Multi Modal Research
Multi Modal Research is an AI-powered research tool that leverages Google’s Gemini 2.5 models for analyzing diverse data types within a single analytical framework. The system is orchestrated through LangGraph, a framework designed for managing complex, multi-step agent workflows. By processing text, images, audio, and video simultaneously, the tool can identify patterns and connections that emerge across different content modalities.
Architecture and Capabilities
The system’s multimodal approach enables it to perform comparative analysis across different data formats without requiring separate specialized tools for each content type. This unified processing allows researchers to examine relationships between textual information, visual data, and temporal content (audio and video) as interconnected components of a broader analytical task.
Applications
Multi Modal Research is suited for research domains where understanding emerges from synthesizing information across multiple formats—such as document analysis supplemented by images or diagrams, qualitative research involving recorded interviews alongside transcripts, or competitive intelligence gathering that spans web content, media coverage, and visual assets. The tool’s ability to maintain context across modalities supports more nuanced analysis than single-format approaches typically provide.
Source Notes
- 2026-04-14: “But OpenClaw is expensive…”