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Generate structured AI agent learning paths, projects, and deployment guidance.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ai-agents-from-zero" yet — see the docs or source repo.
Create an 8-week AI agent learning roadmap for a beginner, covering LLMs, prompting, RAG, LangChain, LangGraph, Dify, Coze, and MCP. List weekly goals, hands-on tasks, and suggested projects.
A structured weekly study plan with knowledge modules, practice tasks, and project suggestions.
Design a hands-on enterprise knowledge base QA agent project, including the tech stack, system architecture, functional modules, development steps, evaluation metrics, and deployment plan.
A complete project implementation plan covering development, evaluation, and production deployment.
Prepare an AI agent interview question bank for an LLM application engineer role. Organize it by fundamentals, framework comparison, RAG, MCP, deployment/operations, and project practice, and provide reference answers.
A clearly organized interview question set with reference answers for systematic review and interview prep.
Learn code-first GenAI agent building from prototype to enterprise deployment.
Explore a trilingual agentic AI roadmap with projects and hands-on practice.
Run AI agents through OpenAI-compatible APIs with memory and multi-step workflows.
Collaborate with AI agents to solve complex tasks, search knowledge, and use tools.
Build, run, inspect, and debug AI agents with better visibility.
Orchestrate multiple AI agents with skills, memory, and cross-agent collaboration.