OpenMemory FAQs

Question 1

Is OpenMemory compatible with existing AI agent frameworks?

Accepted Answer

Yes, OpenMemory is framework-agnostic and designed to connect with any MCP-compatible client. This includes popular AI coding assistants, desktop clients, and agent frameworks like LangChain, CrewAI, AutoGen, and more, often without requiring changes to your agent's code.

Question 2

What search capabilities does OpenMemory offer?

Accepted Answer

OpenMemory uses hybrid search, combining BM25 keyword scoring with vector cosine similarity. This re-ranking process ensures highly accurate recall, even when search queries differ in wording from the stored information.

Question 3

Can OpenMemory be used offline or without external services?

Accepted Answer

Yes, OpenMemory features a 'zero-setup mode' that operates entirely on SQLite with FTS5. This allows for local-first and offline use, requiring no API keys, GPUs, or extra dependencies, while still supporting hybrid search.

Question 4

What is OpenMemory and what problem does it solve?

Accepted Answer

OpenMemory provides persistent, semantic memory for AI agents, solving the 'forgetting' problem common in agent sessions. It enables local-first, framework-agnostic, and production-ready memory management, allowing agents to maintain continuity, accumulate knowledge, and offer personalized interactions.

Question 5

How does OpenMemory organize agent memory?

Accepted Answer

It organizes memory into structured tiers: `MEMORY.md` (long-term facts), `USER.md` (user profiles), `AGENTS.md` (agent instructions), `RELATIONS.md` (entity graph), and `daily/YYYY-MM-DD.md` (append-only logs). These files are assembled for context at session start.

OpenMemory

OpenMemory

Características Principales

Casos de Uso

Características Principales

Casos de Uso