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Agent Memory & RAG

Simplemem

Simplemem is a Python library for LLM-agent memory that stores, semantically compresses and retrieves long-term memories. The repository describes text-memory support through MCP and multimodal support through Python.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Simplemem — 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 Simplemem when an LLM agent needs persistent memory and retrieval across interactions, especially when the project requires either MCP-based text memory or Python-based multimodal handling.

Who it is for

  • Developers building LLM agents with persistent memory.
  • Teams creating retrieval-augmented assistants.
  • Python developers working with text, image, audio and video memory inputs.
  • Developers integrating agent memory through MCP-compatible platforms.

Documented uses

  • Store long-term memories for an LLM agent.
  • Compress memories semantically before retrieval.
  • Retrieve prior interactions as agent context.
  • Handle multimodal memory involving text, images, audio and video.
  • Build knowledge-graph-backed memory features.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. Its documented platforms include CLI, server and self-hosted use.

  • Choose the MCP route when the use case is documented text-based memory.
  • Choose the Python integration when the use case requires text, image, audio and video memory.
  • The project is Python-based and lists MCP among its ecosystems.
  • The repository is public and MIT-licensed.
  • A complete integration example is not included in the supplied material.

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 text memory through MCP

Provide an LLM agent with text-based long-term memory storage and retrieval through an MCP-compatible integration.

Before you start

  • An LLM agent or AI platform that supports MCP.
  • The Simplemem repository documentation.

Steps

  1. Use an MCP-compatible integration with Simplemem.

  2. Connect the LLM agent to Simplemem for text memory.

  3. Use the connected memory layer to store and retrieve long-term text memories.

Expected outcome

The agent has text-based long-term memory storage and retrieval through MCP.

Guide limitations

  • The supplied repository text does not provide complete implementation details or a full integration example.
  • This guidance reflects documented support and is not a human-verified setup test.
Documentation 1 ↗
Source-based practical guide

Set up multimodal memory through Python

Enable an agent memory layer for text, image, audio and video inputs using Simplemem's Python integration.

Before you start

  • A Python-based integration.
  • The Simplemem repository documentation.
  • Text, image, audio or video inputs for the intended memory workflow.

Steps

  1. Use Simplemem through its Python integration.

  2. Provide the intended text, image, audio or video inputs.

  3. Use the integration to handle multimodal memory for the agent.

Expected outcome

The agent can use a Python-based memory layer covering text, image, audio and video.

Guide limitations

  • The supplied repository text does not provide a complete Python integration example.
  • This guidance reflects documented support and is not a human-verified setup test.
Documentation 1 ↗
Evidence and transparency

Sources & review

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