LangGraph-Orchestrated LLM Agents for Scalable Movie Knowledge Graphs and Question Answering

Abstract

Recent advances in large language models (LLMs) and agent-based orchestration are transforming automated knowledge graph (KG) creation as well as robust question answering in complex domains. We present a modular, multi-agent system that extracts, integrates, and reasons over diverse NoSQL movie data, powered by state-of-the-art LLMs such as GPT-4.1. Our architecture converts unstructured plots, cast/crew metadata, and numeric attributes into high-fidelity KGs - enabling both natural language and programmatic queries. To maximize reliability and flexibility, the system unifies multiple retrieval strategies - keyword search, vector similarity, knowledge graph querying, and summarization - each deployed as an autonomous pipeline. Parallel orchestration via LangGraph supports adaptive engine selection, concurrent execution, and robust answer verification with LLM ensemble “jury” scoring. Critically, the framework features comprehensive observability, allowing detailed monitoring and analysis of agent decisions, pipeline performance, and query outcomes. By treating each retrieval method and LLM as a specialized agent, our approach delivers scalable, explainable, and highly accurate results (up to 97%), significantly surpassing monolithic solutions. This agentic, observable architecture paves the way for next-generation autonomous analytics, integration, and decision support across data-rich domains.