#LangChain MCP agent
294 related articles
·2 min
MCP for Beginners: The Essential Protocol for AI Agent Development
Learn what MCP (Model Context Protocol) is, why it's replacing Function Calling, and how it breaks AI data silos to enable remote tool invocation for agents.
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·3 min
AI Agent Development Landscape: From Architecture Design to Production Deployment
A systematic overview of the AI Agent tech stack: RAG retrieval, Agent planning, MCP protocol, AI Gateway, and observability — helping developers build production-grade AI systems.
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WrenAI: An Open-Source GenBI Tool for …
·4 min
WrenAI: An Open-Source GenBI Tool for Querying Databases with Natural Language
WrenAI is an open-source GenBI tool by the Canner team that converts natural language into trusted SQL, charts, and dashboards via a semantic layer. Supports 20+ data sources including BigQuery and Snowflake. 16,000+ GitHub stars.
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·3 min
AI Development for Java Engineers: A Complete Guide to LangChain4j Agents
Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.
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GitHub Daily · July 21: The Agent Skil…
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GitHub Daily · July 21: The Agent Skills Ecosystem Explodes, Intelligence Dashboard Tops the Charts
GitHub Trending July 21: The Agent Skills ecosystem explodes as pluggable skill packs top the charts, MCP becomes the de facto standard, and ai-agent-book surges 4,434 stars in a day.
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The Complete Guide to Agent Skills: Andrew Ng × Anthropic Joint Course Breakdown
Andrew Ng's DeepLearning.ai teams up with Anthropic to teach Agent Skills: file structure, progressive disclosure, MCP integration, and the full path from Claude.ai to the Agent SDK.
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Java AI Tech Stack Explained: Framework Selection & Learning Path Guide
A complete guide to Java AI development: Spring AI, LangChain4j, Spring AI Alibaba, and AgentScope4j — framework comparisons, selection tips, and a clear learning path.
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·3 min
Java Developers Pivoting to AI: Why RAG Knowledge Bases Are the Best Entry Point
Traditional Java roles are shrinking while AI demand surges. Learn the three paths into AI for developers, and why RAG knowledge bases are the highest-ROI entry point for Java engineers.
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·3 min
Frontend to AI Agent: Why TypeScript + Zod Have Become Non-Negotiable
Frontend engineers pivoting to AI Agent development: TypeScript and Zod are now must-have skills. Explore the full progression from API calls to building LangGraph-style frameworks, and nail the 3 core interview topics.
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Java Developers Transitioning to AI: A Practical Guide to Building RAG Knowledge Bases
How can Java developers break into AI? This guide covers the AI application engineer career path, RAG knowledge base fundamentals, vector database retrieval, and enterprise-grade RAG challenges.
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·3 min
LangChain V1.3 in Practice: Mastering AI Engineering Mindset — From Tool User to Tool Master
Why do AI results vary so dramatically? LangChain V1.3 reveals the answer: engineering mindset. Covers LangGraph, Deep Agent, RAG, Time Travel, and more.
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Build an MCP Server in 10 Minutes: Let AI Call Your Code Directly
MCP (Model Context Protocol) is the open standard for AI tool integration. Build your own MCP server with ~20 lines of Python. Learn tools, resources, prompts, and both local and remote deployment.
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Designing APIs for AI Agents: A Paradi…
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Designing APIs for AI Agents: A Paradigm Shift from Human Interfaces to Agent-Friendly Design
When AI Agents become the primary API callers, traditional interface design assumptions break down. This article explores agent-friendly API design principles and how MCP is driving this paradigm shift.
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·2 min
The Four Core Concepts of AI Agents Explained: Function Calling, Tool, MCP, and Skill
A clear breakdown of the four core AI Agent concepts: Function Calling, Tool, MCP, and Skill — understand the full tech stack behind intelligent agent development.
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Adversarial Testing of AI Agents in the Terminal via MCP
Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.
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Spring AI in Action: A Complete Guide to LLM Integration for Java Developers
Spring AI is Java's answer to LangChain — offering unified multi-model APIs, structured output, RAG, Tool Calling, and MCP protocol support for enterprise LLM development.
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PlanWright: A Control Plane and Multi-…
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PlanWright: A Control Plane and Multi-Agent Orchestration Layer for AI Coding Agents
PlanWright is a control plane for AI coding agents, drawing on Kubernetes orchestration principles to tackle multi-agent task assignment, state tracking, and collaboration conflicts.
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Complete AI Agent Development Learning Path: From Core Principles to Enterprise Deployment
A structured AI Agent learning path covering core principles, prompt engineering, tool use, multi-agent systems, and frameworks like LangChain, CrewAI, and Dify for enterprise deployment.
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LangChain4j Getting Started Guide: A Java Engineer's Fast Track to AI Application Development
LangChain4j is the AI application development framework built for Java engineers. Integrate DeepSeek, Qwen, and more into Spring Boot — no Python required.
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·2 min
Complete Spring AI Tutorial: ChatModel, RAG, and MCP Practical Guide
A complete Spring AI guide for Java developers covering ChatModel, EmbeddingModel, ChatMemory, Tool Calling, MCP protocol, and RAG with Milvus. Build LLM apps in Spring Boot.
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