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Tool Integration

246 articles · Page 2 of 3
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 8, 2026 · Artificial Intelligence

From RAG to Deep Research Agent: Building a Multi‑Round AI Agent with ReAct

This article walks through the practical differences between simple Retrieval‑Augmented Generation and a full Deep Research Agent, explains the four pillars that support such agents, demonstrates a minimal ReAct implementation with robust error handling, and shares interview tips for showcasing these systems.

LLMPrompt EngineeringRAG
0 likes · 18 min read
From RAG to Deep Research Agent: Building a Multi‑Round AI Agent with ReAct
Code Mala Tang
Code Mala Tang
Apr 7, 2026 · Artificial Intelligence

Demystifying LLMs: From Tokens to Agents – An Engineer’s Deep Dive

This article provides a comprehensive, engineering‑focused breakdown of large language models, covering their Transformer roots, tokenization, context windows, prompt engineering, tool integration via MCP, and autonomous agents, while offering practical examples and actionable insights for developers.

AI FundamentalsAgentLLM
0 likes · 10 min read
Demystifying LLMs: From Tokens to Agents – An Engineer’s Deep Dive
PaperAgent
PaperAgent
Apr 7, 2026 · Artificial Intelligence

Unlock Production‑Grade AI Agents with the OpenHarness Python Framework

This article introduces OpenHarness, an open‑source Python implementation that simplifies building production‑level AI agents by providing lightweight core infrastructure, detailed feature breakdown, architecture overview, and sample code to help researchers and developers understand and create custom intelligent agents.

Agent ArchitecturePythonTool Integration
0 likes · 5 min read
Unlock Production‑Grade AI Agents with the OpenHarness Python Framework
Wuming AI
Wuming AI
Apr 6, 2026 · Artificial Intelligence

Designing Effective Coding Agents: Six Core Components Explained

This article analyzes the architecture of coding agents and their harnesses, detailing six essential components, how they interact with real‑time repository context, prompt caching, tool validation, context‑bloat control, structured memory, and delegation, while providing concrete Python examples and visual diagrams.

Context ManagementLLMPrompt Engineering
0 likes · 21 min read
Designing Effective Coding Agents: Six Core Components Explained
AI Tech Publishing
AI Tech Publishing
Apr 6, 2026 · Artificial Intelligence

Six Core Components of a Coding Agent Explained with Code

The article systematically breaks down the six essential building blocks of a programming agent—live repository context, prompt shape and cache reuse, structured tool access and validation, context reduction, structured session memory, and bounded sub‑agent delegation—illustrated with a Mini Coding Agent implementation and comparisons to Claude Code, Codex, and OpenClaw.

Coding AgentContext CompressionLLM
0 likes · 15 min read
Six Core Components of a Coding Agent Explained with Code
21CTO
21CTO
Apr 3, 2026 · Artificial Intelligence

How Google’s Java Agent Development Kit Simplifies Enterprise AI Agent Integration

Google’s new Java Agent Development Kit 1.0 provides a structured, plugin‑based framework that lets Java backend teams embed large‑language‑model agents, manage context and token limits, integrate secure tools, persist state, and enable cross‑language Agent2Agent collaboration without rewriting existing architectures.

AIAgent SDKContext Management
0 likes · 11 min read
How Google’s Java Agent Development Kit Simplifies Enterprise AI Agent Integration
Java One
Java One
Apr 3, 2026 · Artificial Intelligence

Can You Pass the Claude Code Official Tutorial Quiz? Test Your Knowledge

This article presents an eight‑question quiz covering Claude Code’s tool system limitations, GitHub integration permissions, planning vs. thinking modes, Claude.md file types, custom command creation, hook behavior, and hook purposes, followed by the correct answer key for self‑assessment.

AI coding assistantClaude CodeHooks
0 likes · 6 min read
Can You Pass the Claude Code Official Tutorial Quiz? Test Your Knowledge
AI Architecture Hub
AI Architecture Hub
Apr 3, 2026 · Artificial Intelligence

Build Your First Real AI Agent: Step‑by‑Step Guide for Beginners

This tutorial walks you through creating a functional AI agent that can receive goals, plan steps, invoke tools, and iterate until task completion, covering environment setup, core loop implementation, tool integration, error handling, and testing without requiring prior programming experience.

AI AgentAutonomous LoopBeginner Tutorial
0 likes · 9 min read
Build Your First Real AI Agent: Step‑by‑Step Guide for Beginners
AgentGuide
AgentGuide
Apr 2, 2026 · Artificial Intelligence

Understanding ReAct: The Reason‑Act Loop Behind LLM Agents

The article explains ReAct—a Reason‑Act framework for large language model agents that observes, reasons, takes actions via tools, receives feedback, and iterates—highlighting its distinction from plain QA, its step‑by‑step workflow, practical importance, and a weather‑query example.

AI workflowLLM AgentsReAct
0 likes · 5 min read
Understanding ReAct: The Reason‑Act Loop Behind LLM Agents
JavaGuide
JavaGuide
Mar 30, 2026 · Backend Development

Interviewers Ask About Claude Code Skills—What If You Haven’t Used /simplify?

The article explains the built‑in Claude Code /simplify command, how it uses three parallel AI agents to review and automatically fix code, demonstrates real‑world bugs it uncovered in Java projects, compares it with traditional linters, and offers practical tips and integration guidance.

/simplifyAI agentsClaude Code
0 likes · 16 min read
Interviewers Ask About Claude Code Skills—What If You Haven’t Used /simplify?
SpringMeng
SpringMeng
Mar 30, 2026 · Artificial Intelligence

Quick Start Guide to Claude Code: Master the AI-Powered Programming Assistant

This comprehensive tutorial walks you through installing, configuring, and using Claude Code, covering its tool‑use mechanism, context management, command shortcuts, custom MCP servers, and practical tips for integrating the assistant into real‑world development workflows.

AI programming assistantClaude CodeContext Management
0 likes · 21 min read
Quick Start Guide to Claude Code: Master the AI-Powered Programming Assistant
Su San Talks Tech
Su San Talks Tech
Mar 30, 2026 · Artificial Intelligence

Mastering LLM Function Calling: Theory, Workflow, and Hands‑On Code

This article explains the fundamentals of large‑model function calling, why it’s needed to bridge language models with real‑world tools, and provides a step‑by‑step implementation in Python—including tool definition, intent extraction, local execution, and result integration—complete with code samples and diagrams.

AI AgentAPIFunction Calling
0 likes · 11 min read
Mastering LLM Function Calling: Theory, Workflow, and Hands‑On Code
ShiZhen AI
ShiZhen AI
Mar 28, 2026 · Artificial Intelligence

GLM-5.1 Now Open to All: Performance vs Claude Opus, Pricing & Setup Guide

GLM-5.1 is now available to all Coding Plan subscribers, including the $10/month Lite tier, scoring 45.3 on SWE‑bench—just 5.4% below Claude Opus 4.6’s 47.9—while offering 20+ tool integrations and a manual switch from the default GLM‑4.7 model.

AI coding modelClaude OpusGLM-5.1
0 likes · 7 min read
GLM-5.1 Now Open to All: Performance vs Claude Opus, Pricing & Setup Guide
DeepHub IMBA
DeepHub IMBA
Mar 27, 2026 · Artificial Intelligence

AI Agent Architecture: Chain‑of‑Thought, ReAct, and Tool Calls

From a simple black‑box view where an agent receives a user request and returns an answer, the article breaks down modern AI agent designs—detailing the pure Chain‑of‑Thought reasoning loop, the ReAct reasoning‑acting cycle, tool integration, iteration tuning, and how to choose the optimal architecture for production.

AI agentsLLM architectureProduction Deployment
0 likes · 9 min read
AI Agent Architecture: Chain‑of‑Thought, ReAct, and Tool Calls
inShocking
inShocking
Mar 24, 2026 · Artificial Intelligence

How to Build Effective AI Agents: Key Principles, Patterns, and When to Use Them

The article analyzes Anthropic's guidance on building effective AI agents, contrasts workflow and agent architectures, outlines criteria for choosing agents, presents six incremental design patterns, and shares practical principles such as simplicity, transparency, and robust tool interfaces.

AI agentsAgent designLLM memory
0 likes · 9 min read
How to Build Effective AI Agents: Key Principles, Patterns, and When to Use Them
Smart Workplace Lab
Smart Workplace Lab
Mar 23, 2026 · Artificial Intelligence

Unlocking Agentic Workflows: How AI Can Operate Like an Autonomous Employee

This article explains the 2026 definition of Agentic Workflow, outlines its four core components, presents a five‑step execution loop, shares real‑world productivity data, and provides ready‑to‑use prompts and tool recommendations for instantly applying the concept in the workplace.

AI agentsAI automationAgentic Workflow
0 likes · 6 min read
Unlocking Agentic Workflows: How AI Can Operate Like an Autonomous Employee
Su San Talks Tech
Su San Talks Tech
Mar 23, 2026 · Artificial Intelligence

How OpenClaw Turns AI Agents into Real‑World Automation Tools

OpenClaw is an AI Agent framework that bridges chat platforms and large language models, enabling automated tasks through context‑engineered prompts, tool usage, memory management, sub‑agents, and security controls, while illustrating practical examples, workflow steps, and mitigation strategies for potential shell‑command exploits.

AI AgentLLMOpenClaw
0 likes · 18 min read
How OpenClaw Turns AI Agents into Real‑World Automation Tools
PaperAgent
PaperAgent
Mar 22, 2026 · Artificial Intelligence

How AI Agents Like OpenClaw Turn LLMs into Autonomous Assistants

This article explains what AI agents are, how they differ from ordinary language‑model interfaces, and walks through OpenClaw’s workflow, tool usage, security challenges, memory handling, and advanced features such as sub‑agents and context compaction, offering practical insights for building safe autonomous AI systems.

AI AgentContext EngineeringOpenClaw
0 likes · 27 min read
How AI Agents Like OpenClaw Turn LLMs into Autonomous Assistants
AI Step-by-Step
AI Step-by-Step
Mar 22, 2026 · Artificial Intelligence

How OpenClaw’s Agent Loop Turns Chat into Actionable Tasks

OpenClaw distinguishes itself from ordinary chatbots by employing an Agent Loop—a task‑driving execution chain that normalizes inputs, assembles context, makes model‑based decisions, suspends for tool results, and writes back state, enabling continuous task progression rather than single‑turn replies.

AI AgentAgent LoopContext Assembly
0 likes · 10 min read
How OpenClaw’s Agent Loop Turns Chat into Actionable Tasks
inShocking
inShocking
Mar 18, 2026 · Artificial Intelligence

Building a Coding Agent with Claude: A 200‑Line Python Walkthrough

This article explains how to construct a functional coding agent by combining a large language model, a bash tool, and a message history loop, showing step‑by‑step code, system prompts, error handling, and a complete execution example.

AI AgentAgent LoopClaude
0 likes · 10 min read
Building a Coding Agent with Claude: A 200‑Line Python Walkthrough
Architect's Ambition
Architect's Ambition
Mar 18, 2026 · Artificial Intelligence

From Zero to a Real AI Agent: Master Its Core Essence, Not Just API Calls

The article explains why an AI Agent is more than a simple LLM API call, outlines its four essential modules—memory, planning, tool use, and feedback—shows how they differ from ordinary models, and offers practical steps and common pitfalls for building a production‑grade single‑agent system.

AI AgentLLMMemory
0 likes · 13 min read
From Zero to a Real AI Agent: Master Its Core Essence, Not Just API Calls
AI Explorer
AI Explorer
Mar 18, 2026 · Artificial Intelligence

Unlock Instant AI Agents with LangGraph‑Powered Deep Agents

Deep Agents, an open‑source framework built on LangGraph, bundles planning, file‑system tools, sub‑agent coordination and context management into a ready‑to‑run AI agent that can be launched with three lines of Python code and fully customized for diverse applications.

AI agentsDeep AgentsLLM
0 likes · 7 min read
Unlock Instant AI Agents with LangGraph‑Powered Deep Agents
Architect's Ambition
Architect's Ambition
Mar 16, 2026 · Artificial Intelligence

Understanding AI Agents: From Chatting to Getting Things Done

The article explains the four essential components of AI Agents—brain, memory, tool, and planning layers—illustrates their implementation with Python code, compares planning strategies, shares a real-world OOM fault‑diagnosis case, and lists common pitfalls to help newcomers build functional agents.

AI AgentLLMMemory Management
0 likes · 17 min read
Understanding AI Agents: From Chatting to Getting Things Done
PaperAgent
PaperAgent
Mar 11, 2026 · Artificial Intelligence

Can Full‑Modal AI Agents Master Vision, Audio, and Tools? Meet OmniGAIA & OmniAtlas

This article introduces OmniGAIA, a challenging full‑modal benchmark with 360 real‑world tasks, and OmniAtlas, a training framework that equips multimodal agents with active perception and tool‑integrated reasoning, showing substantial performance gains over existing open‑source models through extensive experiments and analysis.

AgentOmniAtlasOmniGAIA
0 likes · 16 min read
Can Full‑Modal AI Agents Master Vision, Audio, and Tools? Meet OmniGAIA & OmniAtlas
Xike
Xike
Mar 7, 2026 · Artificial Intelligence

AI Agent Development Guide: Core Concepts, Architecture, and Practical LangChain4j Examples

This guide explains what an AI Agent is, outlines its four core abilities, compares it with traditional programs, presents a layered architecture diagram, shows common use‑case scenarios, and provides step‑by‑step Java code using LangChain4j to build basic, multi‑tool, planning, reflective, and collaborative agents together with optimization tips and FAQs.

AI AgentAgent ArchitectureLangChain4j
0 likes · 19 min read
AI Agent Development Guide: Core Concepts, Architecture, and Practical LangChain4j Examples
Alibaba Cloud Native
Alibaba Cloud Native
Mar 3, 2026 · Artificial Intelligence

Boost AI Coding Efficiency with Qoder Slash Commands: A Practical Guide

This article explains how Qoder’s slash commands can eliminate unnecessary project scans and web searches, showing side‑by‑side comparisons, command file structures, customization tips, and best‑practice recommendations to speed up AI‑assisted coding while saving tokens.

AI codingQoderSlash Commands
0 likes · 8 min read
Boost AI Coding Efficiency with Qoder Slash Commands: A Practical Guide
Tencent Cloud Developer
Tencent Cloud Developer
Mar 3, 2026 · Artificial Intelligence

Why AI Coding Agents Are Just Loops + Context Engineering (And How to Build One)

The article explains that AI coding agents operate as a simple while‑loop driven by context engineering, details their core control flow, compares various tools, and provides a step‑by‑step Python implementation demonstrating how to define tools, system prompts, and the ReAct loop for practical use.

AI codingLLMPython implementation
0 likes · 17 min read
Why AI Coding Agents Are Just Loops + Context Engineering (And How to Build One)
ShiZhen AI
ShiZhen AI
Mar 3, 2026 · Artificial Intelligence

How OpenAkita Makes Three AIs Collaborate Automatically

OpenAkita is an open‑source multi‑Agent AI assistant that automatically splits tasks among specialized agents, offers 89 built‑in tools across 16 categories, supports 30+ large models and six IM platforms, provides a zero‑CLI graphical setup, and includes a three‑layer memory system with self‑evolving capabilities.

AI assistantOpenAkitaTool Integration
0 likes · 9 min read
How OpenAkita Makes Three AIs Collaborate Automatically
AI Tech Publishing
AI Tech Publishing
Feb 27, 2026 · Artificial Intelligence

Step‑by‑Step Guide to Building OpenClaw: A Persistent AI Assistant with Sessions, Tools, and Multi‑Agent Support

This tutorial walks through constructing OpenClaw from scratch, covering persistent JSONL sessions, SOUL.md persona files, tool definitions and an agent loop, permission checks, gateway architecture, context compression, long‑term memory, command queuing, scheduled heartbeats, and multi‑agent routing, all with concrete Python code examples.

AI agentsLLMOpenClaw
0 likes · 38 min read
Step‑by‑Step Guide to Building OpenClaw: A Persistent AI Assistant with Sessions, Tools, and Multi‑Agent Support
Fun with Large Models
Fun with Large Models
Feb 24, 2026 · Artificial Intelligence

DeepAgents Quickstart Guide: A Full Walkthrough of Core Features

This article introduces LangChain's DeepAgents framework, explains its design goals, compares it with LangChain and LangGraph, and provides a step‑by‑step code walkthrough that demonstrates task planning, sub‑agent delegation, tool usage, and result generation for building complex AI agents with just a few lines of code.

AI agentsDeepAgentsLangChain
0 likes · 15 min read
DeepAgents Quickstart Guide: A Full Walkthrough of Core Features
AI Product Manager Community
AI Product Manager Community
Feb 24, 2026 · Artificial Intelligence

Mastering AI Agents: 100 Essential Questions Across 5 Stages

This comprehensive guide walks you through five development stages of AI agents—core concepts, advanced planning, memory management, tool integration, and enterprise deployment—answering 100 practical questions that reveal definitions, architectures, best‑practice patterns, safety measures, and performance‑optimisation techniques for production‑grade agents.

AI agentsAgent ArchitectureLLM
0 likes · 34 min read
Mastering AI Agents: 100 Essential Questions Across 5 Stages
Open Source Tech Hub
Open Source Tech Hub
Feb 20, 2026 · Artificial Intelligence

How to Build AI Agents in PHP with the Model Context Protocol (MCP)

Learn how to connect PHP-based AI agents to the Model Context Protocol (MCP) using the open‑source Neuron AI framework, covering MCP fundamentals, server setup, tool integration, and example code for creating custom agents that can invoke external APIs, databases, and web content.

AI agentsLLMMCP
0 likes · 12 min read
How to Build AI Agents in PHP with the Model Context Protocol (MCP)
AI Tech Publishing
AI Tech Publishing
Feb 16, 2026 · Artificial Intelligence

Mastering MCP: Connecting AI Agents to the World in One Lesson

This tutorial explains how the Model Context Protocol (MCP) standardizes AI agent integration by replacing custom tool code with a JSON‑RPC based, auto‑discovered ecosystem, walks through configuration, core loading logic, code implementation, a runnable example, and compares MCP with traditional tool use.

AI AgentJSON-RPCMCP
0 likes · 8 min read
Mastering MCP: Connecting AI Agents to the World in One Lesson
Data STUDIO
Data STUDIO
Feb 12, 2026 · Artificial Intelligence

How to Add Tools to a LangGraph AI Agent for Real‑World Tasks

This tutorial walks through adding custom, pre‑built, and server‑side tools to a LangGraph AI agent, demonstrates a ReAct workflow, implements conditional edges for web search, enforces structured output for intelligent shutdown, and shows how to monitor token usage with callbacks, all with runnable Python code.

AI AgentLangGraphPython
0 likes · 16 min read
How to Add Tools to a LangGraph AI Agent for Real‑World Tasks
Data Thinking Notes
Data Thinking Notes
Feb 8, 2026 · Artificial Intelligence

How OpenClaw Turns AI into a Hands‑On Digital Assistant (Local‑First, Open‑Source)

OpenClaw is an open‑source, local‑first AI agent platform that acts as a digital employee capable of autonomously executing tasks on your computer, offering multi‑channel interaction, persistent memory, and a modular architecture that bridges the gap between conversational AI and real‑world operations.

AI AgentAutomationDocker deployment
0 likes · 13 min read
How OpenClaw Turns AI into a Hands‑On Digital Assistant (Local‑First, Open‑Source)
AI Tech Publishing
AI Tech Publishing
Feb 5, 2026 · Artificial Intelligence

From Java Backend to AI Agent Engineer: Essential Knowledge for the Transition

This comprehensive guide walks Java backend developers through the fundamentals of AI agents, comparing agents with traditional workflows, detailing core components such as LLMs, tools, and memory, and exploring practical patterns, frameworks, and code examples to help them successfully shift into AI agent development.

AI agentsLLMMemory Management
0 likes · 35 min read
From Java Backend to AI Agent Engineer: Essential Knowledge for the Transition
AI Software Product Manager
AI Software Product Manager
Feb 4, 2026 · Artificial Intelligence

Mastering Agent Skills: A Systematic Guide to Large Model Capabilities

This article traces the evolution of large‑model capabilities from early plugins to the standardized Agent Skills framework, explains the core concepts, technical composition, and progressive disclosure mechanism, and provides a step‑by‑step practical guide for building, configuring, and deploying Skills across ecosystems.

AI architectureAI operationsAgent Skills
0 likes · 11 min read
Mastering Agent Skills: A Systematic Guide to Large Model Capabilities
Shuge Unlimited
Shuge Unlimited
Jan 27, 2026 · Artificial Intelligence

Clawdbot 2026: Why This Open‑Source AI Agent Gateway Is Gaining Massive Attention

Clawdbot, an open‑source AI Agent gateway with 54.6k GitHub stars, offers persistent three‑month memory, 50+ built‑in tools, and multi‑model channel management; built on TypeScript/Node.js, it delivers strong automation but incurs notable API costs and a learning curve, making it ideal for long‑term AI‑driven projects yet less suited for casual users.

AI AgentClawdbotMulti-Channel
0 likes · 13 min read
Clawdbot 2026: Why This Open‑Source AI Agent Gateway Is Gaining Massive Attention
Java One
Java One
Jan 24, 2026 · Artificial Intelligence

Master Claude Code: Unlock AI‑Powered Terminal Coding

This guide explains Claude Code’s agent loop, model choices, built‑in tool categories, project access scope, session handling, checkpoint and permission controls, and practical tips for efficiently using the AI‑driven terminal assistant to write, test, and refactor code.

AI coding assistantAgent LoopCheckpoint
0 likes · 15 min read
Master Claude Code: Unlock AI‑Powered Terminal Coding
Programmer's Advance
Programmer's Advance
Jan 21, 2026 · Artificial Intelligence

Unlocking AI Agents: 12 Proven Secrets from 720 K Users

This guide distills twelve core best‑practice secrets—derived from 720,000 paying users and a billion lines of daily code—on how to make AI agents obey prompts, plan before coding, manage context, use rules, custom commands, hooks, multi‑agent parallelism, and test‑driven development for reliable, high‑productivity outcomes.

AI AgentPrompt EngineeringTest‑Driven Development
0 likes · 18 min read
Unlocking AI Agents: 12 Proven Secrets from 720 K Users
Tech Verticals & Horizontals
Tech Verticals & Horizontals
Jan 10, 2026 · Artificial Intelligence

Intelligent Agent System Levels 0‑4: From Core Reasoning to Self‑Evolving Agents

The article outlines a five‑tier taxonomy of intelligent agents—from a standalone language‑model reasoning engine lacking real‑time perception, through tool‑enabled problem solvers, context‑engineered planners, collaborative multi‑agent teams, up to self‑evolving systems that can create new tools or agents to fill capability gaps.

Agent ArchitectureContext EngineeringIntelligent Agents
0 likes · 9 min read
Intelligent Agent System Levels 0‑4: From Core Reasoning to Self‑Evolving Agents
Tech Verticals & Horizontals
Tech Verticals & Horizontals
Jan 8, 2026 · Artificial Intelligence

Google Agent Whitepaper: Building Production‑Ready AI Agents from Architecture to Ops

This whitepaper explains how modern AI agents evolve from simple language models to autonomous, multi‑step systems, detailing their core components, five‑step reasoning loop, classification levels, design patterns, deployment options, observability, security, and continuous learning with concrete examples.

AI agentsAgent ArchitecturePrompt Engineering
0 likes · 49 min read
Google Agent Whitepaper: Building Production‑Ready AI Agents from Architecture to Ops
Sohu Tech Products
Sohu Tech Products
Jan 7, 2026 · Mobile Development

How Android Studio’s New AI Agent Supercharges Mobile Development

The article explains Android Studio’s latest AI Agent, covering its three core concepts—Tools, Context, and Model Context Protocol—while showing practical examples of built‑in tools, knowledge‑base integration, Figma linking, and a full UI‑generation workflow that lets developers create, refine, and fix Jetpack Compose apps using natural language prompts.

AI AgentAndroid StudioJetpack Compose
0 likes · 7 min read
How Android Studio’s New AI Agent Supercharges Mobile Development
Tencent Cloud Developer
Tencent Cloud Developer
Jan 7, 2026 · Artificial Intelligence

How Context Engineering Powers the Next Generation of AI Agents

Transitioning from simple chatbots to sophisticated agents, this article explains how expanding context becomes a core variable, detailing the evolution from prompt engineering to context engineering, the challenges of managing growing context, and practical solutions like structured context, tool integration, and the MCP framework for reliable AI systems.

AgentLLMTool Integration
0 likes · 20 min read
How Context Engineering Powers the Next Generation of AI Agents
phodal
phodal
Dec 30, 2025 · Industry Insights

Beyond Comfort: 6 Key Trends Driving AI Coding Tools in 2025‑2026

The article analyzes six emerging trends in Chinese AI coding tools—model capability parity, open tool integration, spec‑driven development, lower entry barriers, self‑validation, and full‑stack automation—arguing that future success depends on end‑to‑end engineering reliability rather than mere code generation or emotional support.

AI codingAutomationIndustry Trends
0 likes · 12 min read
Beyond Comfort: 6 Key Trends Driving AI Coding Tools in 2025‑2026
Architecture Digest
Architecture Digest
Dec 25, 2025 · Artificial Intelligence

MCP Explained: The Universal ‘Connector’ Turning AI Models into Extensible Agents

This article introduces the Model Context Protocol (MCP), a universal standard that lets large language models seamlessly connect to databases, APIs, local files, and third‑party services, explains its architecture, core primitives, practical Python implementation, trade‑offs, security considerations, and how it compares with other integration approaches.

AIModel Context ProtocolPython
0 likes · 13 min read
MCP Explained: The Universal ‘Connector’ Turning AI Models into Extensible Agents
DataFunSummit
DataFunSummit
Dec 23, 2025 · Artificial Intelligence

What Core Capabilities Do Mature GUI Agents Need? Expert Insights from the Agentic AI Summit

In a live discussion hosted by Prof. Yang Jian with experts Zhang Xi and Cui Chen, the panel explores the essential abilities of mature GUI agents, the role of multimodal models in visual understanding, the transfer of code‑agent techniques to GUI tasks, edge‑device performance trade‑offs, complex planning, tool ecosystems, deployment challenges, and future breakthrough scenarios.

GUI AgentTool Integrationagentic AI
0 likes · 22 min read
What Core Capabilities Do Mature GUI Agents Need? Expert Insights from the Agentic AI Summit
AI Tech Publishing
AI Tech Publishing
Dec 22, 2025 · Artificial Intelligence

How Agent Skills and MCP Servers Work Together

This article explains how Anthropic's Skills and Model Context Protocol (MCP) servers complement each other to let Claude agents follow specific workflows, access external tools, and produce consistent, reliable outputs, illustrated with real‑world use cases and a quick reference guide.

AI agentsAnthropicClaude
0 likes · 13 min read
How Agent Skills and MCP Servers Work Together
Qborfy AI
Qborfy AI
Dec 16, 2025 · Artificial Intelligence

Mastering AI Function Calling: Turn LLMs into Actionable Assistants

Function Calling lets large language models invoke external tools or APIs during a conversation, transforming them from passive responders into proactive assistants; this guide explains the concept, workflow, and practical implementations with weather, parallel queries, and stock price examples using OpenAI’s Python SDK.

AI Function CallingChatbotLLM
0 likes · 9 min read
Mastering AI Function Calling: Turn LLMs into Actionable Assistants
Tencent Technical Engineering
Tencent Technical Engineering
Dec 15, 2025 · Artificial Intelligence

How to Add Human‑in‑the‑Loop Interrupts to LangGraph Agents for Safe, Controllable AI Workflows

This guide explains the concept of human‑in‑the‑loop (HITL) interruptions in LangGraph, outlines the core mechanisms such as persistent state and dynamic/static interrupts, and provides detailed Python examples for four classic patterns—approval/rejection, state editing, tool‑call review, and input validation—plus advanced topics like parallel interrupts and MCP‑based tool integration.

AI agentsLangGraphMCP
0 likes · 35 min read
How to Add Human‑in‑the‑Loop Interrupts to LangGraph Agents for Safe, Controllable AI Workflows
Bilibili Tech
Bilibili Tech
Dec 12, 2025 · Artificial Intelligence

Turning a Simple JS Function into a Cross‑Platform AI Tool with MCP

This article details how we built an AI‑tool ecosystem by evolving a basic online JS cloud‑function platform into a unified, reusable capability layer that integrates with Flowise, LangChain StructuredTool, and the Model Context Protocol (MCP) to provide secure, cross‑platform tool calls for agents.

AI ToolsLangChainMCP
0 likes · 20 min read
Turning a Simple JS Function into a Cross‑Platform AI Tool with MCP
Alibaba Middleware
Alibaba Middleware
Dec 9, 2025 · Artificial Intelligence

AgentScope Java 1.0 Empowers Java Developers to Build Enterprise‑Grade Agentic Apps

AgentScope Java 1.0 launches with a ReAct‑based AI agent framework that adds real‑time intervention, efficient tool management, sandbox security, high‑performance native optimisations, and seamless enterprise integration, enabling Java developers to create production‑ready, multimodal agent applications.

AI agentsAgentScopeEnterprise AI
0 likes · 13 min read
AgentScope Java 1.0 Empowers Java Developers to Build Enterprise‑Grade Agentic Apps
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
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 7, 2025 · Artificial Intelligence

Key Lessons from Scaling Agent RL Training: Stability, Tooling, and Reward Design

Over recent months of extensive agent reinforcement‑learning experiments across search, data‑analysis, and multi‑source scenarios, the author shares twelve practical insights covering stability, environment‑reward‑algorithm priorities, tool‑call reliability, reward hacking pitfalls, evaluation alignment, and scaling tricks for larger models.

PPO EWMARL scalingReward Design
0 likes · 7 min read
Key Lessons from Scaling Agent RL Training: Stability, Tooling, and Reward Design
Data Party THU
Data Party THU
Nov 29, 2025 · Artificial Intelligence

Unlocking AI Agents: From Fundamentals to Building Your First LLM‑Powered Agent

This comprehensive guide explores the concept of AI agents, detailing their definitions, classifications, and core interaction loops, then walks you through building a functional LLM‑driven travel assistant with step‑by‑step code, tool integration, and practical insights on agent versus workflow paradigms.

AI agentsAgent ArchitectureLLM
0 likes · 39 min read
Unlocking AI Agents: From Fundamentals to Building Your First LLM‑Powered Agent
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Nov 20, 2025 · Artificial Intelligence

How DeepAgent Achieves End‑to‑End Reasoning with 16,000+ Scalable Tools

DeepAgent is a new end‑to‑end reasoning agent that unifies autonomous thinking, dynamic tool search, and execution, handling over 16,000 real APIs, supporting embodied environments and research assistance, and achieving state‑of‑the‑art results across multiple benchmarks through its unified reasoning core, memory‑folding mechanisms, structured memory, and the ToolPO training framework.

AI agentsGeneral AITool Integration
0 likes · 14 min read
How DeepAgent Achieves End‑to‑End Reasoning with 16,000+ Scalable Tools
Wuming AI
Wuming AI
Nov 10, 2025 · Artificial Intelligence

What Exactly Is an AI Agent? A Clear, Practical Guide

This article explains the concept of AI agents, contrasting them with chatbots, detailing their ability and structural layers, summarizing academic surveys and whitepapers, and illustrating how agents plan, perceive, and act to autonomously accomplish user‑defined goals.

AI AgentAgent ArchitectureAutonomous Planning
0 likes · 9 min read
What Exactly Is an AI Agent? A Clear, Practical Guide
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Nov 7, 2025 · Artificial Intelligence

Introducing LangGraph: A Low‑Level Framework for Building Stateful AI Agents

This article explains why modern LLM‑based applications need agent capabilities, introduces LangGraph’s core features such as stateful execution, graph‑based orchestration, tool integration, human‑in‑the‑loop and multi‑agent support, and provides a step‑by‑step Python example that builds a simple chat‑bot agent.

LLM AgentsLangGraphPython example
0 likes · 11 min read
Introducing LangGraph: A Low‑Level Framework for Building Stateful AI Agents
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Nov 3, 2025 · Artificial Intelligence

How AI Agents Are Revolutionizing Technology: The New Engine of Innovation

This article explores the rise of AI agents—from their definition as intelligent digital assistants powered by large language models to their evolution through planning, memory, and tool use—highlighting real‑world applications, core technical mechanisms, code implementations, and future trends such as autonomy, multimodal fusion, standardization, and safety considerations.

AI AgentMultimodalStandardization
0 likes · 24 min read
How AI Agents Are Revolutionizing Technology: The New Engine of Innovation
Goodme Frontend Team
Goodme Frontend Team
Nov 3, 2025 · Artificial Intelligence

Unlock AI Power with Model Context Protocol (MCP): Build LLM‑Enabled Servers in Minutes

This article introduces the Model Context Protocol (MCP) and Large Language Models (LLM), explains their core concepts, transmission mechanisms, lifecycle, and essential modules, and provides step‑by‑step code examples for creating an MCP server, adding tools, resources, prompts, and debugging workflows to accelerate AI‑driven development.

AILLMMCP
0 likes · 15 min read
Unlock AI Power with Model Context Protocol (MCP): Build LLM‑Enabled Servers in Minutes
Practical DevOps Architecture
Practical DevOps Architecture
Oct 14, 2025 · Artificial Intelligence

Master AI Agents: From Basics to Advanced Multi-Model Development

This comprehensive AI agent development course covers 18 chapters, ranging from fundamental concepts and architecture to large‑model integration, tool and browser control, memory, RAG self‑learning, sandboxing, database manipulation, multi‑agent architectures, code assistance, and a real‑world frontend automation project, complete with source code and documentation.

AI agentsLangChainLarge Language Models
0 likes · 3 min read
Master AI Agents: From Basics to Advanced Multi-Model Development
DataFunSummit
DataFunSummit
Oct 7, 2025 · Artificial Intelligence

Deep Thinking in Large Language Models: Overcoming Domain Challenges

This presentation explores how large language models can transcend their general knowledge limits by developing domain‑specific deep thinking abilities, addressing challenges such as complex instruction execution, expert reasoning gaps, and tool integration, and proposes reinforcement‑learning‑driven frameworks, structured thinking pipelines, and tool‑calling mechanisms to achieve rational intelligence.

Domain AdaptationTool Integrationdeep reasoning
0 likes · 27 min read
Deep Thinking in Large Language Models: Overcoming Domain Challenges
AI Cyberspace
AI Cyberspace
Oct 4, 2025 · Artificial Intelligence

Exploring OpenManus: A Deep Dive into an Open‑Source AI Agent Framework

This article provides a comprehensive overview of OpenManus, an open‑source, general‑purpose AI agent framework, covering its installation, configuration, core architecture—including BaseAgent, ReActAgent, ToolCallAgent, and Manus—its extensive tool collection, execution logs, and detailed code analysis for developers and AI researchers.

AI AgentOpenManusPython
0 likes · 74 min read
Exploring OpenManus: A Deep Dive into an Open‑Source AI Agent Framework
BirdNest Tech Talk
BirdNest Tech Talk
Oct 2, 2025 · Artificial Intelligence

How Function Calling Empowers LLMs: A Step‑by‑Step LangChain Guide

This article explains how function (tool) calling lets large language models like GPT or Gemini invoke external APIs, walks through defining tools with LangChain, and demonstrates a complete Python example that fetches real‑time weather data and returns a natural‑language answer.

AI agentsFunction CallingLLM
0 likes · 9 min read
How Function Calling Empowers LLMs: A Step‑by‑Step LangChain Guide
phodal
phodal
Sep 29, 2025 · Artificial Intelligence

How AutoDev Leverages Google’s A2A Protocol for Cross‑Agent Collaboration

This article explains how AutoDev adds support for Google’s Agent‑to‑Agent (A2A) protocol, detailing its architecture, integration with the Model Context Protocol (MCP), configuration steps, debugging tools, and the benefits of a modular, open‑source AI programming ecosystem.

A2AAI agentsAgent-to-Agent
0 likes · 6 min read
How AutoDev Leverages Google’s A2A Protocol for Cross‑Agent Collaboration
Amazon Cloud Developers
Amazon Cloud Developers
Sep 16, 2025 · Artificial Intelligence

Elegant Solution to Prompt Bloat: Semantic Retrieval of Tools for Efficient LLM Inference

The article explains how the limited context window of large language models causes prompt bloat when many tool descriptions are embedded, and presents the RAG‑MCP architecture that stores tool metadata in a vector database, uses semantic retrieval to select only the most relevant tools, dramatically shortens prompts, and improves inference speed and tool‑call accuracy.

Amazon BedrockLLMMCP
0 likes · 25 min read
Elegant Solution to Prompt Bloat: Semantic Retrieval of Tools for Efficient LLM Inference
Data Thinking Notes
Data Thinking Notes
Sep 14, 2025 · Artificial Intelligence

How to Build a Robust Tool Integration Module for AI Agents

This article explains the architecture, core components, and step‑by‑step implementation of a tool usage module that enables AI agents to standardize, select, execute, and transform external tools, illustrated with a sales data analysis case and detailed code snippets.

AI AgentLLMTool Integration
0 likes · 9 min read
How to Build a Robust Tool Integration Module for AI Agents
JD Tech
JD Tech
Sep 3, 2025 · Artificial Intelligence

Launch a Multi‑Agent AI System in 20 Lines with OxyGent

This guide shows how to quickly build, configure, and deploy modular AI agents using the OxyGent framework—covering environment setup, minimal code initialization, tool integration, multi‑agent orchestration, and advanced deployment techniques—all illustrated with concise examples and screenshots.

AI agentsOxyGentTool Integration
0 likes · 4 min read
Launch a Multi‑Agent AI System in 20 Lines with OxyGent
DaTaobao Tech
DaTaobao Tech
Sep 1, 2025 · Artificial Intelligence

Boost Business Automation with AI Agents and MCP: Real-World Insights

This article explores how integrating AI agents with the Model Context Protocol (MCP) and tools like Playwright can automate reporting and batch task creation, detailing practical implementations, challenges, performance comparisons with traditional solutions, and best practices for combining AI and engineering to achieve efficient, reliable business workflows.

AI AgentAutomationMCP
0 likes · 19 min read
Boost Business Automation with AI Agents and MCP: Real-World Insights
JD Tech Talk
JD Tech Talk
Aug 25, 2025 · Artificial Intelligence

Kickstart Multi‑Agent Collaboration with OxyGent: A 20‑Line Setup Guide

This guide introduces the open‑source OxyGent multi‑agent framework, walks through a quick 20‑line installation, demonstrates environment configuration, tool integration, visualization, and advanced features such as RAG, Reflexion, and distributed deployment for AI applications.

AIOxyGentPython
0 likes · 4 min read
Kickstart Multi‑Agent Collaboration with OxyGent: A 20‑Line Setup Guide
JD Cloud Developers
JD Cloud Developers
Aug 25, 2025 · Artificial Intelligence

Kickstart Multi-Agent Collaboration with OxyGent: 20‑Line Setup Guide

This guide introduces the open‑source OxyGent multi‑agent framework, provides step‑by‑step installation, a 20‑line hello‑world example, tool integration via SSE, MCP and FunctionHub, deployment features like data persistence and distributed setup, and outlines advanced use cases such as multimodal agents and plan‑and‑solve paradigms.

AI FrameworkOxyGentPython
0 likes · 5 min read
Kickstart Multi-Agent Collaboration with OxyGent: 20‑Line Setup Guide
Instant Consumer Technology Team
Instant Consumer Technology Team
Aug 15, 2025 · Artificial Intelligence

Why Building Enterprise AI Agents Feels Like Building a Distributed Brain

An engineer recounts the hard‑earned lessons from moving beyond RAG to enterprise‑level AI agents, exposing three critical challenges—scheduling, memory management, and tool integration—and proposes architectural patterns that turn fragile prototypes into robust, observable, and secure AI systems.

AI agentsAgentic EngineeringEnterprise AI
0 likes · 9 min read
Why Building Enterprise AI Agents Feels Like Building a Distributed Brain
Fun with Large Models
Fun with Large Models
Jul 30, 2025 · Artificial Intelligence

LangChain Tool Integration: Step‑by‑Step Guide to Built‑in and Custom Functions

This article walks through how to integrate LangChain's built‑in tools and user‑defined functions into AI agents, covering environment setup, installing dependencies, using the Python code interpreter tool, binding tools to a model, parsing tool calls with JsonOutputKeyToolsParser, and demonstrating both a data‑analysis example and a weather‑lookup function.

AI agentsFunction CallingLangChain
0 likes · 13 min read
LangChain Tool Integration: Step‑by‑Step Guide to Built‑in and Custom Functions
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Jul 28, 2025 · Artificial Intelligence

How Enterprise AI Agents Move From Efficiency Silos to Value Resonance

The article explains how enterprise AI agents shift from a command‑response model to a goal‑execution paradigm, outlines a layered LLM architecture, tool integration, and memory systems, and demonstrates three practical use cases that create clear value loops for R&D, marketing, and customer service.

AI agentsAutomationEnterprise AI
0 likes · 13 min read
How Enterprise AI Agents Move From Efficiency Silos to Value Resonance
DaTaobao Tech
DaTaobao Tech
Jul 18, 2025 · Artificial Intelligence

Build a Minimal Java ReAct Agent in 200 Lines: A Hands‑On Tutorial

This tutorial walks you through constructing a lightweight ReAct agent using Java, explaining the Thought‑Action‑Observation loop, providing a 200‑line code example, and demonstrating a real‑world approval workflow with prompts, tool definitions, and step‑by‑step interaction logs.

AgentLLMPrompt Engineering
0 likes · 21 min read
Build a Minimal Java ReAct Agent in 200 Lines: A Hands‑On Tutorial
Sanyou's Java Diary
Sanyou's Java Diary
Jul 3, 2025 · Artificial Intelligence

How MCP Standardizes AI Tool Calls with JSON‑RPC and Spring AI

This article explains the MCP framework that standardizes AI tool invocation using JSON‑RPC, outlines its client‑server architecture, details communication methods such as STDIO, SSE and streamable HTTP, and provides a Spring AI demo showing tool registration, discovery, and execution.

AIFunction CallingJSON-RPC
0 likes · 14 min read
How MCP Standardizes AI Tool Calls with JSON‑RPC and Spring AI
大转转FE
大转转FE
Jul 1, 2025 · Artificial Intelligence

Boost AI Development Efficiency: Integrating MCP with Cursor

This article explains the Model Context Protocol (MCP), compares it with traditional function calling, and provides a step‑by‑step guide for integrating MCP into the Cursor editor, including token generation, configuration, server setup, and practical examples that dramatically improve AI‑assisted development productivity.

AI developmentCursorMCP
0 likes · 14 min read
Boost AI Development Efficiency: Integrating MCP with Cursor
AI Large Model Application Practice
AI Large Model Application Practice
Jun 23, 2025 · Databases

How Google’s MCP Toolbox Simplifies Enterprise Database Access for LLM Agents

This guide explains Google’s open‑source MCP Toolbox for Databases, covering its core concepts, installation, configuration, two usage modes (native SDK and MCP), example LangGraph agent integration, security features, observability, and practical code snippets for building reliable LLM‑driven database tools.

DatabasesLLM AgentsMCP Toolbox
0 likes · 11 min read
How Google’s MCP Toolbox Simplifies Enterprise Database Access for LLM Agents
Architecture & Thinking
Architecture & Thinking
Jun 23, 2025 · Artificial Intelligence

Building AI Assistants with Eino: A Go Framework for Large‑Model Applications

This article introduces Eino, an open‑source Golang framework for large‑model AI applications, explains its core capabilities, walks through creating a simple AI assistant with message templates and chat model integration, and demonstrates how to extend the system with tools and a modular architecture for future expansion.

AI assistantEinoGo
0 likes · 17 min read
Building AI Assistants with Eino: A Go Framework for Large‑Model Applications
Tech Freedom Circle
Tech Freedom Circle
Jun 21, 2025 · Artificial Intelligence

How MCP + LLM + Agent Architecture Becomes the AI Agent’s Neural Hub and New Infrastructure

The article explains the Model Context Protocol (MCP) as a zero‑code bridge that lets large language models seamlessly access databases, external APIs, and execute code, detailing its benefits for developers and everyday users, its core components, step‑by‑step workflow, real‑world examples, and how it outperforms traditional APIs in modern AI agent systems.

AI AgentLLMMCP
0 likes · 37 min read
How MCP + LLM + Agent Architecture Becomes the AI Agent’s Neural Hub and New Infrastructure
Instant Consumer Technology Team
Instant Consumer Technology Team
Jun 19, 2025 · Artificial Intelligence

Exploring II-Agent: An Open‑Source AI Agent Framework for Multi‑Domain Automation

II-Agent is an open‑source, multi‑domain AI agent framework that leverages powerful large language models, a rich toolset, planning‑and‑reflection mechanisms, and advanced context management to enable autonomous task execution, real‑time interaction, and seamless integration across development, data analysis, and enterprise workflows.

AI AgentAutomationContext Management
0 likes · 21 min read
Exploring II-Agent: An Open‑Source AI Agent Framework for Multi‑Domain Automation
Smart Era Software Development
Smart Era Software Development
Jun 12, 2025 · Artificial Intelligence

Anthropic’s Practical Guide to AI Agents: From Selection to Efficient Implementation

This article offers a detailed, Anthropic‑based guide on building effective AI agents and workflows, covering selection criteria, design patterns such as prompt chains, routing, parallelization, orchestrator‑worker and evaluation‑optimization, real‑world case studies, and concrete implementation recommendations that stress simplicity and composability.

AI agentsAnthropicLLM
0 likes · 26 min read
Anthropic’s Practical Guide to AI Agents: From Selection to Efficient Implementation
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 11, 2025 · Artificial Intelligence

From Chat to Autonomous Agents: Architecture, ReAct, Prompt Engineering

This article chronicles the evolution from simple chat interactions to sophisticated autonomous agents, detailing stages of LLM development, ReAct reasoning, memory management, tool integration, and practical implementation using the browser-use project, while offering prompt design insights and future directions for AI agents.

AI AgentLLMMCP
0 likes · 30 min read
From Chat to Autonomous Agents: Architecture, ReAct, Prompt Engineering
Data Thinking Notes
Data Thinking Notes
Jun 10, 2025 · Artificial Intelligence

Unlocking AI Agents: Architecture, Tools, and Real‑World Applications

This article provides a comprehensive overview of generative AI agents, detailing their core components—model, tools, and orchestration layer—explaining cognitive architectures, tool types, learning strategies, and practical development with LangChain and Vertex AI, while highlighting future prospects and challenges.

AI AgentLangChainPrompt Engineering
0 likes · 24 min read
Unlocking AI Agents: Architecture, Tools, and Real‑World Applications
DaTaobao Tech
DaTaobao Tech
Jun 6, 2025 · Artificial Intelligence

Redefining Business Core Assets in the LLM Era: Agent Evolution & Collaboration

This article examines how the rise of large language models reshapes core business assets, defines agents and tools, explores multi‑agent collaboration patterns, task allocation and conflict resolution mechanisms, and evaluates the MCP protocol and engineering requirements for building scalable, flexible agent platforms.

Agent ArchitectureLLMMCP protocol
0 likes · 9 min read
Redefining Business Core Assets in the LLM Era: Agent Evolution & Collaboration
Sohu Tech Products
Sohu Tech Products
May 21, 2025 · Artificial Intelligence

Beyond LLM Limits: Function Calling, MCP, and A2A Compared

The article examines the inherent knowledge cutoff of large language models, introduces function calling, Model Context Protocol (MCP), and Agent‑to‑Agent (A2A) as solutions for real‑time data access, compares their architectures, communication patterns, and use cases, and discusses their respective strengths and drawbacks.

A2AAI protocolsAgent Communication
0 likes · 17 min read
Beyond LLM Limits: Function Calling, MCP, and A2A Compared
Ubiquitous Tech
Ubiquitous Tech
May 16, 2025 · Backend Development

How to Build a TypeScript Stdio‑Mode MCP Server for Weather Queries

This article explains the motivation behind Model Context Protocol (MCP), compares its communication modes, and provides a step‑by‑step tutorial for creating a TypeScript‑based MCP Server in Stdio mode that exposes a weather‑lookup tool, builds it, runs it, and tests it with the official inspector.

AI AgentMCPServer Development
0 likes · 16 min read
How to Build a TypeScript Stdio‑Mode MCP Server for Weather Queries