 2026 and MCP DevSummit Seoul 2026 to meet open source developers and contributors. At the four-day event, community leaders and users of these Linux Foundation open source projects gathered to share knowledge, collaborate on solutions, and push the projects forward. [...] ## Introducing memory retention for agentic memory in OpenSearch DevFeed: [Introducing memory retention for agentic memory in OpenSearch]() Original publisher: [Read original article]() Author: Erfan Ballew Published: 2026-08-13T22:29:25Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [AI Agent](), [audit trail](), [model-deployment]() Tags: [agentic](), [audit-trail](), [blog](), [compliance](), [cost](), [experimental](), [lifecycle](), [memory](), [opensearch](), [precision](), [retention](), [storage](), [technical]() ### AI overview This article explains OpenSearch 3.8's experimental memory retention feature for agentic memory. It describes age-based and count-based limits for different memory types, how policies prevent stale context and uncontrolled storage growth, and how to enable retention on an existing cluster. ### Source excerpt Learn how the memory retention policy in OpenSearch automatically manages the lifecycle of agentic memory, controlling storage growth while preserving specific memories. The post Introducing memory retention for agentic memory in OpenSearch appeared first on OpenSearch. ## Agentic relevance tuning: Letting LLM agents do the search engineering work DevFeed: [Agentic relevance tuning: Letting LLM agents do the search engineering work]() Original publisher: [Read original article]() Author: Kylie Wagar-Dirks Published: 2026-07-31T15:00:47Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [Large Language Model](), [Framework](), [data](), [Deployment]() Tags: [2026](), [agents](), [aws](), [blog](), [data](), [deployment](), [evaluation](), [framework](), [llm-agents](), [opensearch](), [ranking](), [search]() ### AI overview The article introduces Agentic Relevance Tuning (ART), an end-to-end framework that uses specialized LLM-powered agents to automate search relevance improvement in OpenSearch. ART monitors user behavior, proposes ranking changes, runs offline evaluations, and coordinates deployment when improvements are validated. ### Source excerpt At OpenSearchCon Europe 2026, Bobby Mohammed (AWS) and Daniel Wrigley (OpenSource Connections) introduced Agentic Relevance Tuning (ART), a framework that uses specialized LLM agents to fully automate the search relevance lifecycle in OpenSearch. The post Agentic relevance tuning: Letting LLM agents do the search engineering work appeared first on OpenSearch. ## Connect OpenSearch to private ML endpoints DevFeed: [Connect OpenSearch to private ML endpoints]() Original publisher: [Read original article]() Author: Nathalie Jonathan Published: 2026-07-28T15:00:41Z Content type: tutorial Language: en Sources: [OpenSearch]() Topics: [Amazon OpenSearch Service](), [network security](), [VPC](), [inference-endpoints](), [Security](), [AWS IAM](), [Latency](), [Self-hosted](), [Amazon Web Services]() Tags: [aws](), [aws-identity-and-access-management-iam](), [blog](), [firewalls](), [inference](), [infrastructure](), [opensearch](), [production](), [self-hosted](), [vpc]() ### AI overview This guide explains how to connect OpenSearch and Amazon OpenSearch Service to machine learning models hosted on private infrastructure. It covers VPC-hosted endpoints, private SageMaker endpoints, internal API gateways, and self-hosted inference servers, with configuration steps for connectors, model registration, deployment, testing, and VPC egress. ### Source excerpt Connect OpenSearch to ML models hosted on private infrastructure. Configure ML Commons connectors for VPC-hosted endpoints, private SageMaker models, and internal inference servers without exposing services to the public internet The post Connect OpenSearch to private ML endpoints appeared first on OpenSearch. ## Data Engineering Weekly #276 DevFeed: [Data Engineering Weekly #276]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-06-29T03:52:17Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [schema-evolution](), [data-architecture](), [Amazon OpenSearch Service](), [Resilience](), [data observability]() Tags: [architecture](), [compatibility](), [data-engineering](), [engineering](), [observability](), [opensearch](), [resilience](), [schema-evolution]() ### AI overview Data Engineering Weekly #276 is a newsletter roundup covering data platform fundamentals, storage and workload architecture, schema evolution in Pinterest's ingestion framework, zone-failure-resilient OpenSearch at Uber, AI modernization, and stateful reasoning systems for notebooks. ### Source excerpt The Weekly Data Engineering Newsletter ## Amazon OpenSearch Serverless is now available in the Vercel Marketplace DevFeed: [Amazon OpenSearch Serverless is now available in the Vercel Marketplace]() Original publisher: [Read original article]() Author: Hedi Zandi Published: 2026-05-28T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Amazon OpenSearch Service](), [AI search](), [Retrieval Augmented Generation (RAG)]() Tags: [agentic](), [amazon](), [apis](), [aws](), [cost](), [infrastructure](), [integration](), [rag](), [search](), [serverless](), [vector](), [vercel]() ### AI overview Amazon OpenSearch Serverless is available through the Vercel Marketplace with dashboard provisioning, automatic project configuration, and integrated resource management. The integration supports vector, lexical, hybrid, and agentic search, including a RAG route example. ### Source excerpt Amazon OpenSearch Serverless is now available in the Vercel Marketplace, with guided setup and automatic project configuration built into the Vercel dashboard. Built for agentic infrastructure By bringing Amazon OpenSearch Serverless into the Vercel Marketplace, teams benefit from a unified workflow: In-dashboard provisioning: Spin up OpenSearch collections directly from the Vercel dashboard Automatic configuration: Environment variables are automatically injected into your projects on provisioning Unified management: View and manage search resources alongside the rest of your application infrastructure in the Marketplace resource view, with quick links to AWS for deeper configuration This integration is powered by the next generation of Amazon OpenSearch Serverless APIs, with Vercel participating as a key design partner alongside AWS during development. Unified support for vector, lexical, hybrid, and agentic search in a single collection Autoscales up to 20x faster, built for the bursty, unpredictable load patterns of agentic workloads Scale to zero with no idle costs Up to 60% cost savings by paying for actual usage instead of peak capacity Get started To help you get started, you can create a new AWS Account directly through Vercel and receive $100 USD in credits to try Amazon OpenSearch Serverless. If you already have an active AWS account, you can link it instantly, manage your plan, and view usage details right from your Vercel dashboard. Ready to build? Install Amazon OpenSearch from the Vercel Marketplace or deploy a starter template to see the integration in action. Read more ## The search for speed in Figma DevFeed: [The search for speed in Figma]() Original publisher: [Read original article]() Author: Scott Laird Published: 2024-10-10T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Amazon OpenSearch Service](), [Latency](), [Instrumentation](), [elasticsearch](), [Amazon Web Services](), [Traces](), [telemetry](), [Web]() Tags: [aws](), [elasticsearch](), [figma](), [infrastructure](), [instrumentation](), [latency](), [metrics](), [opensearch](), [performance](), [search](), [traces]() ### AI overview Figma describes an investigation into slow search performance as its search infrastructure grew. The team migrated from an older version of ElasticSearch to OpenSearch through AWS's managed OpenSearch service, then used instrumentation, metrics, and traces to reconcile major differences between reported search latency and application-level latency. The investigation found that API-library calls were substantially slower than OpenSearch's reported averages and helped establish a foundation for future scale. ### Source excerpt Our months-long investigation into slow search speeds led to a solution that not only improved performance but also set the foundation for future scale. ## Chainguard announces a Wolfi-based OpenSearch image with SBOMs and fewer CVEs DevFeed: [Chainguard announces a Wolfi-based OpenSearch image with SBOMs and fewer CVEs]() Original publisher: [Read original article]() Published: 2023-04-07T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [Amazon OpenSearch Service](), [chainguard images](), [Containers](), [Security]() Tags: [chainguard-images](), [containers](), [opensearch](), [provenance](), [sboms](), [security](), [slsa]() ### AI overview Chainguard announces a new OpenSearch container image built on Wolfi with a hardened Java toolchain and runtime. The article says the image is less than 25% the size of an alternative image and has significantly fewer CVEs, and that it includes SBOMs and provenance information. ### Source excerpt Chainguard's new OpenSearch image is smaller, more secure, and perfect for container deployments.