shubhamsaboo/awesome-llm-apps

Analysis updated 2026-06-20

★ 109,044PythonAudience · vibe coderComplexity · 2/5LicenseSetup · easy

Mindmap

mindmap
  root((awesome-llm-apps))
    App categories
      Starter agents
      Multi-agent teams
      Voice AI agents
      RAG tutorials
    AI providers
      OpenAI
      Claude
      Gemini
      Llama
    Tech
      Python
      Streamlit
      MCP agents
      Fine-tuning
    Use cases
      Side projects
      Learning by example
      Rapid prototyping

mindmap root((awesome-llm-apps)) App categories Starter agents Multi-agent teams Voice AI agents RAG tutorials AI providers OpenAI Claude Gemini Llama Tech Python Streamlit MCP agents Fine-tuning Use cases Side projects Learning by example Rapid prototyping

Click or tap to explore — scroll the page freely

What do people build with it?

USE CASE 1

Clone a ready-made RAG template, point it at your own documents, and ship a document Q&A chatbot in a day.

USE CASE 2

Pick a voice AI agent starter and build a customer support bot that takes voice input and responds with speech.

USE CASE 3

Use a multi-agent template to build a team of AI agents that research a topic, write a draft, and review it collaboratively.

USE CASE 4

Learn how agent memory, tool use, and RAG work by running the provided examples and reading the linked step-by-step write-ups.

What is it built with?

PythonStreamlitOpenAI APILangChain

How does it compare?

	shubhamsaboo/awesome-llm-apps	comfy-org/comfyui	deepseek-ai/deepseek-v3
Stars	109,044	111,631	103,409
Language	Python	Python	Python
Setup difficulty	easy	hard	hard
Complexity	2/5	3/5	5/5
Audience	vibe coder	vibe coder	researcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 30min

Each template requires at least one LLM provider API key such as OpenAI or Anthropic, no other infrastructure needed.

Apache 2.0, use, modify, and distribute freely for any purpose including commercial use, as long as you include the license notice.

In plain English

Awesome LLM Apps is a cookbook of more than 100 ready-to-run starter projects for building applications powered by large language models (LLMs, the AI models behind tools like ChatGPT). Instead of asking you to figure out the wiring yourself, each entry is a self-contained template, original code, tested end to end, that you can clone, customize, and ship as a real product. The README's tagline is "clone, customize, ship." The collection is organized into roughly 13 categories: starter agents that run with just an API key, more advanced agents with memory and tools, multi-agent teams that coordinate with each other, voice AI agents, agents built on the MCP (Model Context Protocol) standard, RAG tutorials (Retrieval-Augmented Generation, the pattern of feeding a model your own documents), agent-skills demos, fine-tuning walkthroughs, "chat with X" projects, and several crash courses on popular AI agent frameworks. Each template is provider-agnostic, meaning the README states you can swap between Claude, Gemini, OpenAI, Llama, Qwen, and xAI by changing configuration. Featured examples in the README include an insurance-claim voice agent and a home-renovation agent that takes a photo and redesigns the room. You would use this if you are starting a new AI side project and don't want to rebuild the same RAG pipeline or agent loop from scratch, or if you are learning by example and prefer working code over abstract tutorials. The quick-start in the README is "git clone, pip install, streamlit run", so the stack is Python with Streamlit (a tool for putting a web interface in front of Python scripts) plus whichever model provider you pick. Every featured template links to a free step-by-step write-up on the author's Unwind AI site, and the whole repository is Apache-2.0 licensed.

Copy-paste prompts

Prompt 1

I want to build a chatbot that answers questions about my company's PDF documents using RAG. Which template in awesome-llm-apps should I start with, and how do I set it up with my own PDFs?

Prompt 2

Using one of the awesome-llm-apps starter projects, show me how to build a multi-agent research assistant where one agent searches the web and another writes a summary.

Prompt 3

I want to swap the OpenAI model in an awesome-llm-apps template for Claude. Show me which config lines to change to use Anthropic's API instead.

Prompt 4

Show me how to run the insurance-claim voice agent from awesome-llm-apps locally and what API keys I need to provide.

Prompt 5

I'm learning about AI agent memory. Show me how to run the memory agent example from awesome-llm-apps and explain how it stores and retrieves past conversation context.

Frequently asked questions

What is awesome-llm-apps?

A collection of 100+ ready-to-run AI app starter projects, agents, RAG, voice AI, multi-agent teams, that you clone, customize, and ship as a real product.

What language is awesome-llm-apps written in?

Mainly Python. The stack also includes Python, Streamlit, OpenAI API.

What license does awesome-llm-apps use?

Apache 2.0, use, modify, and distribute freely for any purpose including commercial use, as long as you include the license notice.

How hard is awesome-llm-apps to set up?

Setup difficulty is rated easy, with roughly 30min to a first successful run.

Who is awesome-llm-apps for?

Mainly vibe coder.

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