#langchain4j — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #langchain4j, aggregated by home.social.
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🤖 AI systems are moving beyond prompts to agents that plan, act and interact.
At #BaselOne26, Lize Raes explains why agents aren't a passing hype, but a design pattern for building robust AI systems.Using #LangChain4j and #Embabel, she shows how #Java combines agentic AI with type safety, reliability and testability.
🎙️ Full program: https://baselone.org/#programm
🎟️ Get your tickets: https://eventfrog.ch/BaselOne26
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🤖 AI systems are moving beyond prompts to agents that plan, act and interact.
At #BaselOne26, Lize Raes explains why agents aren't a passing hype, but a design pattern for building robust AI systems.Using #LangChain4j and #Embabel, she shows how #Java combines agentic AI with type safety, reliability and testability.
🎙️ Full program: https://baselone.org/#programm
🎟️ Get your tickets: https://eventfrog.ch/BaselOne26
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🤖 AI systems are moving beyond prompts to agents that plan, act and interact.
At #BaselOne26, Lize Raes explains why agents aren't a passing hype, but a design pattern for building robust AI systems.Using #LangChain4j and #Embabel, she shows how #Java combines agentic AI with type safety, reliability and testability.
🎙️ Full program: https://baselone.org/#programm
🎟️ Get your tickets: https://eventfrog.ch/BaselOne26
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🤖 AI systems are moving beyond prompts to agents that plan, act and interact.
At #BaselOne26, Lize Raes explains why agents aren't a passing hype, but a design pattern for building robust AI systems.Using #LangChain4j and #Embabel, she shows how #Java combines agentic AI with type safety, reliability and testability.
🎙️ Full program: https://baselone.org/#programm
🎟️ Get your tickets: https://eventfrog.ch/BaselOne26
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LLMs anzubinden ist einfach. Eine produktionsreife KI-Anwendung zu bauen, ist die eigentliche Herausforderung. Jean-Claude Brantschen zeigt, wie LangChain4j parallele Multi-LLM-Anfragen, Memory, Tools und sogar einen Chatbot mit Web-UI elegant in Java umsetzt.👉 https://javapro.io/de/langchain4j-teil-2/
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LLMs anzubinden ist einfach. Eine produktionsreife KI-Anwendung zu bauen, ist die eigentliche Herausforderung. Jean-Claude Brantschen zeigt, wie LangChain4j parallele Multi-LLM-Anfragen, Memory, Tools und sogar einen Chatbot mit Web-UI elegant in Java umsetzt.👉 https://javapro.io/de/langchain4j-teil-2/
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Warum für KI-Projekte Python lernen, wenn dein Team bereits Java beherrscht? Jean-Claude Brantschen zeigt, wie LangChain4j OpenAI, Claude, Gemini und Ollama über dieselbe API verbindet – mit weniger Boilerplate und mehr Typsicherheit. Mehr erfahren: https://javapro.io/de/langchain4j-teil-1/
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Warum für KI-Projekte Python lernen, wenn dein Team bereits Java beherrscht? Jean-Claude Brantschen zeigt, wie LangChain4j OpenAI, Claude, Gemini und Ollama über dieselbe API verbindet – mit weniger Boilerplate und mehr Typsicherheit. Mehr erfahren: https://javapro.io/de/langchain4j-teil-1/
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Warum für KI-Projekte Python lernen, wenn dein Team bereits Java beherrscht? Jean-Claude Brantschen zeigt, wie LangChain4j OpenAI, Claude, Gemini und Ollama über dieselbe API verbindet – mit weniger Boilerplate und mehr Typsicherheit. Mehr erfahren: https://javapro.io/de/langchain4j-teil-1/
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Warum für KI-Projekte Python lernen, wenn dein Team bereits Java beherrscht? Jean-Claude Brantschen zeigt, wie LangChain4j OpenAI, Claude, Gemini und Ollama über dieselbe API verbindet – mit weniger Boilerplate und mehr Typsicherheit. Mehr erfahren: https://javapro.io/de/langchain4j-teil-1/
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Warum für KI-Projekte Python lernen, wenn dein Team bereits Java beherrscht? Jean-Claude Brantschen zeigt, wie LangChain4j OpenAI, Claude, Gemini und Ollama über dieselbe API verbindet – mit weniger Boilerplate und mehr Typsicherheit. Mehr erfahren: https://javapro.io/de/langchain4j-teil-1/
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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
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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
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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
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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
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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
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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/
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#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/
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#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/
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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
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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
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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
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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/
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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/
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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/
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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