Documentation Index
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Welcome to EngramDB Documentation
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 EngramDB?
EngramDB is a database system that combines vector, graph, and temporal properties in a single primitive called a MemoryNode. This unified approach allows for rich, multi-dimensional representation of agent memories, enabling complex queries that can leverage semantic similarity, relational connections, and temporal evolution.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
Getting Started
Installation
Learn how to install and set up EngramDB
Core Concepts
Understand the fundamental building blocks of EngramDB
API Reference
Explore the detailed API documentation
Examples
See EngramDB in action with code examples
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
Advanced Topics
Temporal Layers
Learn how to track memory evolution over time
Connections
Create and manage relationships between memories
Storage Engines
Understand the different storage backends
Embeddings
Learn about vector and multi-vector embeddings
Background Processing
Discover sleep-time compute capabilities