awesome-llm-apps

<p align="center">
  <img src="assets/logo.png" alt="Awesome LLM Apps" width="200" />
</p>

<h1 align="center">🧠 Awesome LLM Apps</h1>

<p align="center">
  <strong>A curated collection of production-ready LLM applications with working code</strong>
</p>

<p align="center">
  RAG Pipelines • AI Agents • Chatbots • Web Scrapers • Text-to-SQL
</p>

<p align="center">
  <img src="https://img.shields.io/badge/Python-3.8+-blue?style=flat-square&logo=python" />
  <img src="https://img.shields.io/badge/LangChain-0.1+-green?style=flat-square" />
  <img src="https://img.shields.io/badge/OpenAI-GPT--4-purple?style=flat-square&logo=openai" />
  <img src="https://img.shields.io/badge/ChromaDB-Vector_Store-orange?style=flat-square" />
  <img src="https://img.shields.io/badge/License-MIT-yellow?style=flat-square" />
  <img src="https://img.shields.io/badge/Examples-5-red?style=flat-square" />
  <a href="https://github.com/sponsors/RayeesYousufGenAi"><img src="https://img.shields.io/badge/Sponsor-💖-ff69b4?style=flat-square&logo=github" /></a>
</p>

---

## 🎯 What Is This?

A collection of **production-ready LLM applications** that you can run, learn from, and extend. Each example is a complete, working app with clean code, Streamlit UI, and documentation.

> **No tutorials, no theory** — just real, working applications that solve real problems.

---

## 🚀 Working Examples

| # | App | Description | Stack | Run |
|---|-----|-------------|-------|-----|
| 01 | [🤖 Smart Chatbot](examples/01-smart-chatbot/) | Context-aware chatbot with sliding window memory | GPT-4, LangChain, Streamlit | `streamlit run chatbot.py` |
| 02 | [📄 PDF RAG Q&A](examples/02-pdf-rag-qa/) | Upload PDFs → Ask questions → Get sourced answers | ChromaDB, OpenAI Embeddings, GPT-4 | `streamlit run rag_app.py` |
| 03 | [🕷️ AI Web Scraper](examples/03-ai-web-scraper/) | Scrape any URL → AI extracts what you need | BeautifulSoup, GPT-4, Streamlit | `streamlit run scraper.py` |
| 04 | [🤝 Multi-Agent Crew](examples/04-multi-agent-crew/) | 3 AI agents collaborate: Research → Write → Edit | CrewAI, GPT-4 | `python crew_app.py "topic"` |
| 05 | [🗃️ Text-to-SQL](examples/05-text-to-sql/) | Ask questions in English → Get SQL + live results | SQLite, GPT-4, Streamlit | `streamlit run text_to_sql.py` |

---

## 🏗️ Architecture Patterns

### RAG (Retrieval-Augmented Generation)
```
Document → Chunk → Embed → Vector Store → Retrieve → LLM → Answer
```
Used in: [PDF RAG Q&A](examples/02-pdf-rag-qa/)

### Multi-Agent Systems
```
Task → Agent 1 (Research) → Agent 2 (Write) → Agent 3 (Edit) → Output
```
Used in: [Multi-Agent Crew](examples/04-multi-agent-crew/)

### Conversational Memory
```
User → LLM + Memory Buffer (last N messages) → Context-Aware Response
```
Used in: [Smart Chatbot](examples/01-smart-chatbot/)

### AI-Powered Data Extraction
```
URL/Database → Scrape/Query → LLM Analysis → Structured Output
```
Used in: [AI Web Scraper](examples/03-ai-web-scraper/), [Text-to-SQL](examples/05-text-to-sql/)

---

## ⚡ Quick Start

```bash
# 1. Clone this repo
git clone https://github.com/RayeesYousufGenAi/awesome-llm-apps.git
cd awesome-llm-apps

# 2. Install dependencies
pip install -r requirements.txt

# 3. Set up API keys
cp .env.example .env
# Edit .env with your OpenAI API key

# 4. Run any example
cd examples/01-smart-chatbot
streamlit run chatbot.py
```

---

## 📚 Curated Resources

### 🔗 Frameworks & Libraries

| Category | Tool | Description |
|----------|------|-------------|
| **LLM Framework** | [LangChain](https://langchain.com) | Build context-aware reasoning applications |
| **LLM Framework** | [LlamaIndex](https://llamaindex.ai) | Data framework for LLM applications |
| **Multi-Agent** | [CrewAI](https://crewai.com) | Framework for orchestrating AI agent teams |
| **Multi-Agent** | [AutoGen](https://microsoft.github.io/autogen/) | Microsoft's multi-agent conversation framework |
| **Vector Store** | [ChromaDB](https://trychroma.com) | Open-source AI-native embedding database |
| **Vector Store** | [Pinecone](https://pinecone.io) | Managed vector database for production |
| **Vector Store** | [Weaviate](https://weaviate.io) | Open-source vector search engine |
| **UI** | [Streamlit](https://streamlit.io) | Build data apps with pure Python |
| **UI** | [Gradio](https://gradio.app) | Build ML demos and web apps fast |
| **Orchestration** | [n8n](https://n8n.io) | Workflow automation with AI nodes |

### 🤖 LLM Providers

| Provider | Models | Best For |
|----------|--------|----------|
| [OpenAI](https://openai.com) | GPT-4, GPT-4 Turbo, GPT-3.5 | General purpose, coding, reasoning |
| [Google](https://ai.google.dev) | Gemini Pro, Gemini Ultra | Multimodal, long context |
| [Anthropic](https://anthropic.com) | Claude 3, Claude 3.5 | Long documents, safety, coding |
| [Meta](https://llama.meta.com) | Llama 3, CodeLlama | Open-source, self-hosted |
| [Mistral](https://mistral.ai) | Mistral, Mixtral | Fast, efficient, open-weight |
| [Hugging Face](https://huggingface.co) | 100K+ models | Open-source model hub |

### 📖 Learning Resources

| Resource | Type | Level |
|----------|------|-------|
| [LangChain Docs](https://python.langchain.com/docs/) | Documentation | All |
| [DeepLearning.AI Short Courses](https://www.deeplearning.ai/short-courses/) | Video Courses | Beginner-Advanced |
| [Prompt Engineering Guide](https://www.promptingguide.ai/) | Guide | Beginner |
| [RAG Best Practices](https://docs.llamaindex.ai/en/stable/) | Documentation | Intermediate |
| [Building LLM Apps (Chip Huyen)](https://huyenchip.com/2023/04/11/llm-engineering.html) | Blog | Advanced |

---

## 🗂️ Project Structure

```
awesome-llm-apps/
├── 📄 README.md                  ← You are here
├── 📋 requirements.txt           ← All Python dependencies
├── 🔑 .env.example               ← API key template
├── 📜 LICENSE                     ← MIT License
├── 🎨 assets/
│   └── logo.png                  ← Project logo
└── 📁 examples/
    ├── 01-smart-chatbot/          ← GPT-4 chatbot with memory
    │   ├── chatbot.py
    │   └── README.md
    ├── 02-pdf-rag-qa/             ← RAG pipeline for PDF Q&A
    │   ├── rag_app.py
    │   └── README.md
    ├── 03-ai-web-scraper/         ← AI-powered web scraping
    │   ├── scraper.py
    │   └── README.md
    ├── 04-multi-agent-crew/       ← CrewAI multi-agent system
    │   ├── crew_app.py
    │   └── README.md
    └── 05-text-to-sql/            ← Natural language SQL queries
        ├── text_to_sql.py
        └── README.md
```

---

## 🤝 Contributing

Want to add your own LLM app? Follow these steps:

1. **Fork** this repo
2. **Create** a new folder under `examples/` (e.g., `06-your-app/`)
3. **Include**: working code + `README.md` with run instructions
4. **Submit** a Pull Request

### Guidelines
- ✅ Code must be working and tested
- ✅ Include a `README.md` with setup + run instructions
- ✅ Use `.env` for API keys (never hardcode)
- ✅ Keep dependencies in `requirements.txt`
- ❌ No notebook-only examples — provide `.py` files

---

## 📊 What Makes This Different?

| Feature | This Repo | Other Collections |
|---------|-----------|-------------------|
| **Working Code** | ✅ Every example runs | ❌ Many are snippets |
| **UI Included** | ✅ Streamlit/Gradio apps | ❌ CLI-only |
| **Real Patterns** | ✅ RAG, Agents, Memory | ❌ Basic API calls |
| **Clean Structure** | ✅ Each app self-contained | ❌ Monolithic |
| **Production Ready** | ✅ Error handling, env vars | ❌ Hardcoded keys |

---

## ⭐ Star History

If you find this useful, **give it a ⭐!** It helps others discover these tools.

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## 💖 Sponsors & Support

Building and maintaining high-quality open-source AI applications takes significant time and effort. If you find this repository useful for your learning, projects, or business, please consider supporting the development!

<p align="center">
  <a href="https://github.com/sponsors/RayeesYousufGenAi">
    <img src="https://img.shields.io/badge/Sponsor_Me_On_GitHub-�

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