#1818th of 88 in Learning and cookbooks
awesome-ai-apps
Collection of 141 runnable LLM agent and RAG example apps
- Stars
- 16.7k
- License
- MIT
- Last commit
- Nov 2026
- Language
- Python
10,071 on 21 Apr 202616,745 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
A repository of 141 self-contained example projects for building LLM apps: starter agents per framework (Agno, LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Mastra), voice agents, MCP agents, memory agents, RAG apps and LoRA fine-tuning. Each project has its own folder, README, dependencies and `.env.example`. Many examples default to Nebius Token Factory as the model provider.
Who it is for: Developers learning to build LLM agents and RAG apps from working examples
Strengths
- Each project is isolated with its own README, dependencies and .env.example
- Covers many frameworks side by side, including Agno, LangGraph, CrewAI, Mastra and Google ADK
- Spans agents, voice (LiveKit, Pipecat), MCP, memory, RAG and fine-tuning in one repo
- MIT licensed and actively committed
Weaknesses
- Not one application; each example must be set up and run separately
- Many examples depend on hosted APIs such as Nebius, Exa or Firecrawl
- Sponsor-linked services appear throughout, so provider choice is skewed
- No releases or versioning; example quality and upkeep vary by folder
What it needs
- Needs Nebius Token Factory, OpenAI API, Exa, Firecrawl, LiveKit, Pipecat, Memori
- Models: Nebius Token Factory, OpenAI, Google Gemini
Quick answers
Is awesome-ai-apps open source?
Yes. Its code is on GitHub under the MIT license.
Also in Learning and cookbooks
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|---|---|---|
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| 2 | generative-ai-for-beginners21-lesson course on building generative AI apps in Python and TypeScript | 83 out of 100 |
| 3 | rag_techniquesCollection of 42 runnable notebooks implementing RAG techniques | 81 out of 100 |
| #4 | cs249r_bookOpen textbook and course materials for machine learning systems engineering | 81 out of 100 |
| #5 | ai-for-beginners24-lesson AI curriculum with notebooks in PyTorch and TensorFlow | 80 out of 100 |