diff --git a/python/agents/long-term-memory/.env.example b/python/agents/long-term-memory/.env.example
new file mode 100644
index 000000000..1908ff4bb
--- /dev/null
+++ b/python/agents/long-term-memory/.env.example
@@ -0,0 +1,12 @@
+# .env file
+# For cognee operations
+
+LLM_PROVIDER=gemini
+LLM_MODEL=gemini/gemini-2.5-flash
+LLM_API_KEY=your-openai-api-key      
+EMBEDDING_PROVIDER=fastembed
+EMBEDDING_MODEL=jinaai/jina-embeddings-v2-base-en
+EMBEDDING_DIMENSIONS=768
+
+# For Agents- Gemini models (ADK execution)
+GOOGLE_API_KEY=your-google-api-key    
\ No newline at end of file
diff --git a/python/agents/long-term-memory/.python-version b/python/agents/long-term-memory/.python-version
new file mode 100644
index 000000000..e4fba2183
--- /dev/null
+++ b/python/agents/long-term-memory/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/python/agents/long-term-memory/README.md b/python/agents/long-term-memory/README.md
new file mode 100644
index 000000000..e155ab419
--- /dev/null
+++ b/python/agents/long-term-memory/README.md
@@ -0,0 +1,87 @@
+# Personal Assistant with Memory
+
+An executive assistant for travel planning that remembers your preferences and plans itineraries accordingly using **Cognee** (Semantic memory system) and **Google ADK** (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
+
+- **Google ADK 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
+
+We will directly use the integration provided by Cognee: [Google ADK <> Cognee](https://github.com/topoteretes/cognee-integration-google-adk)
+
+## Prerequisites
+
+- Python >= 3.10 and < 3.14
+- [uv](https://github.com/astral-sh/uv) package manager
+- API keys for your chosen LLM provider (i.e., Google)
+
+## Installation
+
+### 1. Create Virtual Environment
+
+```bash
+uv venv
+source .venv/bin/activate
+```
+
+### 2. Install Dependencies
+
+Uses Gemini (LLM) and [FastEmbed](https://github.com/qdrant/fastembed) (embeddings).
+
+```bash
+uv add cognee-integration-google-adk
+```
+
+## Configuration
+
+Create a `.env` file in the project root with the appropriate environment variables. Similar to `.env.example`
+
+```env
+# For cognee operations
+
+LLM_PROVIDER=gemini
+LLM_MODEL=gemini/gemini-2.5-flash
+LLM_API_KEY=your-openai-api-key      
+EMBEDDING_PROVIDER=fastembed
+EMBEDDING_MODEL=jinaai/jina-embeddings-v2-base-en
+EMBEDDING_DIMENSIONS=768
+
+# For Agents- Gemini models (ADK execution)
+GOOGLE_API_KEY=your-google-api-key    
+```
+
+## Usage
+
+### Running the Default Stack (`app.py`)
+
+```bash
+cd travel_agent
+uv run agent.py
+```
+
+The agent will:
+1. Store your preferences in Cognee's memory
+2. Demonstrate retrieval by planning a restaurant itinerary for Rome or any other place. 
+3. Get personalized suggestion rather than generic responses. 
+
+## How It Works
+
+1. **Memory Storage**: When you provide preferences, the agent uses `add_tool` to store them in Cognee's semantic knowledge graph
+2. **Memory Retrieval**: When planning itineraries, the agent uses `search_tool` to find relevant preferences
+3. **Context-Aware Planning**: The agent applies retrieved preferences to generate personalized recommendations
+
+
+## Troubleshooting
+
+- **Import errors**: Ensure all dependencies are installed with `uv sync`
+- **API key errors**: Verify your `.env` file contains valid API keys
\ No newline at end of file
diff --git a/python/agents/long-term-memory/pyproject.toml b/python/agents/long-term-memory/pyproject.toml
new file mode 100644
index 000000000..fb2281118
--- /dev/null
+++ b/python/agents/long-term-memory/pyproject.toml
@@ -0,0 +1,9 @@
+[project]
+name = "long-term-memory"
+version = "0.1.0"
+description = "Personal Assistant who remembers my preference to plan my itinerary accordingly"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "cognee-integration-google-adk>=0.1.1",
+]
diff --git a/python/agents/long-term-memory/travel_agent/__init__.py b/python/agents/long-term-memory/travel_agent/__init__.py
new file mode 100644
index 000000000..60cac4f44
--- /dev/null
+++ b/python/agents/long-term-memory/travel_agent/__init__.py
@@ -0,0 +1,13 @@
+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
\ No newline at end of file
diff --git a/python/agents/long-term-memory/travel_agent/agent.py b/python/agents/long-term-memory/travel_agent/agent.py
new file mode 100644
index 000000000..e01ef44eb
--- /dev/null
+++ b/python/agents/long-term-memory/travel_agent/agent.py
@@ -0,0 +1,48 @@
+import asyncio
+from google.adk.agents import Agent
+from google.adk.runners import InMemoryRunner
+
+import cognee
+from cognee_integration_google_adk import add_tool, search_tool
+from constant import MY_PREFERENCE, INSTRUCTIONS
+
+from dotenv import load_dotenv
+load_dotenv()
+
+root_agent = Agent(
+    model="gemini-2.5-flash",
+    name="executive_assistant",
+    description="You are a Personal Executive Assistant who remembers my preference to plan my itinerary accordingly",
+    instruction=INSTRUCTIONS,
+    tools=[add_tool, search_tool],
+)
+
+async def main():
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+    
+    runner = InMemoryRunner(agent=root_agent)
+    events = await runner.run_debug(f"""Remember:
+                                    {MY_PREFERENCE}
+                                    """
+    )
+    
+    for event in events:
+        if event.is_final_response() and event.content:
+            for part in event.content.parts:
+                if part.text:
+                    print(part.text)
+    
+    query = "plan 3 days Itinerary for Rome along with restaurants to try food."
+    events = await runner.run_debug(
+        query
+    )
+    
+    for event in events:
+        if event.is_final_response() and event.content:
+            for part in event.content.parts:
+                if part.text:
+                    print(part.text)
+
+if __name__ == "__main__":
+    asyncio.run(main())
\ No newline at end of file
diff --git a/python/agents/long-term-memory/travel_agent/constant.py b/python/agents/long-term-memory/travel_agent/constant.py
new file mode 100644
index 000000000..5670c7714
--- /dev/null
+++ b/python/agents/long-term-memory/travel_agent/constant.py
@@ -0,0 +1,19 @@
+MY_PREFERENCE = """
+- I like to visit places near the beach where I can find the best spots. 
+- I need locations that are rare to find on blogs but are goldmine places for your eyes
+- I prefer Vegetarian meals. Use this when I ask for restaurants recommendation
+- I am a Jain so plan accordingly. Garlic and Onions works. But No eggs and Mushrooms. 
+- For hotel recommendations if I ask: I prefer private bathrooms and minimum 8+ reviews on booking.com and more than 4+ ratings on Google Maps 
+- My hobbies that might also help in planning Itineraries: I love Anime, F1 and Cricket.
+- About me: I am Tarun Jain, AI & Founding Engineer with a YouTube channel: AI with Tarun
+"""
+
+INSTRUCTIONS = """
+You are a travel planning agent. When the user asks for recommendations:
+
+1. Search memory for relevant user preferences
+2. Extract and apply those preferences to filter all results
+3. When retrieving cached recommendations from memory, re-validate against preferences
+4. Present only options that match ALL preference criteria
+5. If preferences conflict with available options, explain why and suggest alternatives
+"""
\ No newline at end of file
diff --git a/python/agents/long-term-memory/uv.lock b/python/agents/long-term-memory/uv.lock
new file mode 100644
index 000000000..69fc8a13e
--- /dev/null
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