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hands-on guide to building and orchestrating modern AI agents — from LangGraph and CrewAI to FastAPI and Streamlit deployment.**\n\nThis repository is the **third module** in the progressive learning path on **Generative AI Engineering**, designed to bridge the gap between basic LangChain concepts and full AI application development.\n\n---\n\n## 📚 Overview\n\nThis course introduces the essential frameworks and architecture patterns needed to **build, manage, and deploy AI agents**.  \nYou'll learn to combine **LangGraph**, **CrewAI**, **FastAPI**, and **Streamlit** into cohesive systems — the same technologies used in production-grade AI applications.\n\n- **Agent Fundamentals**: Learn what AI agents are and how they differ from workflows  \n- **Modern Orchestrators**: Work hands-on with **LangGraph** and **CrewAI**  \n- **Practical Memory Management**: Build short-term and persistent memory using LangGraph  \n- **App Architecture**: Explore modular, layered, and hexagonal structures for real-world projects  \n- **Backend & UI**: Expose your AI through **FastAPI** and connect it to a **Streamlit frontend**\n\nPerfect for AI developers, data scientists, or engineers who want to move from building chains to building full **AI-powered applications**.\n\n---\n\n## 📘 Course Structure\n\n| Notebook | Topic | Description |\n|----------|-------|-------------|\n| `01` | **Intro to AI Agents** | What AI agents are, how they differ from workflows, and overview of orchestrators (LangGraph & CrewAI). |\n| `02` | **LangGraph Basics** | Visual, conceptual intro to LangGraph — nodes, edges, state, memory, and control flow. |\n| `03` | **Memory with LangGraph** | Implement short-term and persistent memory (RAM + SQLite checkpointers). |\n| `04` | **CrewAI Basics** | Understand the CrewAI architecture — agents, tasks, crews, and processes. |\n| `05` | **Code Architecture Patterns** | Learn modular app structures: monolithic, layered, and hexagonal architectures. |\n| `06` | **FastAPI Intro** | Build your first backend API to serve model responses via OpenAI or Groq. |\n| `07` | **Streamlit Intro** | Create a lightweight web UI that interacts with your FastAPI `/chat` endpoint. |\n\n---\n\n## 🚀 Getting Started\n\n### Prerequisites\n\n- **Python**: 3.10, 3.11, or 3.12  \n- **uv**: Fast Python package manager ([installation guide](https://github.com/astral-sh/uv))  \n- **OpenAI or Groq API Key** (for examples using real LLMs)\n\n### Installation\n\n1. **Clone the repository:**\n```bash\ngit clone https://github.com/JaimeLucena/03-agents-and-apps-foundations.git\ncd 03-agents-and-apps-foundations\n```\n\n2. **Install dependencies:**\n```bash\nuv sync\n```\n\n3. **Set up your environment variables:**  \nCreate a `.env` file in the root directory:\n```env\nOPENAI_API_KEY=your_openai_api_key_here\nGROQ_API_KEY=your_groq_api_key_here  # Optional\n```\n\n4. **Launch JupyterLab:**\n```bash\nuv run jupyter lab\n```\n\n---\n\n## 🧠 Run the API + UI\n\nOnce you understand the architecture, you can run the full system locally:\n\n**1️⃣ Start the FastAPI backend**\n```bash\nuv run uvicorn main:app --reload --port 8000\n```\nVisit Swagger docs at `http://localhost:8000/docs`\n\n**2️⃣ Start the Streamlit frontend**\n```bash\nuv run streamlit run app_streamlit.py\n```\nOpen `http://localhost:8501` and interact with your app.\n\n---\n\n## 🛠️ Technology Stack\n\n- **LangGraph** – Graph-based orchestration for agent state management  \n- **CrewAI** – Multi-agent coordination and task execution  \n- **LangChain** – Core LLM interface (LCEL-based)  \n- **FastAPI** – High-performance Python web framework for serving LLM endpoints  \n- **Streamlit** – Python-native web UI for interactive apps  \n- **SQLite / MemorySaver** – Persistent and in-memory state storage  \n- **OpenAI / Groq** – Model providers for LLM inference\n\n---\n\n## 🎓 Learning Path\n\n### Phase 1: Fundamentals (Notebooks 01–03)\nUnderstand what agents are, how LangGraph works, and how to manage memory in your apps.\n\n### Phase 2: Agent Orchestration (Notebooks 04–05)\nDive into CrewAI and learn scalable app architectures (monolithic, modular, hexagonal).\n\n### Phase 3: App Deployment (Notebooks 06–07)\nExpose your models via FastAPI and connect them to a live Streamlit frontend.\n\n---\n\n## 🎯 What You'll Build\n\nBy the end of this repository, you'll be able to:\n\n✅ Build agent graphs using LangGraph  \n✅ Implement short-term and persistent memory  \n✅ Orchestrate multi-agent systems with CrewAI  \n✅ Design modular AI app architectures  \n✅ Serve your model with FastAPI  \n✅ Build a simple UI in Streamlit connected to your backend  \n✅ Run end-to-end AI apps locally\n\n---\n\n## 📋 Recommended Knowledge\n\nThis is the **third module** in the learning series. You should be comfortable with:\n\n- Python fundamentals ([01-python-fundamentals](https://github.com/JaimeLucena/01-python-fundamentals))  \n- Basic LangChain concepts ([02-langchain-beginners](https://github.com/JaimeLucena/02-langchain-beginners))  \n- Working in Jupyter notebooks\n\n---\n\n## 🔗 Related Resources\n\n- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/)  \n- [CrewAI Documentation](https://docs.crewai.com/)  \n- [FastAPI Documentation](https://fastapi.tiangolo.com/)  \n- [Streamlit Documentation](https://docs.streamlit.io/)  \n- [OpenAI API Reference](https://platform.openai.com/docs/api-reference)\n\n---\n\n## 📝 License\n\nThis project is licensed under the MIT License — see the [LICENSE](LICENSE) file for details.\n\n---\n\n## 👨‍💻 Author\n\n**Jaime Lucena**  \nGenerative AI Engineer — Building AI applications & sharing what I learn along the way 🚀\n\n- **GitHub**: [@JaimeLucena](https://github.com/JaimeLucena)  \n- **LinkedIn**: [linkedin.com/in/jaimelucena](https://linkedin.com/in/jaimelucena)\n\n---\n\n## ⭐ Support\n\nIf you find this project useful, consider giving it a star on GitHub ⭐  \nIt helps others discover this learning series!\n\n---\n\n**Ready to build real AI agents?**  \nStart with notebook `01` and move step by step! 🚀","readmeExcerpt":"03-agents-and-apps-foundations **A complete, hands-on guide to building and orchestrating modern AI agents — from LangGraph and CrewAI to FastAPI and Streamlit deployment.** This repository is the **third module** in the progressive learning path on **Generative AI Engineering**, designed to bridge the gap between basic LangChain concepts and full AI application development. --- 📚 Overview This course introduces the","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"git clone https://github.com/JaimeLucena/03-agents-and-apps-foundations.git\ncd 03-agents-and-apps-foundations"},{"language":"bash","snippet":"uv sync"},{"language":"env","snippet":"OPENAI_API_KEY=your_openai_api_key_here\nGROQ_API_KEY=your_groq_api_key_here  # Optional"},{"language":"bash","snippet":"uv run jupyter lab"},{"language":"bash","snippet":"uv run uvicorn main:app --reload --port 8000"},{"language":"bash","snippet":"uv run streamlit run app_streamlit.py"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["python"],"docsSourceLabel":"GITHUB REPOS","editorialOverview":"A hands-on guide to building and orchestrating AI agents with LangGraph and CrewAI, from fundamentals to deployment with FastAPI and Streamlit. 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