Hands-on course material for building AI agents.
| Module | Topics | Stack |
|---|---|---|
| 01 — Introduction to Developing AI Agents | Agent concepts, PEAS framework, LangGraph, CrewAI | Python · CrewAI |
| 02 — Frameworks for Developing LLM Agents | LLM frameworks overview, RAG, tools, memory | Java · Spring AI |
| 03 — Domain-Specific LLM Agents | Fine-tuning, LoRA, data preparation, feedback loops | Python · Hugging Face · PEFT |
| 04 — Evaluating and Optimizing LLM Agents | Agent quality metrics, DeepEval, LLM-as-a-Judge (G-Eval), Open-RAG-Eval, LangSmith observability | Python · LangChain · DeepEval · LangSmith |
Each module has its own README with setup instructions. All projects require an OPENAI_API_KEY — copy .env.example to .env inside the project and fill in your key.
git clone https://github.com/andy489/Agentic-AI-for-Developers.git
cd Agentic-AI-for-DevelopersThen follow the README in the module you want to run.