This repository offers a curated collection of advanced AI applications built with the Reflex framework, demonstrating practical Large Language Model (LLM) use cases. It targets developers and researchers seeking to build scalable, AI-powered solutions, showcasing implementations of AI agents, Retrieval-Augmented Generation (RAG), and best practices for integrating LLMs from various providers.

How It Works

The examples leverage the Reflex framework, a Python-based full-stack web framework, to build and deploy sophisticated LLM applications. This approach allows for rapid development and iteration of complex AI features within a unified Python environment, simplifying the creation of interactive and scalable web interfaces for LLM-powered functionalities.

Quick Start & Requirements

• Install using pip install reflex.
• Requires Python 3.8+.
• See official Reflex documentation for detailed setup.

Highlighted Details

• Demonstrates AI agents and their use cases.
• Features Retrieval-Augmented Generation (RAG) implementations.
• Showcases best practices for building scalable AI solutions.
• Integrates LLMs from Google, Anthropic, OpenAI, and self-hosted models.

Maintenance & Community

• Developed by the Reflex team.
• Community support available via Discord.

Licensing & Compatibility

• Licensed under the MIT License.
• Permissive for commercial use and integration with closed-source projects.

Limitations & Caveats

The repository focuses on showcasing applications and may require further adaptation for production-ready deployments. Specific LLM provider API keys are necessary for many examples.