Engram FAQs

Question 1

How does Engram's dual-memory system work?

Accepted Answer

Engram combines an episodic memory (powered by Qdrant vector store) for temporal events and a semantic graph (NetworkX with SQLite/PostgreSQL) for structured entities and relationships. An LLM reasoning engine then fuses context from both memory types to provide comprehensive understanding.

Question 2

What is Engram and how does it benefit AI agents?

Accepted Answer

Engram is a cutting-edge persistent dual-memory system for AI agents, blending episodic (vector-based) and semantic (graph-based) memory. It empowers agents with deeper context, enhanced reasoning, and long-term knowledge retention, preventing hallucination and improving decision-making.

Question 3

Can Engram integrate with other knowledge systems?

Accepted Answer

Yes, Engram features federated search and auto-discovery, enabling it to query and retrieve information from external knowledge systems like mem0, LightRAG, Graphiti, and custom REST/File/PostgreSQL/MCP providers in parallel, creating a unified knowledge base.

Question 4

What AI agents and interfaces are compatible with Engram?

Accepted Answer

Engram is designed for broad compatibility, integrating seamlessly with agents like Claude Code, Cursor, OpenClaw, and any MCP-compatible client. It offers versatile access via CLI, MCP (stdio), HTTP API, WebSocket, and a Web UI.

Question 5

What enterprise features does Engram offer?

Accepted Answer

Engram is built for enterprise-grade deployments, offering multi-tenancy for isolated stores, JWT/RBAC authentication, Redis-backed caching, audit trails, rate limiting, and robust Docker/Kubernetes deployment options with observability via OpenTelemetry.

Engram

Engram

Características Principales

Casos de Uso

Características Principales

Casos de Uso