An extensible multi-agent Research Assistant that uses Gemini-powered subagents (Researcher, Analyst, Formatter) with ArXiv and custom tools to search, analyze, format, and persist academic findings, and can run locally (Flask) or be deployed to Vertex AI.
An advanced AI agent system designed to automate the academic research process. Powered by Google Gemini 2.5, this project uses a multi-agent architecture to search for papers, analyze publication trends, format citations, and persist findings.
Multi-Agent Architecture: Specialized agents working in concert: 🕵️ Researcher: Searches ArXiv for relevant papers (supports parallel queries). 📊 Analyst: Analyzes data (e.g., publication year distribution) and generates ASCII charts. 📝 Formatter: Formats raw paper data into proper APA citations. 🧠 Root Agent: Orchestrates the workflow and manages memory.
Tools & Integrations: ArXiv API: Real-time access to millions of academic papers. Long-Term Memory: Persists findings to a JSON knowledge base. Parallel Execution: Simulates concurrent research tasks for efficiency.
Deployment Ready: Run locally via Flask API. Deploy to production on Google Cloud Vertex AI Agent Engine. Interactive testing via ADK Web UI.
The system follows a hierarchical multi-agent pattern:
graph TD
User[User Query] --> Root["Root Agent (Orchestrator)"]
Root -->|Delegates Search| Researcher[Researcher Agent]
Researcher -->|Calls| ArXiv[ArXiv API]
Root -->|Delegates Analysis| Analyst[Analyst Agent]
Root -->|Delegates Formatting| Formatter[Formatter Agent]
Root -->|Saves Data| Memory[(Long-Term Memory)]
Root -->|Final Report| User
Clone the repository:
git clone https://github.com/emansarahafi/research-assistant-agent.git
cd research-assistant-agent
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