Engram FAQs

Question 1

Can Engram integrate with Large Language Models (LLMs)?

Accepted Answer

Absolutely. Engram is built for LLM integration, offering natural language query support, LLM tool-calling capabilities, and various API interfaces (HTTP, gRPC, MCP) to seamlessly connect with AI agents and models.

Question 2

What is Engram and its core purpose?

Accepted Answer

Engram is a high-performance AI memory engine designed to provide persistent memory for AI systems. It combines knowledge graph storage, semantic search, logical reasoning, and continuous learning into a single, self-contained binary file.

Question 3

What are the licensing terms for Engram?

Accepted Answer

Engram is free for personal, research, education, and non-profit organizations. Commercial use requires a paid license; please refer to the LICENSE file or contact sven.andreas@gmail.com for commercial inquiries.

Question 4

What are Engram's key features for AI development?

Accepted Answer

Engram offers a single-file, no-dependency setup, hybrid search (full-text, vector similarity, bitmap filtering), an integrated inference engine for logical reasoning, peer-to-peer knowledge mesh synchronization, and multiple API interfaces (HTTP, gRPC, MCP).

Question 5

How does Engram enable intelligent search and retrieval?

Accepted Answer

Engram provides hybrid search capabilities, combining BM25 full-text search with HNSW vector similarity search and bitmap filtering. This allows for precise and semantic retrieval of information from your knowledge base.

Engram

Engram

Características Principales

Casos de Uso

Características Principales

Casos de Uso