OpenSearch Project Blog

The OpenSearch Agent Skills Hackathon is officially underway! Running from July 15 to August 17, 2026, this event offers U.S.-based developers a chance to build production-ready agentic skills, compete for a $5,000 prize pool, and potentially showcase their work at OpenSearchCon North America. Join the developer community today to start building and get your code in front of the OpenSearch maintainers.

Celebrating five years of the OpenSearch Project, true open source governance under the Linux Foundation has proven to be the most sustainable model for enterprise data infrastructure. Backed by over 2 billion downloads, OpenSearch delivers superior predictability, security, and native AI vector capabilities compared to restrictive "source-available" alternatives.

As vector search workloads scale, retrieving vectors from search results becomes a bottleneck. Vector retrieval using doc values in OpenSearch 3.7 bypasses the expensive _source vector reconstruction path, delivering 5.5x faster end-to-end retrieval and 14.7x faster server-side performance.

The OpenSearch Ambassador Program now accepts applications year-round. Apply today to join our next cohort—reviews begin August 7, 2026.

At OpenSearchCon Europe 2026, IBM's Dom Couldwell argued that truly relevant enterprise search requires design based on user intent (query, journey, and product intent) rather than just the literal keywords typed

Deploy the OpenSearch Observability Stack—a single OpenTelemetry-native platform unifying metrics, traces, and logs—and follow a step-by-step root cause analysis from error spike to failing log line, without switching tools.

Data Prepper 2.16 adds end-to-end metrics with a pull-based Prometheus source and OpenSearch-TSDB, experimental OpenSearch pull-based ingestion, other improvements to the OpenSearch sink, and more.

The rise of agentic AI has sparked a debate on whether open-source infrastructure is becoming obsolete. However, as David Nalley of AWS highlighted at OpenSearchCon India 2026, the economics of token consumption make recreating established infrastructure from scratch a waste of resources. Rather than reinventing foundational software, AI agents should utilize proven, open-source building blocks like OpenSearch. Open source provides essential "known good" design patterns and fosters "communities of problems," where human collaboration continues to drive innovation in ways that raw compute power cannot replace.

At OpenSearchCon Europe 2026, Fernando Rejon Barrera from Zeta Alpha challenged standard vector search benchmarks by evaluating Lucene’s HNSW, Faiss, and jVector under realistic production workloads.

Learn how vector search in OpenSearch eliminates zero-result pages in e-commerce by acting as a semantic fallback when lexical query relaxing falls short.

Learn how one engineer went from OpenSearch user to plugin maintainer through community engagement, consistent contributions, and a passion for search relevance.

At OpenSearchCon Europe 2026, Laysa Uchoa from NordCloud reframed generative AI hallucinations as a retrieval problem rather than an LLM limitation. She demonstrated production RAG architectures where hallucination rates dropped by over 80% by prioritizing hybrid retrieval (combining BM25 lexical scoring and k-NN vector search), implementing disciplined chunking strategies, and utilizing rigorous evaluation frameworks over simple "vibes".

Learn how OpenSearch 3.5's LLM as a Judge feature improves search relevance evaluation by using large language models to automatically assess search result quality at scale.

The OpenSearch agent server is a multi-agent orchestration platform that routes tasks to specialized AI agents using MCP tools and intelligent context-aware routing.

At OpenSearchCon Europe 2026, Carl Meadows announced three major shifts positioning OpenSearch as the foundation for agentic AI: OpenSearch Launchpad for natural language application deployment, automated hybrid search configurations, and high-cardinality telemetry tracking. Additionally, CERN joined the OpenSearch Software Foundation to handle massive scale workloads.

A single space in a KNN field name broke OpenSearch snapshots. Learn how Aiven traced the bug, reported it, and helped the community ship a fix in OpenSearch v2.17.

Asymmetric embedding models in OpenSearch 3.5 improve semantic search relevance by up to 125% over symmetric models. Learn how they work and how to implement them.

At OpenSource Summit North America,OpenSearch open source community member Bobby Mohammed took the stage to unpack how OpenSearch is bridging the gap between traditional retrieval and the future of AI. If you weren’t able to catch the session live, here is your breakdown of how OpenSearch is providing the critical primitives required to power multi-agent AI architectures at scale.

Across all SDLC phases, AI agents now generate code, root-cause analyses, remediation plans, performance fixes, and standard operating procedures faster than humans can meaningfully review them. The bottleneck is no longer generation; it's deciding whether to trust what has been generated. This post shares four agents the OpenSearch team built across the SDLC and the harness-based verification loops that make them reliable.