A Spatial Memory Agent designed to act as an external hippocampus for your physical possessions. Built with Expo (React Native) and deployed on the web. Try it live at things.utkarshjoshi.com. Read the full story and technical breakdown in the blog post.
It started in the dead of winter. My mom could not find that heavy jacket she had packed away last year. We turned the house upside down, but it was nowhere to be found. I realized I had the same problem. I’m constantly looking for that specific square piece of wood, a box of 2.5-inch galvanized screws, or where I left my IRFZ44N MOSFET transistors for a circuit I was building. ...
A powerful Python framework for building and deploying AI agents that automate complex business processes. Combines the latest advances in AI with practical automation needs. Key Features Modular Agent Architecture: Build custom agents for specific tasks Natural Language Processing: Process and understand unstructured data Task Orchestration: Coordinate multiple agents for complex workflows Monitoring & Analytics: Track agent performance and efficiency Easy Integration: Simple API for integrating with existing systems Technical Stack Python 3.9+ Flask LangChain OpenAI GPT PostgreSQL Redis for caching Docker for containerization Impact 70% reduction in manual processing time 85% accuracy in automated decision making Significant cost savings in operational processes Code Example from ai_agent_framework import Agent, Workflow # Define a custom agent class DocumentProcessor(Agent): def __init__(self): super().__init__() self.model = self.load_model("gpt-4") async def process(self, document): # Extract key information extracted_data = await self.model.extract(document) # Validate and transform return self.validate_and_transform(extracted_data) # Create a workflow workflow = Workflow("Document Processing") workflow.add_agent(DocumentProcessor()) workflow.add_agent(DataValidator()) workflow.add_agent(ReportGenerator()) # Execute the workflow result = await workflow.execute(document_path) Project Structure ├── src/ │ ├── agents/ # Agent implementations │ ├── core/ # Core framework functionality │ ├── models/ # AI model integrations │ ├── workflows/ # Workflow definitions │ └── utils/ # Utility functions ├── tests/ # Test suite ├── examples/ # Example implementations └── docs/ # Documentation Getting Started Install the framework: pip install ai-agent-framework Create your first agent: from ai_agent_framework import Agent class MyAgent(Agent): async def process(self, input_data): # Your agent logic here return processed_data Start building your automation workflows! Documentation For detailed documentation, visit our GitHub repository. ...
A powerful Python framework for building and deploying AI agents that automate complex business processes. Combines the latest advances in AI with practical automation needs. Key Features Modular Agent Architecture: Build custom agents for specific tasks Natural Language Processing: Process and understand unstructured data Task Orchestration: Coordinate multiple agents for complex workflows Monitoring & Analytics: Track agent performance and efficiency Easy Integration: Simple API for integrating with existing systems Technical Stack Python 3.9+ Flask LangChain OpenAI GPT PostgreSQL Redis for caching Docker for containerization Impact 70% reduction in manual processing time 85% accuracy in automated decision making Significant cost savings in operational processes Code Example from ai_agent_framework import Agent, Workflow # Define a custom agent class DocumentProcessor(Agent): def __init__(self): super().__init__() self.model = self.load_model("gpt-4") async def process(self, document): # Extract key information extracted_data = await self.model.extract(document) # Validate and transform return self.validate_and_transform(extracted_data) # Create a workflow workflow = Workflow("Document Processing") workflow.add_agent(DocumentProcessor()) workflow.add_agent(DataValidator()) workflow.add_agent(ReportGenerator()) # Execute the workflow result = await workflow.execute(document_path) Project Structure ├── src/ │ ├── agents/ # Agent implementations │ ├── core/ # Core framework functionality │ ├── models/ # AI model integrations │ ├── workflows/ # Workflow definitions │ └── utils/ # Utility functions ├── tests/ # Test suite ├── examples/ # Example implementations └── docs/ # Documentation Getting Started Install the framework: pip install ai-agent-framework Create your first agent: from ai_agent_framework import Agent class MyAgent(Agent): async def process(self, input_data): # Your agent logic here return processed_data Start building your automation workflows! Documentation For detailed documentation, visit our GitHub repository. ...