Awesome LLM Apps: 93K Stars for Runnable Code Examples

You've worked through the LangChain quickstart. You've bookmarked a dozen academic papers on retrieval-augmented generation. But when you sit down to build an actual LLM application—one that handles real documents, manages conversation state, or coordinates multiple AI agents—the examples run out.

This is the gap Shubhamsaboo's awesome-llm-apps addresses: a curated collection of runnable LLM projects that demonstrate RAG systems, AI agents, multi-agent teams, and voice applications. Not paper citations. Not framework documentation. Working code you can clone, run, and study.

The Learning Gap Between Papers and Production

Most LLM learning resources fall into two camps. Academic papers explain architectures but rarely include implementation details. Framework documentation assumes you already understand what you're building. Between these extremes, developers circulate through toy examples—chat interfaces that store no history, RAG systems that work on three PDFs, agents that handle exactly one use case.

The repository tackles this by collecting documented examples of complete applications. Instead of "here's how to call an API," the projects show how to structure a lead generation agent, build a research assistant that processes multiple sources, or coordinate agents that hand off tasks to each other.

What Makes This Collection Different

Where Hannibal046/Awesome-LLM catalogs papers and theoretical frameworks, this repository focuses on implementations you can execute locally. The distinction matters for developers who learn by modifying working code rather than translating research into practice.

The collection spans several application categories. RAG implementations demonstrate document processing pipelines. Agent examples show task planning and tool use. Multi-agent projects illustrate how to coordinate specialized AI systems. Voice applications connect speech-to-text, LLM reasoning, and text-to-speech into functional workflows.

Each project includes the code structure, dependencies, and setup instructions developers need to run the example themselves. This differentiates it from web-focused app collections that prioritize deployment-ready interfaces over learning materials.

Community Usage and Adoption

The repository's 93,000 stars suggest broad awareness, but forks tell a different story about actual use. Developers like Madhuvod and peteryxu have forked the collection, indicating they're adapting these examples for their own projects rather than just bookmarking the resource.

Active issue reports show ongoing engagement with the code. Developers don't file bug reports for repositories they haven't tried to run. The issues reveal which examples see the most use and where implementation challenges surface in real environments.

Growing Pains

Growth creates maintenance pressure. Some projects encounter Composio errors when retrieving external data. Others throw agno.storage exceptions in certain runtime configurations. Pull requests and issues accumulate faster than a single maintainer can address them—a moderate delivery risk typical for community resources scaling from thousands to tens of thousands of users in under a year.

These issues are evidence of a living project that prioritizes shipping working examples over perfect code. The examples demonstrate patterns and approaches; developers adapt them to their specific contexts rather than running them unchanged in production.

When Code Examples Beat Documentation

This collection serves developers who've moved past "hello world" LLM experiments but haven't yet built complete applications. If you learn by reading and modifying existing code, if you need to see how pieces fit together before architecting your own system, if you're ready to move from API calls to application design—this repository provides the missing reference implementations.

It won't replace official documentation or academic papers. It complements them by showing what those resources describe: working applications you can run today.