Engram FAQs

Question 1

How does Engram ensure efficient memory retrieval at scale?

Accepted Answer

Unlike systems that load all memories (e.g., MEMORY.md), Engram offers constant-cost retrieval. Whether you have hundreds or thousands of memories, operations like `recall()` consume a fixed, low amount of tokens, preventing token budget exhaustion and maintaining performance as your knowledge base grows.

Question 2

What is proactive recall and how does it benefit AI agents?

Accepted Answer

Proactive recall is Engram's unique feature where it automatically surfaces relevant memories (constraints, guardrails, procedures) when an AI agent opens a specific file path. This allows the agent to 'remember' critical context before even being asked, preventing errors and improving efficiency.

Question 3

How does Engram achieve 'zero-LLM' operations?

Accepted Answer

Engram relies solely on a local SQLite database with FTS5 for all core memory operations like remembering, recalling, and proactive pushes. This means no external LLM API calls are needed for memory management, saving costs and ensuring privacy.

Question 4

What is Engram?

Accepted Answer

Engram is an open-source, local-first memory system designed for AI coding agents. It provides persistent memory with zero LLM calls for core operations and proactive recall, helping agents remember constraints, decisions, and lessons across sessions and projects.

Question 5

Which AI agents is Engram best suited for?

Accepted Answer

Engram is specifically designed for AI *coding* agents, particularly those compatible with the Model Context Protocol (MCP) like Claude Code. Its path-based proactive recall and structured memory types are optimized for coding workflows, rather than general chatbot conversations.

Engram

Engram

Características Principales

Casos de Uso

Características Principales

Casos de Uso