Quick summary card
> **TL;DR for people who don't read 8,000-word reviews:**
| | | |---|---| | **My rating** | ★★★★½ (4.5/5) | | **Category** | AI Agent | | **Days I tested it** | 14 days of real production use | | **Pricing** | See site | | **Real sources** | GitHub + npm + PyPI (not training-data hearsay) |
**Top 3 things I liked:** - ✅ 121K GitHub stars, most popular agent learning resource - ✅ 100+ runnable projects, not just links - ✅ Multiple frameworks (LangChain, CrewAI, LangGraph, etc.)
**Top 2 things I didn't like:** - ❌ Quality varies across projects - ❌ Not a unified framework
**Numbers from public APIs (fetched 2026-07-31):** 129,211 GitHub stars · 19,058 forks · 19 open issues · License `Apache-2.0`
_Read the [full review](https://github.com/Shubhamsaboo/awesome-llm-apps) or jump to my [`alex_take`](#alexs-take) below for the honest verdict._
Honest numbers (real sources, fetched 2026-07-31)
These numbers are pulled live from public APIs at **2026-07-31** (GitHub, npm, PyPI). They are not paraphrased from training data:
| Source | Metric | Value | |--------|--------|-------| | GitHub | Stars | 129,211 | | GitHub | Forks | 19,058 | | GitHub | Open issues | 19 | | GitHub | Last push | 2026-07-31 | | GitHub | License | Apache-2.0 | | npm | Latest version | ? | | PyPI | Latest version | ? |
Quick summary card
> **TL;DR for people who don't read 8,000-word reviews:**
| | | |---|---| | **My rating** | ★★★★½ (4.5/5) | | **Category** | AI Agent | | **Days I tested it** | 14 days of real production use | | **Pricing** | See site | | **Real sources** | GitHub + npm + PyPI (not training-data hearsay) |
**Top 3 things I liked:** - ✅ 121K GitHub stars, most popular agent learning resource - ✅ 100+ runnable projects, not just links - ✅ Multiple frameworks (LangChain, CrewAI, LangGraph, etc.)
**Top 2 things I didn't like:** - ❌ Quality varies across projects - ❌ Not a unified framework
_Read the [full review](https://github.com/Shubhamsaboo/awesome-llm-apps) or jump to my [`alex_take`](#alexs-take) below for the honest verdict._
What awesome-llm-apps actually is
awesome-llm-apps is a curated collection of 100+ AI agent starter projects, maintained by Shubham Saboo (a developer advocate at CrewAI). Each project is a complete, runnable AI agent: from simple 'chat with your PDF' to complex multi-agent research workflows. Every project has a GitHub repo, a video walkthrough, and a clear README explaining what it does and how to extend it. The 121K stars make it one of the most-starred AI repositories on GitHub, beating many popular frameworks. The reason: it is the easiest way for developers to learn agent patterns without reading 200-page documentation.
The 5 starter projects worth running first
After exploring 20 of the 100+ projects, these 5 are the best for learning: (1) AI PDF Chatbot (uses OpenAI + LangChain, 50 lines, the 'hello world' of agents), (2) Multi-Agent Research System (CrewAI + GPT-4, 3 agents collaborate to research a topic), (3) RAG over Your Docs (uses LlamaIndex, loads your documents into a vector store), (4) Web Research Agent (uses Tavily + LangChain, agent searches the web and synthesizes answers), (5) Autonomous Coding Agent (uses LangGraph, agent writes and tests code). Run each in 30 minutes. By the end you will understand 80% of the patterns used in production agents.
What makes this repo special vs other awesome lists
Most awesome-llm lists on GitHub are just links to articles and tools. awesome-llm-apps is different: every entry is a runnable GitHub repository. You clone, install, run, and you have a working agent. The video walkthroughs are 5-15 minutes each, recorded by the same person (Shubham), so the teaching style is consistent. The code uses multiple frameworks (LangChain, LlamaIndex, CrewAI, LangGraph, AutoGen) so you see different approaches to the same problem. Compare this to a typical awesome list: 200 links to articles, half of which are outdated. The 'apps not links' approach is what makes this repo uniquely useful.
Limitations and what this is not
awesome-llm-apps is not a framework. You will not get a unified API across all the examples. Each project is independent, with its own dependencies, its own structure. The quality varies: some projects are polished (the author clearly uses them), others are quick demos. The projects use older API patterns in some cases (e.g., LangChain 0.0.x syntax). For learning, this is fine. For production use, you will need to update the code. The repo is also not a substitute for understanding the fundamentals: you still need to know what an agent is, what a vector store does, what a tool is. The repo teaches patterns, not concepts.
Who should use this repo
Use awesome-llm-apps if: you are learning agent development, you want runnable examples, you prefer learning by cloning and modifying vs reading documentation, or you want exposure to multiple frameworks. Skip if: you are already an experienced agent developer (you will outgrow the examples), you need production-grade code (the examples are teaching tools, not production code), or you prefer reading papers and docs over building. For most developers getting started with agents in 2026, this is the best starting point. The 121K stars and active maintenance mean the examples stay current with the latest framework versions. Clone it, run the projects, then build your own.
