With the rapid growth in the volume of open-source data and the increasing diversity and breadth of information sources, OSINT (Open Source Intelligence) processes are facing numerous challenges. Among these challenges are the extremely high volume of incoming data, the need for fast and efficient data processing, as well as the heterogeneity and structural diversity of the data. This massive amount of information presents analysts with difficulties such as identifying relevant data, extracting valuable insights, and managing unstructured data. In such circumstances, the utilization of advanced artificial intelligence technologies-particularly large language models-can play a significant role in enhancing the efficiency and effectiveness of OSINT processes.This article introduces an innovative, agent-based approach leveraging large language models to enable intelligent management of open-source data. The conceptual framework proposed herein integrates the advanced capabilities of LLMs with the specific requirements of OSINT, providing a platform for intelligent data collection, analysis, and decision-making. Within this framework, tasks are intelligently distributed, and each stage of the process is assigned to specialized, intelligent agents, each responsible for processing and analyzing a portion of the data according to their respective capabilities.