Overview
ResearchMate is an AI-based multi-agent research and report generation system that automates the research-to-report workflow. It gathers information, verifies credibility, synthesizes findings, and generates a structured, cited report. The system uses specialized, collaborating agents coordinated through LangGraph, including an independent Fact Verification Agent that can send work back for re-research when it fails quality checks. The re-search loop (Verify → Increment Retry → Research) is the project's core technical differentiator, proving the system is genuinely agentic rather than a fixed sequential chain of LLM calls. The current build has completed Phase 2 of 6, with the Research Agent, Fact Verification Agent, Summarization Agent, and Report Generation Agent all done. The planned full architecture includes a RAG Agent for PDF processing, a FastAPI + MySQL backend, a React frontend with Google Auth, and deployment via Docker on AWS EC2 with cPanel-hosted MySQL.
Pricing
Free
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Use Cases
- Researchers and students use ResearchMate to automate the process of searching multiple sources, verifying credibility, and compiling findings into structured reports.
- Teams use ResearchMate to generate structured Markdown reports with sections like Introduction, Key Findings, Research Gaps & Open Questions, Sources, and Conclusion.
- Users can rely on ResearchMate to automatically re-search when the Fact Verification Agent fails quality checks, refining the query based on the failure reason.
- In Phase 3, users will be able to upload PDFs and use the RAG Agent to answer questions via FAISS, with a literature-review mode that extracts methodology, datasets, and results into comparison tables.
- The RAG Agent will cross-check document claims against live web findings to flag contradictions.
Features
AI assistant
Natural language interface
Workflow automation
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