Building a simple AI agent with LangChain4j is easy. But add real-world requirements and it falls apart: Suddenly you’re writing while loops, tracking state manually, parsing agent responses, and praying nothing breaks. Real Example: Text-to-SQL Requirements: LangChain4j approach: 78 lines of orchestration code for a simple workflow. LangGraph4j: Define the Flow, Not...
# LangGraph4j Integration Specification ## Table of Contents 1. [Why LangGraph4j?](#why-langgraph) 2. [Core Concepts](#core-concepts) 3. [Java Implementation Example](#java-example) 4. [ColdFusion Integration Proposal](#coldfusion-integration) — ## 1. Why LangGraph4j? {#why-langgraph} ### The Problem: Traditional Workflow Limitations...
Excellent question! This is where the real-world architecture matters. Let me show you the correct pattern: ❌ WRONG Approach (Don’t Do This): ✅ CORRECT Approach: The key insight: The workflow object is just the...
Your agents flow might require interruption or human-in-loop at multiple node executions. Here is the code sample to do so
Here is the java code showing the real world use case of using LangGraph in Java applications
When integrating external model servers with local applications using the Model Context Protocol (MCP), one of the most important safety primitives is roots.Roots are how a client tells a connected server: “these are the...
This example demonstrates how to build a travel assistant using LangGraph and LLM-based tool orchestration — where each tool is represented as an independent node in the workflow graph. Instead of hardcoding if/else logic...
Blackboard If the problem is always the same the previous architectures might have worked … but what if the best next step depends on the results of the previous one? A rigid sequence can...
# How to Implement Sampling in MCP (Model Context Protocol) with Java ## What is MCP Sampling? MCP sampling allows you to capture and process streaming messages from AI models in real-time. It’s useful...
When building AI-powered applications, one of the most common challenges is: 👉 How do we give the LLM access to fresh, structured data without stuffing the entire dataset into its prompt? This is where...