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Agent Memory & RAG

Remind

Remind is a desktop application that captures screenshots, audio and other digital activity, transcribes and summarizes material, indexes it in a vector database, and provides chat-based retrieval over the resulting personal history.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Remind — 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 Remind when you want an AI-assisted record of activity that can be summarized and queried later, and are prepared to follow its local setup requirements.

Who it is for

  • People who want to search and revisit their own captured digital activity.
  • Developers interested in running or adapting a local AI memory application.

Documented uses

  • Capture digital activity for later recall.
  • Extract text from screenshots and transcribe captured material.
  • Review daily summaries of activity.
  • Ask questions through chat about an indexed personal history.
Before you choose

Requirements & limits

The repository describes a first version for macOS and provides source setup instructions. It lists an Apache-2.0 license.

  • The documented source setup requires Node.js 18 or later, npm, Python, and Ollama with llama3.1.
  • The README says to allow about 30 minutes initially for activity to gather in the vector database.
  • The README identifies Enchanted as powering the interface and app.
  • The repository lists TypeScript as its primary language and Apache-2.0 as its 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

Prepare and start Remind from source

Set up the repository and start its development server.

Before you start

  • Node.js 18 or later and npm
  • Python
  • Ollama with llama3.1 pulled

Steps

  1. Clone the Remind repository.

  2. In the project directory, run npm install.

  3. Run npm run dev to start the server.

  4. Follow the linked setup video for the remaining setup stage.

Expected outcome

The repository's development server is started, with the video pointing to the next setup stage.

Guide limitations

  • These steps do not cover the remaining application setup or confirm a completed working installation.
  • The README describes a first macOS version; the listed setup steps are source-based.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
RemindCapture digital activity for later recall.Desktop, macOS, Self-hostedThe repository describes a first version for macOS and provides source setup instructions. It lists an Apache-2.0 license.
Aml Multiagent RagIngest PDF regulations, extract and chunk their text, and build a searchable vector collection.Docker, Self-hostedThe repository is public and specifies the MIT license. The documented setup is self-hosted and requires Python 3.11 or later, Docker Desktop and an OpenAI API key.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
DonTizi/ReMind · Official homepage title · retained in its source language
How we review sourcesSuggest a correction