Building ResearcherAI: A Journey from Idea to Production
Welcome! I'm Adrian, and this is the story of how I built ResearcherAI - a production-ready multi-agent system that helps researchers discover, analyze, and synthesize academic papers using AI.
What You'll Learn
This isn't just another tutorial. It's a real journey through building a complex system from scratch, complete with the challenges I faced, the decisions I made, and the lessons I learned along the way.
📋 Table of Contents
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- How I came up with the idea
- Defining the problem and goals
- Choosing the tech stack
- Planning the architecture
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- Multi-agent system patterns
- Dual-backend strategy (dev vs production)
- Knowledge graphs + vector search
- Event-driven architecture with Kafka
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- Building 6 specialized agents
- Integrating 7 data sources
- LangGraph + LlamaIndex orchestration
- Conversation memory and sessions
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- React + TypeScript setup
- Glassmorphism design system
- Framer Motion animations
- API integration layer
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- Achieving 96.60% test coverage
- Unit, integration, and E2E tests
- Testing dual backends
- CI/CD with GitHub Actions
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- Docker containerization
- Multi-service orchestration
- GitHub Actions workflows
- Production considerations
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- Apache Airflow for ETL
- Kafka UI for event monitoring
- Neo4j and Qdrant dashboards
- Performance optimization
The Journey at a Glance
🎯 What I Built
A sophisticated research assistant that:
- Collects papers from 7 sources (arXiv, PubMed, Semantic Scholar, etc.)
- Analyzes them using knowledge graphs and vector embeddings
- Answers questions with context-aware AI reasoning
- Scales from laptop development to production deployment
📊 By the Numbers
96.60% Test Coverage 291 Tests Passing 6 Specialized Agents 7 Data Sources 2 Deployment Modes⚡ Key Features
- Dual-Backend Architecture: Switch between dev (NetworkX+FAISS) and prod (Neo4j+Qdrant) with zero code changes
- Event-Driven: Kafka-based async communication between agents
- Production-Grade: 40-70% cost reduction with intelligent caching and model selection
- Modern UI: React frontend with glassmorphism design
- Fully Tested: Comprehensive test suite with 96.60% coverage
Why I Built This
I was frustrated with how hard it was to keep up with research papers in AI/ML. There are thousands published every month, and it's impossible to read them all. I wanted a tool that could:
- Find relevant papers across multiple sources automatically
- Understand the relationships between papers, authors, and concepts
- Answer my questions by synthesizing information from multiple sources
- Remember context from our previous conversations
But I also wanted to learn best practices for building production systems with:
- Multi-agent architectures
- Knowledge graphs and vector databases
- Event-driven patterns
- Comprehensive testing
- Modern frontend development
So I built ResearcherAI.
The Tech Stack
Here's what I used and why:
| Component | Technology | Why I Chose It |
|---|---|---|
| LLM | Gemini 2.0 Flash | Fast, accurate, and cost-effective |
| Orchestration | LangGraph | Perfect for multi-agent workflows |
| RAG | LlamaIndex | Best-in-class for retrieval augmentation |
| Graph DB | Neo4j / NetworkX | Dual backend for flexibility |
| Vector DB | Qdrant / FAISS | Fast semantic search |
| Events | Kafka | Async, scalable communication |
| ETL | Apache Airflow | Production-grade workflow automation |
| Frontend | React + TypeScript | Type-safe, modern UI |
| Styling | Tailwind CSS | Rapid development with utility classes |
| Animations | Framer Motion | Smooth, professional animations |
| Containers | Docker | Consistent deployment |
| CI/CD | GitHub Actions | Automated testing and deployment |
The Development Process
Building ResearcherAI took about 3 weeks of focused work. Here's how I approached it:
Week 1: Foundation
- Researched multi-agent patterns and RAG architectures
- Set up basic agent structure with LangGraph
- Integrated first 3 data sources (arXiv, Semantic Scholar, PubMed)
- Got basic knowledge graph working with NetworkX
Week 2: Production Features
- Added Neo4j and Qdrant for production deployment
- Implemented Kafka event system
- Built conversation memory and session management
- Added comprehensive error handling and retry logic
- Integrated Apache Airflow for automated collection
Week 3: Frontend & Testing
- Built React frontend with glassmorphism design
- Wrote comprehensive test suite (291 tests, 96.60% coverage)
- Set up CI/CD with GitHub Actions
- Docker containerization and orchestration
- Documentation and polish
What Makes This Different
Most tutorials show you the happy path. I'm going to show you:
- ✅ Real Challenges: The bugs I hit and how I fixed them
- ✅ Design Decisions: Why I chose certain patterns over others
- ✅ Trade-offs: What I compromised on and why
- ✅ Production Patterns: Circuit breakers, token budgets, caching
- ✅ Testing Strategy: How I achieved 96.60% coverage
- ✅ Cost Optimization: Techniques that reduced costs by 40-70%
Who This Is For
This tutorial is for you if:
- You're building AI/ML applications and want to go beyond prototypes
- You're interested in multi-agent systems and RAG architectures
- You want to learn production-grade patterns for LLM applications
- You're building research tools or knowledge management systems
- You love building things and learning by doing
Prerequisites:
- Python experience (intermediate level)
- Basic understanding of APIs and databases
- Familiarity with React is helpful but not required
- Docker knowledge is helpful for deployment
Ready to Start?
Let's dive in! Click below to start with the planning phase, where I'll walk you through how I went from idea to requirements.
Next: Planning & Requirements →Built with ❤️ by Adrian Sebuliba