docs: actualizar estructura del proyecto y corregir error de seguridad

- Actualizar README principal con rutas correctas a notebooks/
- Crear README en carpeta notebooks con formato consistente
- Actualizar .gitignore para cubrir todas las carpetas del proyecto
- Corregir error medium de seguridad en notebook 03 (eliminar if/else redundante)

Author: Elizabeth

.gitignore

@@ -0,0 +1,133 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual Environment
+.venv/
+venv/
+ENV/
+env/
+.virtualenv/
+deploy-to-production/.venv/
+deploy-to-production/deployment/.venv/
+notebooks/.venv/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+*.ipynb_checkpoints
+
+# Environment variables
+.env
+.env.local
+.env.*.local
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+.project
+.pydevproject
+.settings/
+*.code-workspace
+.devcontainer/
+*.dev
+
+# macOS
+.DS_Store
+.AppleDouble
+.LSOverride
+._*
+
+# Windows
+Thumbs.db
+ehthumbs.db
+Desktop.ini
+
+# Session data
+sessions/
+*.session
+notebooks/sessions/
+
+# Logs
+*.log
+logs/
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+.tox/
+.nox/
+
+# Generated output (ignore all generated content)
+output/
+*.output
+generated_images/
+generated_videos/
+generated_content/
+
+# MCP Server generated files
+mcp_custom_tools_server.py
+
+# Bedrock AgentCore files
+.bedrock_agentcore.yaml
+.bedrock_agentcore/
+deploy-to-production/deployment/.bedrock_agentcore/
+*.hash
+dependencies.zip
+
+# AWS credentials (extra safety)
+*.pem
+*.key
+credentials
+config
+
+# Temporary files
+*.tmp
+*.temp
+*.bak
+*.swp
+*~.nib
+
+# Package files
+*.dmg
+*.gz
+*.iso
+*.jar
+*.rar
+*.tar
+*.zip
+
+# Sample test outputs
+deploy-to-production/sample-test/test_results/
+deploy-to-production/sample-test/*.png
+deploy-to-production/sample-test/*.jpg
+deploy-to-production/sample-test/*.mp4
+
+# Data files (keep sample data but ignore generated)
+notebooks/data-sample/generated/
+notebooks/data-sample/output/

README.md

@@ -1,11 +1,204 @@
-## My Project
+# Strands Agents Tutorial - Build Multi-Modal AI
 
-TODO: Fill this README out!
+## Overview
 
-Be sure to:
+Build production-ready AI agents with the [Strands Agents SDK](https://github.com/strands-agents/sdk-python) and AWS services. This repository demonstrates how you can create multi-modal systems with persistent memory in minimal code. Progress from your first agent to production-ready systems through hands-on chapters.
 
-* Change the title in this README
-* Edit your repository description on GitHub
+Strands Agents create AI agents with minimal code using built-in tools from the community package. No need to write custom tool implementations—focus on your use case while the framework handles the complexity.
+
+## Tutorial Contents - 7 Hands-On Notebooks + Production Deploy
+
+| # | Notebook | Description |
+|---|----------|-------------|
+| **01** | [Hello World - First AI Agent](notebooks/01-hello-world-strands-agents.ipynb) | Create your first AI agent with [Amazon Bedrock](https://aws.amazon.com/bedrock/) in under 10 lines of code. Learn agent basics, system prompts, and execution loops. |
+| **02** | [Custom Tools for Multi-Modal Processing](notebooks/02-custom-tools.ipynb) | Extend agents with custom tools using the `@tool` decorator. Process multi-modal content including images, videos, and documents. |
+| **03** | [MCP Integration for Tool Sharing](notebooks/03-mcp-integration.ipynb) | Share tools across applications with [Model Context Protocol (MCP)](https://modelcontextprotocol.io/). Convert tools to MCP servers and deploy for production. |
+| **04** | [State Management and Sessions](notebooks/04-state-and-sessions.ipynb) | Implement session management with `FileSessionManager`. Maintain conversation context and handle multiple concurrent sessions. |
+| **05** ⭐ | [S3 Vector Memory for Persistent Context](notebooks/05-s3-vector-memory.ipynb) | Build semantic memory with [Amazon S3 Vectors](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html). Create agents that remember across all sessions with production-ready infrastructure. |
+| **06** 🌍 | [Travel Assistant with Cross-Session Memory](notebooks/06-travel-assistant-demo.ipynb) | Real-world demonstration of cross-session memory with a personalized AI travel assistant. Features multimodal analysis (text, images, PDFs) and progressive personalization. |
+| **07** 🎨 | [Travel Content Generator with Built-in Tools](notebooks/07-travel-content-generator.ipynb) | Generate complete travel content packages (images, videos, itineraries) for any destination using built-in tools from [`strands-agents-tools`](https://pypi.org/project/strands-agents-tools/). Fully automated with no manual confirmations. |
+| **Bonus** 🚀 | [Deploy to Production with AgentCore](deploy-to-production/) | Deploy your multimodal travel agent to production using [Amazon Bedrock AgentCore Runtime](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agents-tools-runtime.html). Includes persistent memory, serverless deployment, and comprehensive testing. |
+
+### Recommended Learning Path
+
+- **Notebooks 01-02**: Basic agents with custom capabilities
+- **Notebook 03**: Production-ready tools with MCP standardization
+- **Notebooks 04-05**: Stateful agents with persistent memory
+- **Notebooks 06-07**: Real-world applications demonstrating rapid development with built-in tools
+
+## Quick Start
+
+### Prerequisites
+- AWS Account with [Amazon Bedrock](https://aws.amazon.com/bedrock/) model access enabled
+- Python 3.9+ and Jupyter Notebook
+- AWS CLI configured (`aws configure`)
+
+### Installation
+
+```bash
+# Setup environment
+python -m venv .venv
+source .venv/bin/activate  # Windows: .venv\Scripts\activate
+cd notebooks
+pip install -r requirements.txt
+
+```
+
+Open `notebooks/01-hello-world-strands-agents.ipynb` to begin.
+
+## Why Strands Agents - Key Advantages
+
+### Build Faster with 40+ Pre-Built Tools
+
+Building AI agents traditionally requires extensive boilerplate code for tool integration, error handling, and state management. Strands Agents provides:
+
+**Built-in Tools**: The [`strands-agents-tools`](https://pypi.org/project/strands-agents-tools/) package includes 40+ pre-built tools:
+- **Multi-modal**: `generate_image`, `image_reader`, `nova_reels`, `diagram`
+- **File Operations**: `file_read`, `file_write`, `editor`
+- **Memory**: `memory`, `agent_core_memory`, `mem0_memory`
+- **Web & Network**: `http_request`, `browser`, `slack`
+- **AWS Services**: `use_aws`, `retrieve` (Bedrock Knowledge Bases)
+- **Code Execution**: `python_repl`, `code_interpreter`
+- **And many more**: See [full list](https://github.com/strands-agents/tools)
+
+**Example**: Create an agent with image generation in 5 lines:
+
+```python
+from strands import Agent
+from strands.models import BedrockModel
+from strands_tools import generate_image
+
+agent = Agent(model=BedrockModel(model_id="..."), tools=[generate_image])
+agent("Generate an image of the Eiffel Tower at sunset")
+```
+
+No need to write image generation logic, handle API calls, or manage file storage—the tool handles everything.
+
+### 4-Step Rapid Prototyping Process
+
+Test new ideas quickly:
+
+1. **Import pre-built tools** from strands-agents-tools
+2. **Create an agent** with your chosen model
+3. **Add tools** to the agent's capabilities
+4. **Run and iterate** immediately
+
+This repository demonstrates this approach throughout—especially in the Travel Content Generator (Notebook 08) where you can generate complete content packages with minimal custom code.
+
+## Real-World Demo - AI Travel Assistant with Memory
+
+Experience persistent vector memory with a real-world use case: an AI travel assistant that remembers your preferences across multiple sessions.
+
+### Key Features
+
+- **True Cross-Session Memory**: Agent remembers everything even with fresh instances
+- **Multimodal Analysis**: Processes text preferences, destination photos, and travel itineraries
+- **Semantic Search**: Finds relevant memories intelligently
+- **Progressive Personalization**: Gets smarter with each interaction
+- **Production-Ready**: Built on AWS services for scale
+
+### Try the Demo
+
+```bash
+# 1. Open the demo notebook
+notebook notebooks/06-travel-assistant-demo.ipynb
+
+# 2. Update configuration with your S3 Vector bucket
+# 3. Run all cells to see cross-session memory in action
+```
+
+**Note**: To generate custom travel content for the demo, open and run [notebooks/07-travel-content-generator.ipynb](notebooks/07-travel-content-generator.ipynb) first.
+
+## Production Deployment - AgentCore Runtime Guide
+
+Take your multimodal travel agent from notebook to production with [Amazon Bedrock AgentCore Runtime](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agents-tools-runtime.html).
+
+### What's Included
+
+The [`deploy-to-production/`](deploy-to-production/) directory contains everything you need to deploy a production-ready multimodal travel agent:
+
+- **Persistent Memory**: Cross-session memory using Bedrock AgentCore Memory
+- **Multimodal Support**: Process images, videos, and documents
+- **Serverless Deployment**: Auto-scaling with AWS CodeBuild (no Docker required)
+- **Test Scripts**: Comprehensive testing for memory and multimodal capabilities
+
+### Features
+
+- ✅ **Image Analysis**: Analyze destination photos, food labels, travel documents
+- ✅ **Video Processing**: Process travel videos with Amazon Nova (visual only)
+- ✅ **Document Reading**: Read PDFs, itineraries, and text files
+- ✅ **Memory Persistence**: Remember user preferences across all sessions
+- ✅ **Production Monitoring**: CloudWatch logs and metrics
+
+### Quick Deploy
+
+```bash
+# 1. Navigate to deployment directory
+cd deploy-to-production/deployment
+
+# 2. Install dependencies
+pip install -r requirements.txt
+
+# 3. Configure agent with memory
+agentcore configure -e multimodal_agent.py
+# Select 'yes' for memory and long-term memory extraction
+
+# 4. Deploy to production
+agentcore launch
+
+```
+
+### Testing - Memory & Multimodal Capabilities
+
+```bash
+# Test short-term and long-term memory
+cd ../sample-test
+export AGENT_ARN="your-agent-arn"
+python test_short_memory.py
+python test_long_memory.py
+
+# Test image and video analysis
+python test_multimodal.py path/to/image.jpg
+python test_video.py path/to/video.mp4
+```
+
+**Learn More**: See the complete [deployment guide](deploy-to-production/README.md) for detailed instructions, architecture diagrams, and troubleshooting.
+
+
+## Best Practices & Tips
+
+- Complete notebooks in order—each builds on previous concepts
+- Run cells sequentially to maintain state
+- Experiment with examples to deepen understanding
+- Explore the [strands-agents-tools package](https://github.com/strands-agents/tools) for more pre-built capabilities
+- Monitor costs with [AWS Cost Explorer](https://aws.amazon.com/aws-cost-management/aws-cost-explorer/)
+
+## Common Issues & Solutions
+
+**Import Errors**: Run `pip install --upgrade strands-agents strands-agents-tools boto3`
+
+**AWS Credentials**: Verify with `aws sts get-caller-identity` or reconfigure with `aws configure`
+
+**S3 Vectors** (Notebook 05): Follow the [Getting Started Guide](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors-getting-started.html) and verify bucket and index regions match
+
+**MCP Server** (Notebook 03): Ensure server runs on `http://localhost:8000/sse` with no port conflicts
+
+**Tool Consent Prompts**: Set `BYPASS_TOOL_CONSENT=true` environment variable for automated workflows
+
+## Additional Resources
+
+**Documentation**  
+• [Strands Agents SDK](https://github.com/strands-agents/sdk-python)  
+• [Strands Community Tools](https://github.com/strands-agents/tools)  
+• [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/)  
+• [Amazon Bedrock AgentCore](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html)  
+• [S3 Vectors](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html)  
+• [MCP Specification](https://modelcontextprotocol.io/)  
+• [Amazon Nova](https://aws.amazon.com/bedrock/nova/)
+
+---
+
+**Ready to start?** Open [Notebook 01: Hello World](notebooks/01-hello-world-strands-agents.ipynb) and build your first AI agent.
 
 ## Security