> ## Documentation Index
> Fetch the complete documentation index at: https://engramdb.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction to EngramDB

> An overview of EngramDB and its capabilities

# Introduction to EngramDB

EngramDB is a specialized database system designed specifically for agent memory management. It provides efficient storage, retrieval, and querying of agent memories using a unified memory representation model.

## What is an Engram?

In neuroscience, an engram is a hypothetical means by which memory traces are stored as biophysical or biochemical changes in the brain. EngramDB borrows this concept to represent agent memories as rich, multi-dimensional data structures that combine:

* **Vector embeddings** for semantic content
* **Graph connections** for relational information
* **Temporal layers** for memory evolution
* **Flexible attributes** for structured metadata

## Why EngramDB?

Traditional databases are not optimized for the unique requirements of agent memory systems:

* **Vector databases** excel at similarity search but lack graph capabilities
* **Graph databases** handle relationships well but aren't optimized for vector similarity
* **Document databases** provide flexible schemas but lack specialized memory features
* **Time-series databases** track changes over time but aren't designed for semantic search

EngramDB combines the strengths of these different database types into a unified system specifically designed for agent memory management.

## Key Use Cases

EngramDB is ideal for:

* **AI Agents**: Store and retrieve memories for conversational agents, assistants, and autonomous systems
* **Knowledge Graphs**: Build semantic knowledge graphs with vector similarity capabilities
* **Memory Evolution**: Track how knowledge and beliefs change over time
* **Multi-modal Memory**: Store and query memories across different modalities (text, images, etc.)
* **Cognitive Architectures**: Implement memory systems for cognitive architectures

## Core Features

* **Unified Memory Representation**: Combines graph, vector, and temporal properties in a single primitive
* **Vector Similarity Search**: Find memories with similar semantic content
* **Flexible Storage Options**: In-memory database for testing and development, file-based for persistence
* **Query API**: Rich querying with vector similarity, attribute filters, and temporal constraints
* **Memory Evolution**: Track changes to memories over time with temporal layers
* **Python Bindings**: First-class Python API for integration with ML and AI applications
* **Web Interface**: Browser-based UI for visualization and interaction with the database

## Architecture

EngramDB is built around these core components:

1. **MemoryNode**: The fundamental unit of storage, combining vector embeddings, graph connections, temporal layers, and flexible attributes
2. **Storage Engines**: Both in-memory and file-based storage options
3. **Vector Index**: For efficient similarity search
4. **Query System**: A fluent interface for building complex queries
5. **Database**: A unified interface combining all components
6. **Language Bindings**: Core implementation in Rust with Python bindings

## Next Steps

* [Getting Started](/docs/getting-started): Installation and basic usage
* [Core Concepts](/docs/core-concepts): Detailed explanation of key concepts
* [API Reference](/docs/api-reference/memory-node): Comprehensive API documentation
* [Examples](/docs/examples/rust-examples): Code examples in Rust and Python