Engram FAQs

Question 1

What makes Engram unique compared to other AI memory solutions?

Accepted Answer

Engram distinguishes itself by not involving LLMs in the write path, prioritizing write reliability and speed. It stores episodes first, ensuring writes never fail, and defers complex processing like entity extraction to an optional second layer. It also offers fast queries with DuckDB HNSW indexing and a single, portable binary.

Question 2

What are the core features of Engram?

Accepted Answer

Key features include semantic search with vector similarity, local embedding generation via Ollama, fast queries powered by DuckDB HNSW indexing, a single portable binary across multiple platforms, and native integration with Model Context Protocol (MCP) clients like Claude Desktop, Claude Code, and Cursor.

Question 3

What is Engram?

Accepted Answer

Engram is a private, local, event-sourced memory system designed for AI agents and LLMs. It provides reliable episode storage with powerful semantic search capabilities, ensuring your AI's memory is robust and easily accessible without reliance on external services.

Question 4

How does Engram handle embeddings and data privacy?

Accepted Answer

Engram generates all embeddings locally using Ollama, ensuring zero external API calls for this process. Your data is stored in a portable DuckDB file on your own system, guaranteeing complete privacy and control over your AI's memory.

Question 5

What are the prerequisites for using Engram?

Accepted Answer

To use Engram, you'll need Ollama running locally (or remotely) with an embedding model (e.g., 'nomic-embed-text'). If you plan to build Engram from source, Go 1.25+ is also required.

Engram

Engram

Key Features

Use Cases

Key Features

Use Cases