# OpenSearch OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## FIPS 140-3 support in OpenSearch DevFeed: [FIPS 140-3 support in OpenSearch]() Original publisher: [Read original article]() Author: Karsten Schnitter Published: 2026-09-16T19:12:14Z Content type: article Language: en Sources: [OpenSearch]() Topics: [fips 140-3](), [opensearch](), [Cryptography](), [Encryption](), [Security]() Tags: [aws](), [blog](), [compliance](), [cryptography](), [encryption](), [fips](), [fips-140-3](), [opensearch](), [security](), [technical]() ### AI overview This post explains OpenSearch support for a FIPS 140-3-compliant mode starting with version 3.6. It describes the validated cryptographic modules used for security-relevant operations, the collaboration involving SAP, SAS, and AWS, and how native FIPS mode differs from using a FIPS-validated TLS-terminating proxy. ### Source excerpt OpenSearch now supports running in a mode compliant with FIPS 140-3, contributed through a multi-year collaboration between SAP, SAS, and AWS. The post FIPS 140-3 support in OpenSearch appeared first on OpenSearch. ## And the winners are: Announcing the results of the OpenSearch Agent Skills Hackathon DevFeed: [And the winners are: Announcing the results of the OpenSearch Agent Skills Hackathon]() Original publisher: [Read original article]() Author: James McIntyre Published: 2026-09-14T23:00:09Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Agent Skills](), [Hackathon](), [Code]() Tags: [agent-skills](), [audit](), [blog](), [github](), [hackathon](), [latency](), [opensearch](), [root-cause-analysis](), [technical]() ### AI overview This OpenSearch article announces the winners of the OpenSearch Agent Skills Hackathon. It describes the competition requirements and judging criteria, then highlights the first-place Unclosed skill, which performs auditable log root-cause analysis through premise audits, hypothesis trees, and closure checks. ### Source excerpt Meet the three winners of the OpenSearch Agent Skills Hackathon. Their skills tackle log root-cause analysis, GDPR compliance, and slow-query diagnostics, all built read-only, fully auditable, and shipped with real evaluation. The post And the winners are: Announcing the results of the OpenSearch Agent Skills Hackathon appeared first on OpenSearch. ## OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards DevFeed: [OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards]() Original publisher: [Read original article]() Author: Kristi Piechnik Published: 2026-09-14T12:00:14Z Content type: news Language: en Sources: [OpenSearch]() Topics: [Open Source](), [AI Infrastructure](), [observability](), [AI search](), [Security](), [Retrieval Augmented Generation (RAG)](), [Databases](), [real-time]() Tags: [2026](), [agentic-ai](), [ai-agents](), [ai-infrastructure](), [announcements](), [awards](), [data](), [enterprise](), [observability](), [open-source](), [opensearch](), [real-time](), [recognition](), [retrieval](), [retrieval-augmented-generation-rag](), [search](), [security]() ### AI overview OpenSearch won the Analytics & Data Intelligence Solutions category in SiliconANGLE Media's 2026 TechForward Awards. The recognition highlights its open source, vendor-neutral platform for enterprise search, observability, security analytics, vector databases, and agentic AI workloads. ### Source excerpt Recognition validates open source momentum, architectural consolidation, and enterprise scale as the project marks five years of community growth The post OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards appeared first on OpenSearch. ## 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. ## From Search to Search & Apache Lucene: Growing the heart of OpenSearchCon DevFeed: [From Search to Search & Apache Lucene: Growing the heart of OpenSearchCon]() Original publisher: [Read original article]() Author: Kris Freedain Published: 2026-08-14T21:57:12Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Library](), [Maintainers]() Tags: [2026](), [apache](), [blog](), [community](), [event](), [opensearch](), [search]() ### AI overview Community feedback led OpenSearchCon to expand its Search track into Search & Apache Lucene, creating a dedicated forum for Lucene practitioners, maintainers, and search relevance experts. The article describes the revised track and the strong response to its call for presentations for OpenSearchCon North America 2026. ### Source excerpt Learn how community feedback transformed OpenSearchCon's Search track into a dedicated home for Apache Lucene practitioners. Discover the new Search & Apache Lucene track at OpenSearchCon North America 2026, September 22-24 in San Jose. The post From Search to Search & Apache Lucene: Growing the heart of OpenSearchCon appeared first on OpenSearch. ## 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. ## Building open-source observability together: The OpenSearch Observability TAG turns one DevFeed: [Building open-source observability together: The OpenSearch Observability TAG turns one]() Original publisher: [Read original article]() Author: Dotan Horovits Published: 2026-08-11T17:01:57Z Content type: article Language: en Sources: [OpenSearch]() Topics: [observability](), [Open Source](), [OpenTelemetry](), [Prometheus](), [agent observability]() Tags: [agent-observability](), [blog](), [cncf](), [community](), [metrics](), [observability](), [open-source](), [opensearch](), [opentelemetry](), [prometheus]() ### AI overview The OpenSearch Observability Technical Advisory Group marks its first anniversary after a year of community-driven guidance, RFC reviews, architecture decisions, and collaboration across organizations. The article highlights work involving OpenTelemetry, Prometheus, Perses, KEDA, Kubernetes, and the OpenSearch Observability Stack, and explains how contributors can participate in the group's second year. ### Source excerpt The OpenSearch Observability TAG's first year: 10+ orgs shipped OTLP ingestion, Prometheus metrics, an open-source Observability Stack, and AI agent observability. The post Building open-source observability together: The OpenSearch Observability TAG turns one appeared first on OpenSearch. ## The real cost of your observability stack DevFeed: [The real cost of your observability stack]() Original publisher: [Read original article]() Author: Shenoy Pratik Gurudatt Published: 2026-08-07T15:00:00Z Content type: article Language: en Sources: [OpenSearch]() Topics: [observability](), [telemetry](), [agent observability](), [Artificial Intelligence](), [Open Source]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [blog](), [dashboards](), [infrastructure](), [latency](), [logging](), [logs](), [metrics](), [microservices](), [observability](), [open](), [open-source](), [platform](), [platform-engineering](), [technical](), [telemetry](), [traces](), [vendor-lock-in]() ### AI overview The article examines the operational and financial costs of fragmented observability for agentic workloads. It explains that humans and AI agents must move among separate metrics, logging, and tracing tools, which increases investigation time, token usage, latency, integration work, and the risk of losing correlation context. It presents an open-source OpenSearch Observability Stack as an alternative intended to unify logs, traces, and agent telemetry while avoiding per-GB penalties and vendor lock-in. ### Source excerpt AI agents generate 10x the telemetry, and proprietary per-GB pricing punishes you for it. See how the open-source OpenSearch Observability Stack unifies logs, traces, and agent telemetry without vendor lock-in. The post The real cost of your observability stack appeared first on OpenSearch. ## What's new in OpenSearch 3.8 DevFeed: [What's new in OpenSearch 3.8]() Original publisher: [Read original article]() Author: James McIntyre Published: 2026-08-05T00:50:11Z Content type: release Language: en Sources: [OpenSearch]() Topics: [opensearch](), [Artificial Intelligence](), [observability](), [Model Context Protocol](), [Large Language Model]() Tags: [ai](), [blog](), [large-language-model](), [mcp](), [observability](), [opensearch](), [performance](), [release]() ### AI overview OpenSearch 3.8 adds broader Model Context Protocol integration, lower-latency streaming machine-learning predictions over gRPC, faster vector ingestion and radial search, expanded large-language-model provider support, and new tools for log and time-series analysis. ### Source excerpt OpenSearch 3.8 expands the platform's search, AI, and observability capabilities with enhanced vector performance, broader agent integrations, and new tools to help you simplify analytics workflows from ingestion to investigation. The post What's new in OpenSearch 3.8 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. ## The forbidden index: Building privacy-preserving search with OpenSearch DevFeed: [The forbidden index: Building privacy-preserving search with OpenSearch]() Original publisher: [Read original article]() Author: Kylie Wagar-Dirks Published: 2026-07-24T15:00:22Z Content type: article Language: en Sources: [OpenSearch]() Topics: [Open Source](), [Access Control](), [pii](), [dashboards](), [tokenization](), [data](), [elasticsearch](), [Framework]() Tags: [analytics](), [analytics-stack](), [blog](), [compliance](), [dashboards](), [data](), [dynamic-data](), [open-source](), [opensearch](), [pii](), [plugin](), [privacy](), [search](), [tokenization]() ### AI overview The article describes a talk by Unnati Mishra and Akshat Khanna on building privacy-preserving search and analytics with OpenSearch. Their approach moves privacy controls into the ingest layer, using a custom plugin to redact PII at index time through tokenization and dynamic data masking. It also applies differential privacy to OpenSearch Dashboards by adding calibrated noise to aggregate query results. ### Source excerpt At OpenSearchCon Europe 2026, Unnati Mishra and Akshat Khanna (Angel One) argued that privacy in search must be handled during the ingest phase rather than retroactively through access controls. The post The forbidden index: Building privacy-preserving search with OpenSearch appeared first on OpenSearch.