Books

Metagraphs for Agentic AI: Beyond Triples, Beyond HypergraphsFrom Knowledge Graphs to Knowledge ArchitecturesThe triple is not enough.Every AI engineer building agent memory hits the same wall. You model a meeting as a knowledge graph triple — and immediately lose the fact that five people were in the room, a decision was made, and that decision caused three downstream actions. You reify. You flatten. You create workarounds. And your "knowledge graph" becomes a tangle of auxiliary nodes that machines can traverse but no human can reason about.This book shows you the way out. What You'll Learn Metagraphs are graph structures where edges connect sets of nodes to sets of nodes — and where edges themselves can be referenced as first-class nodes. They are the missing data structure for AI agents that need to remember, reason, and coordinate like humans do.This book takes you on a complete journey:Hypergraphs first. You'll learn what they are, why they matter, and where they break down. You'll implement them three ways — in SQL, in LadybugDB (Cypher), and in TypeDB — so you understand the tradeoffs viscerally, not just theoretically.Then metagraphs. You'll see how metagraphs solve the fundamental hypergraph problem (edges that can't be nodes), explore RDF named graphs as a lightweight metagraph, and implement full metagraphs in the same three database paradigms with production-ready, commented code.Then the big ideas. Semantic Spacetime. Holonic systems. Human cognitive architecture mapped to graph structures. Multi-agent coordination. Promise Theory for autonomous AI networks. This is where metagraphs stop being a data structure and become an architecture for intelligence. Who This Book Is For You're a software engineer, AI researcher, or knowledge graph practitioner who builds real systems. You've used Neo4j or RDF stores. You've built RAG pipelines. You've felt the limits. You want to know what comes next.No PhD required. Every concept comes with working code in SQL, Cypher, TypeQL, SPARQL, and Python. What Makes This Book Different This isn't a theoretical monograph. It's the distillation of two and a half years of research, 130+ published articles, and hands-on implementation at the intersection of knowledge graphs and agentic AI.Every chapter bridges theory and practice. You'll read about Basu and Blanning's formal metagraph definition — and then build the schema in PostgreSQL. You'll learn Mark Burgess's Promise Theory — and then model a multi-agent coordination protocol as a six-layer promise graph. You'll understand why labeled property graphs are secretly metagraphs — and what that means for your Neo4j deployment today. 18 Chapters. Three Parts. One Argument. Part I — The Hypergraph Foundation (7 chapters): From the knowledge representation crisis through hypergraph theory to three complete database implementations.Part II — The Metagraph Solution (5 chapters): Metagraphs as the answer, RDF named graphs as a bridge, and three full metagraph implementations with detailed commentary.Part III — Theory Meets Practice (6 chapters): Semantic Spacetime, labeled property graphs, AI memory and human cognition, holonic systems, agent-to-agent interaction, and Promise Graphs for network-of-networks coordination. The Core Thesis If you want AI agents that reason like humans, you need knowledge structures that capture how humans actually organize knowledge — not as flat collections of facts, but as nested, hierarchical, context-rich, temporally-aware structures where relationships themselves carry meaning and can be the subject of further reasoning.Metagraphs are that structure. This book shows you why, and how to build with them.

This series of test-driven small coding puzzles lets you code a database from scratch (no dependencies).We'll cover KV storage engines, LSM-Tree indexes, SQL, concurrent transactions, ACID, etc.

Learn databases from the bottom up by coding your own, in small steps, and with simple Go code (language agnostic).Atomicity & durability. A DB is more than files!Persist data with fsync.Crash recovery.KV store based on B-tree.Disk-based data structures.Space management with a free list.Relational DB on top of KV.Learn how tables and indexes are related to B-trees.SQL-like query language; parser & interpreter.Concurrent transactions with copy-on-write data structures.

What really happens when PostgreSQL executes your query? Follow a SQL statement through every stage of PostgreSQL's internal pipeline—from raw text to returned results—and gain the deep understanding that transforms how you write, tune, and debug database applications.

Explore the power of Rust with "Rust Projects: Write a Redis Clone". This hands-on guide takes you through building a Redis-inspired database from the ground up, introducing key programming concepts like TCP connections, the RESP protocol, and concurrency. Following the CodeCrafters challenge, this book gradually builds your skills, making complex topics accessible. Whether you're new to Rust or looking to deepen your understanding, this project-based journey offers practical, real-world insights into modern systems programming. The book contains 40% discount code for CodeCrafters.io!

A high-performance data access layer must resonate with the underlying database system. Knowing the inner workings of a relational database and the data access frameworks in use can make the difference between a high-performance enterprise application and one that barely crawls.

When your database outgrows simple optimizations, it's time to think bigger. Lift the Elephant goes beyond query tuning to reveal actionable strategies for scaling PostgreSQL, from partitioning to high-availability architectures. Built on lessons from scaling Hubstaff, this is your playbook for navigating the challenges of database growth.

Build real-world software by coding a Redis server from scratch.Network programming. The next level of programming is programming for multiple machines. Think HTTP servers, RPCs, databases, distributed systems.Data structures. Redis is the best example of applying data structures to real-world problems. Why stop at theoretical, textbook-level knowledge when you can learn from production software?Low-level C. C was, is, and will be widely used for systems programming and infrastructure software. It’s a gateway to many low-level projects.From scratch. A quote from Richard Feynman: “What I cannot create, I do not understand”. You should test your learning with real-world projects!

Desbloquea el poder de DuckDB para la analítica geoespacial moderna. Esta guía práctica ayuda a los profesionales de SIG a dominar la gestión eficiente de datos espaciales, transformando grandes conjuntos de datos del mundo real en conocimientos valiosos mediante SQL, Python y la extensión espacial de DuckDB. La edición impresa a todo color está disponible en Amazon.

You don't need a graph database. You need graph thinking inside DuckDB. GraphDuck takes you from SQL adjacency lists to metagraphs, hypergraphs, and hybrid Graph RAG pipelines — all inside DuckDB. Learn to model knowledge graphs, build AI agent memory systems, run graph algorithms, and combine vector search with graph traversal in a single embedded database. Every concept comes with runnable code. No infrastructure required.

Your AI agent is only as smart as what it remembers. Most developers treat knowledge representation as an afterthought -- a data structure problem, not an architecture problem. They flatten complexity into schemas that can't express relationships, deploy embeddings without grounding, and build agents that degrade under their own reasoning load. This book changes that. **TypeDB for Edge AI Agents** is a practitioner's guide to building knowledge systems that actually work at the scale and complexity your agents demand. TypeDB is built on type theory -- the same mathematics that powers formal verification and programming language safety. For agents, that means you can express constraints that prevent bugs in your knowledge layer, reason about what's possible and what's forbidden, and build memory systems that don't require constant hallucination detection. With the Rust rewrite in TypeDB 3.0, your agents can carry sophisticated knowledge graphs on-device -- no round-trip to a server, no latency, no external dependencies. This book covers everything from the PERA data model and OWL ontologies to promise graphs for multi-agent coordination, giving you the patterns to design knowledge systems that scale without becoming incoherent. Whether you're an ML engineer, AI architect, or backend developer building production agent systems, this book bridges the gap between type theory and working code. You'll learn to design knowledge graphs that don't decay, structure agent memory that doesn't degrade over time, and coordinate multi-agent systems that respect causality and distributed constraints. Stop building agents that confidently hallucinate -- start building agents that reason correctly, remember reliably, and coordinate with certainty.

This book provides an introduction to the high-level concepts behind query engines and walks through all aspects of building a fully working SQL query engine in Kotlin.

"Explore the synergy of AI swarm technology""Integrating Swarm Intelligence and Human Ingenuity: A Vision of Tomorrow's World" Delve into a world where AI enhances human capabilities, transforming industries and society. Uncover the secrets of this groundbreaking tech and its profound impact. A future where AI collaborates with us awaits.

Learn graph databases the hands-on way — no servers, no setup, no fluff.Hands-On LadybugDB Cypher takes you from your first MATCH to complex recursive queries, shortest-path algorithms, and real-world AI agent graphs — all running locally in under a minute. 27 chapters. One evolving project. Every Cypher concept you need. Start querying graphs today.

Learn how real databases work by following this practical guide to building your own in a programming language of your choice.Build a language from scratch- Create an interpreter that can parse, type check and run SQL queries. Start with running simple queries and build up to more complex ones.Learn SQL (the fun way) - Gain a deeper understanding of SQL. Cover the order of execution of queries, how SQL uses ternary logic and the techniques that databases use to join and group data efficiently. Compare your implementation with databases such as PostgreSQL, MySQL, SQLite and DuckDB.Improve as a programmer- The perfect project to practice how to structure and refactor code as you grow the feature set of your database server. The 200+ included test cases provide a safety net for refactoring your code, giving you freedom to experiment. Learn computer science topics including creating a recursive descent parser, building a type checker and implementing iterators.