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AgentGuide is an open-source learning resource designed to provide a structured pathway for understanding and building AI agents. The project aggregates tutorials, research papers, frameworks, and practical resources related to agent development with large language models. Instead of presenting scattered resources, the repository organizes them into a systematic learning roadmap that guides learners from foundational concepts to advanced AI agent systems. The guide covers topics such as agent frameworks, retrieval-augmented generation systems, multi-agent collaboration, memory management, and tool usage. It also includes practical projects, interview preparation materials, and curated research papers related to AI agents and LLM engineering. The project is designed not only for learning but also for career preparation, helping developers understand how to build portfolio projects and prepare for AI engineering roles.
Features
- Structured learning roadmap for AI agent development
- Curated tutorials covering major agent frameworks and tools
- Resources on RAG systems, embeddings, and vector databases
- Guidance on multi-agent systems and workflow orchestration
- Collections of research papers and technical references
- Interview preparation materials and practical project examples
Project Samples
