MongoDB has announced the integration of search and vector search capabilities with MongoDB Community Edition and MongoDB Enterprise Server.
Previously, exclusively except for the fully managed Mongodb Atlas cloud platform, developers and organizations of all sizes now have access to previews of robust full-text search and vector search capabilities in the world's most popular modern database, with Mongodb's local, on-premises, and self-management offering. Starting today, these features have been made public for development and testing purposes.
Today's customers expect the latest real-time applications, high-performance, personalized. To meet these requirements, developers and businesses need comprehensive AI search and search tools integrated into the database where the data is stored. These native outbox search and AI-driven features include full-text, semantic search, and hybrid search to provide a highly accurate, intelligent, context-aware searched luxury generation (RAG) and agent AI user experience.
Allow millions of developers to build more powerful applications
Previously, search functionality had to be added to an external search engine or vector database to integrate into a self-managed MongoDB environment. Managing fragmented search stacks added complexity and risk, creating operational overheads that could lead to fragile extracts, transformation (ETL) pipelines, synchronization errors, and higher costs. This meant that developers needed to use and manage multiple systems from different vendors just to add search functionality.
Currently, with search and search capabilities directly integrated into MongoDB Community Edition and MongoDB Enterprise Server, developers and organizations are:
- Test and build AI applications locally: Vector Search enables semantic information search based on meaning encoded with vector embedding. This allows users to manage all dynamic AI applications that rely on unstructured data, such as text documents, images, videos, audio files, chat messages, and more, all locally or on-premises environments.
- Increase accuracy with hybrid search: Combine keywords and vector searches to return unified results from a single query for more accurate results. Developers who are critical to trusted agent solutions and AI applications can easily take advantage of this powerful feature directly through MongoDB's familiar query framework.
- Power AI Agent with Long-Term Memory: MongoDB's data acts as a long-term memory store for AI Agents, enabling accurate context-aware applications that can respond to real-world situations. Community Edition allows developers to easily prototype lag systems. Organizations built on Enterprise Server can safely ground AI agents with their own data about their own infrastructure.
MongoDB is a unified document database that provides developers with the tools they need to build modern applications to handle all their use cases in one place. Today, Mongodb promotes this commitment with powerful search and search capabilities that help developers build intelligent AI applications to provide context related to their agent systems in their environment of choice.
MongoDB Partners validates new search capabilities in the Community Edition
Many MONGODB partners, including LANGCHAIN, work closely with providers of software development frameworks for building LLM-driven applications and MongoDB, including LlamainDex, an open source framework for LLM applications, including LLMAINDEX, to test search and vector search capabilities in the community version.
MongoDB Search and MongoDB Vector Search are available through Public Preview on MongoDB Community Edition and Enterprise Server. For more information, please see the Mongodb blog.
Devin Pratt, IDC Research Director
According to a 2025 IDC survey, over 74% of organizations will use a unified vector database to store and query vector embeddings within the Agent AI workflow. In a rapidly moving technology era driven by LLMS and AI applications, developers cannot afford to slow down with fragmented systems. Embedding searches and vector searches directly into the database gives you one less complexity to manage and you can concentrate on building intelligent applications.
Bencephalo, Mongodb Core Product Head and Senior Vice President
At Mongodb, we believe in empowering developers everywhere with the tools they need to build next-generation applications. By expanding the search and vector search capabilities, we offer developers the unparalleled flexibility to build their chosen environment with the ultimate customer guarantee that Mongodb Atlas' beloved core database and query capabilities are also freely available in the community. And once your applications are ready to market, you can easily migrate to a fully managed Mongodb Atlas platform for seamless scaling, multi-cloud flexibility and enterprise-grade security.
Harrison Chase, CEO, Run Chaine
MongoDB search and vector search are now accessible in the already popular Mongodb Community Edition. Now, customers can leverage Mongodb and Langchain in their deployment mode and their preferred environment to build cutting-edge LLM applications.
Jerry Liu, CEO of Llamaindex
I'm excited about the next interaction in the Mongodb Community Edition search experience. Customers want the utmost flexibility to run search and GEN AI-enabled applications, and providing this functionality to the community unlocks a whole new way to build and test anywhere.
