Awesome LLM Apps is a GitHub repository that gathers a large number of implementation examples for applications built on LLMs (large language models). Runnable sample code and tutorials are organized by theme: AI agents, multi-agent teams, RAG (retrieval-augmented generation), MCP (Model Context Protocol), voice agents, and more. It is maintained by Shubham Saboo in India, and new templates are added continuously in step with his AI media outlet “Unwind AI.” As of August 2026 the repository has passed 130,000 stars, making it one of the largest sample collections in this space. The easiest way to think about it is as a place to learn from working code when you want to build an AI agent but have no idea where to start writing.
Key Features
- More than 100 implementation samples: From beginner-friendly single-file implementations such as travel planners, data analysis, and podcast generation, through to full-scale agents that combine tool use and memory ─ fraud investigation, home renovation, financial coaching, and similar
- Categories organized by theme: Folders are split by topic, including starter agents, advanced agents, always-on agents, multi-agent teams, voice AI agents, generative UI agents, autonomous game-playing agents, MCP-connected agents, RAG applications, memory systems, and fine-tuning
- Spans major models and frameworks: Alongside commercial models such as Claude, Gemini, and GPT, it supports open source models including DeepSeek, Llama, and Qwen. Frameworks span Google ADK, OpenAI Agents SDK, CrewAI, LangGraph, AG2 and others, so you can compare the same use case implemented with different tools
- Agent Skills included: Reusable skills to give to coding agents are published as well. They can be pulled into your own environment with
npx skills add <URL> - Apache-2.0 license: Commercial use and modification are permitted (attribution required). Beyond reading it to learn, the code is easy to reuse as a foundation for your own product
- Linked to Unwind AI: Explanatory articles for each sample live on the “Unwind AI” media site, and new templates are added and announced weekly
Pricing
| Plan | Price | Main features |
|---|---|---|
| Using the repository | $0 | Browse, clone, and commercially use all sample code (Apache-2.0) |
| Unwind AI (newsletter) | $0 | Announcements of new templates, tutorial articles |
Pricing is current as of August 2026. Please check the official repository and Unwind AI for the latest information. Note that running the samples often requires API keys from OpenAI, Anthropic, Google and others, and those API charges are paid to each provider.
Pros & Cons
✅ Pros
- Because you start from working code, you can grasp the overall picture before working through a framework’s official documentation
- Categories are cut by practical scenario, making it easy to find an implementation close to what you want to do
- The commercially permissive license means it can serve as a starting point for internal tools and products
- Spanning several frameworks and models, it doubles as material for technology selection
- Development is active, so samples for newer mechanisms such as MCP and Agent Skills appear relatively early
⚠️ Cons
- This is a sample collection, not a guarantee of production-ready quality. Error handling, authentication, and cost control are yours to add
- Authors and creation dates vary from sample to sample, and some have outdated dependency versions that will not run as-is
- Explanations and comments are mostly in English; there is no Japanese documentation
- With so many entries, it is easy to get lost if you browse without a clear goal
- Running the code requires a Python environment and various API keys ─ it is not a no-code option
Comparison with Similar Services
| Comparison | Awesome LLM Apps | GenAI_Agents | OpenAI Cookbook | Anthropic Cookbook |
|---|---|---|---|---|
| Provider | Shubham Saboo / Unwind AI | Nir Diamant | OpenAI | Anthropic |
| Main content | 100+ app implementations by use case | Agent-building tutorials (mostly Jupyter) | Recipes for using the OpenAI API | Recipes for using Claude |
| Target models | Spans commercial and open source | Mainly OpenAI plus various frameworks | Mainly OpenAI models | Mainly Claude |
| Frameworks | ADK / OpenAI Agents SDK / CrewAI / LangGraph / AG2 and more | LangGraph / LangChain / CrewAI / AutoGen and more | Mainly the official SDK | Mainly the official SDK |
| License | Apache-2.0 (commercial use allowed) | Custom license (non-commercial) | Follows each repository’s terms | Follows each repository’s terms |
| Character | Broad range of real applications | Careful explanations for learning | Accuracy as a primary source | Accuracy as a primary source |
Who Is It For
- Engineers who want to build AI agents or RAG but do not want to spend time designing from scratch
- People deciding between frameworks such as CrewAI, LangGraph, and the OpenAI Agents SDK
- Anyone who wants to quickly check implementation examples of newer mechanisms like MCP and voice agents
- Teams that want to stand up an internal-tool prototype quickly from commercially usable code
- Learners who want to study LLM app design patterns systematically by reading implementations
Summary
Awesome LLM Apps has effectively become a standard “gallery of examples” for LLM app development. Its strength lies in the combination of a large, use-case-organized body of implementations and an Apache-2.0 license that permits commercial use. That said, the contents are samples, not products. Outdated dependencies and missing error handling are yours to fill in, so the realistic approach is to run and read the code rather than trusting it as-is, and adopt only the parts you need. A good starting point is to pick one folder close to your own use case and run it as the README describes.