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Building Production-Grade Multi-Agent RAG Systems

A Comprehensive Guide to ResearcherAI​

From Concept to Deployment​

Book Cover

Author: Adrian Sebuliba

Version: 2.0

Publication Date: November 2025

About This Book​

This book chronicles the complete journey of building ResearcherAI, a production-ready multi-agent system for research paper analysis. Unlike typical tutorials that show only the happy path, this book shares the real challenges, design decisions, trade-offs, and lessons learned from building a system that achieves:

  • 96.60% test coverage
  • Dual-backend architecture (development & production)
  • Event-driven design with Apache Kafka
  • 7 data sources integrated
  • Modern React frontend with glassmorphism design
  • Complete CI/CD pipeline
  • Production-grade patterns (circuit breakers, token budgets, intelligent caching)

Whether you're building AI applications, multi-agent systems, or simply want to learn production engineering practices, this book provides practical, battle-tested insights you can apply immediately.

Who This Book Is For​

Primary Audience:

  • Software engineers building LLM-powered applications
  • ML engineers implementing RAG systems
  • System architects designing multi-agent platforms
  • DevOps engineers deploying AI services

Prerequisites:

  • Intermediate Python programming
  • Basic understanding of APIs and databases
  • Familiarity with Docker (helpful but not required)
  • General software engineering principles

What You'll Learn:

  • Multi-agent architecture patterns
  • RAG (Retrieval-Augmented Generation) implementation
  • Knowledge graphs with Neo4j and NetworkX
  • Vector databases with Qdrant and FAISS
  • Event-driven architecture with Kafka
  • Apache Airflow for ETL orchestration
  • React frontend development
  • Testing strategies for AI systems
  • Production deployment with Docker
  • Cost optimization techniques (40-70% reduction)
  • Monitoring and observability

How to Use This Book​

Linear Reading: The chapters build on each other and are best read in sequence, especially if you're new to multi-agent systems.

Reference Guide: Each chapter is self-contained enough to serve as a reference for specific topics (testing, deployment, monitoring).

Hands-On Learning: All code is available on GitHub. Follow along by building the system yourself.

Academic Study: Extensive references are provided for deeper exploration of concepts.

Code Repository​

All source code, configuration files, and documentation are available at:

GitHub: https://github.com/Sebuliba-Adrian/ResearcherAI

License: MIT License (see repository for details)

Conventions Used in This Book​

Code Blocks:

# Python code examples look like this
def example_function():
    pass

Terminal Commands:

$ command to execute
output from command

Important Notes:

Important concepts and warnings are highlighted in blockquotes.

File Paths:

  • relative/path/to/file.py
  • /absolute/path/to/file.py

References: Academic papers and technical resources are cited inline with footnotes and compiled in the bibliography at the end of each chapter.

Acknowledgments​

This project stands on the shoulders of giants. Special thanks to:

  • The LangChain and LangGraph teams for pioneering multi-agent frameworks
  • LlamaIndex contributors for excellent RAG tools
  • Google for the Gemini API
  • Neo4j, Qdrant, Apache Kafka, and Apache Airflow communities
  • The open-source community for countless libraries and tools

Technical reviewers and beta readers provided invaluable feedback that shaped this book.

About the Author​

Adrian Sebuliba is a software engineer specializing in AI/ML systems and production infrastructure. With experience building scalable systems, he focuses on practical, production-grade patterns that balance innovation with reliability.

Connect:

Feedback and Corrections​

Despite careful review, errors may remain. If you find technical inaccuracies, typos, or have suggestions for improvement:

Your input helps improve this resource for the community.

Copyright © 2025 Adrian Sebuliba

The content of this book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

The accompanying source code is licensed under the MIT License.

You are free to:

  • Share — copy and redistribute the material
  • Adapt — remix, transform, and build upon the material

Under the following terms:

  • Attribution — You must give appropriate credit
  • NonCommercial — Not for commercial use without permission
  • ShareAlike — Distribute derivatives under the same license

For commercial licensing inquiries, contact the author.

Table of Contents​

Front Matter

  • About This Book
  • Who This Book Is For
  • How to Use This Book
  • Acknowledgments

Introduction: The Problem and the Vision

Chapter 1: Planning & Requirements

  • 1.1 The Problem
  • 1.2 The Vision
  • 1.3 Core Requirements
  • 1.4 Technology Selection
  • 1.5 Architecture Philosophy
  • 1.6 The Development Plan
  • 1.7 Lessons from Planning
  • References

Chapter 2: Architecture Design

  • 2.1 System Overview
  • 2.2 Multi-Agent Architecture
  • 2.3 Dual-Backend Strategy
  • 2.4 Event-Driven Architecture
  • 2.5 RAG Pipeline Design
  • 2.6 Session Management
  • 2.7 Production-Grade Patterns
  • 2.8 Airflow Integration
  • 2.9 Architecture Evolution
  • References

Chapter 3: Backend Development

  • 3.1 Project Structure
  • 3.2 Data Collector Agent
  • 3.3 Knowledge Graph Agent
  • 3.4 Vector Agent
  • 3.5 Reasoning Agent
  • 3.6 Orchestration
  • 3.7 Lessons Learned
  • References

Chapter 4: Frontend Development

  • 4.1 Design Vision
  • 4.2 Technology Stack
  • 4.3 Glassmorphism Design System
  • 4.4 Page Components
  • 4.5 API Integration
  • 4.6 TypeScript Types
  • 4.7 Lessons Learned
  • References

Chapter 5: Testing Strategy

  • 5.1 Testing Philosophy
  • 5.2 Test Structure
  • 5.3 Unit Testing
  • 5.4 Integration Testing
  • 5.5 Coverage Analysis
  • 5.6 CI/CD Integration
  • 5.7 Performance Testing
  • 5.8 Lessons Learned
  • References

Chapter 6: Deployment & CI/CD

  • 6.1 Deployment Options
  • 6.2 Docker Containerization
  • 6.3 GitHub Actions Pipelines
  • 6.4 Configuration Management
  • 6.5 Health Checks
  • 6.6 Scaling Considerations
  • 6.7 Lessons Learned
  • References

Chapter 7: Monitoring & Operations

  • 7.1 Observability Pillars
  • 7.2 Apache Airflow Monitoring
  • 7.3 Kafka Event Monitoring
  • 7.4 Database Dashboards
  • 7.5 Application Metrics
  • 7.6 Alerting
  • 7.7 Cost Monitoring
  • 7.8 Backup and Recovery
  • 7.9 Lessons Learned
  • References

Chapter 8: Conclusion & Future Work

  • 8.1 What We Built
  • 8.2 Key Takeaways
  • 8.3 Future Enhancements
  • 8.4 Final Thoughts

Bibliography

Ready to begin? Turn to the Introduction