awesome-ai-apps README

The project's README from github.com/Arindam200/awesome-ai-apps. As one markdown file: README.md.

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Awesome AI Apps Awesome

Arindam200%2Fawesome-ai-apps | Trendshift

This repository is a comprehensive collection of 132 projects, tutorials, and recipes for building powerful LLM-powered applications, including text agents, voice assistants, RAG apps, and MCP-backed tools. These projects serve as a guide for developers working with various AI frameworks and stacks.

📋 Table of Contents


💎 Sponsors

A huge thank you to our sponsors for their generous support!

Bright Data - Web Data Platform
Web Data Platform
Visit Bright Data website
Nebius Token Factory
AI Inference Provider
Visit Nebius Token Factory
ScrapeGraphAI - Web Scraping Library
AI Web Scraping framework
View ScrapeGraphAI on GitHub
Memori - SQL Native Memory for AI
SQL Native Memory for AI
Visit Memorilabs website
CopilotKit - Agentic Application Platform
Agentic Application Platform
Visit CopilotKit website
ScaleKit - Auth Stack for AI
Auth Stack for AI
Visit ScaleKit website
Okahu - AI Platform
AI Observability Platform
Visit Okahu website
AgentField - Kubernetes for AI Agents
Kubernetes for AI Agents
Visit AgentField website

💎 Become a Sponsor

Interested in sponsoring this project? Feel free to reach out!
Email


🧩 Starter Agents

Quick-start agents for learning and extending different AI frameworks. 20 projects

🪶 Simple Agents

Straightforward, practical use-cases for everyday AI applications. 18 projects

🎙️ Voice Agents

Real-time voice assistants and streaming speech pipelines — including LiveKit, Pipecat, Gradium, and VoxCode (Deepgram + Cursor SDK). 9 projects

🗂️ MCP Agents

Examples using Model Context Protocol for external tool integration. 14 projects

🧠 Memory Agents

Agents with advanced memory capabilities for context retention and personalization. 13 projects

📚 RAG Applications

Retrieval-augmented generation examples for document understanding and knowledge bases. 18 projects

awesome-ai-apps (Arindam200/awesome-ai-apps)

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这是什么?这是一个精选合集,包含 132 个基于大语言模型(LLM)构建应用的项目、教程和示例,涵盖文本智能体、语音助手、RAG 应用以及由 MCP 支持的工具。
这是单个应用,还是许多小项目的索引?它是一个索引:每个条目都是独立、自包含的项目或示例,使用不同的 AI 框架构建(如 LangChain、Agno、CrewAI、LlamaIndex、Mastra 等),并按入门智能体、简单智能体、语音智能体、MCP 智能体、记忆智能体和 RAG 应用等类别组织。
我该怎么使用这些示例项目?浏览目录找到分类,选择一个项目,然后打开它自己的文件夹或 README 查看安装说明。这个仓库本身是一个目录,而不是单个可直接安装的应用。
所有项目都使用同一个 AI 服务商或 API 密钥吗?不是。不同项目使用不同的服务商和 SDK(如 Nebius Token Factory、OpenAI Agents SDK、Google ADK、AWS Strands、Deepgram、LiveKit 等),因此所需的密钥取决于你选择的具体项目。
这些是可用于生产环境的应用,还是仅供学习的演示?大多数是入门级或演示级示例,用于学习和扩展不同框架;不过也有少数(如 Production PDF RAG)被描述为更接近实际生产环境的结构。
包含哪些类别的项目?入门智能体、简单智能体、语音智能体、MCP 智能体、记忆智能体、RAG 应用、高级智能体和微调,另外还有教程和视频。
涵盖哪些框架?范围很广,包括 LangChain、LangGraph、CrewAI、Agno、LlamaIndex、Mastra、AWS Strands、Google ADK、Microsoft AutoGen/Semantic Kernel/Agent Framework、Hugging Face smolagents、DSPy,以及用于语音的 Pipecat 和 LiveKit。
MCP 智能体示例是关于什么的?它们展示了用于连接外部工具的模型上下文协议(MCP)集成,例如 GitHub 仓库分析、Couchbase 和数据库访问、沙箱代码执行,以及安全的 MCP 访问模式。
这个仓库还在接收新项目吗?还在接收。它被标记为 Hacktoberfest 项目,并设有贡献指南部分,表明它正在积极接收社区提交的示例应用。
Creator
Arindam200
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491.6 MB
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Updated
CommitOct 09
Age
1y
Commits · 90 Days