Arindam200/awesome-ai-apps

What it is
A curated, community-maintained list of 132 example projects, tutorials, and recipes for building applications powered by large language models (LLMs). It's a practical reference guide for developers who want to see real code for common AI app patterns.

What kinds of apps are covered
The projects span several categories, each with a clear focus:

• Text agents – assistants that chat, reason, and call tools (e.g., a weather bot, a travel planner, a stock portfolio analyst).
• Voice assistants – real-time speech pipelines (e.g., a customer-support voice agent, a pitch coach, a voice-enabled RSVP caller).
• RAG applications – systems that retrieve relevant documents or data before answering, for tasks like codebase Q&A or enterprise knowledge bases.
• MCP-backed tools – examples using the Model Context Protocol, a standard way for AI agents to connect to external services (databases, GitHub, hotel booking, etc.).
• Memory agents – agents that remember past interactions across sessions (e.g., a study coach, a brand reputation monitor, a job search assistant).
• Starter and advanced agents – from quick-start templates for learning a framework to more complex multi-agent systems.
• Fine-tuning – examples for customizing models on your own data.

How it's organized
The README groups projects into labeled sections (Starter Agents, Simple Agents, Voice Agents, MCP Agents, Memory Agents, RAG Applications, Advanced Agents, Fine-Tuning) plus a separate Tutorials & Videos section. Each entry is a one-line description with a link to its folder in the repo.

Frameworks and stacks
The examples cover a wide range of popular AI frameworks, including AutoGen, CrewAI, LangChain, LangGraph, LlamaIndex, OpenAI Agents SDK, Semantic Kernel, Agno, Mastra, and more. Many projects are powered by specific services (e.g., Nebius for inference, LiveKit for voice, Memori for memory).

Sponsorship and contribution
The project lists several commercial sponsors (Bright Data, Nebius, ScrapeGraphAI, CopilotKit, ScaleKit, Okahu, AgentField, Byteful, Memorilabs) and invites others to sponsor via email. It also has a Contributing section, meaning it's open to community additions.

Notable details

• The repo carries the Awesome badge, a common marker for high-quality curated lists.
• The README includes a Getting Started section (not detailed in the excerpt) and a table of contents for easy navigation.
• The project appears to be actively maintained, with a Trendshift badge indicating popularity.

In short: If you're a developer looking for working examples of LLM-powered apps — from simple chatbots to voice assistants and document-retrieval systems — this repo gives you a browsable catalog of real code, each with a short description and a link to dive in.