Multi Modal Research
Multi Modal Research is an AI-powered research tool designed to process and analyze information across multiple data modalities simultaneously. It accepts text, images, audio, and video as input, allowing researchers to extract insights from diverse content types within a unified analytical framework. This multimodal capability enables more comprehensive analysis than single-modality tools, as patterns and information relevant to a research question may appear in different formats.
Technical Architecture
The tool is built on LangGraph, a framework for orchestrating multi-agent workflows. This foundation allows Multi Modal Research to coordinate complex analytical processes across its various components. The underlying processing is powered by Google’s Gemini 2.5 models, which provide the multimodal understanding capabilities necessary to interpret content across different input types. This combination of LangGraph’s workflow coordination and Gemini’s processing power creates a structured system for handling diverse data simultaneously.
Use Cases
Multi Modal Research is applicable to research scenarios where information exists in mixed formats—such as analyzing documents containing both text and embedded images, reviewing video content with audio tracks, or synthesizing insights from collections of different media types. By processing multiple modalities together rather than separately, the tool can identify cross-modal relationships and provide more holistic analytical results.
Source Notes
- 2026-04-14: “But OpenClaw is expensive…”