Project
LLM Agent with Tool Use, Memory, and Retrieval
Tools & technologies
An agent that remembers, reasons, and reaches for the right tool.
A ReAct-based agent built on DSPy that pairs persistent vector memory with retrieval-augmented generation, enabling grounded, multi-step reasoning across conversations.
Developed an intelligent LLM-powered agent capable of autonomous reasoning, tool invocation, persistent memory, and real-time information retrieval. Implemented a ReAct-based architecture using DSPy to dynamically orchestrate function calls, integrated vector-based memory for personalized multi-turn interactions, and engineered retrieval-augmented generation through web search and document parsing to improve factual accuracy on current information. Designed scalable backend workflows, recommendation algorithms, and state management systems to support reliable multi-step task execution while leveraging modern large language model frameworks for contextual decision making.
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