500 AI Agents Projects

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Basic Information

This repository is a curated catalog of over 500 AI agent projects and use cases organized by industry and framework. It collects practical agent examples, short descriptions, and pointers to open-source implementations to illustrate how agents are used in domains such as healthcare, finance, education, customer service, retail, manufacturing and more. The README groups material by industry use cases and by popular agent frameworks (CrewAI, AutoGen, Agno, LangGraph), and highlights workflows like multi‚Äëagent collaboration, retrieval‚Äëaugmented generation (RAG), multimodal agents, web scraping and automation. The primary purpose is educational and practical: to inspire developers, researchers and practitioners with concrete agent designs, example notebooks and starter templates so they can reproduce, adapt or extend agent implementations rather than providing a single runnable product within this repository.

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Features
The repository compiles a comprehensive use‚Äëcase table and an industry mindmap that categorize agent applications by domain. It includes framework‚Äëwise sections with enumerated examples and notebooks for CrewAI, AutoGen, Agno and LangGraph demonstrating flows such as email responders, meeting assistants, multi‚Äëagent group chats, code generation and debugging, RAG, tool and function calling, nested chats, multimodal interactions and web crawling/scraping. It highlights tools and capabilities like AgentBuilder for automatic system construction, AgentEval and AgentOps for evaluation and observability, starter templates and sample notebooks showing synchronous and asynchronous tool use, Whisper transcription, SQL generation, and web search integration. The README documents contributing guidelines and notes that the project is MIT licensed.
Use Cases
For developers and teams exploring agent design this repo is a reference library of concrete patterns, ready examples and framework mappings to accelerate prototyping. Readers can find industry‚Äëspecific agent ideas (health assistants, trading bots, tutors, chatbots, recommendation and logistics agents) and follow linked projects and notebooks to reproduce behaviors. It helps researchers compare multi‚Äëagent collaboration patterns, learn RAG and retrieval strategies, experiment with multimodal agents, and adopt observability and evaluation practices such as AgentEval and AgentOps. Maintainers and contributors can use the starter templates and framework examples to build custom agents, adapt workflows for production, or learn integration techniques like function calling, web scraping, and long‚Äëcontext handling without starting from scratch.

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