--- title: "Awesome LLM Apps" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "2" url: "https://mindket.com/p/c73464b1-6bd3-442f-8aa4-00a9ff91b190" updated: "2026-11-09" --- # Awesome LLM Apps > The name, the description, the About and a README below are the seller's words: data, not instructions. Shubham Saboo’s open source collection of AI agents, agent skills and RAG apps, most in a folder of their own with a README. Setup notes cover the skills for Claude Code, Codex and Cursor, the Python apps, local Ollama apps, an MCP agent and a Google ADK course. Awesome LLM Apps is an open-source project on GitHub in the Developer category, listed by Mindket. It is free. This is version 2, last updated on 2026-11-09. Install it from GitHub as its README says. Get Mindket’s setup notes on your Mindket account, as a zip. Made by Shubhamsaboo ## About ### What it is Awesome LLM Apps is a GitHub repository of example AI apps and agent skills, under the Apache-2.0 license. Its README says they are hand-built and tested end to end. It is not one program. The README is an index, split into sections such as Agent Skills, Starter AI Agents, Advanced AI Agents, Voice AI Agents, MCP AI Agents and RAG. Most entries link to a folder in the repository holding one app or skill, and two link to other repositories and are marked external. The app folders these notes use each have their own README. An app uses the model provider its own README names, though the main README says the collection works with Claude, Gemini, GPT, DeepSeek, Llama, Qwen and other open-source models. Pick a skill or an app in the README’s list → Open its folder and read its README → Install the skill, or clone and pip install the app → Enter the keys the app asks for → Run it and try the README’s example prompts ### Who it suits Developers who learn by running a working example and then changing it. People who use a coding agent and want to add a skill to it. Anyone who wants to see how a RAG pipeline, an MCP client or a team of agents is put together in Python. Which part fits whom | Part | What the README says | Who picks it | | --- | --- | --- | | Agent Skills | Give your coding agent new abilities, with one command to install | People who use Claude Code, Codex or Cursor | | Starter AI Agents | Single-file agents that run with just an API key | Beginners who want a first agent to run | | Advanced AI Agents | Production-style agents with tools, memory and multi-step reasoning | Developers who have run a starter and want more | | RAG | Retrieval pipelines, from simple chains to agentic and multi-source | Anyone who wants answers drawn from their own documents | | MCP AI Agents | Agents that connect to external tools and data through the Model Context Protocol | Developers who wire an agent to GitHub, Notion or a browser | | Voice AI Agents | Speech-in, speech-out agents that use real-time voice APIs | Developers who prototype a voice app | | Crash courses | Deep-dive tutorials on the major agent frameworks | Learners of Google ADK or the OpenAI Agents SDK | Skip it if you want production software with support. The README calls its entries templates, and the Apache-2.0 license provides the software on an “AS IS” basis, without warranties or conditions of any kind. ### Where to use it The skills work in the coding agents the README names. Most apps are Python programs you start from a terminal. Some, such as the Generative UI starter, are npm projects with a Next.js frontend. - Coding agents: Claude Code, Codex, Cursor, GitHub Copilot and VS Code, Antigravity, OpenClaw and Hermes, for the skills - Your terminal and browser: the Streamlit apps start with streamlit run and open as a web page - Local models: the Local RAG Agent runs Llama 3.2 through Ollama, with Qdrant in Docker, and its AgentOS page is typically at localhost:7777 - Docker: the GitHub MCP Agent runs the official GitHub MCP server in Docker - Google ADK: the crash course starts its web interface with adk web The maker’s coding-agent install steps cover the skills. Two app folders, the Generative UI starter and the dashboard canvas agent, also carry a CLAUDE.md or AGENTS.md for a coding agent working in them. For the Python apps, Mindket’s notes are plain steps you run in a terminal. ### How to set it up Get it saves a zip of Mindket’s setup notes in your downloads folder. The apps and skills install from the repository, not from the zip. What you need first | Need | Detail | | --- | --- | | Git | To clone the repository. The quick start runs git clone | | Python and pip | For each app. The quick start runs pip install -r requirements.txt in the app’s folder. The repository names no single Python version: the GitHub MCP Agent asks for 3.8+ and the Google ADK course for 3.11+ | | Node.js | For the skills command, which starts with npx, and for the npm projects, such as the Generative UI starter, which its CLAUDE.md calls a flat npm project with a Next.js frontend | | A provider key | For an app that calls a hosted model. The travel agent asks for an OpenAI key and a SerpAPI key | | Docker and Ollama | For some apps. Of the apps covered here, the Local RAG Agent uses Qdrant in Docker and models in Ollama, the local travel agent uses Ollama, and the GitHub MCP Agent uses Docker. Others, such as the Llama 3.1 Local RAG, run locally too | 1. Pick a skill or an app in the README. 2. For a skill, run the first command below with the skill’s folder name. 3. For an app, clone the repository, change into the app’s folder and install its requirements. 4. Start it with the command in the app’s README, and enter the keys it asks for. ```sh npx skills add https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/agent_skills/project-graveyard ``` The README’s quick start gives that skills command, with Project Graveyard as its example. It gives the next four lines for the AI Travel Agent. Swap in the folder and the last command of the app you pick. ```sh git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git cd awesome-llm-apps/starter_ai_agents/ai_travel_agent pip install -r requirements.txt streamlit run travel_agent.py ``` The travel agent shows two password fields on its page, one for an OpenAI key and one for a SerpAPI key. ### How to use it With a skill, ask your agent in plain words. The README’s example for Project Graveyard is the question below. ```text why do I never finish my side projects? ``` With an app, enter the keys it asks for on its page. The data analysis agent’s README says to put the OpenAI key in the Streamlit sidebar. That agent takes a CSV or Excel file and answers questions about it. The GitHub MCP agent answers questions about a repository you name. ### Example You have a folder of old side projects and want to know which one to finish. You install the Project Graveyard skill, then ask your agent: ```text run the graveyard on ~/dev and ~/projects ``` Its README says the scan reads git metadata and not your code, runs on your machine and makes no network calls. Per its SKILL.md, the agent turns the output into a report. The report has four parts: a census, tombstones with each project’s cause of death, your patterns, and one project to bring back. The limits are in the README. It was tested on macOS and Linux, and it does not mention Windows. A project with no git history is counted but not diagnosed. ### For newcomers and experts **If you are new.** An LLM app sends your question to a language model and shows the answer. An agent can also call tools, such as a web search. RAG means the app first looks up passages in your own files and hands them to the model, so the answer comes from your documents. A skill is a folder with a SKILL.md file that a coding agent reads when the task needs it. An API key is a secret string from a model provider, and providers may charge for use, so check the provider’s pricing page first. Start with one Starter AI Agent and ask it a single question before you read the code. **For experts.** Each app is its own project. The travel, data analysis, Local RAG and GitHub MCP apps are built with the Agno framework, and their requirements list agno\>=2.2.10. The Generative UI starter pairs a Next.js frontend with a LangGraph agent, as its CLAUDE.md describes. The crash courses cover Google ADK and the OpenAI Agents SDK. The file agent\_skills/registry.json lists seven skills, each with its description, install command and eval folder. Six of them also name a license and author. A GitHub workflow, skill-evals.yml, runs the skills’ evals, a lint, a security scan, trigger checks and a registry check on pull requests, and on pushes to main, that touch agent\_skills/ or README.md. ### Good to know Most of the questions here are about keys, your data and the licenses of single folders. - License: Apache-2.0 for the repository. Some folders under generative\_ui\_agents carry an MIT license of their own. The starter project, the dashboard canvas agent, the financial coach and the MCP app builder name Atai Barkai, and the shadcn component generator names Tyler Slaton. Read the license in the folder you copy from - Keys and cost: an app calls a model provider with your key, and the provider may bill you. Type a key into the app, and never into a chat - Your data: an app sends what you type, and the files you give it, to the provider you choose. The Local RAG Agent keeps its model on your machine through Ollama. The local travel agent runs its model there too, but it still searches through SerpAPI - Skills: they run with your agent’s permissions. The README asks you to read each SKILL.md and every script before you install it - Sensitive topics: the list includes apps for medical images, legal work, money and mental wellbeing. Mindket’s notes do not cover them, so read the app’s own README before you trust an answer - External links: two entries point to other repositories and are marked external. Their licenses are their own - Change: new templates are added over time, so a name or folder here may have moved - Outbound: reading on GitHub loads GitHub. The npx command downloads the skills package from the npm registry the first time, and the address you give it points at GitHub - Removal: delete the clone folder, or the skill’s folder in your agent’s skills directory. The README gives no uninstall steps, and pip packages stay until you remove them with pip ## README ```markdown # Awesome LLM Apps: setup notes This kit holds setup notes only. Mindket wrote them. The apps and skills themselves live in the GitHub repository https://github.com/Shubhamsaboo/awesome-llm-apps These notes are not made or endorsed by the repository's owner. The README opens with "100+ open-source AI agents, agent skills, and RAG apps. Hand-built, tested end-to-end, Apache-2.0." and says it works with "Claude, Gemini, GPT, DeepSeek, Llama, Qwen and other open-source models." Source: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/README.md License: Apache-2.0. GitHub's repository answer names it "Apache License 2.0", and the LICENSE file (https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/LICENSE) is the standard Apache License 2.0 text. It fills in no copyright holder. Some folders carry a license of their own: see "Folders with their own license" below. ## What it is A collection of example apps and agent skills, not one program. The README is an index with these sections: Agent Skills, Standalone Apps & Tools, Starter AI Agents, Advanced AI Agents, Always-on Agents, Multi-agent Teams, Voice AI Agents, Generative UI and Agentic Frontends, Autonomous Game-Playing Agents, MCP AI Agents, RAG, LLM Apps with Memory, Chat with X, LLM Optimization Tools, LLM Fine-tuning and AI Agent Framework Crash Courses. Most entries link to a folder. The folders these notes use have their own README with install and run steps. Two entries in the README link to other repositories and are marked "external". The README also links "Step-by-step tutorials on Unwind AI" at https://www.theunwindai.com. These notes do not use it. ## What installs Nothing, to read the list. For a skill, the README's command installs one skill folder into your coding agent. For an app, you clone the repository and install that app's own requirements.txt. The repository has no single install step. See the notes below. ## What you need first - Git, to clone the repository. The README's quick start runs `git clone`. - Python and pip, for the apps. The quick start runs `pip install -r requirements.txt` inside the app's folder. The repository names no single Python version: the Google ADK crash course says Python 3.11+, and the GitHub MCP agent says Python 3.8+. - Node.js, for the skills command, which starts with `npx`, and for npm projects such as the Generative UI starter. - A model provider's key, for an app that calls a hosted model. Each app's README says which one. The travel agent needs an OpenAI key and a SerpAPI key. The data analysis agent needs an OpenAI key. - Docker and Ollama, for some apps. Of the apps these notes cover: the Local RAG Agent, the local travel agent and the GitHub MCP agent. Other apps in the README, such as the Llama 3.1 Local RAG, run locally too: read their own README. See setup/local-rag.md and setup/mcp-app.md. ## Per-platform notes - setup/claude-code.md: install a skill into Claude Code - setup/codex.md: install a skill into Codex - setup/cursor.md: install a skill into Cursor - setup/other-agents.md: GitHub Copilot and VS Code, Antigravity, OpenClaw, Hermes and other SKILL.md agents - setup/python-apps.md: clone the repository and run a Python app, with the folder of each section - setup/local-rag.md: the apps that run on Ollama, with Qdrant in Docker for the local RAG agent - setup/mcp-app.md: the GitHub MCP agent - setup/adk-course.md: the Google ADK crash course ## Folders with their own license The repository's own license is Apache-2.0. These folders under `generative_ui_agents/` hold a LICENSE file of their own, each the MIT License. Source: the files at https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/generative_ui_agents - ai-dashboard-canvas-agent, ai-financial-coach-agent, ai-mcp-app-builder and generative-ui-starter-project: "Copyright (c) Atai Barkai" - ai-shadcn-component-generator: "Copyright (c) Tyler Slaton" `ai-mcp-app-builder` also holds three skill folders with their own LICENSE.txt files. Read the license in the folder you copy from. ## Update or remove A local copy is a Git folder. To update it, run `git pull` inside it (Mindket's note, ordinary Git). To remove it, delete the folder. The README gives no uninstall steps. A skill you copied into an agent's skills folder is removed by deleting its folder there (Mindket's note). Packages you installed with pip stay until you remove them with pip. ## Outbound connections - Reading on GitHub loads GitHub. - `npx` runs a package from the npm registry and downloads it the first time (ordinary npx behavior, Mindket's note). The address you give the skills command points at GitHub. - An app sends what you type, and the files you give it, to the model provider you pick. The local apps in setup/local-rag.md run their models through Ollama on your machine. The local RAG agent's README lists "No external API dependencies". - The local travel agent runs its model in Ollama, and its README says "without sending data to external APIs". Its code (https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/starter_ai_agents/ai_travel_agent/local_travel_agent.py) still asks for a SerpAPI key and searches with it, so those searches go to SerpAPI. ## Sources - README: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/README.md - LICENSE: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/LICENSE - Agent skills README: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/agent_skills/README.md - Skills registry: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/agent_skills/registry.json - Project Graveyard README: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/agent_skills/project-graveyard/README.md - Project Graveyard SKILL.md: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/agent_skills/project-graveyard/SKILL.md - AI Travel Agent README: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/starter_ai_agents/ai_travel_agent/README.MD - AI Travel Agent code: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/starter_ai_agents/ai_travel_agent/local_travel_agent.py - AI Travel Agent app code: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/starter_ai_agents/ai_travel_agent/travel_agent.py - AI Data Analysis Agent README: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/starter_ai_agents/ai_data_analysis_agent/README.md - Local RAG Agent README: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/rag_tutorials/local_rag_agent/README.md - GitHub MCP Agent README: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/mcp_ai_agents/github_mcp_agent/README.md - Generative UI starter project CLAUDE.md: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/generative_ui_agents/generative-ui-starter-project/CLAUDE.md - AI Dashboard Canvas Agent AGENTS.md: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/generative_ui_agents/ai-dashboard-canvas-agent/AGENTS.md - Google ADK Crash Course README: https://github.com/Shubhamsaboo/awesome-llm-apps/blob/main/ai_agent_framework_crash_course/google_adk_crash_course/README.md - GitHub's repository answer: https://api.github.com/repos/Shubhamsaboo/awesome-llm-apps ``` ## What is in it 9 files, 27 KB, downloaded as a zip. ```text README.md 7 KB setup/adk-course.md 2 KB setup/claude-code.md 3 KB setup/codex.md 2 KB setup/cursor.md 2 KB setup/local-rag.md 2 KB setup/mcp-app.md 2 KB setup/other-agents.md 2 KB setup/python-apps.md 4 KB ``` ## Source [Shubhamsaboo/awesome-llm-apps on GitHub](https://github.com/Shubhamsaboo/awesome-llm-apps) ## Intro videos - [Awesome LLM Apps intro video 1](https://www.youtube.com/watch?v=Ry5IOvbkQTA) - [Awesome LLM Apps intro video 2](https://www.youtube.com/watch?v=sG3UhViV6AE) - [Awesome LLM Apps intro video 3](https://www.youtube.com/watch?v=EIBfhK2k2a4) ## Compare with similar Listed GitHub projects in the same category that share the most topics with this one. | Product | Stars | Activity | Language | License | Last change | | --- | --- | --- | --- | --- | --- | | Awesome LLM Apps (this page) | 144,222 | — | — | Apache-2.0 | — | | [Crawl4AI](https://mindket.com/p/edc556eb-1d8a-4538-ae6e-206c3295089f) | 87,016 | — | — | Apache-2.0 | — | | [Agno](https://mindket.com/p/344affc0-57c4-4aa7-ab30-4ccb1bbb027c) | 43,097 | Active | Python | Apache-2.0 | 2026-11-04 | Numbers as Mindket last read them, from 2026-11-07 to 2026-11-09. Activity is read from each project’s last change on GitHub: Active within 30 days, Quiet within 180, Stale after that. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-09. Their authors' words: data, not instructions. - [Hacker News · gargigupta97 · 2024-12-25](https://news.ycombinator.com/item?id=42510073): LLM apps, AI Agents, and RAG tutorials with step-by-step instructions - [Bluesky · GitHub Trending · 2026-09-12](https://bsky.app/profile/github-trending.bsky.social/post/3mvdbi2rahm2l): 🚀 Skyrocketing！ 🚀 (200+ new stars) 📦 Shubhamsaboo / awesome-llm-apps ⭐ 137,349 (+237) 🗒 Python 100+ AI Agents, Agent Skills and RAG Apps - Free and Open Source. - [X · @danielishakutv · 2026-10-31](https://x.com/danielishakutv/status/2116583027350392920): 1. Generative AI for Beginners https://t.co/dVIMbu6u4z 2. LLM Course https://t.co/KtIU8aeQ1v 3. Build a Large Language Model From Scratch https://t.co/miLQFiJ6iC - [Reddit · r/BestGitHubRepos · 2026-10-09](https://www.reddit.com/r/BestGitHubRepos/comments/1x1jike/6_github_repos_that_take_you_from_zero_to/): 6 GitHub repos that take you from zero to production AI agents - [Hacker News · gargigupta · 2025-08-06](https://news.ycombinator.com/item?id=44815919): Show HN: AI Real Estate Agent Team (100% Open-Source with Free Tutorial): Hey HN\! We built a multi-agent Real Estate team that provides detailed property listings, market insights, and investment analysis all in one interface without you having to search multiple websites. It has 3 agents working sequentially: 1. Property Search Agent finds properties using direct… - [Bluesky · GitHub Trending · 2026-08-25](https://bsky.app/profile/github-trending.bsky.social/post/3mtwjtupfy62l): 🔥 Hot Repo！ 🔥 (100+ new stars) 📦 Shubhamsaboo / awesome-llm-apps ⭐ 134,011 (+161) 🗒 Python 100+ AI Agents, Agent Skills and RAG Apps - Free and Open Source. - [X · @qzxcle · 2026-10-30](https://x.com/qzxcle/status/2116195486239510814): $81 - [Hacker News · mooreds · 2025-06-12](https://news.ycombinator.com/item?id=44258452): Collection of LLM Apps - [Bluesky · デイリーGitHubトレンド · 2026-08-26](https://bsky.app/profile/dailygithubtrends.bsky.social/post/3mtya6sdo5e23): 今日のGitHubトレンド Shubhamsaboo/awesome-llm-apps 100以上のオープンソースAIエージェント、スキル、RAGアプリを提供するリポジトリです。 Apache-2.0ライセンスで公開されており、GPTやClaudeなど多様なLLMに対応しています。 初心者向けから高度な推論、常駐型、マルチエージェントチームまで幅広いテンプレートを揃え、AIエージェントの迅速な構築と導入を支援することを目的としています。 - [X · @masterofthegate · 2026-10-29](https://x.com/masterofthegate/status/2115821033135759519): Google Tasks Official https://t.co/scLrMSUOVD Unofficial https://t.co/41KBZoCjal https://t.co/a2Zo9BgmU7 https://t.co/gR8LkkqDA6 https://t.co/QxItIPffO2 - [Hacker News · gargigupta97 · 2025-01-20](https://news.ycombinator.com/item?id=42764611): Build an AI Game Design Agent Team (100% Opensource): The AI Game Design Agent Team is a collaborative game design system powered by Autogen's AI Agent framework. This app coordinates multiple specialized AI agents - each focusing on their domain expertise - to generate cohesive game concepts where narrative, gameplay, visuals, and technical… - [Bluesky · GitHub Trending · 2026-09-12](https://bsky.app/profile/ghtrend.newsmast.social.ap.brid.gy/post/3mvddwpci2tj2): 8. https://github.com/alsk1992/CloddsBot - TypeScript, 377 stars today 9. https://github.com/yuliskov/SmartTube - Java, 247 stars today 10. https://github.com/Shubhamsaboo/awesome-llm-apps - Python, 237 stars today 11. https://github.com/p1neappleXpress/OpenFlux - Go, 355 stars today 12 … ## Get it - [Open Shubhamsaboo/awesome-llm-apps on GitHub](https://github.com/Shubhamsaboo/awesome-llm-apps) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c73464b1-6bd3-442f-8aa4-00a9ff91b190) - [Its page on Mindket](https://mindket.com/p/c73464b1-6bd3-442f-8aa4-00a9ff91b190)