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Awesome LLM Apps: AI Agents & RAG Examples
A curated collection of awesome LLM applications featuring AI Agents and RAG techniques, utilizing models from OpenAI, Anthropic, Gemini, and various open-source projects.
Python
Added on 2025年6月15日40,691
Stars4,638
ForksPython
LanguageProject Introduction
Summary
This project is a comprehensive list intended to collect and showcase 'awesome' applications built using Large Language Models (LLMs). It focuses on practical examples, particularly those incorporating AI Agents and Retrieval Augmented Generation (RAG).
Problem Solved
Finding practical, implemented examples of LLM applications, especially those employing advanced techniques like Agents and RAG, can be challenging. This repository solves this by providing a single, organized resource.
Core Features
Curated List
Browse a categorized list of real-world LLM applications.
AI Agents Showcase
Discover projects implementing AI Agents for complex tasks.
RAG Implementations
Explore applications leveraging Retrieval Augmented Generation (RAG) for enhanced knowledge recall.
Multi-Model Coverage
Find examples using diverse models including OpenAI, Anthropic, Gemini, and prominent open-source alternatives.
Tech Stack
Large Language Models (LLMs)
AI Agents
Retrieval Augmented Generation (RAG)
OpenAI
Anthropic
Gemini
Open Source LLMs (e.g., Llama, Mistral)
使用场景
Explore this repository to achieve the following goals:
Discovering Application Examples
Details
Browse the list to find specific examples of LLM apps built for tasks like summarization, code generation, creative writing, etc.
User Value
Quickly find inspiration or relevant starting points for your own LLM project ideas.
Learning Implementation Techniques
Details
Identify projects that showcase effective patterns or frameworks for building AI agents or implementing RAG.
User Value
Understand how complex LLM techniques like agents and RAG are applied in practice.
Comparing Model Usage
Details
Compare how different LLM models (OpenAI, Anthropic, Gemini, open source) are used in real applications.
User Value
See which models are popular for certain tasks or how developers approach multi-model support.
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