AI Multi-Agent Research Workspace

• 

  Start a research task by entering a question and selecting the desired research workflow.

• 

  Visual knowledge graph showing how research topics and related information are connected.

• 

  AI-generated research report synthesized from multiple sources and agent outputs.

• 

  Research intelligence dashboard showing insights generated by specialized AI agents.

• 

  Detailed research findings with supporting information and source references.

Inspiration

Researching a topic with AI often means switching between different tools, managing multiple prompts, verifying information, and manually combining the results. I wanted to build a workspace where this process could happen in one place through multiple specialized AI agents working together.

What it does

AI Multi-Agent Research Workspace allows users to work with multiple AI agents for research and analysis. Different agents can focus on different parts of a task, and their outputs can be combined into a more useful final result.

The goal is to make complex research workflows more organized, collaborative, and efficient instead of relying on a single AI response.

How we built it

We built the project as a full-stack web application with a React-based frontend and a Node.js/Express backend, with MongoDB used for persistent data storage.

The application is structured around separate agents and research workflows. The frontend provides the workspace experience, while the backend manages requests, agent execution, data persistence, and communication with AI services.

Challenges we faced

One of the biggest challenges was coordinating multiple agents while maintaining useful context between them. We also had to think about handling unreliable AI outputs, keeping the workflow efficient, and managing API usage and response times.

Building the project helped us understand that multi-agent systems are not only about calling multiple AI models, but also about designing the right workflow, responsibilities, and communication between agents.

What we learned

We learned a lot about designing AI agent workflows, full-stack integration, API handling, asynchronous operations, and building a practical AI product around real user workflows.

The project also gave us a better understanding of the challenges involved in making AI systems reliable, scalable, and useful beyond simple chatbot interactions.

Built With

• express.js
• javascript
• mongodb
• neo4j
• node.js
• pineconedb
• rag
• react