awesome ai apps
Basic Information
This repository is a curated collection of practical examples, tutorials, and recipes for building LLM-powered applications and agents. It collects starter projects, simple and advanced agent implementations, Model Context Protocol (MCP) examples, retrieve-augmented generation (RAG) applications, and demo workflows to help users learn how to assemble end-to-end AI solutions. The README highlights a variety of agent frameworks and tools and notes that the collection is powered by Nebius AI Studio. The repo is organized by topic so readers can find quick-start agents, concrete use cases, and multi-stage pipelines. It also provides basic getting-started instructions, prerequisites, and per-project READMEs to guide setup and experimentation.
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Features
Organized catalog of categorized example projects including Starter Agents, Simple Agents, MCP Agents, RAG Applications, and Advanced Agents. Featured framework list and integrations include Google ADK, OpenAI Agents SDK, LangChain, LlamaIndex, Agno, CrewAI, AWS Strands Agents, and Pydantic AI. Practical, runnable examples cover finance tracking, newsletter generation, web automation, memory agents, PDF chat, OCR processing, and multi-agent research pipelines. Includes demo playlists and tutorial resources, per-project requirements and setup guidance, contribution guidelines for community submissions, and an MIT license. The repo provides links to individual project READMEs and prescribes prerequisites like Python 3.10+, Git, and pip for reproducible local setup.
Use Cases
The collection helps developers, learners, and practitioners accelerate prototyping and learning by offering ready-made templates and end-to-end examples that demonstrate common LLM patterns and agent behaviors. Users can jumpstart projects with quick-start agents for tasks such as email helpers, weather bots, finance monitoring, document Q&A, and job or trend analysis. MCP and RAG examples illustrate document retrieval and context-managed interactions. Advanced pipelines show multi-stage research and production-oriented services. The organized examples, tutorial playlists, and installation instructions reduce setup friction and provide practical references for extending, adapting, or integrating agent patterns into new applications.
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