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Memoryos

Memoryos is a Python memory-management system for personalized AI agents, organized around hierarchical storage and separate storage, updating, retrieval and generation modules.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Memoryos — 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 Memoryos when an agent needs to carry useful user or conversation context across interactions and retrieve it for later responses.

Who it is for

  • Developers building personalized AI agents that need persistent conversation context.
  • Teams integrating memory retrieval into AI applications through Python or the project’s MCP tools.

Documented uses

  • Save user-assistant conversation details for later use.
  • Retrieve relevant historical dialogue, preferences or knowledge in response to a query.
  • Add a memory layer to an AI application or agent workflow.
Before you choose

Requirements & limits

  • The documented Python setup requires Python 3.10 or later.
  • The README example requires an OpenAI API key and configures a local data-storage path.
  • The README names OpenAI, DeepSeek and Qwen as LLM examples and lists BGE-M3, Qwen3-Embedding-0.6B and all-MiniLM-L6-v2 as embedding options.
  • The project is published under the Apache-2.0 license.

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

Save and retrieve a conversation with the Python example

Initialize Memoryos, save a conversation exchange, and request a response using a query about that saved context.

Before you start

  • Python 3.10 or later
  • An OpenAI API key
  • A local data-storage path
  • Memoryos installed through the documented PyPI or GitHub installation path

Steps

  1. Install Memoryos using the README’s PyPI command or its GitHub requirements-file instructions.

  2. Configure the example with user and assistant identifiers, the API key, data-storage path, LLM model, and embedding model.

  3. Initialize the Memoryos instance.

  4. Call add_memory with a user input and assistant response, then call get_response with a query about the saved information.

Expected outcome

The example requests a response to a question about information saved in the conversation memory.

Guide limitations

  • The README’s example uses an OpenAI API key and gpt-4o-mini; it does not provide a complete configuration recipe for every listed LLM.
  • The documented example does not specify operational details such as production deployment or data-retention controls.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
8 October 2026
Original website title
BAI-LAB/MemoryOS · Official homepage title · retained in its source language
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