#persistent reasoning graphs

87 related articles

Jul 20, 2026·4 min

GPT-5.6 & OpenAI Super App Deep Dive: Loop Engineering as the New Paradigm Replacing Prompt Engineering

Deep dive into GPT-5.6 (Sol/Terra/Luna) and OpenAI's Super App: Loop Engineering, Parallel Agents, and Computer Use — unpacking the shift from prompt to loop engineering with real test cases and a startup framework.

Jul 19, 2026·3 min

AI Second Brain Tech Stack: Building a Proactive Intelligent Assistant for ADHD Users

How to build a true AI second brain for ADHD users: LangGraph, n8n, RAG, vector databases, and layered architecture for a proactive personal assistant.

Jul 17, 2026·3 min

Open-Source Dynamic Workflows: How awman Enables Multi-Model Collaborative AI Programming

awman's --dynamic flag enables cross-framework dynamic workflows with multi-model collaboration. Explore its leader agent architecture, shared context design, and auto fault-tolerance mechanisms.

Jul 15, 2026·4 min

LangChain in Practice: Mastering the Four Core Modules of the Agent Ecosystem

A deep dive into LangChain's four core modules: LangChain components, LangGraph orchestration, Deep Agents, and LangSmith. Build your first Agent from scratch.

Jul 14, 2026·3 min

LangChain 1.3 in Practice: A Complete Learning Path from LLMs to Agent Development

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.

Jul 13, 2026·3 min

LangChain 1.3 in Practice: A Complete Technical Guide from LLMs to DeepAgent

An in-depth analysis of the core knowledge system of LangChain 1.3, covering the Harness architecture philosophy, DeepAgent positioning, LangGraph fundamentals, Agent memory, and human-in-the-loop.

Jul 12, 2026·6 min

AI Agent Development Learning Path: Four Stages from Zero to Hands-On Projects

A systematic breakdown of the AI agent development learning path, covering four stages: fundamentals, RAG knowledge bases, tool use, multi-agent collaboration, and hands-on projects.

Jul 12, 2026·5 min

Getting Started with LangGraph: Building AI Agent Workflows with Graph Structures

Master LangGraph core concepts: nodes, edges, and routing functions. Learn StateGraph, MemorySaver, and ToolNode through a weather-query Hello World example, and understand how LangGraph relates to LangChain and powers Agent workflows.

Jul 12, 2026·4 min

Building an Offline RAG Application from Scratch: A Complete Guide to a Private PDF Knowledge Base

A step-by-step breakdown of building a local RAG app: Ollama local models + ChromaDB vector database + Flask, enabling PDF document Q&A, fully offline operation, and zero data leakage. Perfect for developers new to RAG.

Jul 12, 2026·4 min

AI Persistent Memory Three-Layer Architecture Explained: In-Depth Comparison of Mem0, Zep, and ContextNest

Deep dive into the three-layer architecture of AI persistent memory systems—storage, management, and retrieval—with an in-depth comparison of Mem0, Zep, and ContextNest to help developers choose the right memory solution for AI Agents.

Jul 11, 2026·5 min

ECC: An Agent Enhancement Framework for AI Coding Assistants — Five Core Capabilities Explained

ECC is an agent optimization framework for AI coding assistants like Claude Code, Cursor, and Codex, enhancing them with skills, memory, security, and research-first development capabilities.

Jul 10, 2026·8 min

OpenAI Merges Codex with ChatGPT: A Deep Dive into the Unified Desktop App Overhaul

OpenAI officially merges its coding agent Codex with ChatGPT into a unified desktop app, adding new coding workflows, a Chrome extension, a built-in browser, and GPT-5.6-powered Computer Use capabilities.

Jul 10, 2026·5 min

28 Enterprise-Grade AI Agent Projects: A Complete Guide That Even Beginners Can Deploy

A collection of 28 fully reproducible enterprise-grade AI Agent projects covering code debugging, financial analysis, customer service, and multi-agent collaboration—deployable even for beginners.

Jul 10, 2026·4 min

9 Proven Methods to Make Hermes Agent Actually Work for You

9 battle-tested methods from hundreds of hours with Hermes Agent: model selection (Opus/ChatGPT/GLM), multi-agent failover, cross-device coordination via Tailscale, and reverse prompting workflows.

Jul 10, 2026·2 min

GBrain Local Deployment Guide: An AI Knowledge Engine That Outperforms Traditional RAG by 31%

GBrain is an open-source AI knowledge base supporting full local offline deployment. Its 12-step retrieval pipeline and knowledge graph boost accuracy 31% over traditional RAG.

Jul 10, 2026·4 min

Spring AI Alibaba Graph in Practice: HR Recruitment Workflow Agent Design and Core Technical Analysis

Build an HR recruitment workflow Agent with Spring AI Alibaba Graph, covering resume parsing, job matching, tiered question generation, HITL checkpointing, and time travel state rollback across 20 core technical points.

Jul 10, 2026·5 min

Security Swarm Evaluation in Practice: Validating AI Security Tools with Real Vulnerabilities

A deep dive into Security Swarm's evaluation methodology: building test sets from real, recent vulnerabilities to avoid training data contamination and validate its ability to find more bugs at lower cost.

Jul 10, 2026·7 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.

Jul 9, 2026·7 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.

Jul 9, 2026·7 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.