# AI4JVM — the curated guide to AI on the JVM

> The curated guide to AI on the JVM — compare Java AI agent frameworks (Spring AI, LangChain4j, Koog), run LLMs locally, and find MCP tools and resources.

Complete content of https://ai4jvm.com/ in Markdown. This is the full directory; `/llms.txt` has a short index of the same site. Entries are human-curated: each must be specific to Java/JVM developers and actively maintained. See https://github.com/jamesward/ai4jvm/blob/main/CONTRIBUTING.md for the editorial policy.

## Latest Java AI News

Latest news and releases from across the Java AI ecosystem — new framework versions, MCP updates, and inference engines.

- [Embabel Agent 1.5.1 Released](https://github.com/embabel/embabel-agent/releases/tag/v1.5.1) — RAG improvements including a `SectionReader` for document navigation, embedding-driven skills, Atlas Cloud BYOK support, thinking-tag selection control, and fixes for Bedrock chat options and Ollama thinking-mode extraction
- [Micronaut Framework 5.1.2 Released](https://micronaut.io/2026/08/24/micronaut-framework-5-1-2/) — maintenance release with updates across Core, Cache, Test, Data, Security, Serialization, and Flyway modules
- [Spring AI 2.0.1 Available Now](https://spring.io/blog/2026/08/21/spring-ai-2-0-1-available-now/) — first maintenance release since the June 2.0.0 GA, fixing 80+ issues including 7 CVEs (PDF processing, cache, session allocation, file operations, semantic caching, Redis, tool dispatch), adding configurable tool-call limits and OpenAI audio streaming, and removing deprecated Mistral AI chat models
- [MCP Java SDK 2.0.1 Released](https://github.com/modelcontextprotocol/java-sdk/releases/tag/v2.0.1) — first patch on the 2.0 line: upgrades the conformance suite to 0.1.16 and Spring AI conformance to 2.0.0 GA, bounds HTTP and STDIO server/client reads, and fixes SSE event classification, pagination, and unregistered-handler behavior in stateless servers
- [A2A Java SDK 1.2.0.Final Released](https://quarkus.io/blog/a2a-java-sdk-1-2-0-final-released/) — hardens the authorization model with read-authorization enforcement on referenced task lookups, adds a `TaskStreamLifecycleHook` SPI for stream lifecycle management, and ships breaking changes including method renames and a `TaskState` enum change
- [Quarkus LangChain4j 1.13.0 Released](https://github.com/quarkiverse/quarkus-langchain4j/releases/tag/1.13.0) — upgrades to LangChain4j 1.19.0 and the latest MCP client spec, replaces the Qdrant gRPC client with a REST client, adds GPULlama3 tool-calling support, and expands the Dev UI with a Guardrails page and RAG context visibility
- [DevOps Patterns and Java 26 for On-Premises LLM Platforms in Safety-Critical Environments](https://javapro.io/2026/08/18/devops-patterns-and-java-26-for-on-premises-llm-platforms-in-safety-critical-environments/) — Cornelius May covers RAG-on-Kubernetes architecture, Java 26's AOT caching, Vector API, and Structured Concurrency, and treating prompts as versioned, governed code
- [Vibe Coding, Maven, and the Dependencies You Didn't Choose](https://foojay.io/today/vibe-coding-maven-and-the-dependencies-you-didnt-choose/) — Steve Poole warns that AI coding assistants are silently choosing an app's Maven dependencies, transitive packages, and plugins, and argues supply-chain scrutiny needs to keep pace with AI-accelerated dependency selection
- [Micronaut Framework 5.1.1 Released](https://micronaut.io/2026/08/17/micronaut-framework-5-1-1-released/) — maintenance release covering Micronaut Core and Micronaut OpenAPI
- [Spring AI Recipe: Reasoning and Acting (ReAct)](https://www.linkedin.com/pulse/spring-ai-recipe-reasoning-actingreact-craig-walls-c4uvc) — Craig Walls shows how Spring AI's `ChatClient` with tools implements the ReAct pattern via an automatic `ToolCallingAdvisor` loop — the LLM reasons, invokes a tool, observes the result, and iterates — demonstrated with a small Disney Parks example
- [Building AI-Driven Java Systems with Spring AI and Java 26](https://javapro.io/2026/08/13/building-ai-driven-java-systems-with-spring-ai-and-java-26/) — treats LLMs as first-class components with explicit responsibilities, leveraging Java 26's Virtual Threads and Structured Concurrency for efficient I/O-bound agentic workflows
- [LangChain4j 1.19.0 Released](https://github.com/langchain4j/langchain4j/releases/tag/1.19.0) — adds MCP client improvements, Anthropic Batch Chat Model support, a Milvus V2 service update with hybrid search, watsonx.ai Model Gateway support, and Google GenAI thinking support
- [Spring AI Recipe: Building a Graph-Based Agentic Workflow with LangGraph4j](https://thetalkingapp.medium.com/spring-ai-recipe-building-a-graph-based-agentic-workflow-with-langgraph4j-0fbfcce25a57) — Craig Walls builds a customer-support workflow with LangGraph4j nodes for classification, billing, and technical support integrated with Spring AI, contrasting LangGraph4j's framework-neutral graphs with Spring AI Alibaba Graph
- [Evolving a Java MCP Server During MCP Specification Upgrades](https://inside.java/2026/08/12/java-mcp-migration/) — Ana-Maria Mihalceanu shows how to adopt the MCP 2026-07-28 spec while staying compatible with older clients, routing new stateless requests through an adapter layer alongside the legacy initialized workflow
- [Embabel Agent 1.5.0 Released](https://github.com/embabel/embabel-agent/releases/tag/v1.5.0) — migrates to Spring AI 2.0 GA (Spring Boot 4, Jackson 3), adds Alibaba Cloud DashScope model support, RAG chunk-metadata and full-text scoring improvements, and a vendor-neutral streaming tool loop
- [Spring AI Recipe: Filtering RAG Results with Metadata](https://thetalkingapp.medium.com/spring-ai-recipe-filtering-rag-results-with-metadata-bef2f8a7cb72) — Craig Walls shows how attaching metadata to document chunks and filtering vector-store queries with Spring AI's `VectorStore` and `QuestionAnswerAdvisor` narrows RAG retrieval before the LLM sees it
- [Introduction to Retrieval-Augmented Generation with Java and MongoDB](https://foojay.io/today/introduction-to-retrieval-augmented-generation-with-java-and-mongodb/) — tutorial building a JAX-RS/Helidon RAG service with LangChain4j and MongoDB Atlas as a combined vector store and operational database, retrieving context to answer HR policy questions before calling the LLM
- [A2A Java SDK 1.2.0.Final Released](https://github.com/a2aproject/a2a-java/releases/tag/v1.2.0.Final) — adds programmatic auth wiring and a TaskStreamLifecycleHook, upgrades to Quarkus 3.37.3, and ships breaking changes: cross-module split-package resolution, `TaskState.UNRECOGNIZED` renamed to `TASK_STATE_UNSPECIFIED` per spec, and corrected authorization enforcement on referenced task lookups
- [Camel Routes as AI Tools: Unified Tooling and MCP Server in Camel 4.22](https://camel.apache.org/blog/2026/08/camel-ai-tools-mcp-422/) — introduces `camel-ai-tool`, a unified tool abstraction so a route defined once works with LangChain4j, Spring AI, or OpenAI, and `camel-mcp-server`, which exposes tagged routes directly as MCP tools
- [AgentScope Java 2.0.1 Released](https://github.com/agentscope-ai/agentscope-java/releases/tag/v2.0.1) — maintenance release expanding model-provider support (DeepSeek, GLM, Kimi, MiniMax) and hardening Harness subagent/permission handling, plus fixes for memory leaks and streaming tool-arg repair
- [InfoQ Java News Roundup](https://www.infoq.com/news/2026/08/java-news-roundup-jul27-2026/) — weekly Java ecosystem roundup covering two new JDK 28 JEPs, Jakarta Agentic AI's first milestone release, GPULlama3.java 1.0.0 GA, GraalVM 25.2's new Graal Script Agent, and point releases across Micronaut, Quarkus, and JobRunr
- [Jakarta Agentic AI 1.0.0-M1](https://github.com/jakartaee/agentic-ai/releases/tag/1.0.0-M1) — first milestone release of the new Jakarta EE agentic AI specification, adding TCK behavioral-test infrastructure and module/javadoc improvements ahead of 1.0
- [Jakarta Agentic AI Hits Its First Milestone](https://foojay.io/today/jakarta-agentic-ai-hits-its-first-milestone/) — Dominika Tasarz walks through the 1.0.0-M1 release now on Maven Central: the draft spec's annotation model (`@Agent`, `@Trigger`, `@Decision`, `@Action`, `@Outcome`, `@HandleException`), a new CDI `@WorkflowScoped` context, and a vendor-neutral `LargeLanguageModel` facade
- [How to Create a Spring Boot Fraud Scoring Service](https://foojay.io/today/how-to-create-a-spring-boot-fraud-scoring-service/) — builds a production credit-card fraud detection REST API with Spring Boot and the pure-Java Deep Netts deep-learning library, embedding the model directly in the app instead of a separate Python inference server
- [Under the HAT: Empowering GPU Acceleration for Java](https://inside.java/2026/07/30/under-the-hat-empowering-gpu-acceleration-for-java/) — Juan Fumero introduces the Heterogeneous Accelerator Toolkit, which uses OpenJDK Project Babylon's code-reflection APIs to translate sections of Java programs into CUDA and OpenCL at runtime, managing generated code and data migration automatically
- [Connecting Java Reinforcement Learning to Python Gymnasium](https://javapro.io/2026/07/29/connecting-java-reinforcement-learning-to-python-gymnasium/) — bridges Java RL code to Python's Gymnasium library via JavaCPP, covering Python lifecycle management and NumPy data transfer, starting with CartPole before scaling up to CARLA
- [GPULlama3.java 1.0.0 Released](https://github.com/beehive-lab/GPULlama3.java/releases/tag/v1.0.0) — first major release of the GPU-accelerated Java LLM inference engine, now on Maven Central as `io.github.beehive-lab:gpu-llama3` with auto-activating `1.0.0-jdk21` and `1.0.0-jdk25` builds, making TornadoVM-backed GPU inference a plain Maven or Gradle dependency
- [Micronaut LangChain4j 2.2.0 Released](https://github.com/micronaut-projects/micronaut-langchain4j/releases/tag/v2.2.0) — adds LangChain4j agentic support, Chroma embedding-store support, guardrails resolved from Micronaut beans, and evaluation testing to the official Micronaut integration
- [JetBrains Air Adds Java and Kotlin Code Intelligence](https://blog.jetbrains.com/air/2026/07/what-s-new-air-gets-more-agents-local-models-and-java-kotlin-code-intelligence/) — the agent-first tool gains language intelligence powered by the IntelliJ IDEA code engine (Beta) for mixed Java/Kotlin projects — jump-to-definition, find usages, symbol search, and error highlighting on agent-modified code — plus local models via Ollama and LM Studio through ACP-compatible agents
- [Spring AI AgentCore 2.1.0 Released](https://github.com/spring-ai-community/spring-ai-agentcore/releases/tag/v2.1.0) — latest release of the Spring Boot integrations for Amazon Bedrock AgentCore, following the 2.0.0 GA
- [Micronaut Framework 5.1.0 Released](https://micronaut.io/2026/07/27/micronaut-framework-5-1-0-release/) — Micronaut LangChain4j upgraded to 2.2.0 with Oracle chat-memory auto-configuration, Chroma embedding-store support, bean-resolved AI-service guardrails, and agentic support, alongside a Micronaut MCP update to track the latest MCP Java SDK
- [When Prompts Become Plugins: Generating User Extensions with Graal Script Agent](https://medium.com/graalvm/when-prompts-become-plugins-34a737d30f54) — introduces a library for turning end-user prompts into sandboxed, reusable JavaScript or Python extensions that run locally within application-defined boundaries
- [Pairing In-Process and Hosted Embeddings for Java MCP Tool Development](https://inside.java/2026/07/25/design-java-mcp-tool/) — designing an MCP server on Helidon that supports both local (DJL/MiniLM) and hosted (OpenAI) embedding models behind a stable tool interface
- [Embabel 1.0.0 Reaches GA](https://github.com/embabel/embabel-agent/releases/tag/v1.0.0) — the JVM agent framework's first stable release promotes experimental RAG APIs to production status, adds generic media/document support, exposes MCP server health via Spring Boot Actuator, and now resolves entirely from Maven Central
- [TornadoVM 5.2.0 Adds Native FP8 Tensor-Core Support](https://github.com/beehive-lab/TornadoVM/releases/tag/v5.2.0-jdk21) — packed half2 support and native FP8 conversion with FP8/BF16 tensor-core MMA for the CUDA backend, plus expanded BFloat16 array support and new activation-function epilogues (SiLU, Sigmoid, Tanh, HardSwish)
- [Inside Java Podcast: AI Solutions with Spring AI 2.0](https://inside.java/2026/07/23/podcast-063/) — Dan Vega and Lize Raes discuss Spring AI 2.0, deterministic agents, and MCP servers/skills, recorded at JavaOne 2026
- [AI Found the Bugs. Who's Patching Your EOL Java Code?](https://foojay.io/today/ai-found-the-bugs-whos-patching-your-eol-java-code/) — argues AI-assisted vulnerability discovery is now outpacing human-speed patching for unsupported Java dependencies, raising security-economics questions for the ecosystem
- [Koog 1.1.1 Released](https://github.com/JetBrains/koog/releases/tag/1.1.1) — adds Spring Boot 4 / Spring AI 2.0 starters and a WebClient-based HTTP client, non-text tool results (images/files), Amazon Bedrock memory auto-discovery, and a fix for a Spring AI 2.0 ClassCastException on generic ChatOptions
- [Solving Spring AI's UI Challenge with AG-UI's Java SDK](https://javapro.io/2026/07/16/solving-spring-ais-ui-challenge-with-ag-uis-java-sdk/) — walks through bridging Spring AI agent backends with frontend UIs like CopilotKit via a standardized streaming protocol
- [LangChain4j 1.18.0 Released](https://github.com/langchain4j/langchain4j/releases/tag/1.18.0) — introduces a Belief-Desire-Intention (BDI) agentic pattern, crash-resilient Human-in-the-Loop suspend/resume for agentic systems, OpenAI TTS support, and a revamped embedding-model API with per-call parameters and multimodal input
- [JVM Pulse: Profiling Java with GitHub Copilot](https://foojay.io/today/i-asked-github-copilot-to-profile-a-java-app-it-found-a-bug-in-my-heap-sizing-and-offered-to-fix-it/) — new open-source Copilot canvas extension from Bruno Borges automates GC/JFR profiling and hands off findings to Copilot for AI-driven tuning recommendations
- [Compiling OPA Policies to WebAssembly for Quarkus LangChain4j Guardrails](https://quarkus.io/blog/quarkus-langchain4j-opa-guardrails/) — sub-millisecond prompt-injection pattern matching via Open Policy Agent rules compiled to Wasm, falling back to an LLM only for inconclusive cases
- [Spring AI AgentCore SDK v2.0.0 Released](https://github.com/spring-ai-community/spring-ai-agentcore/releases/tag/v2.0.0) — major version release of the open-source Spring Boot integration for Amazon Bedrock AgentCore, with auto-configured invocation endpoints, SSE streaming, and short/long-term memory support
- [AgentScope Java v2.0.0 Tagged on GitHub](https://github.com/agentscope-ai/agentscope-java/releases/tag/v2.0.0) — formal GA release tag for the dual-layer ReActAgent/HarnessAgent architecture, a unified 28-event observability model, and a new permission engine
- [LangChain4j Agentic Workflows: From AI Calls to Multi-Agent Systems](https://javapro.io/2026/07/08/langchain4j-agentic-workflows-from-ai-calls-to-multi-agent-systems-in-java/) — tutorial covering sequential, loop, parallel, and conditional workflow patterns, goal-oriented planning, and supervisor-based multi-agent systems in Java
- [Building AI Systems with MongoDB: Implementing the Planning Pattern](https://foojay.io/today/building-ai-systems-with-mongodb-implementing-the-planning-pattern/) — tutorial building a travel-itinerary AI agent with Jakarta EE, Jakarta Data, LangChain4j, and MongoDB using a Reason-Act-Observe planning loop
- [A2A Java SDK 1.1.0.Final Released](https://a2aproject.github.io/a2a-java/posts/a2a-java-sdk-1-1-0-final-released/) — first feature release after GA, adding a TaskAuthorizationProvider SPI for per-user task authorization in multi-tenant deployments, plus a new project website
- [Self-Correcting Structured Output in Spring AI 2.0](https://spring.io/blog/2026/06/23/spring-ai-self-correcting-structured-output) — Christian Tzolov shows how `validateSchema()` detects malformed model output and retries up to three times with the specific validation errors fed back to the model, while `useProviderStructuredOutput()` enforces the schema natively on OpenAI, Anthropic, and Google GenAI
- [Tool Calling in Spring AI 2.0: A Composable, Agentic Architecture](https://spring.io/blog/2026/06/15/spring-ai-composable-tool-calling) — Christian Tzolov details lifting the tool-execution loop out of each chat model into the advisor chain as a recursive `ToolCallingAdvisor`, with `ToolSearchToolCallingAdvisor` progressive tool disclosure cutting tokens 34–64% across hundreds of tools
- [A2A Java SDK Reaches 1.0 GA](https://quarkus.io/blog/a2a-java-sdk-1-0-0-final-released/) — the Agent2Agent Java SDK ships 1.0.0.Final with spec classes converted to Java records, full JSON-RPC/gRPC/REST transport support, and a cross-SDK interop test kit, followed by 1.1.0 adding a `TaskAuthorizationProvider` SPI for per-user task authorization
- [Spring AI 2.0.0 GA Available Now](https://spring.io/blog/2026/06/12/spring-ai-2-0-0-GA-available-now) — built on Spring Boot 4 with Jackson 3 JSON handling and null-safety annotations, tool calling elevated to a composable advisor-chain component for agentic patterns, and Model Context Protocol integration with annotation-driven programming and enterprise security
- [MCP Java SDK 2.0.0 Reaches GA](https://github.com/modelcontextprotocol/java-sdk/releases/tag/v2.0.0) — first major version since 1.x, tracking the 2025-11-25 MCP spec with spec-accurate JSON Schema 2020-12 validation, enforced required fields, and SSE transport deprecated in favor of Streamable HTTP
- [AgentScope Java v2.0.0 GA](https://java.agentscope.io/v2/en/blogs/agentscope-v2-release.html) — Alibaba's JVM-native agent framework reaches production-ready GA, with event-streaming, permission-gated tool execution, and a Workspace abstraction for long-running, safely sandboxed agents
- [The Gen AI Iceberg: Java Tooling Edition](https://javapro.io/2026/06/03/the-gen-ai-iceberg-java-tooling-edition/) — a tiered tour of Java's generative AI tooling, from mainstream frameworks like Spring AI and LangChain4j down to GPU kernel compilation and Project Babylon, arguing the JVM runs AI workloads natively rather than just calling out to them
- [Koog 1.0 Is Out](https://blog.jetbrains.com/ai/2026/05/koog-1-0-is-out-stable-core-better-interop-and-multiplatform-observability/) — JetBrains' Kotlin/Java agent framework reaches a stable core with a 1-year API guarantee, a redesigned Java interop layer, decoupled HTTP transport, and OpenTelemetry observability

## Agent Frameworks & Libraries

Open-source frameworks and SDKs for building AI-powered applications on the JVM — from full agent platforms to Model Context Protocol implementations.

### Spring AI
- **Badge:** Framework
- **Description:** The Spring ecosystem's official AI framework. Portable abstractions across 20+ model providers, tool calling, RAG, chat memory, vector stores, and MCP support. Built by the Spring team at Broadcom.
- **Links:** [Docs](https://spring.io/projects/spring-ai/) · [GitHub](https://github.com/spring-projects/spring-ai) · [Overview](https://spring.io/ai/)

### LangChain4j
- **Badge:** Framework
- **Description:** The most popular Java LLM library. Unified API across 20+ LLM providers and 20+ embedding stores. Three levels of abstraction from low-level prompts to high-level AI Services. Supports RAG, tool calling, MCP, and agents.
- **Links:** [Docs](https://docs.langchain4j.dev/) · [GitHub](https://github.com/langchain4j/langchain4j)

### Embabel
- **Badge:** Framework
- **Description:** Created by Rod Johnson (Spring Framework creator). JVM agent framework using Goal-Oriented Action Planning (GOAP) for dynamic replanning. Strongly typed, Spring-integrated, MCP support. Written in Kotlin with full Java interop.
- **Links:** [GitHub](https://github.com/embabel/embabel-agent) · [Blog](https://medium.com/@springrod/embabel-a-new-agent-platform-for-the-jvm-1c83402e0014)

### Google ADK for Java
- **Badge:** Framework
- **Description:** Google's Agent Development Kit — code-first Java toolkit for building, evaluating, and deploying AI agents. Supports Gemini natively plus third-party models via LangChain4j integration. A2A protocol for agent-to-agent communication.
- **Links:** [Docs](https://google.github.io/adk-docs/get-started/java/) · [GitHub](https://github.com/google/adk-java) · [Codelab](https://codelabs.developers.google.com/adk-java-getting-started)

### Quarkus LangChain4j
- **Badge:** Framework
- **Description:** Enterprise-grade Quarkus extension for LangChain4j. Native compilation with GraalVM, built-in observability (metrics, tracing, auditing), and Dev UI tooling. Maintained by Red Hat & IBM.
- **Links:** [Docs](https://docs.quarkiverse.io/quarkus-langchain4j/dev/index.html) · [GitHub](https://github.com/quarkiverse/quarkus-langchain4j)

### Helidon LangChain4j
- **Badge:** Framework
- **Description:** Oracle's Helidon framework integration with LangChain4j. Declarative AI Services via Helidon Inject, build-time code generation for GraalVM native images, streaming chat over Java Streams, guardrails, built-in metrics, and agentic support (workflows and dynamic agents). Runs on virtual threads.
- **Links:** [Docs](https://helidon.io/docs/v4/se/ai/langchain4j/langchain4j) · [GitHub](https://github.com/helidon-io/helidon-examples/tree/helidon-4.x/examples/integrations/langchain4j)

### Helidon MCP
- **Badge:** Framework
- **Description:** Helidon's Model Context Protocol server and client implementation. Declarative and imperative APIs for building MCP servers with tools, resources, and prompts. Streamable HTTP and SSE transports, virtual threads, build-time processing. From Oracle's Helidon team.
- **Links:** [Docs](https://helidon.io/docs/v4/se/ai/mcp) · [GitHub](https://github.com/helidon-io/helidon-mcp)

### Micronaut MCP
- **Badge:** Framework
- **Description:** Official Micronaut module for building Model Context Protocol servers and clients. STDIO and Streamable HTTP transports, annotation and factory-based tools/prompts/resources, a LangChain4j-backed client option, and GraalVM native image support. Maintained by the Micronaut project team.
- **Links:** [Docs](https://micronaut-projects.github.io/micronaut-mcp/latest/guide/) · [GitHub](https://github.com/micronaut-projects/micronaut-mcp)

### Micronaut LangChain4j
- **Badge:** Framework
- **Description:** Official Micronaut integration for LangChain4j. Compile-time-generated AI services with modules for OpenAI, Anthropic, Azure, Bedrock, Google AI, and Mistral, plus embedding stores for PostgreSQL, Redis, MongoDB, Elasticsearch, Neo4j, Chroma, and Qdrant. Version 2.2.0 adds agentic support, Chroma embedding stores, and guardrails resolved from Micronaut beans.
- **Links:** [Docs](https://micronaut-projects.github.io/micronaut-langchain4j/latest/guide/) · [GitHub](https://github.com/micronaut-projects/micronaut-langchain4j)

### LangChain4j-CDI
- **Badge:** Framework
- **Description:** CDI extension for LangChain4j (part of the LangChain4j project) that brings AI services to Jakarta EE and MicroProfile applications. Inject AI services as CDI beans with `@RegisterAIService`, configure via MicroProfile Config, and add resilience with Fault Tolerance. Supports Quarkus, Helidon, WildFly, Payara, GlassFish, Liberty, and any CDI-capable runtime.
- **Links:** [GitHub](https://github.com/langchain4j/langchain4j-cdi)

### LangGraph4j
- **Badge:** Framework
- **Description:** Build stateful, multi-agent applications with cyclical graphs. Inspired by Python's LangGraph, works with both LangChain4j and Spring AI. Persistent checkpoints, deep agent architectures, and a Studio web UI.
- **Links:** [Docs](https://langgraph4j.github.io/langgraph4j/) · [GitHub](https://github.com/langgraph4j/langgraph4j)

### Akka Agents
- **Badge:** Framework
- **Description:** Agentic AI platform built on Akka's actor model for distributed, resilient systems. Declarative Effects API for building goal-directed agents with durable memory, multi-agent orchestration, and automatic scaling. Task-based autonomous agents with pause/resume/terminate lifecycle control and task result subscriptions, alongside request-based agents. MCP and A2A protocol support, pluggable LLM providers, runtime prompt updates, and agents auto-exposed as HTTP, gRPC, or MCP endpoints. Java and Scala SDKs.
- **Links:** [Docs](https://doc.akka.io/) · [GitHub](https://github.com/akka/akka-sdk) · [Website](https://akka.io/akka-agents)

### Koog (JetBrains)
- **Badge:** Framework
- **Description:** Kotlin-native agent framework from JetBrains. Type-safe DSL, multiplatform (JVM, JS, WasmJS, Android, iOS), A2A protocol support, fault tolerance with persistence, and multi-LLM support.
- **Links:** [Website](https://www.jetbrains.com/koog/) · [GitHub](https://github.com/JetBrains/koog) · [Docs](https://docs.koog.ai/)

### Semantic Kernel (Java)
- **Badge:** Framework
- **Description:** Microsoft's AI orchestration SDK with first-class Java support. Provides prompt chaining, planning, memory, and agent framework abstractions with deep Azure integration.
- **Links:** [GitHub](https://github.com/microsoft/semantic-kernel-java)

### JamJet
- **Badge:** Framework
- **Description:** Production-grade agent runtime with native Java SDK. Rust core (Tokio) for performance, graph-based durable workflow orchestration with event-sourced state, automatic crash recovery, audit trails, and first-class human-in-the-loop. Native MCP client/server and A2A protocol support. Java SDK uses records, virtual threads, and fluent builder API. Apache 2.0.
- **Links:** [Docs](https://docs.jamjet.dev) · [GitHub](https://github.com/jamjet-labs/jamjet) · [Examples](https://github.com/jamjet-labs/jamjet/tree/main/sdk/java/examples)

### Spring AI AgentCore SDK
- **Badge:** SDK
- **Description:** Spring Boot integrations for Amazon Bedrock AgentCore. Auto-configures `/invocations` and `/ping` endpoints, SSE streaming, short- and long-term memory, browser automation via Playwright, and a secure code interpreter. Deploy to AgentCore Runtime (managed, scales to zero) or standalone on EKS/ECS.
- **Links:** [Docs](https://springaicommunity.mintlify.app/projects/incubating/spring-ai-bedrock-agentcore) · [GitHub](https://github.com/spring-ai-community/spring-ai-agentcore)

### MCP Java SDK
- **Badge:** SDK
- **Description:** The official Java SDK for Model Context Protocol servers and clients. Maintained by the Spring AI team. Sync/async, STDIO/Streamable HTTP transports (SSE deprecated as of 2.0), OAuth support via Spring integration. Reached v2.0.0 GA in June 2026 with spec-accurate JSON Schema 2020-12 validation and enforced required fields.
- **Links:** [Docs](https://modelcontextprotocol.io/sdk/java/mcp-overview) · [GitHub](https://github.com/modelcontextprotocol/java-sdk)

### Anthropic Java SDK
- **Badge:** SDK
- **Description:** Official Java SDK for the Claude Messages API. Streaming, retries, structured outputs, extended thinking, code execution, and files API. Build Java apps powered by Claude.
- **Links:** [GitHub](https://github.com/anthropics/anthropic-sdk-java)

### GitHub Copilot SDK for Java
- **Badge:** SDK
- **Description:** Official Java SDK for embedding the GitHub Copilot agentic engine directly into Java applications. Uses the same agentic harness that powers the Copilot CLI — exposes planning, tool calling, file editing, and MCP integration via a simple Java API. Currently in technical preview.
- **Links:** [Docs](https://github.github.com/copilot-sdk-java/) · [GitHub](https://github.com/github/copilot-sdk-java)

### Graal Script Agent
- **Badge:** Library
- **Description:** Early-preview Java library for adding prompt-authored extensions to Java applications. Developers define where an application can be extended; users prompt for the behavior they need. It produces reusable JavaScript or Python extensions that execute locally in a sandbox without another model call.
- **Links:** [Docs](https://graalvm.github.io/graal-script-agent/latest/) · [GitHub](https://github.com/graalvm/graal-script-agent) · [Blog](https://medium.com/graalvm/when-prompts-become-plugins-34a737d30f54)

### Tracy (JetBrains)
- **Badge:** Library
- **Description:** AI tracing library for Kotlin and Java. Captures structured traces from LLM interactions — messages, cost, token usage, and execution time. Implements OpenTelemetry Generative AI Semantic Conventions with exports to Langfuse, Weights & Biases, and more.
- **Links:** [Docs](https://jetbrains.github.io/tracy/latest) · [GitHub](https://github.com/JetBrains/tracy)

### Docling Java
- **Badge:** Library
- **Description:** Official Java client for Docling Serve — invoke document conversion, table detection, formula recognition, reading order analysis, OCR, and more from Java via the Docling Serve backend.
- **Links:** [Docs](https://docling-project.github.io/docling-java/current) · [GitHub](https://github.com/docling-project/docling-java)

### OmniHai
- **Badge:** Library
- **Description:** Unified Java AI utility library for Jakarta EE and MicroProfile. Single API across 10 providers with zero external runtime dependencies — just java.net.http.HttpClient. Chat, streaming, structured outputs, web search, translation, and moderation in a lightweight JAR.
- **Links:** [Website](https://omnihai.org) · [GitHub](https://github.com/omnifaces/omnihai) · [Javadoc](https://javadoc.io/doc/org.omnifaces/omnihai)

### ACP Langchain4j bridge
- **Badge:** Library
- **Description:** An ACP client bridging the official [Kotlin ACP sdk](https://agentclientprotocol.com/libraries/kotlin) to [LangChain4j](https://docs.langchain4j.dev/intro/) and [LangGraph4j](https://github.com/langgraph4j/langgraph4j).
- **Links:** [GitHub](https://github.com/OsgiliathEnterprise/acp-langgraph-langchain-bridge)

### A2A Java SDK
- **Badge:** SDK
- **Description:** The official Java SDK for [Agent-2-Agent Protocol (A2A)](https://a2a-protocol.org) servers and clients. Reference implementation based on Quarkus. Reached 1.0 GA in June 2026 with full JSON-RPC/gRPC/REST transport support, OpenTelemetry integration, and a cross-SDK interop test kit; 1.1.0 added a `TaskAuthorizationProvider` SPI for per-user task authorization; 1.2.0 (Aug 2026) adds programmatic auth wiring and a `TaskStreamLifecycleHook`, with breaking changes to `TaskState` naming and cross-module package resolution.
- **Links:** [GitHub](https://github.com/a2aproject/a2a-java)

### A2A Java SDK for Jakarta Servers
- **Badge:** SDK
- **Description:** Integration of the [A2A Java SDK](https://github.com/a2aproject/a2a-java) for use in Jakarta EE servers (WildFly, Tomcat, Jetty, OpenLiberty, and others).
- **Links:** [GitHub](https://github.com/wildfly-extras/a2a-java-sdk-server-jakarta)

### WildFly AI Feature Pack
- **Badge:** Framework
- **Description:** A feature pack for WildFly, providing seamless LangChain4j-CDI integration and exposing Jakarta EE code as MCP tools via MCP_JAVA Annotations.
- **Links:** [GitHub](https://github.com/wildfly-extras/wildfly-ai-feature-pack)

### MCP_JAVA Annotations
- **Badge:** Library
- **Description:** A framework-agnostic Java library providing core annotations and APIs for implementing Model Context Protocol (MCP) servers and clients. Used by WildFly AI Feature Pack and LangChain4j-CDI. Compatible with OpenLiberty, Quarkus, and other Java frameworks.
- **Links:** [GitHub](https://github.com/mcp-java/java-mcp-annotations)

### Atmosphere
- **Badge:** Framework
- **Description:** A portable layer across Java AI runtimes. Write `@Agent` once against a unified API (tool calling, memory, streaming, structured output); swap the runtime — Spring AI, LangChain4j, Google ADK, Embabel, Koog, or built-in OpenAI — by changing one dependency. `@Coordinator` orchestrates multi-agent fleets with parallel, sequential, and conditional routing. Served over transports (WebTransport/HTTP3, WebSocket, SSE, long-polling, gRPC) and protocols (MCP, A2A, AG-UI). Built by Async-IO.
- **Links:** [Docs](https://atmosphere.github.io/docs/) · [GitHub](https://github.com/Atmosphere/atmosphere) · [Async-IO](https://async-io.live)

### Spring AI Agent Utils
- **Badge:** Library
- **Description:** Community library from the Spring AI team that brings Claude Code-inspired agentic primitives to Spring AI applications — file operations, shell execution, web fetch, task/subagent orchestration, auto-memory, and a portable Agent Skills implementation. Built on Spring AI 2.0's agentic foundation.
- **Links:** [Docs](https://springaicommunity.mintlify.app/projects/incubating/spring-ai-agent-utils) · [GitHub](https://github.com/spring-ai-community/spring-ai-agent-utils)

### Spring AI A2A
- **Badge:** SDK
- **Description:** Spring Boot integration for building Agent2Agent (A2A) protocol servers. Auto-configured JSON-RPC endpoints and task lifecycle handling wired to Spring AI's `ChatClient`, with full `@Tool` support and multi-agent orchestration. Complements the A2A Java SDK for Spring applications.
- **Links:** [GitHub](https://github.com/spring-ai-community/spring-ai-a2a)

### Spring AI Session
- **Badge:** Library
- **Description:** Event-sourced conversation memory for Spring AI. Structured events with identity, timestamps, and session ownership; turn-aware compaction that trims history on turn boundaries; multi-agent branch isolation; keyword search over history; and pluggable in-memory or JDBC repositories via an SPI.
- **Links:** [Docs](https://spring-ai-community.github.io/spring-ai-session/) · [GitHub](https://github.com/spring-ai-community/spring-ai-session)

### Spring AI Playground
- **Badge:** Framework
- **Description:** Cross-platform desktop app from the Spring AI Community that acts as a local execution layer for AI agent tools. Build, test, and publish MCP tools with a deny-first sandbox, per-tool risk levels, and human-in-the-loop approval for sensitive operations, plus observability dashboards for tokens, cost, traces, and tool execution.
- **Links:** [GitHub](https://github.com/spring-ai-community/spring-ai-playground)

### Jakarta Agentic AI
- **Badge:** Framework
- **Description:** Eclipse Foundation specification proposal defining vendor-neutral APIs for building, deploying, and running AI agents on Jakarta EE runtimes. Annotation-based programming model (`@Agent`, `@Trigger`, `@Decision`, `@Action`, `@Outcome`) built on CDI, REST, and Config — pluggable with existing frameworks like LangChain4j and Spring AI rather than replacing them. Proposed by Payara and Reza Rahman, backed by Oracle, Fujitsu, and OmniFish; version 1.0 under active development.
- **Links:** [GitHub](https://github.com/jakartaee/agentic-ai) · [Spec](https://jakarta.ee/specifications/agentic-ai/1.0/) · [Announcement](https://payara.fish/blog/announcing-the-jakarta-agentic-ai-project/)

### AgentScope Java
- **Badge:** Framework
- **Description:** Alibaba's JVM-native framework for production-ready, distributed agents. Event-streaming architecture, permission-gated tool execution, sandboxed tool execution (local, Docker, or Kubernetes), and distributed session/memory backed by Redis, MySQL, or PostgreSQL. Reached v2.0.0 GA in July 2026.
- **Links:** [Website](https://java.agentscope.io/) · [GitHub](https://github.com/agentscope-ai/agentscope-java)

### JVector
- **Badge:** Library
- **Description:** DataStax's pure-Java embedded vector search engine combining DiskANN and HNSW graph designs for approximate nearest-neighbor search, SIMD-accelerated via the Java Vector API. Supports indexes larger than available memory via two-pass compressed search. Powers Apache Cassandra and AstraDB; has an official LangChain4j embedding-store integration.
- **Links:** [GitHub](https://github.com/datastax/jvector) · [LangChain4j Integration](https://docs.langchain4j.dev/integrations/embedding-stores/jvector/)

### Milvus Java SDK
- **Badge:** Library
- **Description:** Official Java client for Milvus, one of the most widely used open-source vector databases. Type-safe API for collection management, ANN and hybrid search, and bulk import, with modular packaging (core plus an optional BulkWriter module). Commonly paired with LangChain4j and Spring AI vector-store integrations in Java RAG stacks.
- **Links:** [GitHub](https://github.com/milvus-io/milvus-sdk-java) · [Website](https://milvus.io)

### ClawRunr
- **Badge:** Framework
- **Description:** Open-source personal AI agent runtime built on Spring Boot, Spring AI, and JobRunr — runs entirely on your own hardware. Schedules and executes background tasks, browses the web, and manages work via human-readable Markdown files, with multi-channel support (chat UI, Telegram, Discord) and pluggable OpenAI, Anthropic, or Ollama providers. Built by the JobRunr team; started as a demo and grew into a community project.
- **Links:** [GitHub](https://github.com/ClawRunr/JavaClaw) · [Website](https://clawrunr.io/)

### MCP Toolbox Java SDK
- **Badge:** SDK
- **Description:** Official Google Java client library for MCP Toolbox. Lets Java agents (Spring Boot, Quarkus, Jakarta EE) discover and invoke Toolbox-defined tools — database queries, API calls — via a type-safe, async-first `CompletableFuture` API with Google Cloud ADC authentication. Apache 2.0; feature set is still catching up to the Python, JS, and Go SDKs.
- **Links:** [Docs](https://mcp-toolbox.dev/documentation/introduction/) · [GitHub](https://github.com/googleapis/mcp-toolbox-sdk-java)

### Tools4AI
- **Badge:** Framework
- **Description:** Pure-Java agentic framework that converts natural-language prompts into executable actions — Java method calls, REST endpoints, shell commands, or Swagger API calls — with multi-provider support across Gemini, OpenAI, Anthropic, and LocalAI. No Spring dependency required. MIT licensed, published to Maven Central.
- **Links:** [GitHub](https://github.com/vishalmysore/Tools4AI) · [Javadoc](https://javadoc.io/doc/io.github.vishalmysore/tools4ai/latest/index.html)

### EclipseStore
- **Badge:** Library
- **Description:** Eclipse Foundation's Java-native persistence engine — stores and loads full object graphs with microsecond response times, no JPA/ORM overhead. Version 4 integrates JVector into its GigaMap indexing, turning EclipseStore into an embedded, pure-Java vector database: HNSW similarity search, on-disk indexing for datasets larger than memory, and Product Quantization to shrink embedding footprints by up to 90%.
- **Links:** [GitHub](https://github.com/eclipse-store/store) · [Docs](https://docs.eclipsestore.io/manual/gigamap/index.html) · [Blog](https://microstream.one/blog/2026/02/13/eclipsestore-4-beta-build-vector-database-apps-with-pure-java/)

### OpenAI Java SDK
- **Badge:** SDK
- **Description:** Official Java client library for the OpenAI API, maintained by OpenAI. Written in Kotlin with full Java interop. Typed access to Chat Completions and the Responses API, streaming, structured outputs, function calling, and asynchronous execution via `CompletableFuture`.
- **Links:** [GitHub](https://github.com/openai/openai-java)

### Google Gen AI Java SDK
- **Badge:** SDK
- **Description:** Google's official Java SDK unifying access to the Gemini Developer API and Vertex AI. Supports Gemini text/chat, Imagen image generation, Veo video generation, embeddings, token counting, and automatic function calling. Distinct from Google ADK for Java, which is an agent-orchestration framework built on top of it.
- **Links:** [GitHub](https://github.com/googleapis/java-genai) · [Docs](https://googleapis.github.io/java-genai/javadoc/)

### IBM watsonx.ai Java SDK
- **Badge:** SDK
- **Description:** IBM's official Java SDK for the watsonx.ai enterprise AI platform. Chat completions, streaming, tool calling, embeddings, text classification/extraction/detection, reranking, and time-series forecasting, for both IBM Cloud and on-premises deployments. Integrates with LangChain4j, Quarkus, and Apache Camel.
- **Links:** [GitHub](https://github.com/IBM/watsonx-ai-java-sdk) · [Docs](https://ibm.github.io/watsonx-ai-java-sdk/)

### Spring AI — watsonx.ai
- **Badge:** SDK
- **Description:** Official Spring AI integration for IBM watsonx.ai. Spring Boot auto-configuration for chat (IBM Granite, Meta Llama, Mistral AI), embeddings, content moderation (HAP, PII, Granite Guardian), and document reranking via Spring AI's portable abstractions. Supports streaming, function calling, and reactive programming with WebFlux.
- **Links:** [Docs](https://spring-ai-community.github.io/spring-ai-watsonx-ai) · [GitHub](https://github.com/spring-ai-community/spring-ai-watsonx-ai)

### SAP AI SDK for Java
- **Badge:** SDK
- **Description:** SAP's official Java SDK for integrating generative AI into enterprise applications via SAP AI Core and the Generative AI Hub. Orchestration service integration, prompt templating, grounding, data masking, content filtering, and Spring AI compatibility.
- **Links:** [GitHub](https://github.com/SAP/ai-sdk-java)

### Tiberius
- **Badge:** Library
- **Description:** Java security and safety testing framework for Java LLM-enriched applications, integrating with JUnit 5 and Spring Boot so adversarial testing lives in the standard test suite. 200+ attack probes across the OWASP LLM Top 10, probabilistic testing (via PUnit) for non-deterministic LLM outputs, fixture-based regression testing, bias testing, model fingerprinting, and LangChain4j guardrail validation. Apache 2.0.
- **Links:** [Docs](https://github.com/tiberius-security/tiberius/tree/main/docs) · [GitHub](https://github.com/tiberius-security/tiberius) · [Blog](https://foojay.io/today/tiberius-a-security-testing-framework-for-llm-applications-in-java/) · [Article](https://dev.karakun.com/2026/07/20/llm-security-testing-java-tiberius.html) · [Podcast](https://foojay.io/today/foojay-podcast-99/)

### Ollama4j
- **Badge:** SDK
- **Description:** Java client library for running models via a local Ollama server. Chat, streaming, tool/function calling (including MCP tools), vision-model image inputs, embeddings, and model management, with Prometheus metrics support. Requires Java 17+, published to Maven Central.
- **Links:** [GitHub](https://github.com/ollama4j/ollama4j)

### Cohere Java SDK
- **Badge:** SDK
- **Description:** Official Java SDK for the Cohere API, generated and maintained by Cohere. Chat, embeddings, reranking, and classification endpoints, published to Maven Central.
- **Links:** [GitHub](https://github.com/cohere-ai/cohere-java)

### kyo-ai
- **Badge:** Framework
- **Description:** AI module for Kyo, the Scala 3 algebraic-effects toolkit. Makes an LLM call a typed, composable value — schema derived from your result type, tool-call loop and conversation threading handled automatically, streaming and persistent actor-backed agents included. Multi-provider (OpenAI-compatible, Anthropic). As of Kyo 1.0.0-RC5 (July 2026), `kyo-ai` moved into the main Kyo monorepo alongside a new `kyo-mcp` module for MCP server/client support. Apache 2.0.
- **Links:** [GitHub](https://github.com/getkyo/kyo/tree/main/kyo-ai) · [Website](https://getkyo.io/)

### zio-bedrock-converse
- **Badge:** SDK
- **Description:** Typed Scala 3 access to the Amazon Bedrock Converse API, built on ZIO and ZIO HTTP. Three abstraction levels — low-level wire control, single-turn requests, and multi-turn agentic loops with automatic tool dispatching — with tool schemas derived from Scala types and streaming text responses. Typed end-to-end with no `DynamicValue` in the public API. Apache 2.0.
- **Links:** [GitHub](https://github.com/jamesward/zio-bedrock-converse)

### zio-http-mcp
- **Badge:** Library
- **Description:** MCP server and client library for Scala 3, ZIO, and ZIO HTTP. Typed tool inputs and outputs via ZIO Schema, resources and prompt templates, OAuth 2.1 authorization, stateful or stateless HTTP transports, and server-initiated messaging (sampling, elicitation) over SSE. Supports the 2025-11-25 and 2026-07-28 MCP protocol revisions with automatic negotiation.
- **Links:** [GitHub](https://github.com/jamesward/zio-http-mcp)

### Tachyon
- **Badge:** Framework
- **Description:** Java 21+ runtime for building high-performance MCP servers — write blocking handler code and virtual threads run it off the Netty event loop, no reactive boilerplate. Stateless by default with optional sessions, native Netty transports (io_uring/epoll/kqueue), a Kotlin DSL, and full MCP 2025-11-25 and 2026-07-28 support verified against the official conformance tests. Apache 2.0, currently in beta.
- **Links:** [GitHub](https://github.com/kpavlov/tachyon)

### Solon-AI
- **Badge:** Framework
- **Description:** Full-scenario Java AI application module of the Solon framework ecosystem — LLM chat and tool/skill calling, RAG knowledge bases, MCP client/server support, and ReAct/multi-agent Team orchestration. Embeddable in Spring Boot, jFinal, Vert.x, and Quarkus; supports Java 8–25.
- **Links:** [GitHub](https://github.com/opensolon/solon-ai)

### AWS SDK for Java v2 — Bedrock
- **Badge:** SDK
- **Description:** Official AWS SDK for Java v2 modules (`bedrockruntime`, `bedrockagentruntime`) for invoking Amazon Bedrock foundation models and Bedrock Agents from Java. Part of the actively-released `aws-sdk-java-v2` monorepo, joining the Google Gen AI, IBM watsonx.ai, and SAP AI Java SDKs already listed here.
- **Links:** [GitHub](https://github.com/aws/aws-sdk-java-v2) · [Maven Central](https://central.sonatype.com/artifact/software.amazon.awssdk/bedrockruntime)

### Qdrant Java Client
- **Badge:** Library
- **Description:** Official Java client for the Qdrant vector database — collection management, vector upsert, and similarity search over gRPC, with sync and async (`ListenableFuture`) support.
- **Links:** [GitHub](https://github.com/qdrant/java-client)

### Weaviate Java Client
- **Badge:** Library
- **Description:** Official Java client for the Weaviate vector database. The v6 line is a from-scratch rewrite for Weaviate ≥1.32, replacing the deprecated v5 client, with a "Tucked Builder" API for data ingestion, semantic search, filtering, and collection management.
- **Links:** [GitHub](https://github.com/weaviate/java-client) · [Docs](https://docs.weaviate.io/weaviate/client-libraries/java)

### Pinecone Java Client
- **Badge:** Library
- **Description:** Official Java client for the Pinecone vector database. Covers index management (serverless, pod-based, BYOC, sparse indexes), collection operations, vector upsert/query/fetch/update/delete, namespace management, and Pinecone's hosted inference API (embeddings, reranking, model listing). Joins the site's other official vector-database clients for Qdrant, Weaviate, and Milvus.
- **Links:** [GitHub](https://github.com/pinecone-io/pinecone-java-client)

### Testcontainers Ollama Module
- **Badge:** Library
- **Description:** Official Testcontainers-for-Java module for spinning up disposable Ollama containers in integration tests — pull models and commit custom images for reuse. Commonly paired with Spring AI, LangChain4j, and Quarkus LangChain4j to test local-LLM code paths.
- **Links:** [Docs](https://java.testcontainers.org/modules/ollama/) · [GitHub](https://github.com/testcontainers/testcontainers-java/tree/main/modules/ollama)

### Camel LangChain4j Components
- **Badge:** Framework
- **Description:** Official Apache Camel components that let Camel routes call any LangChain4j-supported LLM. The chat component (since Camel 4.5) covers single, prompted, and multi-turn LLM calls with RAG enrichment; the agent component (since Camel 4.14) adds stateful/stateless AI agents that can invoke Camel routes as tools plus MCP client integration. Producer-only endpoints (`langchain4j-chat:id`, `langchain4j-agent:id`) fit naturally into existing integration pipelines. Camel 4.22 generalized tool exposure beyond LangChain4j: `camel-ai-tool` defines a route-as-tool once for use with LangChain4j, Spring AI, or OpenAI, and `camel-mcp-server` exposes tagged routes directly as MCP tools.
- **Links:** [Chat Component Docs](https://camel.apache.org/components/next/langchain4j-chat-component.html) · [Agent Component Docs](https://camel.apache.org/components/next/langchain4j-agent-component.html) · [GitHub](https://github.com/apache/camel)

### Google ADK for Kotlin
- **Badge:** Framework
- **Description:** Google's official Kotlin port of the Agent Development Kit — code-first orchestration of tools, agents, and multi-agent hierarchies in Kotlin, with an included dev UI for testing and evaluation. A companion Android flavor adds on-device agent support (Gemini Nano) with cloud fallback.
- **Links:** [Docs](https://adk.dev/) · [GitHub](https://github.com/google/adk-kotlin) · [Announcement](https://developers.googleblog.com/adk-kotlin-android-building-ai-agents/)

### LLM4S
- **Badge:** Framework
- **Description:** Scala 3 framework for building LLM applications — multi-provider support (OpenAI, Anthropic, Azure OpenAI, Gemini, DeepSeek, Cohere, Mistral, OpenRouter, Ollama), an agent framework with tool calling via ScalaMeta, RAG/vector stores, multimodal input, and OpenTelemetry/Langfuse tracing. Broader in scope than the site's other Scala entries — closer to a "LangChain4j for Scala." Pre-1.0, MIT licensed, active development with weekly community dev-hours.
- **Links:** [Website](https://llm4s.org/) · [GitHub](https://github.com/llm4s/llm4s)

### Agents-Flex
- **Badge:** Framework
- **Description:** Lightweight Java AI application framework positioned as a Spring AI counterpart — unified abstractions for LLM calls, tool calling, agents (ReAct, routing, sub-agents), RAG with vector stores, MCP, a Skills system, and Text2SQL. Runs on plain Java, Spring Boot, or other JVM stacks (Java 8+). Apache 2.0, with frequent releases.
- **Links:** [GitHub](https://github.com/agents-flex/agents-flex)

### Spring AI Alibaba
- **Badge:** Framework
- **Description:** Alibaba Cloud's production-ready framework for agentic, workflow, and multi-agent Java applications, built on top of Spring AI. Graph-based orchestration (`SequentialAgent`, `ParallelAgent`, `RoutingAgent`, `LoopAgent`), multimodal ReactAgent support, MCP integration, and a built-in Admin observability/eval console. JDK 17+, Apache 2.0.
- **Links:** [GitHub](https://github.com/alibaba/spring-ai-alibaba)

### BoxLang AI
- **Badge:** Framework
- **Description:** Unified AI platform for the JVM from Ortus Solutions (makers of BoxLang and ColdBox). One API across 15+ providers, plus multi-agent orchestration with parent-child hierarchies, an Agent Skills system implementing Anthropic's open standard, MCP support (consuming and serving), 20+ memory types with vector RAG, and a composable middleware pipeline. Reached v3.0 in 2026. Apache 2.0.
- **Links:** [Website](https://ai.boxlang.io/) · [Docs](https://ai.ortusbooks.com) · [GitHub](https://github.com/ortus-boxlang/bx-ai)

## Java with Code Assistants

Technologies that supercharge Java development when paired with AI code assistants — from MCP servers that give agents live Javadoc access, to reusable skill packages and IDE integrations.

### AI-Git-Bot
- **Badge:** Assistant
- **Description:** Self-hosted AI workflow automation platform for Git repositories. Automates pull request reviews, test generation, issue management and documentation synchronization with the support of multiple AI backends. Pure Java application with a Spring-Boot based architecture.
- **Links:** [Website](https://gitbot.eu/) · [GitHub](https://github.com/tmseidel/ai-git-bot)

### Javadocs.dev MCP Server
- **Badge:** MCP Server
- **Description:** Gives AI assistants live access to Java, Kotlin, and Scala library documentation from Maven Central. Six tools including latest-version lookup, Javadoc symbol browsing, and source file retrieval. Connect any MCP client via Streamable HTTP.
- **Links:** [Javadocs.dev MCP Server](https://www.javadocs.dev/)

### JetBrains AI
- **Badge:** Assistant
- **Description:** AI-powered coding assistance built into IntelliJ IDEA and all JetBrains IDEs. Context-aware code completion, next-edit suggestions, and an agent-mode chat for refactoring, test generation, and complex tasks. Deep understanding of Java, Kotlin, and Scala project conventions. Supports cloud LLMs (Gemini, OpenAI, Anthropic) plus bring-your-own-key.
- **Links:** [Website](https://www.jetbrains.com/ai/) · [Docs](https://www.jetbrains.com/help/idea/ai-assistant.html)

### SkillsJars
- **Badge:** Skills
- **Description:** A packaging format and registry for distributing reusable AI agent skills as Maven/Gradle JARs. Skills are Markdown files (`SKILL.md`) under `META-INF/skills/` that teach AI agents domain-specific patterns. Discover and load skills on demand in Claude Code, Kiro, and Spring AI apps.
- **Links:** [SkillsJars](https://www.skillsjars.com/)

### jvm-skills
- **Badge:** Skills
- **Description:** Curated directory of AI coding skills from JVM ecosystem engineers. Opinionated best-practice guides that AI tools (Claude Code, Cursor, Copilot) use as context — covering Spring Boot, jOOQ, Testcontainers, Docker, and more. Only lists skills that teach AI something it wouldn't know on its own.
- **Links:** [Website](https://jvmskills.com) · [GitHub](https://github.com/jvm-skills/jvm-skills)

### Awesome GitHub Copilot
- **Badge:** Skills
- **Description:** Awesome Copilot Skills is a curated registry of reusable AI agent skills that developers can plug into agents, providing ready-made capabilities, prompts, and workflows. It helps Java AI developers quickly extend agent functionality without building everything from scratch.
- **Links:** [Website](https://awesome-copilot.github.com/skills/?q=java)

### jOOQ MCP Server
- **Badge:** MCP Server
- **Description:** Gives AI assistants live access to jOOQ documentation and examples. Connect any MCP client via Streamable HTTP.
- **Links:** [jOOQ MCP Server](https://jooq-mcp.martinelli.ch/)

### Vaadin MCP Server
- **Badge:** MCP Server
- **Description:** Gives AI assistants live access to Vaadin documentation and examples. Connect any MCP client via Streamable HTTP.
- **Links:** [Vaadin MCP Server](https://mcp.vaadin.com/docs/)

### Quarkus Agent MCP
- **Badge:** MCP Server
- **Description:** Official standalone MCP server from the Quarkus team that teaches AI coding agents to work with Quarkus applications — scaffolding new projects, controlling the app lifecycle, proxying Dev MCP tools, and searching Quarkus documentation. Runs as a separate process that survives app crashes, so agents can read logs and fix code after failures. Works with Claude Code, GitHub Copilot, Cursor, and JetBrains AI.
- **Links:** [Blog](https://quarkus.io/blog/introducing-agent-mcp/) · [GitHub](https://github.com/quarkusio/quarkus-agent-mcp)

### Open Liberty MCP Server
- **Badge:** MCP Server
- **Description:** Built-in Open Liberty runtime feature (`mcpServer-1.0`) that exposes Jakarta EE and CDI business logic as MCP tools for agentic AI workflows — role-based authorization, dynamic tool registration, and streamable transport. Actively developed through 2026 beta releases from IBM's Open Liberty team.
- **Links:** [Blog](https://openliberty.io/blog/2025/10/23/mcp-standalone-blog.html) · [Website](https://openliberty.io/)

### Maven Tools MCP
- **Badge:** MCP Server
- **Description:** Gives AI agents dependency intelligence from Maven Central — version lookup, upgrade comparisons, CVE and license health signals, and POM-aware resolution across Maven, Gradle, SBT, and Mill projects.
- **Links:** [Maven Tools MCP](https://github.com/arvindand/maven-tools-mcp)

### JVM Pulse
- **Badge:** Extension
- **Description:** GitHub Copilot canvas extension that profiles a Java project's garbage collection and JFR telemetry — detects the build tool and JDK, runs a representative workload, and analyzes it with Microsoft's GCToolkit and the JDK `jfr` CLI. Surfaces throughput, pause times, heap usage, and allocation hot spots in an interactive dashboard, with an "Analyze with AI" hand-off into Copilot for tuning recommendations. Created by Bruno Borges. MIT license.
- **Links:** [GitHub](https://github.com/brunoborges/jvm-pulse) · [Blog](https://foojay.io/today/i-asked-github-copilot-to-profile-a-java-app-it-found-a-bug-in-my-heap-sizing-and-offered-to-fix-it/)

### IntelliJ IDEA MCP Server
- **Badge:** MCP Server
- **Description:** IntelliJ IDEA's built-in Model Context Protocol server, bundled and enabled by default since version 2025.2. Exposes over 100 IDE tools — code analysis, refactoring, debugging, run configurations, database operations — to external MCP clients like Claude Code, Claude Desktop, Cursor, and VS Code. Distinct from the JetBrains AI Assistant plugin above.
- **Links:** [IntelliJ IDEA MCP Server](https://www.jetbrains.com/help/idea/mcp-server.html)

### Apache Camel MCP Server
- **Badge:** MCP Server
- **Description:** Official MCP server shipped with Apache Camel 4.18 (`camel-jbang-mcp`). Exposes the live Camel and Kamelet catalogs, endpoint-URI validation, route understanding, and security analysis as 16 tools for AI coding assistants. Built on Quarkus, runs via JBang, supports STDIO and HTTP/SSE transports.
- **Links:** [Blog](https://camel.apache.org/blog/2026/02/camel-jbang-mcp/) · [Manual](https://camel.apache.org/manual/camel-jbang-mcp.html)

### Micronaut Fun MCP Server
- **Badge:** MCP Server
- **Description:** Official MCP server from the Micronaut project team giving AI assistants search access to Micronaut documentation and Guides via OpenSearch. Confirmed integrations for Claude Code, Claude Desktop, VS Code, cline, and IntelliJ IDEA.
- **Links:** [Website](https://micronaut.fun/) · [GitHub](https://github.com/micronaut-projects/micronaut-fun)

### Develocity MCP Servers
- **Badge:** MCP Server
- **Description:** Two official MCP servers from Gradle Inc.'s Develocity platform — one for per-build data (exceptions, stack traces, test outcomes, cache performance) to investigate failures, and an Analytics server for org-wide queries like flaky-test trends and cache effectiveness. Covers Gradle, Maven, sbt, and Bazel builds.
- **Links:** [Develocity MCP Servers](https://docs.develocity.ai/2026.1/integrations/mcp-servers/)

### AssistAI (Eclipse IDE MCP Server)
- **Badge:** Extension
- **Description:** Community Eclipse IDE plugin that exposes the Eclipse workspace as an MCP server — code analysis, refactoring, build/debug control, and Git operations via EGit — so external AI agents edit through Eclipse's JDT APIs instead of the raw filesystem, keeping incremental compilation and error highlighting in sync. Also bundles an inline AI chat view. MIT licensed.
- **Links:** [Eclipse Marketplace](https://marketplace.eclipse.org/content/assistai-eclipse-ide-mcp-server-ai-agents) · [GitHub](https://github.com/gradusnikov/eclipse-chatgpt-plugin)

### SonarQube MCP Server
- **Badge:** MCP Server
- **Description:** Official MCP server from SonarSource exposing SonarQube Cloud/Server code-quality and security analysis to AI coding agents — issue management, security hotspots, quality gates, coverage, and dependency risk across 50+ tools. Distributed as a Docker image.
- **Links:** [SonarQube MCP Server](https://github.com/SonarSource/sonarqube-mcp-server)

### TDA (Thread Dump Analyzer)
- **Badge:** MCP Server
- **Description:** JVM diagnostics tool for analyzing thread dumps and heap data — deadlocks, bottlenecks, virtual-thread pinning — across JDK 1.4–21+. Usable as a standalone Swing GUI, a JConsole/VisualVM plugin, or a headless MCP server for AI tools like Claude and Cursor.
- **Links:** [GitHub](https://github.com/irockel/tda)

### JAFAR
- **Badge:** MCP Server
- **Description:** Fast, modern Java Flight Recorder (JFR) parser for the JVM with typed and untyped parsing APIs, heap-dump analysis, and an interactive analysis shell. Its `jfr-mcp` module exposes JFR analysis as MCP tools, letting AI agents like Claude directly investigate JVM performance recordings. Apache 2.0, early-stage but active.
- **Links:** [GitHub](https://github.com/btraceio/jafar) · [Blog](https://jbachorik.github.io/posts/jfr-mcp-serve)

### MCP JDWP Java
- **Badge:** MCP Server
- **Description:** MCP server giving AI agents full debugger control over running Java applications via JDWP/JDI — inspect locals, fields, threads, and object graphs; set conditional, deferred, and chained breakpoints; evaluate expressions in suspended frames; and trace execution with non-intrusive logpoints and field watchpoints, all without restarting the JVM. Runs entirely locally over STDIO. Built on Spring Boot and Spring AI MCP. MIT licensed.
- **Links:** [GitHub](https://github.com/fgforrest/mcp-jdwp-java)

### SolonCode
- **Badge:** Assistant
- **Description:** Open-source, provider-agnostic AI coding agent built on the Solon-AI framework, targeting Java 8–26 runtimes. CLI, web, and desktop-IDE interfaces with auto-edit, approval-based execution, and planning agent modes — an open alternative to Claude Code.
- **Links:** [SolonCode](https://github.com/opensolon/soloncode)

### Junie
- **Badge:** Assistant
- **Description:** JetBrains' autonomous coding agent — plans and executes multi-step edits, runs tests and the debugger, and opens PRs. Reached GA in June 2026 with a standalone bring-your-own-key CLI (Anthropic, OpenAI, Google, xAI, OpenRouter, Copilot) alongside its JetBrains IDE integration. Distinct from the general-purpose JetBrains AI Assistant above.
- **Links:** [Docs](https://www.jetbrains.com/help/ai-assistant/junie-agent.html) · [Marketplace](https://plugins.jetbrains.com/plugin/26104-junie-the-ai-coding-agent-by-jetbrains)

## Inference & Training

Run models, train classifiers, and do ML inference directly on the JVM — no Python required.

### Deliverance
- **Badge:** Inference
- **Description:** Deliverance is a Java inference engine capable of generating text, tokenizing input, computing embeddings, and more. Can be used as embedded library inside your Java application or as an HTTP server /chat/completion). Deliverance also provides chat and Rag Chat through vibrant-maven-plugin allowing you to chat with your code!
- **Links:** [GitHub](https://github.com/edwardcapriolo/deliverance) · [Dockerhub](https://hub.docker.com/repository/docker/ecapriolo/deliverance/general)

### Jlama
- **Badge:** Inference
- **Description:** ⚠️ No longer actively maintained. Modern LLM inference engine written in pure Java. Runs Llama, Gemma, Mistral, and more locally on CPU. Uses Java's Vector API (Project Panama) for SIMD-accelerated matrix math. Supports SafeTensors format, quantized models, and distributed inference.
- **Links:** [GitHub](https://github.com/tjake/Jlama) · [Tutorial](https://www.baeldung.com/java-jlama-llm)

### Deep Java Library (DJL)
- **Badge:** Inference
- **Description:** AWS's high-level, engine-agnostic deep learning framework. Supports PyTorch, TensorFlow, ONNX Runtime, and XGBoost backends. DJLServing provides high-performance model serving.
- **Links:** [GitHub](https://github.com/deepjavalibrary/djl) · [InfoQ](https://www.infoq.com/articles/java-machine-learning-djl/)

### ONNX Runtime Java
- **Badge:** Inference
- **Description:** Run transformer and classical ML models directly on the JVM. Hardware acceleration via CUDA, DirectML, CoreML, and more. Enables deploying scikit-learn, PyTorch, and HuggingFace models as ONNX in Java without Python at inference time.
- **Links:** [Docs](https://onnxruntime.ai/docs/get-started/with-java.html) · [InfoQ Guide](https://www.infoq.com/articles/onnx-ai-inference-with-java/)

### Tribuo
- **Badge:** Training
- **Description:** Oracle Labs' ML library for classification, regression, clustering, and anomaly detection. Strong typing, provenance tracking for reproducibility, and integrations with XGBoost, ONNX Runtime, TensorFlow, and LibSVM.
- **Links:** [Website](https://tribuo.org/) · [GitHub](https://github.com/oracle/tribuo)

### GPULlama3.java
- **Badge:** Inference
- **Description:** Java-native LLM inference with automatic GPU acceleration via TornadoVM. Supports Llama 3, Mistral, Qwen, Phi-3, and IBM Granite models in GGUF format. TornadoVM translates Java bytecode to GPU kernels (OpenCL, PTX, SPIR-V). Reached 1.0.0 in July 2026 and is published to Maven Central as `io.github.beehive-lab:gpu-llama3` with auto-activating JDK 21 and JDK 25 builds. From the University of Manchester's Beehive Lab.
- **Links:** [GitHub](https://github.com/beehive-lab/GPULlama3.java) · [InfoQ](https://www.infoq.com/news/2025/06/gpullama3-java-gpu-llm/)

### TornadoVM
- **Badge:** Inference
- **Description:** GPU programming framework for Java — JIT-compiles Java bytecode into CUDA, OpenCL, and Apple Metal at runtime, running on GPUs and multi-core CPUs. Powers GPULlama3.java's GPU acceleration. v5.2.0 added AI-focused kernels: native FP8 conversion, FP8/BF16 tensor-core matrix multiply, cuBLAS/CUTLASS-compatible BFloat16 arrays, and batched FP16 GEMM. From the University of Manchester's Beehive Lab.
- **Links:** [Website](https://www.tornadovm.org) · [GitHub](https://github.com/beehive-lab/TornadoVM) · [Docs](https://tornadovm.readthedocs.io/en/latest/)

### TensorFlow Java
- **Badge:** Training
- **Description:** Java bindings for TensorFlow, maintained by the TensorFlow JVM SIG. Train and deploy TF models entirely in Java. Available as an optional Tribuo integration. Suitable for teams that want to stay within the JVM ecosystem while using TensorFlow's model formats.
- **Links:** [Docs](https://www.tensorflow.org/jvm) · [GitHub](https://github.com/tensorflow/java)

### Eclipse Deeplearning4j (DL4J)
- **Badge:** Training
- **Description:** Long-standing JVM deep-learning suite — DL4J for model building, ND4J for linear algebra, SameDiff for automatic differentiation, and DataVec for ETL. GPU/CPU acceleration and distributed training via Spark, plus model import from Keras, TensorFlow, and ONNX/PyTorch. Maintained by Konduit under the Eclipse Foundation.
- **Links:** [Website](https://deeplearning4j.konduit.ai/) · [GitHub](https://github.com/deeplearning4j/deeplearning4j)

### Apache OpenNLP
- **Badge:** Training
- **Description:** Apache's Java-native NLP toolkit — tokenization, part-of-speech tagging, named entity recognition, chunking, parsing, and language detection, with pluggable MaxEnt, Perceptron, Naive Bayes, and SVM classifiers. Actively maintained, with a Java 21-targeted 3.0 branch in progress.
- **Links:** [Website](https://opennlp.apache.org) · [GitHub](https://github.com/apache/opennlp)

## People to Follow

Key voices at the intersection of Java and AI.

### Sandra Ahlgrimm
- **Bio:** Senior Cloud Advocate for Java — Microsoft/GitHub; focused on GitHub Copilot for Java developers and LangChain4j integrations, co-leads a local JUG, and represents Microsoft on the GraalVM Program Advisory Board
- **Links:** [GitHub](https://github.com/sandraahlgrimm) · [LinkedIn](https://www.linkedin.com/in/sandraahlgrimm/)

### Jean-François Arcand (Java Champion)
- **Bio:** Creator of the original Atmosphere Framework, Grizzly, and AsyncHttpClient; now building the new Atmosphere real-time transport layer for Java AI agents
- **Links:** [@jfarcand](https://twitter.com/jfarcand) · [GitHub](https://github.com/jfarcand) · [LinkedIn](https://www.linkedin.com/in/jfarcand/)

### Jaroslav Bachorik
- **Bio:** JVM serviceability engineer — Datadog; creator of BTrace and JAFAR, a modern JFR parser whose `jfr-mcp` module lets AI agents analyze JVM Flight Recorder data directly
- **Links:** [@BachorikJ](https://twitter.com/BachorikJ) · [GitHub](https://github.com/jbachorik) · [Blog](https://jbachorik.github.io)

### Zineb Bendhiba
- **Bio:** Principal Software Engineer — IBM; Apache Camel PMC member, maintains Camel Quarkus and Quarkus Qdrant, lead author of Camel 4.22's `camel-ai-tool` and `camel-mcp-server`
- **Links:** [@ZinebBendhiba](https://x.com/ZinebBendhiba) · [Bluesky](https://bsky.app/profile/zinebbendhiba.com) · [GitHub](https://github.com/zbendhiba) · [LinkedIn](https://www.linkedin.com/in/zbendhiba/) · [Website](https://zinebbendhiba.com/)

### Bruno Borges (Java Champion)
- **Bio:** Principal Program Manager — Microsoft Java Engineering Group
- **Links:** [@brunoborges](https://twitter.com/brunoborges) · [Bluesky](https://bsky.app/profile/brunoborges.bsky.social) · [GitHub](https://github.com/brunoborges) · [LinkedIn](https://ca.linkedin.com/in/brunocborges) · [Blog](https://blog.brunoborges.info/)

### Vadim Briliantov
- **Bio:** Technical Lead of Koog / AI Agents Platform — JetBrains
- **Links:** [GitHub](https://github.com/Ololoshechkin) · [LinkedIn](https://hu.linkedin.com/in/vadim-briliantov) · [Blog](https://medium.com/@vadim.briliantov)

### Holly Cummins (Java Champion)
- **Bio:** Senior Principal Software Engineer — IBM Quarkus team
- **Links:** [@holly_cummins](https://twitter.com/holly_cummins) · [Bluesky](https://bsky.app/profile/hollycummins.com) · [GitHub](https://github.com/holly-cummins) · [LinkedIn](https://linkedin.com/in/holly-k-cummins/) · [Website](https://hollycummins.com)

### Eric Deandrea (Java Champion)
- **Bio:** [Docling Java](https://docling-project.github.io/docling-java/current) project lead, contributor to LangChain4j, Sr. Principal Software Engineer at IBM
- **Links:** [Bluesky](https://bsky.app/profile/ericdeandrea.dev) · [@edeandrea](https://x.com/edeandrea) · [GitHub](https://github.com/edeandrea) · [LinkedIn](https://www.linkedin.com/in/edeandrea/)

### Ronald Dehuysser
- **Bio:** Creator of JobRunr and ClawRunr
- **Links:** [@rdehuyss](https://twitter.com/rdehuyss) · [GitHub](https://github.com/rdehuyss) · [LinkedIn](https://www.linkedin.com/in/ronalddehuysser)

### Sébastien Deleuze
- **Bio:** Spring Framework core committer, Spring AI/MCP integration — Broadcom
- **Links:** [@sdeleuze](https://x.com/sdeleuze) · [Bluesky](https://bsky.app/profile/seb.deleuze.fr) · [GitHub](https://github.com/sdeleuze) · [LinkedIn](https://www.linkedin.com/in/deleuze) · [Blog](https://seb.deleuze.fr)

### Iryna Dohndorf
- **Bio:** Creator of Tiberius — Java security testing framework for LLM applications; software engineer at Karakun; her work sits at the intersection of Java engineering, AI safety, and antifragile system design
- **Links:** [GitHub](https://github.com/IDohndorf) · [LinkedIn](https://www.linkedin.com/in/iryna-dohndorf/) · [Website](https://iryna-dohndorf.netlify.app/)

### Julien Dubois (Java Champion)
- **Bio:** Creator of JHipster; leads a Developer Relations team at Microsoft focused on agentic developer tools for Java/Spring Boot
- **Links:** [@juliendubois](https://twitter.com/juliendubois) · [Bluesky](https://bsky.app/profile/jdubois.bsky.social) · [GitHub](https://github.com/jdubois) · [LinkedIn](https://www.linkedin.com/in/juliendubois/) · [Website](https://www.julien-dubois.com/)

### Markus Eisele (Java Champion)
- **Bio:** Developer Advocate — IBM Research, JavaLand founder
- **Links:** [@myfear](https://twitter.com/myfear) · [Bluesky](https://bsky.app/profile/myfear.com) · [GitHub](https://github.com/myfear) · [LinkedIn](https://www.linkedin.com/in/markuseisele/) · [Blog](https://blog.eisele.net/)

### Clement Escoffier (Java Champion)
- **Bio:** Red Hat Distinguished Engineer, Quarkus co-creator, works on the Quarkus LangChain4j extension
- **Links:** [GitHub](https://github.com/cescoffier) · [foojay](https://foojay.io/today/author/clement-escoffier/)

### Mario Fusco (Java Champion)
- **Bio:** LangChain4j core team, Sr. Principal Software Engineer at IBM
- **Links:** [@mariofusco](https://x.com/mariofusco) · [GitHub](https://github.com/mariofusco) · [LinkedIn](https://www.linkedin.com/in/mario-fusco-3467213/)

### Antonio Goncalves (Java Champion)
- **Bio:** Principal Software Engineer at Microsoft CoreAI, ParisJUG, Devoxx France, Café IA, book author
- **Links:** [@agoncal](https://twitter.com/agoncal) · [Bluesky](https://bsky.app/profile/agoncal.bsky.social) · [GitHub](https://github.com/agoncal) · [LinkedIn](https://www.linkedin.com/in/agoncal/) · [Blog](https://antoniogoncalves.org)

### Ilayaperumal Gopinathan
- **Bio:** Spring AI team, Broadcom; regularly drives Spring AI release announcements
- **Links:** [GitHub](https://github.com/ilayaperumalg) · [Spring.io](https://spring.io/authors/ilayaperumalg/)

### Frank Greco (Java Champion)
- **Bio:** NYJavaSIG founder, AI 4 Java educator, JSR 381 co-author
- **Links:** [@frankgreco](https://twitter.com/frankgreco) · [Bluesky](https://bsky.app/profile/frankgreco.bsky.social) · [GitHub](https://github.com/fgreco55) · [LinkedIn](https://www.linkedin.com/in/frankdgreco/) · [Website](https://ai4java.com)

### Ivar Grimstad (Java Champion)
- **Bio:** Jakarta EE Developer Advocate — Eclipse Foundation; speaks on bringing AI to Jakarta EE (Jakarta Agentic AI)
- **Links:** [@ivar_grimstad](https://twitter.com/ivar_grimstad) · [Bluesky](https://bsky.app/profile/theguywiththeduketattoo.com) · [GitHub](https://github.com/ivargrimstad) · [LinkedIn](https://www.linkedin.com/in/ivargrimstad/) · [Website](https://www.agilejava.eu)

### Emmanuel Hugonnet
- **Bio:** Software Engineer — IBM; WildFly core contributor driving most of the recent AI work across WildFly AI Feature Pack and LangChain4j-CDI
- **Links:** [@ehsavoie](https://twitter.com/ehsavoie) · [Bluesky](https://bsky.app/profile/ehsavoie.bsky.social) · [GitHub](https://github.com/ehsavoie) · [LinkedIn](https://www.linkedin.com/in/ehsavoie/) · [Website](https://www.ehsavoie.com)

### Rod Johnson (Java Champion)
- **Bio:** Creator of Spring Framework, CEO of Embabel
- **Links:** [@springrod](https://twitter.com/springrod) · [GitHub](https://github.com/johnsonr) · [LinkedIn](https://www.linkedin.com/in/johnsonroda/) · [Blog](https://medium.com/@springrod)

### Daniel Kec
- **Bio:** Helidon developer — Oracle
- **Links:** [@danielkec](https://twitter.com/danielkec) · [Bluesky](https://bsky.app/profile/kec.bsky.social) · [GitHub](https://github.com/danielkec) · [LinkedIn](https://www.linkedin.com/in/danielkec/)

### Kenneth Kousen (Java Champion)
- **Bio:** Author of six books including Kotlin Cookbook and Modern Java Recipes. O’Reilly instructor for AI + Java courses. Professor of Practice in Computer Science at Trinity College. President of Kousen IT, Inc.
- **Links:** [@kenkousen](https://twitter.com/kenkousen) · [Bluesky](https://bsky.app/profile/kousenit.com) · [GitHub](https://github.com/kousen) · [LinkedIn](https://www.linkedin.com/in/kenkousen/) · [Website](https://www.kousenit.com/) · [Newsletter](https://kenkousen.substack.com) · [YouTube](https://youtube.com/@talesfromthejarside)

### Guillaume Laforge (Java Champion)
- **Bio:** Google Developer Advocate — Java, Groovy, AI
- **Links:** [@glaforge](https://twitter.com/glaforge) · [Bluesky](https://bsky.app/profile/glaforge.bsky.social) · [GitHub](https://github.com/glaforge) · [LinkedIn](https://www.linkedin.com/in/glaforge/) · [Blog](https://glaforge.dev/)

### Dmytro Liubarskyi
- **Bio:** Creator of LangChain4j, Principal Architect — IBM
- **Links:** [Bluesky](https://bsky.app/profile/dmythro.bsky.social) · [GitHub](https://github.com/dliubarskyi) · [LinkedIn](https://www.linkedin.com/in/dmytro-liubarskyi/)

### Josh Long (Java Champion)
- **Bio:** Spring Developer Advocate at Broadcom
- **Links:** [@starbuxman](https://twitter.com/starbuxman) · [Bluesky](https://bsky.app/profile/starbuxman.joshlong.com) · [GitHub](https://github.com/joshlong) · [LinkedIn](https://www.linkedin.com/in/joshlong/) · [Spring Blog](https://spring.io/authors/joshlong/)

### T. Jake Luciani
- **Bio:** Creator of Jlama — Java LLM inference
- **Links:** [@tjake](https://twitter.com/tjake) · [GitHub](https://github.com/tjake) · [LinkedIn](https://www.linkedin.com/in/tjake/)

### Loïc Magnette
- **Bio:** Senior Software Engineer at Oniryx; BeJUG (Belgian Java User Group) co-organizer, writes and speaks on LangChain4j agentic workflows for Quarkus
- **Links:** [@LoMagnette](https://x.com/LoMagnette) · [Bluesky](https://bsky.app/profile/lomagnette.bsky.social) · [GitHub](https://github.com/lomagnette) · [LinkedIn](https://www.linkedin.com/in/lomagnette/) · [Blog](https://lomagnette.github.io/)

### François Martin
- **Bio:** International speaker and author, Oracle ACE Associate, senior full-stack software engineer
- **Links:** [@fmartin_](https://x.com/fmartin_) · [GitHub](https://github.com/martinfrancois) · [LinkedIn](https://www.linkedin.com/in/fran%c3%a7oismartin/) · [Bluesky](https://bsky.app/profile/fmartin.ch) · [YouTube](https://www.youtube.com/@fmartindev) · [Website](https://fmartin.ch)

### Simon Martinelli
- **Bio:** Creator of AI Unfied Process and the jOOQ MCP Server
- **Links:** [@simas_ch](https://twitter.com/simas_ch) · [GitHub](https://github.com/simach) · [LinkedIn](https://www.linkedin.com/in/simonmartinelli/)

### Ana-Maria Mihalceanu (Java Champion)
- **Bio:** Senior Developer Advocate, Java Platform Group at Oracle; writes and speaks on Java MCP tooling and AI, co-founder of the Bucharest Software Craftsmanship Community
- **Links:** [@ammbra1508](https://twitter.com/ammbra1508) · [GitHub](https://github.com/ammbra) · [LinkedIn](https://www.linkedin.com/in/ana-maria-mihalceanu-1508/)

### Vishal Mysore
- **Bio:** Creator of Tools4AI
- **Links:** [GitHub](https://github.com/vishalmysore) · [LinkedIn](https://www.linkedin.com/in/vishalrow/) · [Blog](https://medium.com/@visrow)

### Daniel Oh (Java Champion)
- **Bio:** Senior Principal Developer Advocate — Red Hat; CNCF Ambassador speaking on agentic AI and cloud-native Java
- **Links:** [@danieloh30](https://twitter.com/danieloh30) · [Bluesky](https://bsky.app/profile/danieloh30.bsky.social) · [LinkedIn](https://www.linkedin.com/in/daniel-oh-083818112/)

### Michalis Papadimitriou
- **Bio:** Research Fellow, University of Manchester and Senior Software Engineer at Neo4j; TornadoVM core maintainer and lead author of GPULlama3.java, GPU-accelerated LLM inference in pure Java
- **Links:** [GitHub](https://github.com/mikepapadim) · [LinkedIn](https://www.linkedin.com/in/michalis-papadimitriou/) · [Website](https://mpapadimitriou.com/)

### Konstantin Pavlov
- **Bio:** Creator of Tachyon; maintainer of mokksy.dev and LangChain4j Kotlin extensions
- **Links:** [Bluesky](https://bsky.app/profile/kpavlov.me) · [GitHub](https://github.com/kpavlov) · [Blog](https://kpavlov.me)

### Susanne Pieterse
- **Bio:** Senior Software Engineer & iSAQB-certified Software Architect — OPEN.nl; LangChain4j contributor teaching Java teams to build reliable AI agents
- **Links:** [Bluesky](https://bsky.app/profile/susivic.bsky.social) · [GitHub](https://github.com/Sus4nne)

### Mark Pollack
- **Bio:** Spring AI project lead
- **Links:** [@markpollack](https://twitter.com/markpollack) · [GitHub](https://github.com/markpollack) · [LinkedIn](https://www.linkedin.com/in/marklpollack/)

### Lize Raes
- **Bio:** LangChain4j core team, Developer Advocate at Oracle
- **Links:** [@LizeRaes](https://twitter.com/LizeRaes) · [GitHub](https://github.com/LizeRaes) · [LinkedIn](https://www.linkedin.com/in/lize-raes-a8a34110/)

### Reza Rahman (Java Champion)
- **Bio:** Jakarta Agentic AI project lead at Payara/Azul, Jakarta EE Ambassadors founder
- **Links:** [GitHub](https://github.com/m-reza-rahman) · [LinkedIn](https://www.linkedin.com/in/javareza) · [Website](https://reza-rahman.me)

### K. Siva Prasad Reddy
- **Bio:** Developer Advocate at JetBrains, author of Beginning Spring Boot 3
- **Links:** [@sivalabs](https://twitter.com/sivalabs) · [GitHub](https://github.com/sivaprasadreddy) · [LinkedIn](https://in.linkedin.com/in/ksivaprasadreddy) · [YouTube](https://youtube.com/sivalabs) · [Blog](https://sivalabs.in)

### Jennifer Reif (Java Champion)
- **Bio:** Developer Advocate at Neo4j
- **Links:** [@JMHReif](https://twitter.com/JMHReif) · [GitHub](https://github.com/JMHReif) · [LinkedIn](https://www.linkedin.com/in/jmhreif/) · [Website](https://jmhreif.com)

### Victor Rentea (Java Champion)
- **Bio:** Independent trainer and consultant; runs a full-day "Agentic Engineering" workshop covering MCP, context/prompt engineering, and multi-agent orchestration for Java teams, and speaks on AI-augmented engineering at Java conferences
- **Links:** [@victorrentea](https://x.com/victorrentea) · [GitHub](https://github.com/victorrentea) · [LinkedIn](https://ro.linkedin.com/in/victor-rentea-trainer) · [Website](https://victorrentea.ro/)

### Baruch Sadogursky (Java Champion)
- **Bio:** Developer Advocate at Tessl, co-author of Liquid Software and DevOps Tools for Java Developers
- **Links:** [@jbaruch](https://twitter.com/jbaruch) · [Bluesky](https://bsky.app/profile/jbaru.ch) · [GitHub](https://github.com/jbaruch) · [LinkedIn](https://www.linkedin.com/in/jbaruch/) · [Website](https://speaking.jbaru.ch)

### Timo Salm
- **Bio:** Principal Solutions Engineer, VMware Tanzu at Broadcom; frequent conference speaker comparing Java agentic-AI frameworks (Spring AI, LangChain4j, Embabel)
- **Links:** [@salmto](https://twitter.com/salmto) · [GitHub](https://github.com/timosalm) · [LinkedIn](https://www.linkedin.com/in/timosalm)

### Otavio Santana (Java Champion)
- **Bio:** Jakarta Data/Jakarta NoSQL spec lead, Eclipse JNoSQL creator, writes on building AI agents with Jakarta EE and LangChain4j
- **Links:** [@otaviojava](https://twitter.com/otaviojava) · [GitHub](https://github.com/otaviojava) · [LinkedIn](https://www.linkedin.com/in/otaviojava/) · [Website](https://otaviojava.com/) · [YouTube](https://www.youtube.com/@otaviojava)

### Oleg Šelajev (Java Champion)
- **Bio:** Developer Relations Lead for AI — Docker
- **Links:** [@shelajev](https://twitter.com/shelajev) · [GitHub](https://github.com/shelajev) · [LinkedIn](https://www.linkedin.com/in/shelajev/)

### Zoran Sevarac (Java Champion)
- **Bio:** Associate Professor, University of Belgrade; creator of Deep Netts and Neuroph, pure-Java deep learning libraries, and JSR-381 (Visual Recognition API) co-lead
- **Links:** [GitHub](https://github.com/sevarac) · [LinkedIn](https://www.linkedin.com/in/zoran-sevarac-phd-49a9a411/) · [Website](https://www.zoransevarac.com/)

### Bartosz Sorrentino
- **Bio:** LangGraph4j creator, Principal Software Architect
- **Links:** [@bsorrentinoJ](https://twitter.com/bsorrentinoJ) · [GitHub](https://github.com/bsorrentino) · [LinkedIn](https://www.linkedin.com/in/bartolomeosorrentino/)

### Alex Soto Bueno (Java Champion)
- **Bio:** Director of Developer Experience — Red Hat, co-author of "AI Agents with Java" (O'Reilly)
- **Links:** [@alexsotob](https://twitter.com/alexsotob) · [GitHub](https://github.com/lordofthejars) · [Blog](https://www.lordofthejars.com)

### Venkat Subramaniam (Java Champion)
- **Bio:** Founder of Agile Developer, creator of the dev2next and Arc of AI conferences, award-winning author and instructor at the University of Houston
- **Links:** [@venkat_s](https://twitter.com/venkat_s) · [Bluesky](https://bsky.app/profile/venkats.bsky.social) · [GitHub](https://github.com/venkats) · [LinkedIn](https://www.linkedin.com/in/vsubramaniam/)

### Christian Tzolov
- **Bio:** Spring AI lead, MCP Java SDK founder, Spring team at Broadcom
- **Links:** [@christzolov](https://twitter.com/christzolov) · [Bluesky](https://bsky.app/profile/tzolov.bsky.social) · [GitHub](https://github.com/tzolov) · [LinkedIn](https://www.linkedin.com/in/tzolov/)

### Dan Vega (Java Champion)
- **Bio:** Spring Developer Advocate, YouTube educator
- **Links:** [@therealdanvega](https://twitter.com/therealdanvega) · [Bluesky](https://bsky.app/profile/danvega.dev) · [GitHub](https://github.com/danvega) · [LinkedIn](https://www.linkedin.com/in/danvega/) · [Blog](https://www.danvega.dev/)

### Dmitry Vinnik
- **Bio:** Lead Developer Advocate at Meta
- **Links:** [@DmitryVinnik](https://twitter.com/DmitryVinnik) · [GitHub](https://github.com/dmitryvinn) · [LinkedIn](https://www.linkedin.com/in/dmitry-vinnik/) · [Blog](https://dvinnik.dev/)

### Thomas Vitale (Java Champion)
- **Bio:** Spring AI Lead Contributor, creator of Arconia, author of Cloud Native Spring in Action
- **Links:** [@vitalethomas](https://x.com/vitalethomas) · [Bluesky](https://bsky.app/profile/thomasvitale.com) · [GitHub](https://github.com/ThomasVitale) · [LinkedIn](https://www.linkedin.com/in/vitalethomas/) · [Website](https://www.thomasvitale.com)

### Craig Walls (Java Champion)
- **Bio:** Author of Spring AI in Action
- **Links:** [@habuma](https://twitter.com/habuma) · [Bluesky](https://bsky.app/profile/habuma.com) · [GitHub](https://github.com/habuma) · [LinkedIn](https://www.linkedin.com/in/habuma)

### James Ward (Java Champion)
- **Bio:** Developer Advocate — Java, Kotlin, Cloud, AI
- **Links:** [@_JamesWard](https://twitter.com/_jamesward) · [Bluesky](https://bsky.app/profile/jamesward.com) · [GitHub](https://github.com/jamesward) · [LinkedIn](https://www.linkedin.com/in/jamesward) · [Blog](https://jamesward.com)

### Pascal Wilbrink
- **Bio:** Senior Software Developer at OpenValue; creator of the Spring AI AG-UI Java SDK, bringing the AG-UI protocol to Java/Spring AI
- **Links:** [GitHub](https://github.com/pascalwilbrink) · [Website](https://pascalwilbrink.github.io)

## FAQ

Frequently asked questions about AI development on the JVM.

### What is the best Java framework for building AI agents?
The most popular choices are [Spring AI and LangChain4j](#frameworks). Spring AI is ideal if you’re already in the Spring ecosystem, offering portable abstractions across 20+ model providers. LangChain4j provides a standalone library with three levels of abstraction, from low-level prompts to high-level AI Services. Other options include Google ADK for Java, Embabel, and Akka Agents — each with different strengths for specific use cases.

### Can Java run LLMs locally?
Yes. Projects like [Jlama and GPULlama3.java](#inference) run Llama, Mistral, and other models directly on the JVM. Jlama uses Java’s Vector API for SIMD-accelerated inference on CPU, while GPULlama3.java leverages TornadoVM for GPU acceleration. For production deployments, ONNX Runtime Java supports hardware-accelerated inference across CUDA, DirectML, and CoreML.

### What is MCP and how does it work with Java?
The Model Context Protocol (MCP) is an open standard that lets AI assistants interact with external tools and data sources. The official MCP Java SDK, maintained by the Spring AI team, provides both client and server implementations with sync/async support and multiple transports (STDIO, Streamable HTTP; SSE deprecated as of 2.0). Helidon MCP and several frameworks also offer MCP support.

### Is Kotlin supported by Java AI frameworks?
Yes. Most Java AI frameworks run on any JVM language. Embabel is written in Kotlin with full Java interop, Koog from JetBrains is a Kotlin-native agent framework, and Tracy provides AI observability for Kotlin. LangChain4j and Spring AI work seamlessly from Kotlin code.

## Recent & Noteworthy Content, Communities, and Resources

Talks, tutorials, books, and communities for learning AI development on the JVM.

### Java Conferences Tracker
- **Badge:** Community
- **Description:** Community-maintained calendar of all Java conferences worldwide
- **Links:** [Java Conferences Tracker](https://javaconferences.org/)

### Java Relevance in the AI Era
- **Badge:** Blog
- **Description:** RedMonk analysis of Java's position as agent frameworks emerge
- **Links:** [Java Relevance in the AI Era](https://redmonk.com/jgovernor/java-relevance-in-the-ai-era-agent-frameworks-emerge/)

### Awesome Spring AI
- **Badge:** Resource
- **Description:** Curated list of Spring AI resources, tools, and tutorials
- **Links:** [Awesome Spring AI](https://github.com/spring-ai-community/awesome-spring-ai)

### Spring AI in Action (Manning)
- **Badge:** Book
- **Description:** Book by Craig Walls — comprehensive guide to building AI apps with Spring
- **Links:** [Spring AI in Action (Manning)](https://www.manning.com/books/spring-ai-in-action)

### Understanding LangChain4j
- **Badge:** Book
- **Description:** Book by Antonio Goncalves — explore the fundamentals of AI, learn the history and evolution of AI models, and understand the core concepts of LangChain4j
- **Links:** [Understanding LangChain4j](https://www.amazon.com/Understanding-LangChain4j-2nd-agoncal-fascicles-ebook/dp/B0FDQVSLXK)

### AI Agents with Java (O'Reilly)
- **Badge:** Book
- **Description:** Early-release book by Java Champions Alex Soto Bueno, Markus Eisele, and Mario Fusco — building long-running, stateful agents on the JVM with zero-trust security, RAG, and multi-agent coordination
- **Links:** [AI Agents with Java (O'Reilly)](https://www.oreilly.com/library/view/ai-agents-with/0642572245856/)

### Applied AI for Enterprise Java Development (O'Reilly)
- **Badge:** Book
- **Description:** Book by Alex Soto Bueno, Markus Eisele, and Natale Vinto — prompt engineering, RAG, guardrails, fault tolerance, and enterprise AI architecture using Quarkus, LangChain4j, and vector stores
- **Links:** [Applied AI for Enterprise Java Development (O'Reilly)](https://www.oreilly.com/library/view/applied-ai-for/9781098174491/)

### Production LangChain4j — Inside.java
- **Badge:** Resource
- **Description:** Advanced RAG, agentic workflows, and production tips from Devoxx Belgium
- **Links:** [Production LangChain4j — Inside.java](https://inside.java/2026/02/01/devoxxbelgium-production-langchain4j/)

### Google ADK Java Codelab
- **Badge:** Resource
- **Description:** Hands-on: build AI agents in Java with Google's ADK
- **Links:** [Google ADK Java Codelab](https://codelabs.developers.google.com/adk-java-getting-started)

### Devoxx YouTube
- **Badge:** Videos
- **Description:** Thousands of conference talks on Java, AI, cloud, and architecture
- **Links:** [Devoxx YouTube](https://www.youtube.com/@DevoxxForever)

### Coffee + Software
- **Badge:** Videos
- **Description:** Spring ecosystem, AI integration, and Java community
- **Links:** [Coffee + Software](https://youtube.com/@coffeesoftware)

### Foojay Podcast: Java AI Revolution
- **Badge:** Resource
- **Description:** Agents, MCP, graph databases — developers navigate the AI revolution
- **Links:** [Foojay Podcast: Java AI Revolution](https://foojay.io/today/foojay-podcast-86/)

### Building Java AI Agents with Spring AI (AWS)
- **Badge:** Workshop
- **Description:** Hands-on AWS workshop for building intelligent AI agents with Spring AI and AWS services, including deployment to EKS
- **Links:** [Building Java AI Agents with Spring AI (AWS)](https://catalog.workshops.aws/java-spring-ai-agents/en-US)

### AI & Java on Serverless Office Hours
- **Badge:** Livestream
- **Description:** James Ward and Julian Wood explore building AI-powered Java apps — MCP integration, agent architectures with AgentCore, GraalVM optimization for AI workloads, and secure auth patterns for AI services on serverless
- **Links:** [AI & Java on Serverless Office Hours](https://www.youtube.com/watch?v=my2bQtHBUeY)

### Java for an AI World — JavaOne 2026 Opening Keynote
- **Badge:** Videos
- **Description:** Opening keynote of JavaOne 2026, featuring Rod Johnson, Josh Long, and engineering leads from Oracle, Microsoft, NVIDIA, JetBrains, and Uber on Java's AI direction, including the GitHub Copilot SDK for Java
- **Links:** [Java for an AI World — JavaOne 2026 Opening Keynote](https://www.youtube.com/watch?v=3fLCOqpIfI0)

### Bootiful Spring AI — Josh Long & James Ward @ Spring I/O 2026
- **Badge:** Videos
- **Description:** Talk from Spring I/O 2026 in Barcelona demystifying AI integration with Spring Boot — agentic patterns, Spring AI, and MCP integration
- **Links:** [Bootiful Spring AI — Josh Long & James Ward @ Spring I/O 2026](https://www.youtube.com/watch?v=nHnKReitDXc)

### Java and AI in 2026 and Beyond — James Ward Interview
- **Badge:** Videos
- **Description:** 68-minute interview with James Ward on the Code With Ease channel, recorded at GIDS Bangalore — the Java AI landscape from Spring AI, LangChain4j, Koog, and Embabel to MCP, agent memory, RAG vs tools vs skills, and production deployment with AWS AgentCore
- **Links:** [Java and AI in 2026 and Beyond — James Ward Interview](https://www.youtube.com/watch?v=-TWXNYRE0Q8)

### Liberty LangChain4j Workshop
- **Badge:** Workshop
- **Description:** Hands-on, self-paced Open Liberty workshop that progresses from a simple chatbot to agentic systems using LangChain4j — prompt engineering, streaming, RAG, tool calling, MCP, guardrails/observability, and multi-agent supervisor patterns
- **Links:** [Workshop](https://openliberty.github.io/liberty-workshop-langchain4j/) · [Blog](https://openliberty.io/blog/2026/07/10/liberty-langchain4j-workshop.html)

### ScarfBench
- **Badge:** Resource
- **Description:** IBM Research's open benchmark for evaluating AI agents on enterprise Java framework migration — 102 applications and 204 tasks (~1,331 tests) covering Jakarta EE, Quarkus, and Spring, with a public leaderboard
- **Links:** [GitHub](https://github.com/scarfbench/benchmark) · [Announcement](https://www.ibm.com/new/announcements/scarfbench-a-public-benchmark-for-java-framework-migration)

### Kotlin Benchmark for AI Coding Agents
- **Badge:** Resource
- **Description:** JetBrains' open benchmark for evaluating AI coding agents on real-world Kotlin tasks — 105 issues drawn from active open-source Kotlin repositories including ktlint, detekt, and ort, each requiring the agent to read an issue, navigate the project, and produce a patch that passes the project's tests. Public leaderboard, built on the Multi-SWE-bench methodology.
- **Links:** [Leaderboard](https://kotlinlang.org/benchmark/) · [GitHub](https://github.com/Kotlin/kotlin-swe-bench) · [Announcement](https://blog.jetbrains.com/kotlin/2026/07/introducing-the-kotlin-benchmark-evaluate-ai-coding-agents-on-real-world-kotlin-tasks/)

---

AI4JVM — Curating the Java AI ecosystem. Contributions welcome on [GitHub](https://github.com/jamesward/ai4jvm).