BestHub
Sign in
Tagged articles
64 articles
Page 1 of 1
java1234
java1234
May 10, 2026 · Artificial Intelligence

Bringing Python‑Level AI Agents to Java Production: A Deep Dive into AgentScope Java

AgentScope Java is an open‑source, agent‑oriented framework that brings ReAct, tool calling, memory, multi‑agent collaboration, runtime intervention, plug‑in integration, reactive architecture, GraalVM native images and OpenTelemetry observability to Java, enabling production‑grade AI agents with familiar Java tooling.

AI agentsAgentScope JavaJava
0 likes · 9 min read
Bringing Python‑Level AI Agents to Java Production: A Deep Dive into AgentScope Java
CodeTrend
CodeTrend
Apr 29, 2026 · Artificial Intelligence

qwen2API: Turning Qwen Web Chat into OpenAI, Claude, and Gemini Compatible APIs

The qwen2API project offers a FastAPI backend and React+Vite frontend that expose the Qwen web chat as OpenAI Chat Completions, Anthropic Messages, and Gemini GenerateContent interfaces, featuring tool calling, image generation, account pool management, multiple deployment options, and various execution engines.

AnthropicFastAPIGemini
0 likes · 6 min read
qwen2API: Turning Qwen Web Chat into OpenAI, Claude, and Gemini Compatible APIs
IT Services Circle
IT Services Circle
Apr 28, 2026 · Artificial Intelligence

Agent Tool Calls vs. Regular Function Calls: Key Differences Explained

The article explains how LLM‑driven agent tool calls differ from traditional function calls in timing, parameter sourcing, error handling, call‑chain observability, and performance, and it provides concrete examples, failure modes, and interview‑ready summaries.

AI InterviewAgentError Handling
0 likes · 14 min read
Agent Tool Calls vs. Regular Function Calls: Key Differences Explained
James' Growth Diary
James' Growth Diary
Apr 28, 2026 · Artificial Intelligence

Mastering LangGraph Multi‑Agent Collaboration: The Supervisor Pattern from Theory to Practice

This article explains why single‑agent LLM pipelines fail when many tools are attached, introduces the Supervisor pattern that separates routing and execution across specialized agents, compares Tool‑Calling and Handoff approaches, provides a complete TypeScript implementation—including hierarchical supervisors—and lists five common pitfalls with concrete fixes.

HandoffLLM OrchestrationLangGraph
0 likes · 17 min read
Mastering LangGraph Multi‑Agent Collaboration: The Supervisor Pattern from Theory to Practice
James' Growth Diary
James' Growth Diary
Apr 28, 2026 · Artificial Intelligence

Mastering LangGraph Multi‑Agent Collaboration: The Supervisor Pattern Explained from Theory to Practice

The article examines why single‑agent setups fail, introduces the Supervisor pattern for clear responsibility separation, compares Tool‑Calling and Handoff approaches, provides a complete TypeScript implementation, explores hierarchical supervisors, and outlines five common pitfalls with concrete fixes.

HandoffLangGraphMulti-Agent
0 likes · 15 min read
Mastering LangGraph Multi‑Agent Collaboration: The Supervisor Pattern Explained from Theory to Practice
The Dominant Programmer
The Dominant Programmer
Apr 28, 2026 · Backend Development

Spring Boot, LangChain4j & Ollama: Chain for Intent Recognition and Task Dispatch

The article demonstrates how to construct a Spring Boot application that orchestrates multiple AI services using LangChain4j and Ollama, defining intent‑classification and tool‑based assistants, registering them as beans, and routing user requests through a controller to achieve multi‑step intent recognition and task dispatch in a simulated intelligent customer‑service workflow.

AI orchestrationLangChain4jOllama
0 likes · 13 min read
Spring Boot, LangChain4j & Ollama: Chain for Intent Recognition and Task Dispatch
Architecture and Beyond
Architecture and Beyond
Apr 25, 2026 · Artificial Intelligence

Practical Insights on Recent AI Engineering Deployments

The article examines how large language models function as probabilistic components within deterministic software, discusses fault‑tolerance limits for viable AI use cases, and offers detailed engineering guidance on RAG pipelines, tool‑calling determinism, agent fragility, testing, monitoring, and privacy‑conscious deployment in finance.

AI EngineeringAgent ArchitectureLLM
0 likes · 14 min read
Practical Insights on Recent AI Engineering Deployments
IT Services Circle
IT Services Circle
Apr 24, 2026 · Artificial Intelligence

What’s the Real Difference Between LLMs and Agents? What Does an Agent Add?

The article explains that the fundamental gap between LLMs and Agents is state: LLMs perform single, stateless inferences, while Agents maintain execution history, intermediate results, and goal tracking to enable multi‑step, dynamic decision‑making, but this brings uncertainty, higher token costs, and debugging challenges.

AgentLLMMulti-step Reasoning
0 likes · 14 min read
What’s the Real Difference Between LLMs and Agents? What Does an Agent Add?
Coder Circle
Coder Circle
Apr 23, 2026 · Backend Development

How to Use Spring AI MCP to Let Large Language Models Call Your Java APIs

This article walks through the complete process of building a Spring AI MCP server and client in Java, covering protocol layers, Maven setup, configuration, tool definition with @Tool, bean registration, client integration, common pitfalls, and the language‑agnostic benefits of the MCP protocol.

AI integrationMCPTool Calling
0 likes · 10 min read
How to Use Spring AI MCP to Let Large Language Models Call Your Java APIs
AI Architecture Hub
AI Architecture Hub
Apr 22, 2026 · Artificial Intelligence

Build a Minimal AI Agent Loop in 30 Minutes and Turn It into a Stable Production System

This article walks through constructing a tiny, runnable AI agent loop that reads a user task, lets the model choose the next step, calls a tool, feeds the observation back, and repeats, then explains how to add harness, memory, permission, and validation layers to make the agent reliable in real‑world engineering environments.

AI AgentAgent LoopHarness
0 likes · 30 min read
Build a Minimal AI Agent Loop in 30 Minutes and Turn It into a Stable Production System
Architect's Must-Have
Architect's Must-Have
Apr 21, 2026 · Artificial Intelligence

30 Essential AI Agent Concepts: From LLMs to Multi‑Agent Systems

This comprehensive guide systematically explains thirty core terms of AI agents—covering foundational large language models, fine‑tuning techniques, multimodal vision‑language models, agent architectures such as ReAct and CoT, tool‑calling protocols, retrieval‑augmented generation, workflow orchestration, and emerging product forms like autonomous and embodied agents—while detailing the reasoning, trade‑offs, and concrete examples that shape modern agent engineering.

AI agentsEmbodied AIPrompt engineering
0 likes · 36 min read
30 Essential AI Agent Concepts: From LLMs to Multi‑Agent Systems
AI Engineer Programming
AI Engineer Programming
Apr 16, 2026 · Artificial Intelligence

Choosing the Right LLM: A Complete Guide to Selecting from Over 2 Million Models

With more than two million LLMs available, this guide explains how to evaluate functional capabilities, latency, throughput, cost, tool‑calling reliability, context‑window size and compliance, and presents a step‑by‑step framework for picking the most suitable model for each business scenario.

BenchmarkingContext WindowCost Optimization
0 likes · 25 min read
Choosing the Right LLM: A Complete Guide to Selecting from Over 2 Million Models
James' Growth Diary
James' Growth Diary
Apr 11, 2026 · Artificial Intelligence

Deep Dive into Tools: Function Calling Mechanics and LangChain Toolchain Design

This article explains how LLMs use Function Calling to output structured JSON for tool execution, walks through the full multi‑turn tool call loop, shows how LangChain standardizes disparate vendor APIs with BaseTool and bind_tools, and shares practical pitfalls, best‑practice guidelines, and security considerations for building robust agents.

AgentFunction CallingLLM
0 likes · 16 min read
Deep Dive into Tools: Function Calling Mechanics and LangChain Toolchain Design
Big Data and Microservices
Big Data and Microservices
Apr 11, 2026 · Artificial Intelligence

How AI Agents Turn LLMs into Autonomous Executors: The ReAct Paradigm Explained

This article analyzes how AI agents extend large language models with perception‑reason‑action loops, comparing them to traditional chatbots and RPA, and demonstrates their planning, memory, tool‑use, and action capabilities through detailed examples and a step‑by‑step research workflow.

AI AgentAgent ArchitectureAutonomous AI
0 likes · 12 min read
How AI Agents Turn LLMs into Autonomous Executors: The ReAct Paradigm Explained
James' Growth Diary
James' Growth Diary
Apr 9, 2026 · Artificial Intelligence

How ReAct Enables Agents to Think While Acting

This article explains the ReAct pattern—interleaving reasoning and acting for LLM agents—by defining its core loop, comparing it with plain tool‑calling, providing a step‑by‑step hand‑written implementation in JavaScript, showing the LangChain.js wrapper, streaming output, and detailing five common pitfalls and a pre‑deployment checklist.

JavaScriptLLMLangChain
0 likes · 16 min read
How ReAct Enables Agents to Think While Acting
Old Zhang's AI Learning
Old Zhang's AI Learning
Apr 3, 2026 · Artificial Intelligence

Qwopus3.5‑v3: From Reason‑Then‑Act to Act‑Then‑Refine – Claude‑Opus Distillation Turns Qwen3.5 into a Tool‑Using Agent

The newly released Qwopus3.5‑v3 model combines higher‑quality reasoning chains, dedicated tool‑calling reinforcement learning, and an act‑then‑refine paradigm, delivering a 5‑point HumanEval boost, a 1.43‑point MMLU‑Pro gain, 31.7% faster inference and 24% lower token cost, while remaining runnable on a 3090 or a 16 GB MacBook, with easy deployment via GGUF, LM Studio, Ollama or llama.cpp.

Claude OpusDistillationHumanEval
0 likes · 12 min read
Qwopus3.5‑v3: From Reason‑Then‑Act to Act‑Then‑Refine – Claude‑Opus Distillation Turns Qwen3.5 into a Tool‑Using Agent
Old Zhang's AI Learning
Old Zhang's AI Learning
Mar 28, 2026 · Artificial Intelligence

Qwen3.5-27B Outperforms the 397B Model in Tool Calling – Q6 Quantization Is Optimal

Using the open‑source ToolCall‑15 benchmark, the author shows that the 27‑billion‑parameter Qwen3.5 model consistently scores full marks while the 397‑billion‑parameter version fails on several tasks, and that the Q6 quantized variant offers the best trade‑off between size and tool‑calling accuracy.

AILLM BenchmarkModel Evaluation
0 likes · 7 min read
Qwen3.5-27B Outperforms the 397B Model in Tool Calling – Q6 Quantization Is Optimal
AI Engineer Programming
AI Engineer Programming
Mar 24, 2026 · Artificial Intelligence

Tool Calling 2.0: Autonomous Discovery and Multi‑Agent Workflow

The article traces the evolution from early function calls to Tool Calling 2.0, explaining how Model Context Protocol standardizes tool interfaces, how Agent‑to‑Agent communication enables multi‑AI collaboration, and how Skills combine tool orchestration into autonomous workflows, while also discussing remaining challenges such as intent misinterpretation and security risks.

AI skillsAgent CollaborationFunction Calling
0 likes · 14 min read
Tool Calling 2.0: Autonomous Discovery and Multi‑Agent Workflow
Old Zhang's AI Learning
Old Zhang's AI Learning
Mar 22, 2026 · Artificial Intelligence

Hands‑On Review: Unsloth Studio’s One‑Stop Local LLM Console (Windows‑Ready)

The author tests Unsloth Studio, a local web UI that unifies model download, execution, dataset handling, training, fine‑tuning and export, supporting GGUF and safetensors formats across Windows, macOS and Linux, and highlights its integrated tool‑calling, data‑recipe workflow, observability features, installation quirks, and target user scenarios.

GGUFLocal-LLMModel Training
0 likes · 9 min read
Hands‑On Review: Unsloth Studio’s One‑Stop Local LLM Console (Windows‑Ready)
Architect's Ambition
Architect's Ambition
Mar 19, 2026 · Artificial Intelligence

Fix AI Agent Tool-Calling Chaos with Prompt Engineering and MCP Protocol

The article explains how poorly designed prompts cause AI agents to invoke unnecessary or incorrect tools, and shows how a structured prompt template combined with the Model Context Protocol (MCP) and three safety measures can raise tool‑calling accuracy from about 30% to over 95%.

AI AgentJavaMCP protocol
0 likes · 13 min read
Fix AI Agent Tool-Calling Chaos with Prompt Engineering and MCP Protocol
AgentGuide
AgentGuide
Mar 19, 2026 · Artificial Intelligence

What Exactly Is an AI Agent? Complete Interview Guide

This article breaks down the concept of AI agents for interview preparation, covering their definition, core components like planning, memory, and tool use, differences from plain LLM chats, real‑world challenges, typical use cases, detailed component analysis, and a runnable pseudo‑code example.

AI AgentLLMMemory
0 likes · 9 min read
What Exactly Is an AI Agent? Complete Interview Guide
Data STUDIO
Data STUDIO
Mar 18, 2026 · Artificial Intelligence

Building a Smart Web AI Agent with FastAPI, LangGraph, and MCP

This article walks through the design and implementation of a production‑ready Web AI agent that uses FastAPI as the HTTP layer, LangGraph to orchestrate multi‑step reasoning, and MCP to expose external tools, showing how to manage state, integrate multiple LLM providers, and extend the system with persistence, rate‑limiting, and monitoring.

AI AgentFastAPILLM
0 likes · 20 min read
Building a Smart Web AI Agent with FastAPI, LangGraph, and MCP
AI Engineering
AI Engineering
Mar 16, 2026 · Artificial Intelligence

How GLM-5‑Turbo’s “Lobster” Package Powers a Self‑Built Product Operations Assistant

The article introduces GLM‑5‑Turbo, a model specially optimized for OpenClaw autonomous‑agent tasks, details its benchmark superiority on the ZClawBench suite, and walks through a step‑by‑step tutorial that uses AutoClaw and Chrome MCP to create a product‑operations assistant, while also noting token costs and the new Lobster pricing plan.

AI agentsAutoClawGLM-5-Turbo
0 likes · 8 min read
How GLM-5‑Turbo’s “Lobster” Package Powers a Self‑Built Product Operations Assistant
PaperAgent
PaperAgent
Mar 16, 2026 · Artificial Intelligence

How GLM-5-Turbo Turns an AI Research Lab into a 24‑Hour Autonomous Writer

The article details how the newly released GLM-5-Turbo "lobster" model powers an AI research Lab that automatically generates a complete OpenClaw survey paper—from topic brainstorming and literature mining to outline drafting, manuscript writing, and AAAI‑style submission—within an hour, showcasing benchmark results, prompt templates, and practical skill installations.

AI research automationAutoClawGLM-5-Turbo
0 likes · 10 min read
How GLM-5-Turbo Turns an AI Research Lab into a 24‑Hour Autonomous Writer
Baobao Algorithm Notes
Baobao Algorithm Notes
Mar 2, 2026 · Artificial Intelligence

How “Skills” Turn LLM Prompts into Portable, Engineered Workflows

This article dissects the evolution of LLM prompts into structured, version‑controlled skill packages, explains the AgentSkills specification, details OpenClaw’s implementation, compares prompts, memory, MCP and skills, and provides end‑to‑end examples with code, flowcharts and best‑practice recommendations.

Agent SkillsAutomationLLM
0 likes · 40 min read
How “Skills” Turn LLM Prompts into Portable, Engineered Workflows
Code Mala Tang
Code Mala Tang
Feb 20, 2026 · Artificial Intelligence

How to Integrate Claude Code with Ollama for Local and Cloud LLM Workflows

This guide walks you through installing Claude Code and Ollama, pulling and configuring various open‑source models, setting environment variables, and running Claude Code with both local and cloud‑hosted models, while covering context length, performance considerations, and tool‑calling examples.

Claude CodeEnvironment VariablesLLM integration
0 likes · 14 min read
How to Integrate Claude Code with Ollama for Local and Cloud LLM Workflows
AI Tech Publishing
AI Tech Publishing
Feb 15, 2026 · Artificial Intelligence

Mastering AI Agent Engineering in 9 Days: Lesson 1 – The Core Agent Loop

This tutorial introduces the foundational Agent Loop that powers modern AI agents, explains why it is needed, breaks down its four core components, compares workflow‑based and agent‑based designs, and provides a minimal Python implementation with code, pitfalls, and a concrete RSS‑news use case.

AI AgentAgent LoopOpenAI
0 likes · 17 min read
Mastering AI Agent Engineering in 9 Days: Lesson 1 – The Core Agent Loop
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Feb 12, 2026 · Artificial Intelligence

Fast Generation, Weak Intelligence? The Harsh Reality of Diffusion Models for Agents

A comprehensive evaluation shows that while diffusion language models achieve higher generation speed through parallel decoding, they suffer from severe causal reasoning and formatting deficiencies, lagging far behind autoregressive models on embodied and tool‑calling agent tasks.

AI EvaluationAutoregressive ModelsTool Calling
0 likes · 8 min read
Fast Generation, Weak Intelligence? The Harsh Reality of Diffusion Models for Agents
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Feb 4, 2026 · Artificial Intelligence

Why LLM Agents Rush to Call Tools and How to Stop Them

The article explains that premature tool calls in LLM agents stem from a data‑distribution bias in fine‑tuning, and it presents practical fixes such as adding non‑tool samples, enforcing a Thought chain, and using negative sampling to teach the model when to think before acting.

AgentLLMThought Chain
0 likes · 10 min read
Why LLM Agents Rush to Call Tools and How to Stop Them
AI Engineering
AI Engineering
Jan 20, 2026 · Artificial Intelligence

How mcpx Cuts Token Overhead in MCP Tool Calls for Local LLMs

The article explains how mcpx reduces MCP tool definition tokens from tens of thousands to a few hundred by discovering tools at execution time, improving accuracy and speed for local large language models while preserving prompt cache integrity.

AnthropicLocal-LLMMCP
0 likes · 6 min read
How mcpx Cuts Token Overhead in MCP Tool Calls for Local LLMs
AI Insight Log
AI Insight Log
Jan 19, 2026 · Artificial Intelligence

Run Claude Code for Free? Ollama Adds Anthropic API Compatibility

Ollama v0.14.0 now supports the Anthropic API, letting you run Claude Code locally with open‑source models like Qwen or Llama without an API key, network, or cost, and the article provides a step‑by‑step setup, SDK examples, and an objective assessment of the approach.

Anthropic APIClaude CodeLocal-LLM
0 likes · 7 min read
Run Claude Code for Free? Ollama Adds Anthropic API Compatibility
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Dec 26, 2025 · Artificial Intelligence

Introducing MCP: A Standard Protocol to Empower Large Models with System Capabilities

MCP (Model Context Protocol) is an open standard that lets AI applications connect to external systems through a unified client‑server model, exposing Tools, Resources, and Prompts, while addressing security, permission, and audit concerns to make large‑model deployments more reusable and controllable.

AI integrationModel Context ProtocolTool Calling
0 likes · 4 min read
Introducing MCP: A Standard Protocol to Empower Large Models with System Capabilities
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 9, 2025 · Artificial Intelligence

Building Human‑in‑the‑Loop Agent Workflows with MCP on OpenLM

This article explains how to design and implement Human‑in‑the‑Loop (HITL) interactions for large‑model agents on Alibaba's OpenLM platform, covering the challenges of server‑side execution, MCP transport extensions, tool‑calling patterns, timeout handling, and UI rendering strategies across multiple client devices.

AgentHuman-in-the-LoopMCP
0 likes · 39 min read
Building Human‑in‑the‑Loop Agent Workflows with MCP on OpenLM
Wuming AI
Wuming AI
Dec 7, 2025 · Artificial Intelligence

What Is MCP and How It Revolutionizes AI Tool Integration

This article explains the MCP protocol for AI agents, detailing why a universal tool‑calling standard is needed, how it solves the M×N integration nightmare, the roles and execution stages involved, and demonstrates its use with Cherry Studio while highlighting current limitations.

AI AgentCherry StudioLLM
0 likes · 20 min read
What Is MCP and How It Revolutionizes AI Tool Integration
Code Wrench
Code Wrench
Dec 6, 2025 · Artificial Intelligence

Build a Local Go AI Agent with Ollama and DeepSeek – MVP Guide

This article walks you through creating a fully offline, extensible AI programming assistant in Go, using Ollama and DeepSeek‑R1, covering project layout, message formats, function calling, tool integration, a simple WebSocket UI, and future extension ideas.

AI AgentGoLocal-LLM
0 likes · 10 min read
Build a Local Go AI Agent with Ollama and DeepSeek – MVP Guide
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Nov 27, 2025 · Artificial Intelligence

Why Your Enterprise AI Agent Fails and How to Fix the Four Biggest Pitfalls

This article explains why many enterprise AI agents break down in real projects, identifies four common pitfalls—including mistaking agents for chatbots, lacking schema‑level tool logic, missing memory and variable injection, and absent end‑to‑end pipelines—and offers concrete engineering solutions to build robust, task‑driven agents.

AI AgentEnd-to-End PipelineEnterprise AI
0 likes · 8 min read
Why Your Enterprise AI Agent Fails and How to Fix the Four Biggest Pitfalls
Tencent Technical Engineering
Tencent Technical Engineering
Nov 24, 2025 · Artificial Intelligence

Inside Google gemini-cli: Turning the Terminal into an AI Agent with ReAct Architecture

This article systematically dissects Google’s open‑source gemini‑cli, revealing how it transforms a traditional command‑line terminal into an AI‑driven collaborative interface by detailing its ReAct loop, tool‑calling mechanisms, context management, and extensible architecture for building similar terminal agents.

AI AgentCLIGemini CLI
0 likes · 27 min read
Inside Google gemini-cli: Turning the Terminal into an AI Agent with ReAct Architecture
Fun with Large Models
Fun with Large Models
Nov 4, 2025 · Artificial Intelligence

Mastering LangChain 1.0’s create_agent API: Basics, Message Types, and Stream Modes

This tutorial walks through setting up a Python environment, explains the three essential components of LangChain 1.0’s create_agent API, details the built‑in message types, and demonstrates four streaming output modes using a weather‑assistant example to help developers quickly adopt the new agent framework.

AI agentsLangChainPython
0 likes · 11 min read
Mastering LangChain 1.0’s create_agent API: Basics, Message Types, and Stream Modes
DaTaobao Tech
DaTaobao Tech
Oct 29, 2025 · Artificial Intelligence

Master AI Coding: From Token Mechanics to Practical Best Practices

This comprehensive guide explains the underlying principles of AI coding assistants—including token calculation, tool calling, codebase indexing, and Merkle tree synchronization—while offering actionable best‑practice recommendations for prompt engineering, incremental development, documentation, security compliance, and effective team adoption.

AI CodingTool Callingcodebase indexing
0 likes · 45 min read
Master AI Coding: From Token Mechanics to Practical Best Practices
dbaplus Community
dbaplus Community
Oct 26, 2025 · Artificial Intelligence

How MCP Turns AI into a Universal Plug‑In: A Deep Dive into Model Context Protocol

This article explains the Model Context Protocol (MCP) – an open, universal standard that lets large language models seamlessly interact with external tools and data – covering its core architecture, why it’s needed, underlying principles, tool‑selection mechanics, a step‑by‑step Python server implementation, and practical usage tips.

AI integrationLLMMCP
0 likes · 20 min read
How MCP Turns AI into a Universal Plug‑In: A Deep Dive into Model Context Protocol
Senior Tony
Senior Tony
Sep 16, 2025 · Artificial Intelligence

What Is MCP? Exploring the AI‑LLM Interaction Protocol

MCP, a protocol from Anbhropic, standardizes how large language models communicate with external tools, databases, and APIs through a client‑server architecture, offering three communication modes (Stdio, HTTP with SSE, Streamable HTTP) and enabling use cases such as intelligent analytics, knowledge hubs, AI chatbots, BPM, API integration, automated testing, and programming assistance.

AI protocolLLM integrationMCP
0 likes · 9 min read
What Is MCP? Exploring the AI‑LLM Interaction Protocol
Baidu Geek Talk
Baidu Geek Talk
Sep 15, 2025 · Artificial Intelligence

How Baidu’s AI Navigation Turns Voice Commands into Precise Actions

This article explains how Baidu Map’s AI navigation system converts spoken queries into accurate map instructions by combining speech recognition, intent parsing, large‑language‑model reasoning, tool calling, and memory‑reflection techniques, showcasing the underlying technologies that enable instant, context‑aware responses.

AILLMMap Services
0 likes · 13 min read
How Baidu’s AI Navigation Turns Voice Commands into Precise Actions
Fun with Large Models
Fun with Large Models
Sep 1, 2025 · Artificial Intelligence

Build a LangGraph AI Agent in Two Lines Using the Prebuilt Graph API

This tutorial shows how to set up a Python environment, install LangGraph, and use its high‑level prebuilt graph API—specifically create_react_agent—to quickly create a weather‑assistant AI agent with just two lines of code, illustrating the full tool‑calling workflow and ReACT loop.

AI agentsLangGraphPython
0 likes · 11 min read
Build a LangGraph AI Agent in Two Lines Using the Prebuilt Graph API
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 28, 2025 · Artificial Intelligence

How AI Agents and MCP Revolutionize Smart Reporting and Batch Task Automation

This article explores the practical integration of AI agents with Model Context Protocol (MCP) to build a smart reporting assistant and automate batch task creation, detailing the technical workflow, tool‑calling capabilities, implementation steps, challenges faced, and the benefits of combining agents with traditional engineering systems.

AI AgentBrowser AutomationMCP
0 likes · 18 min read
How AI Agents and MCP Revolutionize Smart Reporting and Batch Task Automation
BirdNest Tech Talk
BirdNest Tech Talk
Jun 30, 2025 · Artificial Intelligence

Build a Weather‑Query ReAct Agent with LangGraph: Step‑by‑Step Guide

This article walks through constructing a stateful ReAct‑style LLM agent using LangGraph, detailing the core components—State, Nodes, Edges—defining a weather‑lookup tool with Open‑Meteo, configuring the graph’s nodes and conditional edges, and executing the workflow with streaming to observe each step in real time.

LLM agentsLangGraphPython
0 likes · 16 min read
Build a Weather‑Query ReAct Agent with LangGraph: Step‑by‑Step Guide
Coder Circle
Coder Circle
May 28, 2025 · Artificial Intelligence

Core AI Concepts Every Spring AI Developer Should Know

This article explains fundamental AI concepts—including models, prompts, prompt templates, embeddings, tokens, structured output, data integration, RAG, and tool calling—and shows how Spring AI simplifies their use for Java developers building intelligent applications.

AI modelsPrompt engineeringRAG
0 likes · 13 min read
Core AI Concepts Every Spring AI Developer Should Know
Programmer DD
Programmer DD
May 21, 2025 · Artificial Intelligence

What’s New in Spring AI 1.0 GA? A Deep Dive into Java AI Features

Spring AI 1.0 GA introduces a comprehensive suite of AI capabilities for Java developers, including a ChatClient supporting 20 models, vector‑store integrations, RAG pipelines, advanced chat memory, @Tool function calling, model evaluation, observability, Model Context Protocol, and autonomous agents, with examples for major cloud providers.

AI modelsJavaMCP
0 likes · 6 min read
What’s New in Spring AI 1.0 GA? A Deep Dive into Java AI Features
Java Architecture Diary
Java Architecture Diary
May 21, 2025 · Artificial Intelligence

Spring AI 1.0 Launch: Production‑Ready Java AI Framework Unveiled

Spring AI 1.0, the first production‑grade Java AI framework, introduces ready‑to‑use APIs, seamless model integration, enterprise‑level RAG engine, smart tool calling, and three development modes, empowering developers to rapidly build, customize, and fully control AI applications with major model providers like OpenAI, Anthropic, DeepSeek.

AI FrameworkDeepSeekJava AI
0 likes · 13 min read
Spring AI 1.0 Launch: Production‑Ready Java AI Framework Unveiled
Alibaba Cloud Developer
Alibaba Cloud Developer
May 14, 2025 · Artificial Intelligence

Deploy Alibaba’s Qwen3 LLM in 10 Minutes with Bailei Platform

Learn how to quickly set up Alibaba Cloud’s Bailei platform to call the open-source Qwen3 large language model, explore its cost‑effective performance, dual‑mode reasoning, multilingual support, and enhanced agent capabilities, and follow step‑by‑step instructions for API key configuration, Cherry Studio integration, and tool‑calling setup.

AI deploymentAlibaba CloudMLOps
0 likes · 6 min read
Deploy Alibaba’s Qwen3 LLM in 10 Minutes with Bailei Platform
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Apr 21, 2025 · Artificial Intelligence

What Is the Model Context Protocol (MCP) and How Can It Supercharge Your AI Projects?

Discover the Model Context Protocol (MCP)—an open standard from Anthropic that unifies AI model access to external data sources, enabling real‑time search, tool integration, and bidirectional communication, with practical examples, setup guides, and code snippets for developers to quickly build AI‑driven applications.

AI integrationAgentMCP
0 likes · 12 min read
What Is the Model Context Protocol (MCP) and How Can It Supercharge Your AI Projects?
Java Captain
Java Captain
Apr 17, 2025 · Artificial Intelligence

Demonstrating the Full Lifecycle of Model Context Protocol (MCP) with Tool Calls

This article explains how the Model Context Protocol (MCP) enables large language models to retrieve up‑to‑date external information through standardized tool calls, illustrating the complete end‑to‑end workflow with Python code for the MCP server, client, and host, and discussing its advantages for building AI agents.

AI AgentLLMMCP
0 likes · 21 min read
Demonstrating the Full Lifecycle of Model Context Protocol (MCP) with Tool Calls
Open Source Tech Hub
Open Source Tech Hub
Apr 14, 2025 · Artificial Intelligence

What Is Model Context Protocol (MCP) and How It Turns AI Into a Universal Interface?

This article explains the Model Context Protocol (MCP) – an open, consensus‑based standard that lets large language models seamlessly interact with external tools and data, describes its architecture, why it’s needed, how models choose tools, and provides a step‑by‑step Python server implementation with code examples.

LLMMCPTool Calling
0 likes · 22 min read
What Is Model Context Protocol (MCP) and How It Turns AI Into a Universal Interface?
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
Apr 1, 2025 · Backend Development

Boost Spring AI with Tool Calls: Real‑World Spring Boot 3 Examples

This article explains how Spring AI's tool‑calling feature extends large language models by integrating custom Java tools for information retrieval and operations, and provides step‑by‑step Spring Boot 3 examples—including date‑time and weather tools—complete with configuration, code snippets, and execution results.

AI integrationJavaSpring Boot
0 likes · 9 min read
Boost Spring AI with Tool Calls: Real‑World Spring Boot 3 Examples
Tencent Cloud Developer
Tencent Cloud Developer
Mar 26, 2025 · Artificial Intelligence

Introduction to Anthropic's Model Context Protocol (MCP) with Example Implementations

The article presents Anthropic’s open‑source Model Context Protocol (MCP) – a client‑server framework that standardizes how large language models securely access resources, prompts, and tools (the “HTTP of AI”) – and demonstrates its use through a hot‑fix scraper and a dynamic chatbot that discovers and invokes tools via JSON‑formatted calls.

AI protocolsLLM integrationMCP
0 likes · 15 min read
Introduction to Anthropic's Model Context Protocol (MCP) with Example Implementations
Sohu Tech Products
Sohu Tech Products
Mar 19, 2025 · Artificial Intelligence

Debugging MCP Service Registration Issues in Cursor IDE

The article details how the author discovered a bug in Cursor’s closed‑source IDE where correctly registered MCP services, such as a weather query tool, are omitted from the LLM’s tool list, preventing calls, and explains the debugging steps that compared Cursor with the open‑source Cline implementation to pinpoint the registration flaw.

AI debuggingCursorLLM integration
0 likes · 12 min read
Debugging MCP Service Registration Issues in Cursor IDE
Baidu Geek Talk
Baidu Geek Talk
Mar 19, 2025 · Artificial Intelligence

Inside Baidu’s New Wenxin 4.5 & X1: Multimodal Breakthroughs and Tool‑Enabled AI

Baidu officially launched the Wenxin 4.5 and X1 large language models, showcasing native multimodal foundations, advanced attention masks, heterogeneous expert extensions, and tool‑calling capabilities, while offering low‑cost API access on the Qianfan platform and outlining the underlying technical innovations that drive their performance gains.

AI PlatformBaiduMultimodal AI
0 likes · 8 min read
Inside Baidu’s New Wenxin 4.5 & X1: Multimodal Breakthroughs and Tool‑Enabled AI
Alibaba Cloud Native
Alibaba Cloud Native
Feb 12, 2025 · Artificial Intelligence

Boost AI Agents with Spring AI Alibaba: 20+ RAG Sources & Tool‑Calling Integrations

This article explains how Spring AI Alibaba enables AI agents to leverage Retrieval‑Augmented Generation and Tool Calling by providing over twenty ready‑made RAG data source connectors and more than twenty function‑calling interfaces, along with practical code examples for integrating document readers and weather services.

Document ReaderFunction CallingJava
0 likes · 12 min read
Boost AI Agents with Spring AI Alibaba: 20+ RAG Sources & Tool‑Calling Integrations
DataFunSummit
DataFunSummit
Jul 27, 2024 · Artificial Intelligence

Introducing ModelScope-Agent: An Open‑Source Multi‑Modal Multi‑Agent System

This article presents ModelScope‑Agent, an open‑source multi‑modal multi‑agent framework built on the ModelScope community, explains its underlying agent concepts, outlines its architecture and key features, showcases several real‑world applications such as ModelScope GPT, Story‑Agent and Facechain‑Agent, and includes a detailed Q&A on future directions and challenges.

ModelScope-AgentTool Callingartificial intelligence
0 likes · 15 min read
Introducing ModelScope-Agent: An Open‑Source Multi‑Modal Multi‑Agent System
Baobao Algorithm Notes
Baobao Algorithm Notes
Jun 5, 2024 · Artificial Intelligence

Is GLM‑4‑9B the New Powerhouse? A Deep Dive into Its Performance and Usage

This article reviews the open‑source 9‑billion‑parameter GLM‑4‑9B model, covering installation, quick‑start inference code, quirky Chinese riddles that highlight its strengths over GPT‑4, extensive benchmark tables for dialogue, multilingual, tool‑calling and multimodal tasks, and its broader impact on the Chinese AI ecosystem.

AIGLM-4-9BModel Evaluation
0 likes · 14 min read
Is GLM‑4‑9B the New Powerhouse? A Deep Dive into Its Performance and Usage