diff --git a/examples/car_assistant/README.md b/examples/car_assistant/README.md
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+# Simple Visual Question Answering Example
+
+This example demonstrates how to use the framework for visual question answering (VQA) tasks. The example code can be found in the `examples/step1_simpleVQA` directory.
+
+```bash
+   cd examples/step1_simpleVQA
+```
+
+## Overview
+
+This example implements a simple Visual Question Answering (VQA) workflow that consists of two main components:
+
+1. **Input Interface**
+   - Handles user input containing questions about images
+   - Processes and manages image data
+   - Extracts the user's questions/instructions
+
+2. **Simple VQA Processing**
+   - Takes the user input and image
+   - Analyzes the image based on the user's question
+   - Generates appropriate responses to visual queries
+
+The workflow follows a straightforward sequence:
+
+## Prerequisites
+
+- Python 3.10+
+- Required packages installed (see requirements.txt)
+- Access to OpenAI API or compatible endpoint (see configs/llms/gpt.yml)
+- Redis server running locally or remotely
+- Conductor server running locally or remotely
+
+## Configuration
+
+The container.yaml file is a configuration file that manages dependencies and settings for different components of the system, including Conductor connections, Redis connections, and other service configurations. To set up your configuration:
+
+1. Generate the container.yaml file:
+   ```bash
+   python compile_container.py
+   ```
+   This will create a container.yaml file with default settings under `examples/step1_simpleVQA`.
+
+
+2. Configure your LLM settings in `configs/llms/gpt.yml`:
+   - Set your OpenAI API key or compatible endpoint through environment variable or by directly modifying the yml file
+   ```bash
+   export custom_openai_key="your_openai_api_key"
+   export custom_openai_endpoint="your_openai_endpoint"
+   ```
+   - Configure other model settings like temperature as needed through environment variable or by directly modifying the yml file
+
+3. Update settings in the generated `container.yaml`:
+   - Modify Redis connection settings:
+     - Set the host, port and credentials for your Redis instance
+     - Configure both `redis_stream_client` and `redis_stm_client` sections
+   - Update the Conductor server URL under conductor_config section
+   - Adjust any other component settings as needed
+
+## Running the Example
+
+3. Run the simple VQA example:
+
+   For terminal/CLI usage:
+   ```bash
+   python run_cli.py
+   ```
+
+   For app/GUI usage:
+   ```bash
+   python run_app.py
+   ```
+
+## Troubleshooting
+
+If you encounter issues:
+- Verify Redis is running and accessible
+- Check your OpenAI API key is valid
+- Ensure all dependencies are installed correctly
+- Review logs for any error messages
+
+
+## Building the Example
+
+Coming soon! This section will provide detailed instructions for building and packaging the step1_simpleVQA example step by step.
+
diff --git a/examples/car_assistant/__init__.py b/examples/car_assistant/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/car_assistant/compile_container.py b/examples/car_assistant/compile_container.py
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+++ b/examples/car_assistant/compile_container.py
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+# Import core modules and components
+from omagent_core.utils.container import container
+
+# Import workflow related modules
+from pathlib import Path
+from omagent_core.utils.registry import registry
+
+# Set up path and import modules
+CURRENT_PATH = root_path = Path(__file__).parents[0]
+registry.import_module()
+
+# Register required components
+container.register_callback(callback='AppCallback')
+container.register_input(input='AppInput')
+container.register_stm("RedisSTM")
+# Compile container config
+container.compile_config(CURRENT_PATH)
+
+
+
diff --git a/examples/car_assistant/configs/llms/gpt.yml b/examples/car_assistant/configs/llms/gpt.yml
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+++ b/examples/car_assistant/configs/llms/gpt.yml
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+name: OpenaiGPTLLM
+model_id: gpt-4o
+api_key: ${env| custom_openai_key, sk-4zr6uGzVbNfIiq7U513aCc94Af614792938cE9AdB7D0E295}
+endpoint: ${env| custom_openai_endpoint, http://36.133.246.107:11002/v1}
+temperature: 0
+vision: true
\ No newline at end of file
diff --git a/examples/car_assistant/configs/workers/simple_vqa.yaml b/examples/car_assistant/configs/workers/simple_vqa.yaml
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+++ b/examples/car_assistant/configs/workers/simple_vqa.yaml
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+name: SimpleVQA
+llm: ${sub|gpt}
\ No newline at end of file
diff --git a/examples/car_assistant/input_interface/__init__.py b/examples/car_assistant/input_interface/__init__.py
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index 0000000..e69de29
diff --git a/examples/car_assistant/input_interface/input_interface.py b/examples/car_assistant/input_interface/input_interface.py
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+++ b/examples/car_assistant/input_interface/input_interface.py
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+from pathlib import Path
+
+from omagent_core.utils.registry import registry
+from omagent_core.utils.general import read_image
+from omagent_core.engine.worker.base import BaseWorker
+from omagent_core.utils.logger import logging
+
+CURRENT_PATH = Path(__file__).parents[0]
+
+
+@registry.register_worker()
+class InputInterface(BaseWorker):
+    """Input interface processor that handles user instructions and image input.
+    
+    This processor:
+    1. Reads user input containing question and image via input interface
+    2. Extracts text instruction and image path from the input
+    3. Loads and caches the image in workflow storage
+    4. Returns the user instruction for next steps
+    """
+
+    def _run(self, *args, **kwargs):
+        # Read user input through configured input interface
+        user_input = self.input.read_input(workflow_instance_id=self.workflow_instance_id, input_prompt='Please tell me a question about car and a image.')
+        
+        image_path = None
+        # Extract text and image content from input message
+        content = user_input['messages'][-1]['content']
+        for content_item in content:
+            if content_item['type'] == 'text':
+                user_instruction = content_item['data']
+            elif content_item['type'] == 'image_url':
+                image_path = content_item['data']
+        
+        logging.info(f'User_instruction: {user_instruction}\nImage_path: {image_path}')
+        
+        # Load image from file system
+        if image_path:
+            img = read_image(input_source=image_path)
+            
+            # Store image in workflow shared memory with standard key
+            image_cache = {'<image_0>' : img}
+            self.stm(self.workflow_instance_id)['image_cache'] = image_cache
+
+        return {'user_instruction': user_instruction}
diff --git a/examples/car_assistant/run_app.py b/examples/car_assistant/run_app.py
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+++ b/examples/car_assistant/run_app.py
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+# Import required modules and components
+from omagent_core.utils.container import container
+from omagent_core.engine.workflow.conductor_workflow import ConductorWorkflow
+from omagent_core.engine.workflow.task.simple_task import simple_task
+from pathlib import Path
+from omagent_core.utils.registry import registry
+from omagent_core.clients.devices.app.client import AppClient
+from omagent_core.utils.logger import logging
+logging.init_logger("omagent", "omagent", level="INFO")
+
+# Import agent-specific components
+from agent.input_interface.input_interface import InputInterface 
+
+# Set current working directory path
+CURRENT_PATH = root_path = Path(__file__).parents[0]
+
+# Import registered modules
+registry.import_module(project_path=CURRENT_PATH.joinpath('agent'))
+
+container.register_stm("RedisSTM")
+# Load container configuration from YAML file
+container.from_config(CURRENT_PATH.joinpath('container.yaml'))
+
+
+
+# Initialize simple VQA workflow
+workflow = ConductorWorkflow(name='step1_simpleVQA')
+
+# Configure workflow tasks:
+# 1. Input interface for user interaction
+task1 = simple_task(task_def_name='InputInterface', task_reference_name='input_task')
+# 2. Simple VQA processing based on user input
+task2 = simple_task(task_def_name='SimpleVQA', task_reference_name='simple_vqa', inputs={'user_instruction': task1.output('user_instruction')})
+
+# Configure workflow execution flow: Input -> VQA
+workflow >> task1 >> task2
+
+# Register workflow
+workflow.register(True)
+
+# Initialize and start app client with workflow configuration
+config_path = CURRENT_PATH.joinpath('configs')
+agent_client = AppClient(interactor=workflow, config_path=config_path, workers=[InputInterface()])
+agent_client.start_interactor()
diff --git a/examples/car_assistant/run_cli.py b/examples/car_assistant/run_cli.py
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+++ b/examples/car_assistant/run_cli.py
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+# Import required modules and components
+from omagent_core.utils.container import container
+from omagent_core.engine.workflow.conductor_workflow import ConductorWorkflow
+from omagent_core.engine.workflow.task.simple_task import simple_task
+from pathlib import Path
+from omagent_core.utils.registry import registry
+from omagent_core.clients.devices.cli.client import DefaultClient
+from omagent_core.utils.logger import logging
+
+from agent.input_interface.input_interface import InputInterface
+
+# Initialize logging
+logging.init_logger("omagent", "omagent", level="INFO")
+
+# Set current working directory path
+CURRENT_PATH = Path(__file__).parents[0]
+
+# Import registered modules
+registry.import_module(project_path=CURRENT_PATH.joinpath('agent'))
+
+container.register_stm("RedisSTM")
+# Load container configuration from YAML file
+container.from_config(CURRENT_PATH.joinpath('container.yaml'))
+
+
+
+# Initialize simple VQA workflow
+workflow = ConductorWorkflow(name='car_assistant')
+
+# Configure workflow tasks:
+# 1. Input interface for user interaction
+task1 = simple_task(task_def_name='InputInterface', task_reference_name='input_task')
+# 2. Simple VQA processing based on user input
+task2 = simple_task(task_def_name='SimpleVQA', task_reference_name='simple_vqa', inputs={'user_instruction': task1.output('user_instruction')})
+
+# Configure workflow execution flow: Input -> VQA
+workflow >> task1 >> task2
+
+# Register workflow
+workflow.register(True)
+
+# Initialize and start CLI client with workflow configuration
+config_path = CURRENT_PATH.joinpath('configs')
+cli_client = DefaultClient(interactor=workflow, config_path=config_path, workers=[InputInterface()])
+cli_client.start_interactor()