#Agent tool calling
2412 related articles
·2 min
General Agents Are Essentially Coding Agents: The Paradigm Shift from Tool Calling to Code Execution
Explore why general AI agents are essentially coding agents. From Turing completeness to composability and verifiability, discover the paradigm shift from Function Calling to Code as Action.
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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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·2 min
AI Agent Development in Practice: A Complete Guide to Framework Selection, Tool Calling, and Production Deployment
A comprehensive guide to AI Agent development: covering Agent vs. Chatbot differences, framework selection, tool calling design, RAG pipeline setup, and production deployment best practices.
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·3 min
Local Gemma 3 12B as an AI Agent: A Practical Guide to Tool Calling and Framework Selection
Running Gemma 3 12B locally via Ollama and want to build an AI Agent? This guide covers tool calling, n8n/LangChain/CrewAI comparisons, context limits, and more.
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·5 min
GPT-5.6 Released: Hands-On Analysis of Programmatic Tool Calling and Autonomous Agents
GPT-5.6 is officially released with core upgrades including programmatic tool calling, autonomous subagent delegation, and higher token information density. A hands-on card game build reveals its Agentic power.
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·4 min
The Three Eras of AI Agents: The Capability Leap from Tool Calling to Autonomous Orchestration
An in-depth look at the three core eras of AI Agent development: reliable tool calling, coherent long-task execution, and autonomous orchestration with metacognition. Helps developers match tasks to model capabilities.
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·4 min
Enterprise AI Agent Development Guide: Multi-Task Recognition and Dynamic Tool Calling in Practice
A deep dive into AI Agent architecture and enterprise deployment. From LangChain and ReAct design to dynamic tool calling and multi-task recognition — build autonomous enterprise AI assistants.
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·4 min
Building an AI Agent Framework from Scratch: Deep Dive into Tool Calling, Memory, and Dual-Model Scheduling
Build an AI Agent from scratch — no frameworks. Deep dive into Function Call schema design, MCP remote mirroring, dual-model routing, and short-term memory management.
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Tutorials·3 min
ReAct vs. CodeAct: Two Core Approaches to Agent Tool Calling
In-depth comparison of ReAct and CodeAct — two core Agent tool-calling architectures. From paper principles to code implementation, learn the trade-offs between reasoning+action and code execution.
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Tutorials·3 min
MCP Protocol Explained: Differences from Function Calling and Agent Tool Development in Practice
Deep dive into MCP (Model Context Protocol): its principles, communication architecture, and practical applications. Compare MCP vs Function Calling, explore client-server communication and security.
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Tutorials·2 min
LangChain AI Agent in Practice: Complete Tutorial on Streaming Output + Parallel Tool Calling
Learn how to build a production-grade AI Agent chat app with LangChain and FastAPI, covering streaming tokens, asyncio parallel tool calling, SerpAPI async conversion, and structured output.
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Deep Dives·3 min
NVIDIA Dynamo Multi-Turn Agentic Interaction: Deep Integration of Streaming Tokens and Tool Calling
Deep dive into NVIDIA Dynamo's multi-turn agentic interaction support, covering streaming token output, structured tool calling, state management, and MoE synergy for production-grade AI agents.
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Tutorials·3 min
Dify Agent Building Tutorial: Tool Calling & ESA Search Practical Guide
Complete guide to building Dify Agents: Function Call tool calling, ESA search configuration, time awareness solutions, and 161 plugin tools for no-code AI agent development.
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Tutorials·4 min
Dify AI Agent Tutorial: Build Tool-Calling & Smart Search with Zero Code
Learn how to build an AI Agent on Dify with zero code, covering Function Call vs ReAct modes, Exa search tool setup, time-awareness solutions, and Agent best practices.
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·3 min
Xberg v1 Open-Source Document Extraction Engine: CPU-Only Local Processing Supporting 101 Formats
Xberg v1 is an MIT-licensed open-source local document extraction engine. CPU-only, supporting 101 formats with built-in SPLADE and ColBERT retrieval, Rust-powered for RAG and ML pipelines.
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·3 min
LiveKit Agents: A Comprehensive Guide to the Open-Source Framework for Building Real-Time Voice AI Agents
Deep dive into the LiveKit Agents open-source framework for building real-time voice AI agents using STT, LLM, and TTS modules with production-ready deployment capabilities.
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·2 min
Qwen3-Max Deep Dive: Major Upgrades in Coding and Collaboration, Qwen Studio as an All-in-One AI Platform
Alibaba launches flagship model Qwen3-Max focused on coding and collaboration, paired with Qwen Studio platform integrating multimodal AI, tool calling, and Artifacts to compete with GPT-4o and Gemini.
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·3 min
MCP Session Analytics: Building Production-Grade Observability and Evaluation for AI Agents
How to build product analytics and evaluation capabilities for AI Agents at the MCP protocol layer, covering session-level tracing, tool call observability, and quality Evals.
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·3 min
GitHub Daily · August 4th: Uber Open-Sources AI Agent Security Framework, Enterprise-Grade Agent Protection Takes Center Stage
Uber open-sources ADR, an enterprise AI Agent security framework gaining 140 stars in one day. Plus webpack, Deno, Angular, Tailwind CSS hold steady.
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·2 min
MicroCodex: A C++ Programming Agent Under 1MB — A Fresh Take on Minimalist AI Tools
MicroCodex re-implements OpenAI's Codex programming agent in C++ with a binary under 1MB. This article analyzes its engineering design, minimalist philosophy, and use cases in CI/CD and embedded systems.
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