New tutorial on The Main Thread: Build an AI-powered PDF processing pipeline with Quarkus, Apache Camel, and LangChain4j.
Virus scan with ClamAV
Extract text from PDFs
Summarize with a local LLM via Ollama
https://www.the-main-thread.com/p/quarkus-camel-langchain4j-pdf-processing-pipeline

LangChain4j 1.7.1 is out and now integrates GPU-accelerated large language model (LLM) via GPULlama3.java and TornadoVM

#ai #gpu #java #langchain4j #llama #llm #tornadovm

https://github.com/langchain4j/langchain4j/releases/tag/1.7.1

Just dropped a full hands-on guide:
Build an MCP Server & Client in Quarkus with LangChain4j + Ollama.

Learn how to connect LLMs to real-world tools using the Model Context Protocol: the USB-C for AI tools.

https://www.the-main-thread.com/p/java-quarkus-langchain4j-ollama-mcp-tutorial

Building a Web-Searching Agent with Ollama, Langchain4j, and Quarkus https://eldermoraes.com/building-a-web-searching-agent-with-ollama-langchain4j-and-quarkus/
#Java #quarkus #langchain4j #ollama

New on The Main Thread: Your Path to LangChain4j Mastery
A learning journey for Java devs exploring Quarkus + LangChain4j. From first prompts → enterprise AI apps.

Read here https://www.the-main-thread.com/p/langchain4j-learning-path-java-quarkus-ai

Excited to announce LangChain4j v1.6.0!
- Agent API improvements (Human-in-the-Loop, async robustness, listeners)
- New integrations: Azure Dynamic Sessions & Markdown parsing
- MCP registry + SSE parsing improvements
- Anthropic Claude vision w/ image URLs
Full release notes https://github.com/langchain4j/langchain4j/releases/tag/1.6.0

Dive into the new Langchain4j Agentic Tutorial! Learn how to build intelligent, autonomous agents in Java step-by-step. Perfect for devs ready to take ML / AI workflows to the next level.

Check it out: https://github.com/langchain4j/langchain4j-examples/tree/main/agentic-tutorial/src/main/java

I'm very happy that I've been able to contribute to making this happen! #Open #LLM #Java #AI #Agent #LangChain4j https://developers.googleblog.com/en/adk-for-java-opening-up-to-third-party-language-models-via-langchain4j-integration/

Use any #LLM with your ADK for #Java #AI #agent thanks to the integration with #langchain4j

With some examples calling local models too, with #Docker Model Runner and #ollama

https://developers.googleblog.com/en/adk-for-java-opening-up-to-third-party-language-models-via-langchain4j-integration/

The next release of langchain4j is out! 1.5.0!

Highlights:
Initial MCP (Model Context Protocol) client implementation
Agentic: asynchronous agents + more tool support
Google AI Gemini now generates image outputs
Prompt caching for AWS Bedrock

Thanks to all contributors who made this release possible!

Read the full changelog here https://github.com/langchain4j/langchain4j/releases/tag/1.5.0

New tutorial: From JSON Chaos to AI-Powered Memes
Clean messy JSON with Quarkus REST-Jackson, validate inputs, and enrich your API with LangChain4j + Ollama.
A playful example that teaches serious enterprise skills.

https://www.the-main-thread.com/p/quarkus-json-jackson-langchain4j-meme-service

Building AI-infused apps with Java?
Quarkus + SmallRye Fault Tolerance gives you retries, circuit breakers & fallbacks for safe LLM calls. Combine it with LangChain4j + Ollama to make AI services resilient by design.
https://www.the-main-thread.com/p/quarkus-fault-tolerance-langchain4j-ai-resilience

Ever wondered why some images feel naturally beautiful? It’s the Golden Ratio.
I built an AI-Powered Design Critic in Java using Quarkus, LangChain4j & Ollama that analyzes images and overlays the ratio. Full hands-on tutorial here:
https://www.the-main-thread.com/p/ai-golden-ratio-design-critic-quarkus-java

LangChain4J CDI 0.0.3 is out!!!!! With support for #langchain4j 1.4.0
https://github.com/langchain4j/langchain4j-cdi/releases/tag/langchain4j-cdi-0.0.3
#JakartaEE #Microprofile #Java

Has anyone compared #langgraph4j to the agentic features of #langchain4j?

Next-Level Features of LangChain4j for Production-Ready AI Applications

On Oct 16, @lizeraes dives deep into tools for moderation, multi-LLM integration, robust evaluation methods & advanced Retrieval-Augmented Generation (RAG) techniques to build reliable, scalable AI apps that meet real-world business needs including privacy and cost considerations.

Full program: https://baselone.org/#programm
Tickets: https://eventfrog.ch/BaselOne2025

Negotiating with AI Managers: Build a Role‑Playing Simulator Using Quarkus and Langchain4j
A hands‑on guide for Java developers to create an AI‑powered negotiation training app with a local LLM and a clean frontend.
https://www.the-main-thread.com/p/quarkus-java-ai-negotiation-simulator
#Java #Quarkus #LangChain4j #Memory

Infusing AI into Java applications boosts both the app’s capabilities & the end-user experience.

Discover how Java frameworks like #Quarkus & #LangChain4j make it easy and efficient to interact with LLMs and create satisfying applications for end-users.

Read the #InfoQ article: https://bit.ly/4oNmLqH