#AutoGen framework
712 related articles
·3 min
Claude Code's Hidden Advantages Explained: Design Choices Every AI Coding Tool Should Copy
Deep dive into Claude Code's leading design in agentic coding: skill script execution, CLAUDE.md imports, remote control, dynamic workflow orchestration, and why Cursor, Codex and others should adopt these features.
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·4 min
Harness Engineering in Practice: Taming AI Agents with Hermes Agent
Deep dive into Harness Engineering: using the open-source Hermes Agent framework's four-layer memory system and Skill evolution to build controllable, evolvable AI agents.
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·3 min
The Complete Guide to OpenAI Codex CLI: From Installation and Configuration to Enterprise-Level Practice
In-depth guide to OpenAI Codex CLI: covering installation, agents.md design, multi-agent collaboration, MCP protocol integration, and a RAG customer service project.
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·3 min
Coze Tutorial: A Complete Guide to Building Multi-Agent Systems from Scratch
A complete guide to building AI agents on Coze, covering core features, Dify comparison, skill store usage, workflow building, and multi-Agent collaboration — no coding required.
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·3 min
The Complete Evolution of RAG: A Six-Generation Journey from Naive Retrieval to Multimodal
A complete guide to RAG evolution from Naive RAG through Advanced, Agentic, Graph, and Multimodal RAG — covering core techniques, pain points solved, and real-world use cases.
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·3 min
ARS Framework from Shanghai Jiao Tong University: Enabling Automated AI Research with Trustworthy Conclusions
Shanghai Jiao Tong University's ARS open-source framework solves trustworthiness challenges in autonomous AI research with evidence traceability and independent verification. Papers completed via ARS have been accepted at academic conferences.
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·3 min
Multi-Agent Cost-Cutting Guide: 4 Documents to Slash 60-80% of Your Token Spending
Multi-agent bills out of control? This article breaks down two core token cost pain points and provides 4 actionable documents to cut multi-agent task costs by 60-80%.
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·2 min
Agent Middleware: Adding Interceptors to Model Calls
Learn how AI Agent middleware works through two practical examples — logging and security checks. Master the Observer and Guardian design patterns to build extensible, production-grade Agents.
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·3 min
AI Agent Hands-On Learning Path: A Complete Guide from Beginner to Enterprise-Level Development
A systematic AI Agent development learning roadmap covering prompt engineering, RAG, multi-Agent collaboration, tool calling, and more—with phased learning advice and 28 hands-on project references.
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·3 min
Harness Engineering in Practice: Building an Enterprise E-Commerce System with Claude Code — A Complete Walkthrough
A deep dive into Harness Engineering for AI programming, from concept to implementation. Build an enterprise Java e-commerce system using Claude Code with Skill-driven AI development pipelines.
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·3 min
Loopcraft Explained: Loop-Stacking Architecture Design in AI Agent Development
Deep dive into Loopcraft loop-stacking architecture for AI Agent development, covering retry, self-validation, and meta-learning loops to boost reliability.
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·3 min
AI Agent Learning Roadmap: A Four-Step Practical Guide from Zero to Job-Ready
A proven AI Agent learning roadmap covering four core elements, mainstream architecture patterns, multi-agent collaboration, and hands-on projects to go from zero to job-ready in three months.
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·2 min
Harness: An Open-Source Framework That Generates Multi-Agent Teams from a Single Sentence
Harness is a 4.6K-star open-source multi-Agent framework that auto-generates AI teams from a single sentence, with six built-in collaboration architectures.
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·4 min
Claude Code Codex Plugin Integration: A Practical Guide to Dual-AI Adversarial Review for Better Code Quality
Learn how to install and configure the Codex plugin in Claude Code, leveraging dual-AI adversarial review to uncover code vulnerabilities across seven attack surfaces.
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·2 min
Struggling to Deploy AI Agents? Engineering Is the Key to Going from Demo to Product
57% of projects have deployed AI Agents, but 40% will be killed. This article analyzes the engineering methodology for taking AI Agents from Demo to enterprise product, covering the full process from requirements to deployment.
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·3 min
5 Ways to Deploy LLMs Locally: From Getting Started to Production
A complete guide to 5 local LLM deployment methods: LlamaCPP, Ollama, LM Studio, vLLM/SGLang, and MLX-LM — from personal dev to production environments.
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·4 min
AI Agent Systematic Learning Path: From Zero to Independent Development
A systematic AI Agent learning path covering core principles, Prompt engineering, RAG, multi-Agent collaboration, and hands-on projects for beginners.
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·3 min
AI Large Model Learning Roadmap Breakdown: Three Stages from Application Development to Model Fine-Tuning
Deep breakdown of a popular AI large model learning roadmap covering LangChain, RAG, Agent, and LoRA fine-tuning across three stages, with analysis of its strengths and limitations for career changers.
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·4 min
AI Agent Development: A Complete 6-Week Systematic Learning Roadmap
A 6-week systematic learning roadmap for AI Agent development, covering core architecture, ReAct principles, multi-agent collaboration, RAG integration, and deployment.
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·4 min
Superpowers: Installing Work Standards for Your AI Coding Assistant
How the Superpowers methodology constrains AI coding assistants through requirement clarification, task decomposition, TDD, and verification loops — with setup tips for Trae.
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