StudySphere

StudySphere is a decentralized AI learning and research platform built for students, independent learners, and researchers. It combines structured study tools with AI assistance, so users can move from reading and note-taking into guided learning and deeper analysis in one workspace.

For students, StudySphere provides a curriculum-based study environment where users can explore subjects, generate topic lessons, chat with an AI assistant, create study libraries, and use a Study Coach agent that helps turn a learning goal into a study plan. The agent can recommend next steps, generate lessons and quizzes, grade responses, and track progress over time.

For researchers, StudySphere supports custom workspaces where users can upload documents to 0G Storage, organize materials, add notes, and use a Research Workflow Agent to generate literature maps, synthesis notes, and reading plans based on their research goal and uploaded corpus.

Under the hood, the project uses 0G Compute for AI generation and reasoning, and 0G Storage for decentralized file and artifact persistence. The goal of StudySphere is to make learning and research more structured, interactive, and accessible while taking advantage of decentralized infrastructure.

StudySphere was built as a full-stack Next.js application using the App Router, React, and TypeScript. The frontend handles the learning workspace, research environment, dashboard flows, and agent interfaces, while the backend logic is handled through Next.js API routes. Styling and UI composition were done with Tailwind CSS and reusable React components, which made it easier to build a consistent experience across the study and research flows.

The AI layer is powered by 0G Compute. We use it both for standard chat-style interactions and for more structured agent workflows. For example, the Study Coach agent uses 0G Compute to turn a user’s learning goal into a plan, recommend the next best action, generate lessons and quizzes, and grade quiz answers. The Research Workflow Agent uses the same compute layer to generate literature maps, synthesis notes, and reading plans from uploaded research documents and researcher notes. One of the important implementation details was forcing the model to return structured JSON for planning and decision steps, then letting the backend validate that output before executing the next action. That helped us move beyond simple chat into more reliable agent-style behavior.

For decentralized storage, we integrated 0G Storage. Uploaded research files are pushed to 0G Storage, and generated artifacts such as study plans, lessons, quizzes, literature maps, and synthesis outputs are also persisted there. This gave us a decentralized persistence layer for both source materials and AI-generated outputs.

We also integrated Web3Auth and wallet support to support decentralized identity and wallet-based interactions, and Interswitch for payments. A notable engineering decision was keeping orchestration on the backend and state persistence lightweight on the frontend for this version, which let us move quickly while still proving the core agent workflows on top of 0G Compute and 0G Storage.