Agent Frameworks & Libraries
Spring AI
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.
LangChain4j
The most popular Java LLM library. Unified API across 20+ LLM providers and 30+ embedding stores. Three levels of abstraction from low-level prompts to high-level AI Services. Supports RAG, tool calling, MCP, and agents.
Embabel
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.
Google ADK for Java
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.
Quarkus LangChain4j
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.
LangGraph4j
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.
Koog (JetBrains)
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.
Semantic Kernel (Java)
Microsoft's AI orchestration SDK with Java support. Merged with AutoGen into a unified Microsoft Agent Framework with deep Azure integration. Supports prompt chaining, planning, and memory.
MCP Java SDK
The official Java SDK for Model Context Protocol servers and clients. Co-maintained by the Spring AI team and Anthropic. Sync/async, STDIO/SSE/Streamable HTTP transports, OAuth support.
Anthropic Java SDK
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.
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.
Javadocs.dev MCP Server
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.
JetBrains AI
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.
Inference & Training
Run models, train classifiers, and do ML inference directly on the JVM — no Python required.
Jlama
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 GGUF and SafeTensors formats, quantized models, and distributed inference.
Deep Java Library (DJL)
AWS's high-level, engine-agnostic deep learning framework. Supports PyTorch, TensorFlow, and MXNet backends. Used in production at Netflix and Amazon for real-time inference. DJLServing provides high-performance model serving.
ONNX Runtime Java
Run transformer and classical ML models directly on the JVM. Hardware acceleration via CUDA, ROCm, DirectML, and more. Enables deploying scikit-learn, PyTorch, and HuggingFace models in Java without Python or REST wrappers.
Tribuo
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.
GPULlama3.java
First Java-native Llama 3 implementation with automatic GPU acceleration via TornadoVM. No CUDA or native code needed — GPU-accelerated LLM inference in pure Java. From the University of Manchester's Beehive Lab.
People to Follow
Key voices at the intersection of Java and AI.
Recent & Noteworthy Content, Communities, and Resources
Spring AI in Action (Manning)
Book by Craig Walls — comprehensive guide to building AI apps with Spring
Production LangChain4j — Inside.java
Advanced RAG, agentic workflows, and production tips from Devoxx Belgium
Foojay Podcast: Java AI Revolution
Agents, MCP, graph databases — developers navigate the AI revolution
Building Java AI Agents with Spring AI (AWS)
Hands-on AWS workshop for building intelligent AI agents with Spring AI and AWS services, including deployment to EKS