diff --git a/README.md b/README.md
index 19600c28..06ed20b0 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@ Take control of your data and context with **TrustGraph**. Connect to the latest
 - [**Getting Started**](#getting-started)<br>
 - [**Configuration Builder**](#configuration-builder)<br>
 - [**GraphRAG**](#graphrag)<br>
-- [**Knowledge Packages**](#knowledge-packages)<br>
+- [**Knowledge Cores**](#knowledge-cores)<br>
 - [**Platform Architecture**](#platform-architecture)<br>
 - [**Integrations**](#integrations)<br>
 - [**Observability & Telemetry**](#observability--telemetry)<br>
@@ -80,16 +80,16 @@ TrustGraph features an advanced GraphRAG approach that automatically constructs
 - **Hybrid Retrieval:** When an agent needs to perform deep research, it first performs a **cosine similarity search** on the vector embeddings to identify potentially relevant concepts and relationships within the knowledge graph. This initial vector search **pinpoints relevant entry points** within the structured Knowledge Graph.
 - **Context Generation via Subgraph Traversal:** Based on the ranked results from the similarity search, agents are provided with only the relevant subgraphs for **deep context**. Users can configure the **number of 'hops'** (relationship traversals) to extend the depth of knowledge availabe to the agents. This structured **subgraph**, containing entities and their relationships, forms a highly relevant and context-aware input prompt for the LLM that is endlessly configurable with options for the number of entities, relationships, and overall subgraph size.
 
-## Knowledge Packages
+## Knowledge Coress
 
-One of the biggest challenges currently facing RAG architectures is the ability to quickly reuse and integrate knowledge sets. **TrustGraph** solves this problem by storing the results of the data ingestion process in reusable Knowledge Packages. Being able to store and reuse the Knowledge Packages means the data transformation process has to be run only once. These reusable Knowledge Packages can be loaded back into **TrustGraph** and used for GraphRAG. Some sample knowledge packages are available for download [here](https://github.com/trustgraph-ai/catalog/tree/master/v3).
+One of the biggest challenges currently facing RAG architectures is the ability to quickly reuse and integrate knowledge sets like long-term memory for LLMs. **TrustGraph** solves this problem by storing the results of the data ingestion process in reusable Knowledge Cores. Being able to store and reuse the Knowledge Cores means the data transformation process has to be run only once. These reusable Knowledge Cores can be loaded back into **TrustGraph** and used for GraphRAG. Some sample knowledge cores are available for download [here](https://github.com/trustgraph-ai/catalog/tree/master/v3).
 
-A Knowledge Package has two components:
+A Knowledge Core has two components:
 
 - Set of Graph Edges
 - Set of mapped Vector Embeddings
 
-When a Knowledge Package is loaded into TrustGraph, the corresponding graph edges and vector embeddings are queued and loaded into the chosen graph and vector stores.
+When a Knowledge Core is loaded into TrustGraph, the corresponding graph edges and vector embeddings are queued and loaded into the chosen graph and vector stores.
 
 ## Platform Architecture
 
@@ -97,7 +97,7 @@ The platform orchestrates a comprehensive suite of services to transform externa
 
 Within the **TrustGraph** Platform, the services are grouped as follows:
 
-- **Data Orchestration:** This crucial set of services manages the entire lifecycle of ingesting and preparing data to become AI-ready knowledge. It includes **Data Ingest** capabilities for various data types, a *Data Librarian* for managing and cataloging this information, *Data Transformation* services to clean, structure, and refine raw data, and ultimately produces consumable *Knowledge Packages* – the structured, enriched knowledge artifacts for AI.
+- **Data Orchestration:** This crucial set of services manages the entire lifecycle of ingesting and preparing data to become AI-ready knowledge. It includes **Data Ingest** capabilities for various data types, a *Data Librarian* for managing and cataloging this information, *Data Transformation* services to clean, structure, and refine raw data, and ultimately produces consumable *Knowledge Cores* – the structured, enriched knowledge artifacts for AI.
 - **Data Storage:** The platform relies on a flexible storage layer designed to handle the diverse needs of AI applications. This includes dedicated storage for *Knowledge Graphs* (to represent interconnected relationships), *VectorDBs* (for efficient semantic similarity search on embeddings), and *Tabular Datastores* (for structured data).
 - **Intelligence Orchestration:** This is the core reasoning engine of the platform. It leverages the structured knowledge from the Storage layer to perform *Deep Knowledge Retrieval* (advanced search and context discovery beyond simple keyword matching) and facilitate *Agentic Thinking*, enabling AI agents to process information and form complex responses or action plans.
 - **Agent Orchestration:** This group of services is dedicated to managing and empowering the AI agents themselves. The *Agent Manager* handles the lifecycle, configuration, and operation of agents, while *Agent Tools* provide a framework or library of capabilities that agents can utilize to perform actions or interact with other systems.