The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, MCP compatibility, and more.
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Updated
Aug 22, 2025 - JavaScript
The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, MCP compatibility, and more.
Harness LLMs with Multi-Agent Programming
Local Deep Research achieves ~95% on SimpleQA benchmark (tested with GPT-4.1-mini) and includes benchmark tools to test on your own setup. Searches 10+ sources - arXiv, PubMed, GitHub, web, and your private documents. Everything Local.
Free, high-quality text-to-speech API endpoint to replace OpenAI, Azure, or ElevenLabs
A simple "Be My Eyes" web app with a llama.cpp/llava backend
a magical LLM desktop client that makes it easy for *anyone* to use LLMs and MCP
A local, privacy-first résumé builder using LLMs and Markdown to generate ATS-ready DOCX files with Pandoc — no cloud, no tracking.
Chrome Extension to Summarize or Chat with Web Pages/Local Documents Using locally running LLMs. Keep all of your data and conversations private. 🔐
Code with AI in VSCode, bring your own ai.
Your fully proficient, AI-powered and local chatbot assistant🤖
React Native Apple LLM plugin using Foundation Models
Local coding agent with neat UI
Local, OpenAI-compatible text-to-speech (TTS) API using Chatterbox, enabling users to generate voice cloned speech anywhere the OpenAI API is used (e.g. Open WebUI, AnythingLLM, etc.)
A curated list of awesome platforms, tools, practices and resources that helps run LLMs locally
A text-based user interface (TUI) client for interacting with MCP servers using Ollama. Features include multi-server, dynamic model switching, streaming responses, tool management, human-in-the-loop, thinking mode, full model parameters configuration, custom system prompt and saved preferences. Built for developers working with local LLMs.
💻一款简洁实用轻量级的本地AI对话客户端,采用Tauri2.0和Next.js编写 A simple, practical, and lightweight local AI chat client, written in Tauri 2.0 & Next.js.
LLM story writer with a focus on high-quality long output based on a user provided prompt.
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