1870 related articles

A comprehensive guide to LangChain 1.3 — covering the full learning path from Models to Agent development, including Harness architecture, LangGraph, memory management, HITL, and Guardrails.

What is an AI Agent? This guide explains the key differences between LLMs and Agents, breaks down the Agent formula (LLM + Workflow + Knowledge Base), and compares tools like Dify, Coze, LangChain, and LlamaIndex.

Prompt engineering and RAG are just the basics. Real enterprise AI runs on Agents. Explore the 4 stages of LLM deployment, Agent core capabilities, and industry trends.

Master LangChain from scratch: the three limitations of LLMs, init_chat_model unified interface config, the Message type system, and the path from LLM calls to Agent development.

A deep dive into LLM selection for LangChain and MCP agent development, comparing DeepSeek V3/R1 vs Qwen3 on Function Calling and MCP support with practical tips.

A systematic guide to LangChain LLM application development, covering environment setup, core components (RAG, Chain, Memory), and Agent development to help developers master LLM app building.
TutorialsA systematic guide to LangChain's core features, covering LLM vs. Agent concepts, unified interface design, multi-provider support, environment setup, and hands-on code examples for AI app development.
TutorialsHow to start LLM application development from scratch? A complete roadmap covering Python basics, RAG knowledge bases, and Agent development with LangChain.
TutorialsA dedicated AI learning roadmap for Java developers covering Spring AI, LangChain4J, RAG, and Agent development — from fundamentals to production deployment.
TutorialsA systematic three-step learning path for LLM Agent development: from Prompt Engineering and API calls, to RAG and vector databases, to ReAct and multi-agent systems.
TutorialsA deep dive into LangChain framework essentials, clarifying the key differences between LLMs and Agents, covering Agent development, DeepAgent advanced topics, and learning paths.

Deep analysis of the dangerous disconnect between HTTP 200 OK and actual business outcomes in AI Agent workflows, with solutions for building reliable production-grade Agent systems.

Deep analysis of the dangerous disconnect between HTTP 200 OK and actual business results in AI Agent workflows, with solutions for building reliable production-grade Agent systems.

Learn how to advance from linear pipeline to state machine Agent architecture through a YouTube script-to-storyboard case study, covering fault tolerance, LLM evaluation frameworks, and LangGraph vs AutoGen selection.

Numbat is an open-source AI Agent security detection and response tool supporting cross-framework deployment with Agent behavior visibility and pre-execution interception capabilities.

Exploring whether AI can proactively file tickets for programmers. From architectural constraints and security risks to AI Agent solutions, analyzing the current state and future of AI feedback loops.

Explore AI agent delegation boundaries: from code completion to autonomous agents across three levels, analyzing verifiability, error costs, and context to build pragmatic trust strategies.

Exploring how AI drives large-scale MMO development, from scalable content generation to dynamic NPC interaction, analyzing technical pathways, challenges, and industry implications.

Deep analysis of Hugging Face's frontier lab AI agent intrusion report, covering indirect prompt injection, lateral movement, data exfiltration, and defense-in-depth strategies for AI agent security.

Guide to running Claude Code via Ollama locally: troubleshooting API errors, output token limits, model freezes, with model selection, parameter tuning, and alternative tool recommendations.