GraphRAG is an agentic architecture designed to navigate complex domains like legal contracts by leveraging large language models (LLMs) and structured tools. By structuring legal information as a knowledge graph, users can increase answer accuracy using a LangGraph agent. The system involves constructing a knowledge graph in Neo4j, building a LangGraph agent that allows users to ask specific questions about the contracts, and implementing a contract retrieval tool with various attributes for filtering and aggregation. The LLM acts as the decision-maker, dynamically selecting which tools to invoke and executing multiple tools in sequence to fulfill complex requests. The system has been benchmarked using a dataset of 22 questions, showing promising results, but there is room for growth, including expanding clause coverage and refining tool design.