Engram FAQs

Question 1

What is Engram and its core purpose?

Accepted Answer

Engram provides persistent geometric memory for AI agents, acting as a high-speed local vector database. Its core purpose is to give AI agents long-term, associative memory without relying on cloud services or external APIs, running entirely on your machine.

Question 2

How does Engram manage and enhance AI agent intelligence?

Accepted Answer

Engram leverages Vector Symbolic Architecture (NVSA) for semantic content merging and binding. It offers 21 Model Context Protocol (MCP) tools for comprehensive memory management, intelligence, knowledge graph operations, and includes an Autonomous Agentic Daemon for background memory maintenance.

Question 3

Does Engram require cloud services or an API key?

Accepted Answer

No, Engram is designed for complete local operation. It functions as a high-speed local vector database, eliminating the need for external vector hosting, cloud dependencies, or API keys, ensuring data privacy and speed.

Question 4

What is unique about Engram's data storage and performance?

Accepted Answer

Engram utilizes Rust-native O_DIRECT NVMe storage with 256KB HolographicBlocks. This innovative approach bypasses the OS page-cache, allowing tensors to stream directly from SSD to VRAM for unparalleled high-speed data access and processing.

Question 5

Can Engram be used for knowledge graph creation and traversal?

Accepted Answer

Yes, Engram features an integrated knowledge graph that supports directional relations, semantic traversal, and visualization. You can bind concepts with `op_bind` and generate Mermaid diagrams to explore relationships within your agent's memory.

Engram

Engram

Key Features

Use Cases

Key Features

Use Cases