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

主要功能

使用案例

主要功能

使用案例