AI Agents Memory empowered by Knowledge Graphs
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AI Agents Memory empowered by Knowledge Graphs
connecting the dots for better conversational agents
About the Book
I built a few conversational agents that are privacy-focused, and we realized that we need a memory.
It is a compilation of my Medium articles on graph and memory topics. We need a memory to make the agent smart, and this book is not a solution, nor does it guide you on how to build a memory. Still, it explains how knowledge graphs and metagraphs are essential for memory and how to build a better memory for AI agents based on the graph approach. So it's a graph book, actually, but with the artificial intelligence flavour.
We will touch:
- Why is memory not RAG
- how to model AI memory
- graphs
- graph evolution for a memory
- meta and hypergraphs
- temporal aspect of memory
- event graphs
- casual graph
- semantic space time
License
About the Author
Table of Contents
- About Author and Book
- Why Build Our Memory System for AI Agents
- Why Are We Building Our Memory System?
- Why It Will Be Better Than Anything Else Out There
- Why We Do It This Way
- AI Memory is Not RAG, RAG is Not Enough for AI Agents
- The Current State of RAG
- Why RAG is Not True Memory
- 1. Lack of Episodic Context
- 2. Limited Association Building
- 3. Retrieval Without Understanding
- 4. No Forgetting Mechanism
- Why RAG is Not Enough for Advanced AI Systems
- 1. Context Window Constraints
- 2. Retrieval Quality Bottlenecks
- 3. Static Knowledge Representation
- 4. Limited Self-Reflection
- What Makes Memory Truly Memory?
- 1. Multi-modal Memory Structures
- 2. Active Reconstruction
- 3. Associative Memory Networks
- 4. Adaptive Forgetting
- 5. Hierarchical Organization
- Promising Directions for AI Memory Systems
- Conclusion
- Forgetting in AI Agent Memory Systems
- Beyond RAG Systems: The Need for True Memory
- Human Forgetting vs. Machine Forgetting
- Temporal Awareness in Memory Systems
- The Problem with Perfect Memory
- The Challenge of Importance and Relevance
- Current Approaches and Limitations
- Designing Effective Forgetting Mechanisms
- Ethical Considerations in AI Forgetting
- Conclusion
- Unlearning in AI Agents: A Philosophical Deep Dive
- The Agent’s Imperative: Beyond Hyped Learning
- Memory Pruning Is Not Unlearning
- Memory, Forgetting, and Attention as Part of Unlearning
- Unlearning Is Not Learning; It Is More Complex
- Unlearning as Part of the Reflection Process
- The Path Forward: Building Adaptive Agents
- Advanced Memory Systems for AIAgents: Beyond RAG to Metagraphs
- The Data Challenge in Modern AI
- The Case for Sovereign Data
- Embracing Small Data
- Why RAG Is Not Enough
- The Need for True Memory Systems
- Why Build a Custom Memory System?
- Graphs as the Foundation of Memory
- Beyond Simple Directed Graphs: Why We Need to Evolve Our Thinking
- The Evolution to Hypergraphs and Metagraphs
- Practical Solutions: Layered Graphs and Concept Graphs
- The Future of AI Memory
- Evolution of Knowledge Graphs and AI Agents
- Static Graphs
- Dynamic Graphs
- Temporal Graphs
- Event Graphs
- LLMs in Graph Construction
- Dynamic Graphs and Entity/Relation Extraction with LLMs
- Temporal Graphs and Temporal Context Extraction
- Event Graphs and Event Context Extraction
- Event Graphs and Episodic Memory for AI Agents
- Conclusion
- Hypergraph & Metagraph for AI Agent Memory — Practical Approach
- Why Simple Graphs Are Not Enough
- What Is a Hypergraph
- Limitations of Hypergraphs
- Metagraph
- Metagraph as Fractal Structure
- Metagraph as Homomorphic Structure (Data, Ontology, and Metadata)
- Metagraph as Hierarchical
- Modern Tech Limitations and Metagraphs
- Meta Nodes as a Compromise for Hyper Edges
- Relations as Nodes
- Subgraphs as Nodes
- Metagraphs and Hypergraphs for complex AI agent memory and RAG
- Knowledge graphs and not just graphs
- Strings not things
- Knowledge Graphs are not enough, and Tripples is not enough.
- Temporal aware Semantic and Episodic memory for AI agents
- Hypergraphs as rescue
- Named Graphs and Graph of Graphs for Multi-model and multilingual data
- Humman-like memories for AI Agent with Metagraph
- Concept Graphs on Event “Steroids” for AI Memory
- Concept Graphs as Bipartite Graphs
- Vocabulary and Ontology of Concept Graphs
- Relations as Nodes
- Generic and Type Entities
- Event Graphs
- Events as Relations
- Events as Nodes and Subgraphs
- Events that Model Episodic Memory of AI Agents
- Conclusion
- Temporal Semantics for AI Memory Agents
- Declarative Memory of the Human Brain
- Temporal Semantics
- Time in a Semantic context is not a timestamp.
- Blockchain of connected events and facts
- Time anchors
- Time in AI Agent Memory: Beyond Simple Timestamps
- Why Memory Matters and Why It Is Not Just RAG
- Why Time Matters in Memory
- Evolution of Knowledge Graphs
- Static Graphs
- Dynamic Graphs
- Temporal Graphs
- Event Graphs
- ZEP and Validity Pairs: Why They’re Not Enough
- Multiple Timelines and Lifespans
- DAG of Timelines
- Temporal Entities
- Points
- Time Intervals
- Abstract Time
- Abstract Time: “Soon” and “Future”
- Temporal Chains and Anchors
- Conclusion
- Event Graphs as Self-Referencing DAGs
- Beyond Traditional DAGs
- Natural Representation of Causality
- Efficient Change Propagation
- Temporal Reasoning with Flexibility
- Simplified Debugging and Auditing
- Practical Implementation Patterns
- Vector Clocks Enhanced
- Incremental Graph Construction
- Specialized Storage Engines
- Real-World Applications
- Distributed Tracing Systems
- Supply Chain Management
- Financial Transaction Processing
- Challenges and Future Directions
- Scalability Concerns
- Query Complexity
- Standardization
- Conclusion
- Time-Aware Knowledge Graphs for Episodic Memory
- Modeling Time in Knowledge Graphs
- Multiple Timestamps and the Graphiti Project
- Why Simple Interval Timestamps Fall Short
- Time as a Structure in Knowledge Graphs: Time Trees
- Entity State Relation Model: A Time Machine in Graphs
- Multilayered Graphs: Modeling Entity States Over Time
- Skip Lists for Layer Connection
- Human Usability of Complex Graphs
- Time and Episodic Memory: The Road Ahead
- Conclusion and Future Directions
- Time Traveling for Knowledge Graphs and AI Agents
- Science Fiction, Time Travel, and Modern Data Systems
- Time Travel in Business Domains
- Moving Time from Data to a Data Engine
- Temporal Tables in MS SQL Server
- Datomic and Time Travel
- Digital Twin Databases and Time Travel
- Event-Based vs. State-Based Time Tracking in Knowledge Graphs
- The Role of Logs and Append-Only Storage
- Structured Sharing and Functional Persistent Structures for Time Travel
- Time Travel for Property Graphs
- Time Travel for Property Graphs with Layered Graphs
- Why It Matters for AI Agents
- In a Look of Time Travel Engine for Graph Data
- Conclusion
- Event Graphs: Modeling Reality’s Temporal and Causal Fabric
- The Evolution of Graph-Based Knowledge Representation
- Anatomy of Event Graphs
- Event Nodes: The Heartbeat of Dynamic Systems
- Entity Nodes: The Persistent Actors
- The Rich Tapestry of Relationships
- Causal Relationships
- Participation Relationships
- Hierarchical Relationships
- Advanced Event Graph Paradigms
- Causal Event Graphs
- Probabilistic Event Graphs
- Hypergraph Event Representations
- Complex Temporal Event Graphs
- Events as Relations: An Alternative Perspective
- Case Studies: Event Graphs in Action
- Implementation Approaches and Technical Considerations
- Querying Event Graphs
- Scalability Challenges
- Event Graphs for AI Agent Memory
- Episodic Memory and Retrieval
- From Events to Abstractions
- Applications in Agent Systems
- Technical Implementation
- Challenges and Future Directions
- The Future of Event Graphs
- Standardization Efforts
- Conclusion: The Power of Event-Centric Thinking
- Temporal Reasoning in AI Agent Memory: Allen’s Interval Algebra and Event Graphs
- The Challenge of Time in AI Memory
- Allen’s Interval Algebra: A Foundation for Temporal Reasoning
- The 13 Basic Relations
- Event Graphs: Structuring Temporal Knowledge
- Key Properties of Event Graphs for AI Memory
- Implementing Event Graphs in AI Agent Memory Systems
- Constraint Satisfaction Networks
- Temporal Knowledge Graphs
- Hybrid Neuro-Symbolic Architectures
- Applications in Advanced AI Agents
- Episodic Memory
- Narrative Understanding
- Planning and Prediction
- Anomaly Detection
- Challenges and Future Directions
- Conclusion
- The Past and Future in AI Agent Memory: DAG as a Data Structure for Past and Future Events
- The Past and Future in Event Graphs — Chains and DAGs
- Why Past is DAG Also and Why Could the Analysis of an Alternative Version of the Past Shape the Future
- What Doesn’t Happen Matters
- Temporal, Causality, Superseding, and Probability Relations in Events
- Focus on Why in Event Graph
- Past is Still Dynamic and Could Get More Events and Details: Open World in Event Graphs
- Conclusion
- Relations Between Events in AI Memory: From Temporal to Causal Understanding
- Temporal Entities: The Building Blocks of Memory
- Temporal Relations: Connecting Events in Time
- Temporal Intervals: The Framework of Allen’s Algebra
- Causal Relations Matter More
- Causal Analysis: The Future of AI Memory
- Conclusion
- Causality Graphs for AI Agent Memory: Enabling Coherent Reasoning Across Time
- The Memory Challenge in AI Agents
- Understanding Causality Graphs
- Constructing Causal Memory Structures
- Temporal Dynamics and Graph Evolution
- Applications in Agent Reasoning
- Implementation Considerations
- Challenges and Future Directions
- Conclusion
- Relations Between Events in AI Memory: From Temporal to Causal Understanding
- Temporal Entities: The Building Blocks of Memory
- Temporal Relations: Connecting Events in Time
- Temporal Intervals: The Framework of Allen’s Algebra
- Causal Relations Matter More
- Causal Analysis: The Future of AI Memory
- Conclusion
- Pragmatics and Context in AI Agent Memory
- Pragmatics: What Is It?
- Pragmatics That Extend and Enrich Semantic Meaning
- Context in AI Memory
- Context and Pragmatics in Episodic Memories
- Bigraphs for Context in Memory
- Semantic Space-Time and Context
- Conclusion
- Semantic Spacetime: Understanding Graph Relationships in Knowledge Representation
- The Four Fundamental Relationships in Semantic Spacetime
- NEAR/SIMILAR TO (Proximity Relationship)
- LEADS TO (Causal Relationship)
- CONTAINS (Hierarchical Relationship)
- EXPRESSES PROPERTY (Attributive Relationship)
- Combining Relationships in Semantic Spacetime
- Practical Applications of Semantic Spacetime
- Knowledge Management
- Software Architecture
- Natural Language Processing
- Machine Learning and AI
- Data Integration
- Cognitive Computing
- Complex Systems Modeling
- Conclusion
- Semantic Spacetime and Causality Relations: Building AI Memory Through Causal Graphs
- From Physical Spacetime to Semantic Spacetime: A Foundational Shift
- The Physics Origins and Transformation
- Graph-Based Modeling as Semantic Infrastructure
- The Ontological Framework: Simplicity with Profound Implications
- Core Link Types in Semantic Spacetime
- The Path to Directed Acyclic Graphs
- Causality Relations: The Heart of Intelligent Memory
- Beyond Traditional Temporal and Semantic Focus
- The Dynamic Nature of Causal Relationships
- Causal Subspaces: Contextual Memory Architecture
- Creating Contextual Memory Domains
- Analytical Capabilities of Causal Subspaces
- Crucial Applications for Personal AI Assistance
- The Philosophical Shift: Why Over Where, When, and Who
- Fundamental Reorientation of Analytical Focus
- The Primacy of Causal Understanding
- Implementation Architecture for AI Agents
- Memory Systems Based on Causal Graphs
- Integration with Causal Inference Algorithms
- Future Research and Development Directions
- Expanding Causal Understanding in AI Systems
- Practical Implementation Challenges
- Conclusion: The Transformative Potential of Causality-Based AI
- Bigraphs for Semantic Spacetime: A Promising Framework for Representing Meaning and Structure
- What Are Bigraphs?
- Key Components of Bigraphs
- Interfaces and Composition
- Signatures and Semantic Typing
- Time and Motion in Bigraphs
- Application to Semantic Spacetimes
- Limitations and Extensions
- Conclusion
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