#langchain4j — Public Fediverse posts

Connecting ☕ #Java 25 + 🦜 #langchain4j 1.18.0 to local #MLX (via OpenAI-compatible API server).
Sub-10ms TTFT, 100% offline & private! ⚡

Connecting ☕ #Java 25 + 🦜 #langchain4j 1.18.0 to local #MLX (via OpenAI-compatible API server).
Sub-10ms TTFT, 100% offline & private! ⚡

Kevin Dubois & Mario Fusco tested whether a code assistant could design a multi-agent system using only the LangChain4j documentation.

The result? A multi-agent system capable of writing, testing, and debugging code like human engineers.

📰 Check out the #InfoQ article to see how the experiment went, what worked, and what the resulting project looks like 👉 https://bit.ly/4yXe1Ds

#Java #AI #LangChain4j #AgenticAI

Kevin Dubois & Mario Fusco tested whether a code assistant could design a multi-agent system using only the LangChain4j documentation.

The result? A multi-agent system capable of writing, testing, and debugging code like human engineers.

📰 Check out the #InfoQ article to see how the experiment went, what worked, and what the resulting project looks like 👉 https://bit.ly/4yXe1Ds

#Java #AI #LangChain4j #AgenticAI

As it's still built in #Java with the #micronaut framework, the #langchain4j LLM orchestration library, and @graalvm, you can download pre-built binaries from GitHub for your platform of choice:
https://github.com/glaforge/antigravity-brain-visualizer/releases/tag/v0.4.1

As it's still built in #Java with the #micronaut framework, the #langchain4j LLM orchestration library, and @graalvm, you can download pre-built binaries from GitHub for your platform of choice:
https://github.com/glaforge/antigravity-brain-visualizer/releases/tag/v0.4.1

#AI can query your DB—but can it do it correctly? That’s the hard part. @MarcoBelladelli shows how #Hibernate + #Quarkus + #LangChain4j add validation & control back. Want fewer production surprises?

Dive in: https://javapro.io/2026/04/03/talk-to-your-data-natural-language-data-access-in-java/

#LLM #Java @Hibernate @QuarkusIO @langchain4j

#AI can query your DB—but can it do it correctly? That’s the hard part. @MarcoBelladelli shows how #Hibernate + #Quarkus + #LangChain4j add validation & control back. Want fewer production surprises?

Dive in: https://javapro.io/2026/04/03/talk-to-your-data-natural-language-data-access-in-java/

#LLM #Java @Hibernate @QuarkusIO @langchain4j

What happens when one #AI call isn’t enough? You don’t add more prompts—you add agents. @kevindubois & Laura Cowen show how enterprise AI really scales.

Curious how production systems are built? Dive in: https://javapro.io/2026/03/31/agentic-ai-patterns-for-enterprise-software/

#Quarkus #LangChain4j #Microservices @QuarkusIO

What happens when one #AI call isn’t enough? You don’t add more prompts—you add agents. @kevindubois & Laura Cowen show how enterprise AI really scales.

Curious how production systems are built? Dive in: https://javapro.io/2026/03/31/agentic-ai-patterns-for-enterprise-software/

#Quarkus #LangChain4j #Microservices @QuarkusIO

As #AI applications grow, prompts become workflows—and workflows become systems. Read @LoMagnette's guide to mastering #LangChain4j Agentic Workflows, from simple agents to supervisor patterns, error handling & production-ready orchestration: https://javapro.io/2026/07/08/langchain4j-agentic-workflows-from-ai-calls-to-multi-agent-systems-in-java/

As #AI applications grow, prompts become workflows—and workflows become systems. Read @LoMagnette's guide to mastering #LangChain4j Agentic Workflows, from simple agents to supervisor patterns, error handling & production-ready orchestration: https://javapro.io/2026/07/08/langchain4j-agentic-workflows-from-ai-calls-to-multi-agent-systems-in-java/

Confused by the exploding number of #AI tools in the #JVM ecosystem? Teams mix #SpringAI, #LangChain4j, MCP & #Ollama without understanding the layers underneath. Artur Skowronski explains what each part of the #Java AI stack is actually for: https://javapro.io/2026/06/03/the-gen-ai-iceberg-java-tooling-edition/

@langchain4j

Confused by the exploding number of #AI tools in the #JVM ecosystem? Teams mix #SpringAI, #LangChain4j, MCP & #Ollama without understanding the layers underneath. Artur Skowronski explains what each part of the #Java AI stack is actually for: https://javapro.io/2026/06/03/the-gen-ai-iceberg-java-tooling-edition/

@langchain4j

Chat memory gets fuzzy fast once the UI hides what LangChain4j is actually retaining.

I wrote a Quarkus tutorial that makes retained-memory pressure visible with `TokenWindowChatMemory`, Ollama request counts, a turn ledger, and OpenTelemetry attributes. The useful split is simple: your app-level eviction budget is not the model context limit. https://www.the-main-thread.com/p/quarkus-langchain4j-chat-memory-budget #Java #Quarkus #LangChain4j #Ollama #OpenTelemetry

Chat memory gets fuzzy fast once the UI hides what LangChain4j is actually retaining.

I wrote a Quarkus tutorial that makes retained-memory pressure visible with `TokenWindowChatMemory`, Ollama request counts, a turn ledger, and OpenTelemetry attributes. The useful split is simple: your app-level eviction budget is not the model context limit. https://www.the-main-thread.com/p/quarkus-langchain4j-chat-memory-budget #Java #Quarkus #LangChain4j #Ollama #OpenTelemetry

Local AI gets risky when the first confident answer becomes the system answer.

I wrote a Quarkus tutorial that sends the same text to two Ollama models, uses Quarkus Signals to escalate only on disagreement, and keeps `UNCERTAIN` separate from `FAILED`. https://www.the-main-thread.com/p/quarkus-langchain4j-ollama-signals #Java #Quarkus #LangChain4j #Ollama

Local AI gets risky when the first confident answer becomes the system answer.

I wrote a Quarkus tutorial that sends the same text to two Ollama models, uses Quarkus Signals to escalate only on disagreement, and keeps `UNCERTAIN` separate from `FAILED`. https://www.the-main-thread.com/p/quarkus-langchain4j-ollama-signals #Java #Quarkus #LangChain4j #Ollama

Most #AI prototypes work. Until the next model update breaks half the system. Lutske de Leeuw & Maarten Vandeperre show how #CleanArchitecture, ports & adapters keep AI integrations from becoming spaghetti code.

Read: https://javapro.io/2026/03/17/ai-without-spaghetti-clean-architecture-in-the-age-of-ai/
#LangChain4j #Quarkus QuarkusIO #LLM

Most #AI prototypes work. Until the next model update breaks half the system. Lutske de Leeuw & Maarten Vandeperre show how #CleanArchitecture, ports & adapters keep AI integrations from becoming spaghetti code.

Read: https://javapro.io/2026/03/17/ai-without-spaghetti-clean-architecture-in-the-age-of-ai/
#LangChain4j #Quarkus QuarkusIO #LLM

Our next #JCON2026 session is live: 'Talk to Your Data: Natural Language Data Access in #Java with #Hibernate #Quarkus and LangChain4j' with Marco Melladelli

Explore how Hibernate ORM, Quarkus, and #LangChain4j come together to enable …

Grab your coffee and hit play: https://youtu.be/tMW5jxX6DoA

Our next #JCON2026 session is live: 'Talk to Your Data: Natural Language Data Access in #Java with #Hibernate #Quarkus and LangChain4j' with Marco Melladelli

Explore how Hibernate ORM, Quarkus, and #LangChain4j come together to enable …

Grab your coffee and hit play: https://youtu.be/tMW5jxX6DoA

LangChain4j CDI 1.3.1 released -- simpler Human-in-the-Loop agents, @RegisterSimpleAgent alignment, nested scope fix, new WildFly example, and LangChain4j 1.15.1. AI + Jakarta EE keeps getting better! #Java #AI #JakartaEE #OpenSource #langchain4j

LangChain4j CDI 1.3.1 released -- simpler Human-in-the-Loop agents, @RegisterSimpleAgent alignment, nested scope fix, new WildFly example, and LangChain4j 1.15.1. AI + Jakarta EE keeps getting better! #Java #AI #JakartaEE #OpenSource #langchain4j

Tired of stitching #AI SDKs into your #JakartaEE stack manually? #LangChain4J-CDI lets you declare an interface, annotate it, & inject it anywhere — REST, EJB, schedulers. @EliteGentleman demonstrates the model-driven approach.

Worth a closer look? Read: https://javapro.io/2026/02/25/bring-ai-into-your-jakarta-ee-apps-with-langchain4j-cdi-formerly-smallrye-llm/

Tired of stitching #AI SDKs into your #JakartaEE stack manually? #LangChain4J-CDI lets you declare an interface, annotate it, & inject it anywhere — REST, EJB, schedulers. @EliteGentleman demonstrates the model-driven approach.

Worth a closer look? Read: https://javapro.io/2026/02/25/bring-ai-into-your-jakarta-ee-apps-with-langchain4j-cdi-formerly-smallrye-llm/

Confused by the exploding number of #AI tools in the #JVM ecosystem? Teams mix #SpringAI, #LangChain4j, MCP & #Ollama without understanding the layers underneath. Artur Skowronski explains what each part of the #Java AI stack is actually for: https://javapro.io/2026/06/03/the-gen-ai-iceberg-java-tooling-edition/

@langchain4j

Confused by the exploding number of #AI tools in the #JVM ecosystem? Teams mix #SpringAI, #LangChain4j, MCP & #Ollama without understanding the layers underneath. Artur Skowronski explains what each part of the #Java AI stack is actually for: https://javapro.io/2026/06/03/the-gen-ai-iceberg-java-tooling-edition/

@langchain4j

Cheap questions should not burn the same local model as real debugging work.

I wrote a Quarkus + LangChain4j tutorial that classifies prompts, routes them between two Ollama models, and keeps the decision observable with CDI events and tests. https://www.the-main-thread.com/p/quarkus-langchain4j-model-routing #Java #Quarkus #LangChain4j #Ollama

Cheap questions should not burn the same local model as real debugging work.

I wrote a Quarkus + LangChain4j tutorial that classifies prompts, routes them between two Ollama models, and keeps the decision observable with CDI events and tests. https://www.the-main-thread.com/p/quarkus-langchain4j-model-routing #Java #Quarkus #LangChain4j #Ollama

LangChain4j: Chat With Documents

#ai #java #langchain4j

https://mydeveloperplanet.com/2024/01/24/langchain4j-chat-with-documents/

LangChain4j: Chat With Documents

#ai #java #langchain4j

https://mydeveloperplanet.com/2024/01/24/langchain4j-chat-with-documents/

LangSmith does not need to stay in the Python corner.

I wrote a Quarkus walkthrough that sends LangChain4j traces to LangSmith over OTLP, including plain chat, tool calls, and controlled failures. It also covers the two easy footguns: /otel vs /otel/v1/traces, and region-specific endpoints.

https://www.the-main-thread.com/p/quarkus-langchain4j-langsmith

#Quarkus #LangChain4j #OpenTelemetry #Observability

LangSmith does not need to stay in the Python corner.

I wrote a Quarkus walkthrough that sends LangChain4j traces to LangSmith over OTLP, including plain chat, tool calls, and controlled failures. It also covers the two easy footguns: /otel vs /otel/v1/traces, and region-specific endpoints.

https://www.the-main-thread.com/p/quarkus-langchain4j-langsmith

#Quarkus #LangChain4j #OpenTelemetry #Observability

🚀 langchain4j-cdi 1.2.0 is out!

🤖 @RegisterAgent: 8 agentic topologies (SIMPLE→A2A)
🔌 MCP Server support for CDI beans
⚡ ${config} & #{EL} expressions in annotations
All CDI-native, enterprise-ready.
Thanks to @yblazart.bsky.social , Buhake Sindi and Don Bourne
#Java #AI #LangChain4j #JakartaEE

🚀 langchain4j-cdi 1.2.0 is out!

🤖 @RegisterAgent: 8 agentic topologies (SIMPLE→A2A)
🔌 MCP Server support for CDI beans
⚡ ${config} & #{EL} expressions in annotations
All CDI-native, enterprise-ready.
Thanks to @yblazart.bsky.social , Buhake Sindi and Don Bourne
#Java #AI #LangChain4j #JakartaEE

Every deterministic workflow step does not need a planner call.

This piece shows how to keep MCP tools boring inside a LangChain4j graph: one Quarkus MCP server, one workflow app, /topology, and tests that hit real Ollama instead of stubs.

https://www.the-main-thread.com/p/quarkus-langchain4j-mcp-tool-agents

#Java #Quarkus #LangChain4j #MCP

Every deterministic workflow step does not need a planner call.

This piece shows how to keep MCP tools boring inside a LangChain4j graph: one Quarkus MCP server, one workflow app, /topology, and tests that hit real Ollama instead of stubs.

https://www.the-main-thread.com/p/quarkus-langchain4j-mcp-tool-agents

#Java #Quarkus #LangChain4j #MCP

Agent demos love static diagrams. Production gives you a different graph.

This post shows how to expose a live LangChain4j topology from Quarkus with AgentMonitor, HtmlReportGenerator, and an SSE feed for recent runs.

https://www.the-main-thread.com/p/quarkus-langchain4j-topology-http

#Java #Quarkus #LangChain4j #Observability

Agent demos love static diagrams. Production gives you a different graph.

This post shows how to expose a live LangChain4j topology from Quarkus with AgentMonitor, HtmlReportGenerator, and an SSE feed for recent runs.

https://www.the-main-thread.com/p/quarkus-langchain4j-topology-http

#Java #Quarkus #LangChain4j #Observability

Agent names are not a routing strategy.

This walkthrough builds a Quarkus + LangChain4j sample that uses filesystem Skills to make ownership explicit, keeps a baseline supervisor for comparison, and proves the routing with HTTP tests.

https://www.the-main-thread.com/p/agent-skills-quarkus

#Quarkus #LangChain4j #Java #AIEngineering

Agent names are not a routing strategy.

This walkthrough builds a Quarkus + LangChain4j sample that uses filesystem Skills to make ownership explicit, keeps a baseline supervisor for comparison, and proves the routing with HTTP tests.

https://www.the-main-thread.com/p/agent-skills-quarkus

#Quarkus #LangChain4j #Java #AIEngineering

Once a tool-calling assistant grows from 5 tools to 50, the problem stops being “prompting” and starts being context geometry.

This walkthrough builds a Quarkus + LangChain4j + Ollama example and shows what tool search actually changes: smaller working sets, visible search rounds, and more prompt headroom even when local latency is messy.

https://www.the-main-thread.com/p/langchain4j-tool-search-quarkus-ollama

#Quarkus #LangChain4j #Ollama #Java

Once a tool-calling assistant grows from 5 tools to 50, the problem stops being “prompting” and starts being context geometry.

This walkthrough builds a Quarkus + LangChain4j + Ollama example and shows what tool search actually changes: smaller working sets, visible search rounds, and more prompt headroom even when local latency is messy.

https://www.the-main-thread.com/p/langchain4j-tool-search-quarkus-ollama

#Quarkus #LangChain4j #Ollama #Java

Still building #AI with one model per request? You’re already behind. @kevindubois & Laura Cowen map the shift to agentic systems & why orchestration beats prompts.

Ready to design real AI architectures? Dive in: https://javapro.io/2026/03/31/agentic-ai-patterns-for-enterprise-software/

#Java #LangChain4j @langchain4j

Still building #AI with one model per request? You’re already behind. @kevindubois & Laura Cowen map the shift to agentic systems & why orchestration beats prompts.

Ready to design real AI architectures? Dive in: https://javapro.io/2026/03/31/agentic-ai-patterns-for-enterprise-software/

#Java #LangChain4j @langchain4j

In this #InfoQ article, Vignesh Durai explains how agentic and multimodal AI systems can be engineered using #ApacheCamel & #LangChain4j.

The solution combines LLM-based reasoning, retrieval-augmented generation (RAG), and image classification.

🔗 Read now: https://bit.ly/4sXdlcM

#AI #LLMs #DataPipelines

In this #InfoQ article, Vignesh Durai explains how agentic and multimodal AI systems can be engineered using #ApacheCamel & #LangChain4j.

The solution combines LLM-based reasoning, retrieval-augmented generation (RAG), and image classification.

🔗 Read now: https://bit.ly/4sXdlcM

#AI #LLMs #DataPipelines

Tomorrow I’m speaking at the Bangalore JUG Special Talk on Open-Source GPU-Powered Image Generation with LangChain4j. We’ll walk through a Java-based stack using Spring, LangChain4j, ONNX Runtime, CUDA, and SD4J to build a cloud-ready image generation service with monitoring and a real-time UI.
RSVP: https://www.meetup.com/bangalorejug/events/314000765/

#Java #AI #LangChain4j #OpenSource #GPU

Tomorrow I’m speaking at the Bangalore JUG Special Talk on Open-Source GPU-Powered Image Generation with LangChain4j. We’ll walk through a Java-based stack using Spring, LangChain4j, ONNX Runtime, CUDA, and SD4J to build a cloud-ready image generation service with monitoring and a real-time UI.
RSVP: https://www.meetup.com/bangalorejug/events/314000765/

#Java #AI #LangChain4j #OpenSource #GPU

Exclusive Devoxx UK Discount for Quarkus Devs!

Want to catch all the supersonic, subatomic Java talks at Devoxx UK? Grab £50 off your registration with the exclusive code DVX26QUARKUS.

Don't miss the Quarkus sessions, and be sure to drop by the Quarkus Community Booth. Let's talk high-performance, cloud-native apps, and our latest Generative AI integrations with LangChain4j.

See you in London!

#DevoxxUK #Quarkus #Java #AI #LangChain4j #OpenSource #quarkusworldtour

Exclusive Devoxx UK Discount for Quarkus Devs!

Want to catch all the supersonic, subatomic Java talks at Devoxx UK? Grab £50 off your registration with the exclusive code DVX26QUARKUS.

Don't miss the Quarkus sessions, and be sure to drop by the Quarkus Community Booth. Let's talk high-performance, cloud-native apps, and our latest Generative AI integrations with LangChain4j.

See you in London!

#DevoxxUK #Quarkus #Java #AI #LangChain4j #OpenSource #quarkusworldtour

Cheerio, London! The Quarkus community is heading to Devoxx UK on May 6th and 7th!

We are proud to sponsor a booth at this year's event in the Business Design Centre. Stop by to chat with our community members about how to supercharge your skillset with supersonic, subatomic Java. We'd love to talk to you about live coding, developer joy, & how to easily build enterprise-grade AI apps using our native LangChain4j integration!
️
See you there!
@DevoxxUK #DevoxxUK #Quarkus #Java #AI #LangChain4j

Cheerio, London! The Quarkus community is heading to Devoxx UK on May 6th and 7th!

We are proud to sponsor a booth at this year's event in the Business Design Centre. Stop by to chat with our community members about how to supercharge your skillset with supersonic, subatomic Java. We'd love to talk to you about live coding, developer joy, & how to easily build enterprise-grade AI apps using our native LangChain4j integration!
️
See you there!
@DevoxxUK #DevoxxUK #Quarkus #Java #AI #LangChain4j

☕Break time at #JCON2026! Curious about what’s coming next? Warm up with some #JAVAPRO background reading from today’s speakers:

📄 @vilojona (11:30)
👉 https://javapro.io/2026/03/12/7-habits-of-highly-effective-ai-java-coding/

📄 @kevindubois (12:30)
👉 https://javapro.io/2026/03/31/agentic-ai-patterns-for-enterprise-software/

📄 Florian Sommer (12:30)
👉 https://javapro.io/de/wird-ki-mich-ersetzen-der-wert-unserer-expertise-im-ki-zeitalter/

🚀Insight now, questions later!

#AI #LangChain4j

☕Break time at #JCON2026! Curious about what’s coming next? Warm up with some #JAVAPRO background reading from today’s speakers:

📄 @vilojona (11:30)
👉 https://javapro.io/2026/03/12/7-habits-of-highly-effective-ai-java-coding/

📄 @kevindubois (12:30)
👉 https://javapro.io/2026/03/31/agentic-ai-patterns-for-enterprise-software/

📄 Florian Sommer (12:30)
👉 https://javapro.io/de/wird-ki-mich-ersetzen-der-wert-unserer-expertise-im-ki-zeitalter/

🚀Insight now, questions later!

#AI #LangChain4j

Java developers: this guest post by Sebastian Kühnau shows how to build a streaming AI chat with Quarkus, Vaadin Flow, and LangChain4j — all in plain Java. A nice example of token-by-token UI updates without switching to a separate frontend stack.
https://www.the-main-thread.com/p/streaming-ai-chat-java-quarkus-vaadin-langchain4j

#Java #Quarkus #Vaadin #LangChain4j #AI

Java developers: this guest post by Sebastian Kühnau shows how to build a streaming AI chat with Quarkus, Vaadin Flow, and LangChain4j — all in plain Java. A nice example of token-by-token UI updates without switching to a separate frontend stack.
https://www.the-main-thread.com/p/streaming-ai-chat-java-quarkus-vaadin-langchain4j

#Java #Quarkus #Vaadin #LangChain4j #AI