#persistent reasoning graphs
90 related articles
·7 min
Getting Started with LangChain 1.3: Core Concepts of LLMs and Agents
An in-depth breakdown of LangChain 1.3's core concepts, covering the three major limitations of LLMs, Agent architecture, memory management, and a complete learning path. Master LangChain and LangGraph to quickly build AI development skills.
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·7 min
Dify 1.8.0 in Practice: A Complete Guide to Building Enterprise-Grade AI Workflows with Zero Code
An in-depth walkthrough of deploying Dify 1.8.0 and building applications: three-step Docker deployment, five app types compared, and Workflow vs Chatflow use cases—build enterprise AI apps with zero code.
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·7 min
Zero-Basis Learning Path for AI Agents: A Three-Stage Journey from Beginner to Deployment
A systematic zero-basis learning path for AI Agent development, covering Python and LLM fundamentals, five core capabilities like task planning and RAG, and LangChain hands-on practice.
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·6 min
LangChain 1.3 in Practice: A Complete Guide to DeepAgent Architecture and the Harness Philosophy
An in-depth look at LangChain 1.3's core modules and DeepAgent architecture—covering the Harness philosophy, LangGraph internals, HITL, memory management, and guardrails to master production-grade AI Agent development.
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·5 min
Context Graphs: The Key Technology for AI Agents to Remember and Reuse Past Decisions
Context Graphs use graph structures to store decisions, causal relationships, and outcomes, enabling AI agents to accumulate experience and reuse historical decisions without modifying model weights.
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·7 min
Electron + LangGraph in Practice: Building an AI Agent Visual Orchestration App from Scratch
A hands-on guide to building an enterprise-grade AI Agent workflow orchestration app with Electron Forge and LangGraph, covering local LLM deployment (Qwen3-0.6B), node-based visual canvas design, and full Function Calling integration.
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·7 min
A Complete Guide to LangGraph's Core Concepts: The Framework of Choice for Enterprise Agent Development
An in-depth analysis of LangGraph's core concepts: short-term and long-term storage mechanisms, its differences from LangChain, the MIT open-source license, and private deployment solutions for enterprise Agent development.
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·7 min
Harness Engineering: Core Methodology and Practical Guide for Enterprise-Level AI Programming
Can AI really replace programmers? This article explains Harness Engineering principles and its three evolutionary stages, revealing real pain points of enterprise AI programming.
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·6 min
LangChain Open-Sources OpenWiki: Automatically Generate and Maintain AI-Readable Wikis for Your Codebase
LangChain open-sources OpenWiki, a tool that auto-generates and maintains AI-readable wiki documentation for codebases via a single command, powered by Git history and agents.md integration.
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·5 min
Retracing the Armada's Homeward Route: A Modern Voyage Reconstructing the 1588 Shipwreck Course
After the 1588 Spanish Armada's devastating defeat, survivors were forced around Scotland and past Ireland's west coast, losing dozens of ships to storms. This article explores a modern voyage retracing that route, combining shipwreck archaeology with digital technology.
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·7 min
Inside the AI Fraud Industry Chain: Deepfakes and Identity Deception Revealed Through the Lily Jay Incident
The Lily Jay incident exposes the AI fraud industry chain: how deepfakes, image synthesis, and content automation create fake identities. Practical methods for identifying false content in the AI era.
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·5 min
UE5.8 + Claude Code: A Complete Guide to AI Agents Directly Controlling Unreal Engine
UE5.8's official MCP plugin enables Claude Code and other AI Agents to directly control Blueprints, PCG graphs, materials, and level design. Complete setup and testing guide included.
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·7 min
Getting Started with LangGraph Multi-Agent Development: Architecture Analysis and Three Practical Case Studies
Learn LangGraph multi-agent development covering Supervisor and Collaboration architectures, with three hands-on projects: code assistant, prompt assistant, and WebRTC digital human.
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·7 min
Agent Loop Explained: The Core Runtime Mechanism and Think-Act Cycle of AI Agents
Deep dive into Agent Loop mechanics: the think-act cycle, how agents differ from LLMs, termination conditions, and design principles for building autonomous AI Agent systems.
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·7 min
AI Agent Memory Layer Comparison: Mem0, Zep, Letta, and Cloudflare — Four Major Solutions Evaluated
In-depth comparison of four AI agent memory layer solutions: Mem0's extract-retrieve approach, Zep's temporal knowledge graphs, Letta's self-editing memory, and Cloudflare Durable Objects as infrastructure primitives.
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·4 min
AI Agent Development Learning Roadmap: A Complete Four-Stage Plan from Zero to Production
A systematic AI Agent development learning roadmap covering LLM fundamentals, ReAct paradigm, memory & tool calling, and multi-agent collaboration across four stages with project suggestions.
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·3 min
Building AI Agents from Scratch: LangGraph Architecture and Practical Guide
A deep dive into AI agent principles and development practices, covering agent definitions, leading products (Deep Research, ChengPian, Manus), and the complete LangGraph + LangChain + MCP architecture.
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·4 min
Building a Coding Agent from Scratch: Creating a Codex-like AI Programming Tool
Deep dive into AI coding agent architecture: from interview-level cognition to building a Codex-like CLI agent tool, covering agents.md, Skills systems, context management, and more.
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·3 min
AI Agent Development: A Three-Stage Learning Roadmap from Zero to One
A systematic three-stage AI Agent development roadmap: from Python basics and LLM fundamentals, through five core capabilities like planning and tool use, to hands-on RAG projects for real-world deployment.
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·3 min
Cursor Practical Tutorial: A Complete Guide from Installation to Efficient Workflows
A detailed guide to Cursor AI programming tool covering installation, prompting techniques, model selection, Rules configuration, and practical workflows for developers and content creators.
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