#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! ⚡

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! ⚡

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

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

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

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

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

#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

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/

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

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

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

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

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

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

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

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

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

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/

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/

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

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

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