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MultiMind Introduction

Overview

I designed Multimind to be easier to install and manage than Open WebUI, and more capable than LM Studio. The idea is that the app is well-packaged and easy to deploy with support for: - integrated organizational tools for agents to use (chat messaging, tasks, and artifacts [documents]) - supports cloud-hosted and local models - local STT and TTS (via whisper and piper). - built-in vector database for RAG, and local embedding support via Llama CPP.

Key Features

  • Conversational Interface: Interact through chat messages
  • Task Management: Agents use task management to structure their multi-step workflows.
  • Document Generation: Agents can create structured documents such as spreadsheets, diagrams, and Markdown documents.
  • Research Capabilities: Web search and content summarization via embedded browser
  • Configuration-driven agents: An agent builder allows customization or creation of new agents using their access to composable steps.

For best results, we recommend using one of these high-quality language models:

  • OpenAI GPT-4o (via OpenAI API)
  • Anthropic Claude 3.5 Sonnet (via Anthropic API)
  • DeepSeek V3 (via DeepSeek API)
  • Qwen 2.5 72B Instruct (qwen/qwen-2.5-72b-instruct, available via OpenRouter)
  • NVIDIA Nemotron 70B Instruct (nvidia/llama-3.1-nemotron-70b-instruct, available via OpenRouter)

System Requirements

  • Operating System: Windows 11, Mac OS X Sonoma, or a Linux distribution capable of running AppImage packaged applications
  • Supported LLM provider:
  • Cloud providers: Open Router, Anthropic, OpenAI, AWS Bedrock
  • Local providers: LM Studio, Embedded Local Llama.CPP
  • Optional: ChromaDB vector database, Brave Search API key

Getting Help

For support, you can: - Open issues on the GitHub public issues tracker - Join our Discord community for real-time help and discussions

Screenshot

Here is a screenshot of the welcome screen:

Welcome Screen