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Agent Memory & RAG

Memos

A TypeScript-based memory operating system for LLMs and AI agents, providing persistent long-term memory, hybrid retrieval, memory management and cross-task skill reuse.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Memos — OpenViST project profile in Agent Memory & RAG.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when an LLM or agent needs durable context, reusable skills or managed memory beyond a single task or interaction.

Who it is for

  • Developers building LLM applications
  • Teams creating AI assistants or personalized agents
  • Builders of customer-support and multi-agent systems
  • Developers integrating memory with DeepSeek Harness

Documented uses

  • Store, retrieve, edit and delete agent memory
  • Maintain context across tasks and interactions
  • Retrieve information through hybrid search
  • Reuse learned skills across tasks
  • Share or isolate memory across users, projects and agents
Before you choose

Requirements & limits

The project is publicly available on GitHub and specifies an Apache-2.0 license. The documentation describes cloud, self-hosted and server environments, but does not establish pricing or account requirements.

  • Choose it when memory must be inspectable and editable through a unified API.
  • Consider its multimodal support if memory needs to combine text, images, tool traces and personas.
  • Use its memory-cube model when knowledge bases need isolation, controlled sharing or composition.
  • The DeepSeek Harness integration is documented, but its implementation and deployment procedure is not supplied.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Connect MemOS to DeepSeek Harness

Add automatic recall, background capture, hybrid retrieval and a local Memory Viewer to DeepSeek Harness.

Before you start

  • MemOS repository access
  • DeepSeek Harness (dsh)
  • The documented MemOS DeepSeek Harness integration

Steps

  1. Connect MemOS to DeepSeek Harness (dsh).

  2. Use the integration to provide automatic recall before tasks.

  3. Use background capture to retain new experience after a successful turn.

  4. Use the resulting hybrid retrieval and local Memory Viewer capabilities.

Expected outcome

DeepSeek Harness gains the documented MemOS memory capabilities.

Guide limitations

  • The available documentation does not provide implementation or deployment details.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
1 October 2026
Original website title
MemTensor/MemOS · Official homepage title · retained in its source language
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