Multi Modal Researcher
A Multi Modal Researcher is an AI agent built using Google’s Gemini 2.5 models and orchestrated through the LangGraph framework. It is designed to conduct comprehensive investigations by processing and analyzing multiple data types simultaneously, including text, images, and other modalities. This architecture allows the agent to extract insights from complex information sources that combine different formats in a single analysis task.
Capabilities and Design
The agent leverages Gemini 2.5’s multimodal processing capabilities to handle investigations that would require human researchers to switch between different tools or analysis methods. By integrating multiple data types within a single reasoning loop, the Multi Modal Researcher can identify patterns and connections across different information sources more efficiently. The LangGraph orchestration layer manages the agent’s workflow, enabling structured reasoning and state management across investigation steps.
Applications
Multi Modal Researchers are useful for tasks requiring synthesis of heterogeneous information—such as analyzing documents alongside supporting imagery, processing research datasets with visual components, or investigating scenarios where context spans text and visual evidence. The multimodal approach reduces the friction of converting between different input formats and allows for more holistic analysis of complex subjects.