[ { "id": "ravatar", "name": "Ravatar", "logo": "/assets/logo_ravatar.png", "description": "AI-powered platform specializing in interactive, real-time 3D AI avatars and digital humans for businesses and individuals.", "website": "https://ravatar.com/", "tags": [ "Business", "Enterprise" ], "sponsor": "silver", "sponsor_primary": true, "details": { "sections": [ { "title": "About Ravatar", "content": "It operates as an Avatar-as-a-Service (AaaS) provider, leveraging Generative AI, Conversational AI, voice cloning, and synthetic data to create lifelike avatars that mimic human appearance, speech, and behavior with high accuracy." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Generates realistic video and 3D avatars using audio and video inputs, enabling creation of custom digital identities.", "Supports natural-sounding speech, lip-syncing, adaptive intelligence, and real-time responses across web, mobile apps, messengers, kiosks, and holographic displays.", "Deployed for customer service (AI call center agents, helpdesk specialists), education (digital instructors), healthcare (AI wellness coaches), government (AI government assistants), live events (holographic keynote speakers), and personal branding (AI influencers)." ] } }, { "title": "MariaDB Support", "content": "RAVATAR leverages MariaDB Vector, a feature that enables scalable vector search and retrieval-augmented generation (RAG), to enhance the intelligence and responsiveness of its AI avatars." } ], "links": [ { "label": "Official Website", "url": "https://ravatar.com/" }, { "label": "Documentation", "url": "https://ravatar.com/docs/" }, { "label": "Sponsorship Announcement", "url": "https://mariadb.org/ravatar-becomes-silver-sponsor-of-mariadb-foundation/" } ] } }, { "id": "zencoder", "name": "Zencoder", "logo": "/assets/logo_zencoder.png", "description": "AI coding agent helping developers write, analyze, and optimize code in their IDEs.", "website": "https://zencoder.ai/", "tags": [ "Free", "Business", "Enterprise", "Windows", "macOS", "Linux", "Plugin" ], "sponsor": "silver", "sponsor_primary": true, "details": { "sections": [ { "title": "About Zencoder", "content": "Zencoder integrates directly into VS Code, JetBrains IDEs, and Android Studio. It automates repetitive coding tasks, helps resolve issues, and improves code quality through contextual analysis of your codebase. By handling routine coding operations, Zencoder lets you focus on solving core problems and building better software." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Specialized AI agents handle specific tasks like code generation, unit testing, debugging, and documentation. Each agent is optimized for its purpose, delivering higher quality results than general-purpose assistants.", "Your code is never retained unless you explicitly permit it, all data is encrypted during processing, and Zencoder supports custom compliance frameworks for regulated industries.", "Zencoder’s agentic pipeline runs generated code through a series of verification and repair steps. Instead of just spitting out the first solution, it tests the code, fixes errors, and refines the implementation until it actually works in your repo context.", "Connect with Jira and 20+ other tools, plus Model Context Protocol (MCP) support for seamless workflow integration." ] } }, { "title": "MariaDB Support", "content": "The MariaDB Foundation and Zencoder partnership aims to integrate MariaDB into Zencoder’s AI developer training as well as the development of agents that generate, verify, and optimize MariaDB queries and schemas." } ], "links": [ { "label": "Official Website", "url": "https://zencoder.ai/" }, { "label": "Download", "url": "https://zencoder.ai/download/all" }, { "label": "Documentation", "url": "https://docs.zencoder.ai/get-started/quickstart" }, { "label": "Sponsorship Announcement", "url": "https://mariadb.org/zencoder-becomes-silver-sponsor-of-mariadb-foundation/" } ] } }, { "id": "uibakery", "name": "UI Bakery", "logo": "/assets/logo_uibakery.jpg", "description": "Low-code application development platform designed for building internal tools, admin panels, dashboards, and business applications quickly and efficiently.", "website": "https://uibakery.io/", "tags": [ "Free", "Business", "Enterprise", "Windows", "Linux", "Docker" ], "sponsor": false, "details": { "sections": [ { "title": "About UI Bakery", "content": "UI Bakery is built with a focus on empowering developers, technical teams, and business users to create custom web applications without the overhead of traditional full-stack development." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Supports both developers who prefer visual builders and teams ready to harness the power of AI. Build, iterate, and scale internal apps faster than before.", "Users can describe an app in plain text (e.g., 'Create a dashboard to track sales data'), and App Agent generates the UI, logic, and data bindings automatically.", "Available as a cloud-hosted service or self-hosted (on-premise), ideal for organizations with compliance, data residency, or security requirements." ] } }, { "title": "MariaDB Support", "content": "UI Bakery allows direct connection to MariaDB without requiring APIs or third-party services." } ], "links": [ { "label": "Official Website", "url": "https://uibakery.io/" }, { "label": "Documentation", "url": "https://docs.uibakery.io/" } ] } }, { "id": "aqtra", "name": "Aqtra", "logo": "/assets/logo_aqtra.jpeg", "description": "Aqtra is a Development Infrastructure Layer (DIL) platform for building ERP solutions, business applications, portals, and workflow automation systems.", "website": "https://aqtra.io/", "tags": [ "Business", "Enterprise", "Linux" ], "sponsor": "gold", "sponsor_primary": true, "details": { "sections": [ { "title": "About Aqtra", "content": "Aqtra is a Development Infrastructure Layer (DIL) platform for building and operating business applications, ERP solutions, portals, and workflow automation systems. Built on a metadata-driven architecture, it separates business models, processes, and integrations from application code — enabling faster delivery, easier maintenance, and greater reuse across applications. It supports ERP workloads, CRM, procurement and supplier portals, customer portals, inventory management, help desks, and approval workflows." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Applications, processes, data models, and business logic are defined through metadata and executed by a common runtime layer.", "Design and automate cross-system business processes spanning ERP, CRM, HR, procurement, and operational systems.", "Connect databases, APIs, enterprise systems, and third-party applications through a unified integration layer.", "Build internal, customer, supplier, and partner portals from a shared platform foundation.", "Deploy within customer-controlled cloud, private cloud, or hosted environments while maintaining data governance and control." ] } }, { "title": "MariaDB Support", "content": "MariaDB serves as the relational database foundation of the Aqtra platform, managing business application data, application metadata, configuration and platform settings, workflow execution state, and operational information." } ], "links": [ { "label": "Official Website", "url": "https://aqtra.io/" }, { "label": "Documentation", "url": "https://docs.aqtra.io/" }, { "label": "Contact", "url": "https://aqtra.io/contact-us/" }, { "label": "Sponsorship Announcement", "url": "https://mariadb.org/aqtra-joins-mariadb-foundation-as-a-gold-sponsor/" } ] } }, { "id": "vaadin", "name": "Vaadin", "logo": "/assets/logo_vaadin.svg", "description": "Open-source full-stack Java UI framework for building data-rich business web applications without writing HTML or JavaScript.", "website": "https://vaadin.com/", "tags": [ "Open Source", "Free", "Business", "Enterprise", "Plugin" ], "sponsor": false, "sponsor_primary": false, "contributor": false, "details": { "sections": [ { "title": "About Vaadin", "content": "Vaadin streamlines web application development for Java developers. Combining frontend and backend, developers can create rich user interfaces entirely in Java, without JavaScript, HTML or REST plumbing. The framework manages rendering, event handling, and communication, allowing teams to focus on what really matters: building dynamic applications effortlessly." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "All-Java development — Build both UI and backend in Java, no separate HTML/JavaScript required.", "Enterprise-grade UI components — Pre-built components like Grid, Chart, and Form. Grid supports sorting, filtering, and lazy loading out of the box for handling large datasets efficiently.", "Built-in security — Server-side architecture with automatic CSRF/XSS protection; business logic never reaches the browser.", "Open source & flexible — Apache 2.0 licensed, free to use and extend, with up to 15 years of commercial support available.", "Legacy modernization — Automated tooling to migrate from Swing, SWT, or JavaFX to modern web UIs." ] } }, { "title": "MariaDB Support", "content": "Vaadin applications connect seamlessly to MariaDB through the official MariaDB JDBC driver, with additional support for Spring Data and jOOQ, giving developers a reliable, high-performance data layer for enterprise-grade Java apps. With native integration support and detailed documentation, teams can wire up MariaDB in minutes and take advantage of its open-source flexibility alongside Vaadin's all-Java development model, no extra glue code or complex configuration required." } ], "links": [ { "label": "Official Website", "url": "https://vaadin.com/" }, { "label": "Pricing", "url": "https://vaadin.com/pricing" }, { "label": "Enterprise", "url": "https://vaadin.com/enterprise" }, { "label": "Documentation", "url": "https://vaadin.com/docs/latest/" }, { "label": "Download", "url": "https://vaadin.com/flow" }, { "label": "GitHub", "url": "https://github.com/vaadin" } ] } }, { "id": "langchain", "name": "LangChain", "logo": "/assets/logo_langchain.png", "description": "An open-source framework for building LLM-powered applications, including RAG pipelines, chatbots, agents, and data-aware workflows.", "website": "https://www.langchain.com/", "tags": [ "Open Source", "Free" ], "sponsor": false, "sponsor_primary": false, "contributor": false, "details": { "sections": [ { "title": "About LangChain", "content": [ "LangChain is the most widely used open-source framework for building applications with large language models — retrieval-augmented generation (RAG), chatbots, agents, and data-aware pipelines. It provides the standard building blocks developers reach for first when connecting an LLM to their own data. ", "LangChain.js is the official JavaScript and TypeScript implementation of the same framework, targeting Node.js and browser environments." ] }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Standard abstractions for RAG pipelines: document loaders, text splitters, embeddings, vector stores, and retrievers that compose into production applications.", "Agent tooling and orchestration, including memory, tool calling, and multi-step reasoning chains.", "The largest integration ecosystem in the LLM space — hundreds of models, vector stores, and tools behind one consistent interface, so components can be swapped with configuration changes.", "LangSmith observability and LangGraph orchestration extend the same stack into production monitoring and complex agent workflows." ] } }, { "title": "MariaDB Support", "content": "The official langchain-mariadb package provides a MariaDB vector store and chat message history. LangChain applications can store embeddings, run similarity search, and keep agent conversation memory in MariaDB — the same database that holds their application data — with a configuration change. Documented in LangChain's own integration docs." } ], "links": [ { "label": "Official Website", "url": "https://www.langchain.com/" }, { "label": "GitHub", "url": "https://github.com/langchain-ai" }, { "label": "Documentation", "url": "https://docs.langchain.com/" }, { "label": "LangChain Vector Stores", "url": "https://python.langchain.com/docs/integrations/vectorstores/mariadb/" }, { "label": "langchain-mariadb", "url": "https://github.com/mariadb-corporation/langchain-mariadb" }, { "label": "MariaDB Vector", "url": "https://mariadb.org/projects/mariadb-vector/" }, { "label": "LangChain.js", "url": "https://js.langchain.com/" } ] } }, { "id": "llamaindex", "name": "LlamaIndex", "logo": "/assets/logo_llamaindex.png", "description": "An open-source data framework designed to connect large language models (LLMs) to external, private, or domain-specific data sources.", "website": "https://www.llamaindex.ai/", "tags": [ "Open Source", "Free" ], "sponsor": false, "sponsor_primary": false, "contributor": false, "details": { "sections": [ { "title": "About LlamaIndex", "content": "LlamaIndex is a leading open-source data framework for connecting large language models to private and domain-specific data. It specializes in the ingestion, indexing, and retrieval layer of RAG applications — turning documents, databases, and APIs into queryable knowledge for LLMs." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Data connectors for files, databases, APIs, and SaaS tools, feeding a unified indexing pipeline.", "Flexible index and retrieval strategies — vector, keyword, hybrid, and graph-based — tunable per use case.", "Query engines and chat engines that turn indexed data into question-answering and conversational interfaces.", "A large catalog of interchangeable vector store backends behind one VectorStore interface." ] } }, { "title": "MariaDB Support", "content": "The MariaDBVectorStore integration makes MariaDB a drop-in vector store for LlamaIndex pipelines: store embeddings and run similarity search in MariaDB alongside the application's relational data. Listed in LlamaIndex's own vector store catalog with a dedicated documentation page." } ], "links": [ { "label": "Official Website", "url": "https://www.llamaindex.ai/" }, { "label": "GitHub", "url": "https://github.com/run-llama/" }, { "label": "Documentation", "url": "https://docs.llamaindex.ai/" }, { "label": "MariaDBVectorStore", "url": "https://developers.llamaindex.ai/python/framework-api-reference/storage/vector_store/mariadb/" }, { "label": "MariaDB Vector", "url": "https://mariadb.org/projects/mariadb-vector/" } ] } }, { "id": "springai", "name": "Spring AI", "logo": "/assets/logo_spring.svg", "description": "Spring Framework's official AI integration layer, providing portable abstractions for chat models, embedding models, and vector stores with native Spring Boot support.", "website": "https://spring.io/projects/spring-ai", "tags": [ "Open Source", "Free" ], "sponsor": false, "sponsor_primary": false, "contributor": false, "details": { "sections": [ { "title": "About Spring AI", "content": "Spring AI is the Spring project's official framework for building AI-powered applications in Java. It brings Spring's portable-abstraction philosophy to AI engineering: one programming model across LLM providers, embedding models, and vector stores, integrated with Spring Boot configuration and dependency injection." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Portable abstractions for chat models, embeddings, and vector stores across major AI providers — swap providers without rewriting application code.", "First-class RAG support: document ETL pipelines, embedding generation, similarity search, and prompt construction as Spring components.", "Native Spring Boot auto-configuration, so AI capabilities wire into existing enterprise Java applications the standard Spring way.", "Function calling and structured output mapping for agent-style workflows on the JVM." ] } }, { "title": "MariaDB Support", "content": "Spring AI ships an official MariaDB Vector Store integration: Spring applications store embeddings and run vector similarity search natively on MariaDB through the standard VectorStore abstraction, with Spring Boot auto-configuration." } ], "links": [ { "label": "Official Website", "url": "https://spring.io/projects/spring-ai" }, { "label": "GitHub", "url": "https://github.com/spring-projects/spring-ai" }, { "label": "Documentation", "url": "https://docs.spring.io/spring-ai/reference/" }, { "label": "MariaDB Vector Store", "url": "https://docs.spring.io/spring-ai/reference/api/vectordbs/mariadb.html" }, { "label": "MariaDB Vector", "url": "https://mariadb.org/projects/mariadb-vector/" } ] } }, { "id": "langchain4j", "name": "LangChain4j", "logo": "/assets/logo_langchain4j.png", "description": "An idiomatic open-source Java library for integrating large language models into JVM applications.", "website": "https://docs.langchain4j.dev/", "tags": [ "Open Source", "Free" ], "sponsor": false, "sponsor_primary": false, "contributor": false, "details": { "sections": [ { "title": "About LangChain4j", "content": "LangChain4j is the Java implementation of the LangChain concept: an open-source framework for integrating large language models into Java applications. It gives JVM developers idiomatic building blocks for RAG, agents, and AI-assisted features without leaving the Java ecosystem." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Unified Java APIs for chat models, embedding models, and embedding stores across major AI providers.", "RAG building blocks — document ingestion, splitting, embedding, and retrieval — designed for Java applications.", "AI Services: declarative, annotation-driven interfaces that turn LLM interactions into typed Java methods.", "Integrations with Spring Boot, Quarkus, and Micronaut for mainstream enterprise Java stacks." ] } }, { "title": "MariaDB Support", "content": "The MariaDB Embedding Store lets LangChain4j applications store and search embeddings in MariaDB through the framework's standard embedding-store interface — vector search on the JVM, in the database Java applications already use." } ], "links": [ { "label": "Official Website", "url": "https://docs.langchain4j.dev/" }, { "label": "GitHub", "url": "https://github.com/langchain4j/langchain4j" }, { "label": "Documentation", "url": "https://docs.langchain4j.dev/get-started" }, { "label": "MariaDB Embedding Store", "url": "https://docs.langchain4j.dev/integrations/embedding-stores/mariadb" }, { "label": "MariaDB Vector", "url": "https://mariadb.org/projects/mariadb-vector/" } ] } }, { "id": "mariadb-mcp-server", "name": "MariaDB MCP Server", "logo": "/assets/logo_MariaDB_Foundation_vertical.png", "description": "The official Model Context Protocol server for MariaDB, enabling MCP-compatible AI assistants and agents to query and search MariaDB databases directly.", "website": "https://github.com/mariadb/mcp/", "tags": [ "Open Source", "Free" ], "sponsor": false, "sponsor_primary": false, "contributor": false, "details": { "sections": [ { "title": "About MariaDB MCP Server", "content": "The MariaDB MCP Server is the official Model Context Protocol server for MariaDB. MCP is the emerging open standard for connecting AI assistants and agents to external systems; this server makes MariaDB one of those systems — exposing relational queries and MariaDB Vector similarity search to any MCP-compatible AI client." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Connects AI assistants and agents (Claude, Cursor, and any MCP-compatible client) directly to MariaDB databases.", "Supports querying relational data and vector search from within an AI conversation or agent workflow.", "Standards-based: one server, usable by the growing ecosystem of MCP clients without per-tool integration work.", "Configurable read-only mode and SSL/TLS support with optional OAuth authentication keep AI agent access to MariaDB within controlled boundaries." ] } } ], "links": [ { "label": "GitHub", "url": "https://github.com/mariadb/mcp/" }, { "label": "MCP Release Notes", "url": "https://mariadb.com/docs/release-notes/mcp-server-release-notes" }, { "label": "MariaDB Vector", "url": "https://mariadb.org/projects/mariadb-vector/" }, { "label": "AI RAG Hackathon", "url": "https://mariadb.org/model-context-protocol-mcp-hackathon-integration-track-winner/" } ] } }, { "id": "laravel", "name": "Laravel", "logo": "/assets/logo_laravel.png", "description": "A batteries-included PHP framework for building web applications, with first-party MariaDB support and native vector column types for AI-powered features.", "website": "https://laravel.com/", "tags": [ "Open Source", "Free" ], "sponsor": false, "sponsor_primary": false, "contributor": false, "details": { "sections": [ { "title": "About Laravel", "content": "Laravel is one of the most widely used PHP frameworks, providing a full-stack foundation for web applications: routing, Eloquent ORM, authentication, queuing, and more. It lists MariaDB as one of its five officially supported databases alongside MySQL, PostgreSQL, SQLite, and SQL Server, with a dedicated MariaDB driver." }, { "title": "laravel-mariadb-vector", "content": "[laravel-mariadb-vector](https://packagist.org/packages/devilsberg/laravel-mariadb-vector) is a community package that brings MariaDB native vector search to Laravel's query and Eloquent layer. It lets Laravel applications run similarity search on MariaDB with idiomatic Eloquent syntax — no separate vector database and no raw SQL." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "First-party MariaDB 10.3+ support with a dedicated database driver, configured independently from MySQL.", "Eloquent ORM — an expressive ActiveRecord implementation for working with MariaDB tables as typed PHP models.", "Native vector() migration column type maps directly to MariaDB's VECTOR column, enabling embedding storage without additional packages.", "Artisan CLI for migrations, schema inspection (db:show, db:table), and database management.", "First-party AI toolkit (laravel/ai) for building AI-native Laravel applications." ] } }, { "title": "MariaDB Support", "content": "Laravel lists MariaDB 10.3+ as one of its five officially supported databases with a dedicated driver. The vector() schema method maps natively to MariaDB's VECTOR column type, so Laravel applications can store embeddings and run similarity search in MariaDB without additional packages. For higher-level Eloquent abstractions — VectorCast, nearestNeighbors() query macros, and ANN index support — the community package devilsberg/laravel-mariadb-vector extends this further." } ], "links": [ { "label": "Official Website", "url": "https://laravel.com/" }, { "label": "GitHub", "url": "https://github.com/laravel/framework" }, { "label": "Documentation", "url": "https://laravel.com/docs/" }, { "label": "Vector Column Docs", "url": "https://laravel.com/docs/13.x/migrations#column-method-vector" }, { "label": "MariaDB Vector", "url": "https://mariadb.org/projects/mariadb-vector/" }, { "label": "laravel-mariadb-vector", "url": "https://packagist.org/packages/devilsberg/laravel-mariadb-vector" } ] } }, { "id": "typeorm", "name": "TypeORM", "logo": "/assets/logo_typeorm.png", "description": "An open-source ORM for TypeScript and JavaScript supporting both ActiveRecord and DataMapper patterns across multiple databases including MariaDB.", "website": "https://typeorm.io/", "tags": [ "Open Source", "Free" ], "sponsor": false, "sponsor_primary": false, "contributor": false, "details": { "sections": [ { "title": "About TypeORM", "content": "TypeORM is one of the most widely used ORMs in the TypeScript and JavaScript ecosystem, running on Node.js, browsers, React Native, Electron, and more. It provides a type-safe API for database interactions, with support for both the DataMapper and ActiveRecord architectural patterns, making it adaptable to different project structures and team preferences." }, { "title": "Key Features", "content": { "type": "ul", "items": [ "Supports both DataMapper and ActiveRecord patterns — choose the approach that fits your architecture.", "Full TypeScript support with type-safe entities, columns, and relationships.", "Powerful QueryBuilder for constructing complex queries with joins, pagination, and subqueries.", "Schema migrations with automatic migration generation from entity changes.", "Comprehensive relationship support: one-to-one, one-to-many, many-to-many, bidirectional and self-referenced." ] } }, { "title": "MariaDB Support", "content": "TypeORM connects to MariaDB via the mysql2 driver, with MariaDB listed as a supported database alongside MySQL. Standard TypeORM entities, repositories, QueryBuilder, and migrations work against MariaDB without additional configuration. Vector columns are supported for MariaDB 11.7+, allowing embedding storage to be defined directly in TypeORM entities." } ], "links": [ { "label": "Official Website", "url": "https://typeorm.io/" }, { "label": "GitHub", "url": "https://github.com/typeorm/typeorm" }, { "label": "Documentation", "url": "https://typeorm.io/docs/getting-started" }, { "label": "MySQL/MariaDB Driver", "url": "https://typeorm.io/docs/drivers/mysql/" }, { "label": "MariaDB Vector", "url": "https://mariadb.org/projects/mariadb-vector/" } ] } } ]