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+# Personal Assistant with Memory
+
+An executive assistant for travel planning that remembers your preferences and plans itineraries accordingly using **Cognee** (Semantic memory system) and **Agno** (Agent framework).
+
+## Overview
+
+This example demonstrates how to build a personal assistant that:
+- **Remembers** your travel preferences and dietary restrictions
+- **Plans** personalized itineraries based on stored preferences
+- **Searches** memory to retrieve relevant information when making recommendations
+- **Adapts** recommendations based on your specific needs (e.g., vegetarian meals, hotel preferences, beach locations)
+
+The assistant uses Cognee's Semantic Memory layer to store and retrieve user preferences, enabling context-aware travel planning.
+
+## Architecture
+
+- **Agent Framework**: [Agno](https://github.com/agno-agi/agno/) - For building intelligent agents
+- **Memory Layer**: [Cognee](https://github.com/topoteretes/cognee/) - Semantic knowledge graph for persistent memory
+- **Two Implementation Options**:
+  - `app.py`: Uses OpenAI (default stack)
+  - `custom_stack.py`: Uses Gemini + Qdrant + FastEmbed (custom stack)
+
+## Prerequisites
+
+- Python >= 3.10 and < 3.14
+- [uv](https://github.com/astral-sh/uv) package manager
+- API keys for your chosen LLM provider (OpenAI or Google)
+
+## Installation
+
+### 1. Create Virtual Environment
+
+```bash
+uv venv
+source .venv/bin/activate
+```
+
+### 2. Install Dependencies
+
+Choose one of the following based on which implementation you want to use:
+
+#### Option A: Default Stack (`app.py`)
+
+Uses OpenAI for both agent execution and memory layer.
+
+```bash
+uv add cognee agno
+```
+
+#### Option B: Custom Stack (`custom_stack.py`)
+
+Uses Gemini (LLM), Qdrant (vector store), and FastEmbed (embeddings).
+
+```bash
+uv add google-genai cognee-community-vector-adapter-qdrant fastembed
+```
+
+## Configuration
+
+Create a `.env` file in the project root with the appropriate environment variables.
+
+### For `app.py` (OpenAI Stack)
+
+```env
+# OpenAI API key for Agent execution
+OPENAI_API_KEY=<your-openai-api-key>
+
+# OpenAI API key for Cognee memory layer
+LLM_API_KEY=<your-openai-api-key>
+```
+
+### For `custom_stack.py` (Gemini + Qdrant + FastEmbed)
+
+```env
+# Google API key for agent execution
+GOOGLE_API_KEY=<your-google-api-key>
+
+# Cognee LLM configuration
+LLM_PROVIDER=gemini
+LLM_MODEL=gemini/gemini-2.5-flash
+LLM_API_KEY=<your-google-api-key>
+
+# Cognee embedding configuration
+EMBEDDING_PROVIDER=fastembed
+EMBEDDING_MODEL=jinaai/jina-embeddings-v2-base-en
+EMBEDDING_DIMENSIONS=768
+
+# Qdrant vector store configuration
+QDRANT_URL=<your-qdrant-cluster-endpoint>
+QDRANT_API_KEY=<your-qdrant-api-key>
+```
+
+**Stack Components:**
+- **Vector Store**: Qdrant
+- **LLM**: Gemini 2.5 Flash
+- **Embedding**: FastEmbed (Jina embeddings v2)
+
+## Usage
+
+### Running the Default Stack (`app.py`)
+
+```bash
+python app.py
+```
+
+The agent will:
+1. Store your preferences in Cognee's memory
+2. Demonstrate retrieval by planning a restaurant itinerary for Rome
+
+### Running the Custom Stack (`custom_stack.py`)
+
+```bash
+uv run custom_stack.py
+```
+
+Same functionality as above, but using the custom stack configuration.
+
+## How It Works
+
+1. **Memory Storage**: When you provide preferences, the agent uses `add_memory` to store them in Cognee's semantic knowledge graph
+2. **Memory Retrieval**: When planning itineraries, the agent uses `search_memory` to find relevant preferences
+3. **Context-Aware Planning**: The agent applies retrieved preferences to generate personalized recommendations
+
+### Example Interaction
+
+```python
+# Store preferences
+agent.print_response(MY_PREFERENCE, stream=True)
+
+# Get recommendations based on stored preferences
+agent.print_response("I am visiting Rome, give me restaurants list to stop by", stream=True)
+```
+## Project Structure
+
+```
+agno-agents/
+├── app.py              # Default implementation (OpenAI)
+├── custom_stack.py     # Custom implementation (Gemini + Qdrant)
+├── tools.py            # CogneeTools integration for Agno
+├── constants.py        # Agent instructions and user preferences
+├── README.md           # This file
+└── .env                # Environment variables (create this)
+```
+
+## Customization
+
+You can customize the assistant by modifying:
+
+- **`constants.py`**: Update `MY_PREFERENCE` with your own preferences and `INSTRUCTIONS` to change agent behavior
+- **`tools.py`**: Extend `CogneeTools` to add more memory operations
+- **Environment variables**: Switch between different LLM providers, vector stores, or embedding models
+
+## Troubleshooting
+
+- **Import errors**: Ensure all dependencies are installed with `uv sync`
+- **API key errors**: Verify your `.env` file contains valid API keys
+- **Qdrant connection**: For `custom_stack.py`, ensure your Qdrant cluster is accessible and credentials are correct
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