CodeZero: Building an AI Agent That Learns Using Hindsight
AI assistants are becoming increasingly capable, but one major limitation remains: they often don't remember what happened before. A conversation can contain important business information, decisions, preferences, and context. Without persistent memory, that information can easily be lost. For HackwithHyderabad 3.0, we built CodeZero, an AI assistant designed to explore how persistent memory can…
CodeZero is an AI assistant developed for the HackwithHyderabad 3.0 challenge that explores how persistent memory can enhance AI agents' usefulness over time. The core of the project, Hindsight by Vectorize, provides a memory layer that allows CodeZero to store and retrieve relevant information from previous interactions, enabling more contextual responses.
Traditional chatbots only have access to the current conversation, missing out on important business information and context. CodeZero's architecture consists of four main parts: a Flutter frontend for user authentication and chat interface, a FastAPI backend that handles chat requests, memory creation, retrieval, and LLM requests, Hindsight for memory retrieval, and Ollama with Qwen as the LLM for response generation.
To demonstrate the difference between a traditional chatbot and an AI agent with persistent memory, CodeZero uses a fictional business scenario. It first stores information about the business, customers, marketing activities, and previous decisions. When asked about the next campaign focus, CodeZero retrieves relevant memories using Hindsight and combines them with the current question to generate a contextual response.
This approach shows that AI agents can remember and learn from experience, making them more useful over time.
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