Skip to content
Get packs

Guide · Open source

How to use 500-AI-Agents-Projects: clone, run a sample agent

Updated · written with AI from the project's own documentation and checked against our catalogue data

500-AI-Agents-Projects is a curated GitHub collection of AI agent use cases, organized by framework (LangGraph, CrewAI, AutoGen, Agno) and by industry such as healthcare, finance and education. It mostly links to other open-source projects, and it includes one runnable sample agent. This guide shows you how to clone the repo, run that sample, and find the examples that match your goal.

500 AI Agents Projectsashishpatel26/500-AI-Agents-Projects · ★ 39.2k · MIT

Quick start

  1. 1Clone the repo. Run git clone on the repository URL, then cd into the 500-AI-Agents-Projects folder.
  2. 2Pick an agent folder. Go to agents/01-web-research-agent, the sample the README uses.
  3. 3Install requirements. Run pip install -r requirements.txt inside the agent folder.
  4. 4Create the environment file. Run cp .env.example .env and add your API key to the new .env file.
  5. 5Run the agent. Start the sample with python agent.py and check the output.
  6. 6Explore other examples. Use the framework comparison or industry tables to find the next project to study.

What you need before you start

The examples are Python code, so you need Python and Git installed on your machine. The sample agent also needs an API key for the model it calls, which you add to a .env file.

  • Python
  • Git
  • An API key for the model the agent uses (add it to .env)

Clone the repository

Download the repo to your computer with Git, then move into the folder.

git clone https://github.com/ashishpatel26/500-AI-Agents-Projects.git cd 500-AI-Agents-Projects

Run a sample agent from the agents folder

Each agent in the agents/ folder is self-contained, with its own requirements.txt and .env.example. You do not need a monorepo setup. The README's example uses the web research agent. Install its requirements, create your environment file, then add your API key to .env and run agent.py.

  • Open the agent folder, install its requirements, and copy the example environment file.
  • Edit .env and add your API key.
  • Run python agent.py from that folder.
cd agents/01-web-research-agent pip install -r requirements.txt cp .env.example .env # add your API key python agent.py

Find the examples you need

Use the README's navigation guide to choose a path. If you are picking a framework, the comparison table lists each framework's best-fit use cases. If you want a specific sector, the industry table lists use cases with a link to the code for each one.

The README's quick decision guide suggests Agno or CrewAI for beginners, LangGraph for stateful graphs and RAG, AutoGen for code-writing and research agents, and LlamaIndex for enterprise document pipelines.

  • Run a working agent: open the agents/ folder.
  • Compare frameworks: see the Framework Comparison section.
  • Explore by sector: see the Industry Use Cases table, for example Healthcare or Finance.
  • Learn with a course: see the crewai_mcp_course/ folder.

Check each linked project before relying on it

Most industry entries link to separate projects, and those projects vary in maturity. The README does not describe how entries are vetted beyond its contribution guide, so read each linked project's own documentation and license before you reuse its code.

If you want to add your own agent, the README points to CONTRIBUTION.md.

  • Check the linked repo's last activity and documentation.
  • Confirm its license before reuse. This collection is MIT licensed.

What people use it for

  • Developers comparing LangGraph, CrewAI, AutoGen and Agno before starting a project
  • Students studying how agent architectures are built from real examples
  • Teams looking for healthcare or finance agent examples to study
  • Researchers surveying which industries are trying AI agents

Frequently asked questions

Is 500-AI-Agents-Projects free to use?

The repository is released under the MIT licence, so you can use and modify its code under those terms. Many of the linked industry projects are separate repos with their own licences, so check each one before reuse.

What do I need to install to run it?

You need Python and Git. The sample agent also needs its own Python requirements, installed with pip install -r requirements.txt, and an API key in a .env file.

Is 500-AI-Agents-Projects a finished app I can use directly?

No. It is a curated collection that points to other open-source projects of varying maturity. Only the sample in the agents/ folder is runnable from this repo, and it is meant for study.

Can I run it without programming experience?

Not easily. The examples require Python and API keys, so this collection is aimed at developers, students and researchers who can work with code.

How does it compare with awesome-llm-apps, GenAI_Agents or awesome-ai-agents?

All of these are collections of agent projects. This repo is organized by framework and by industry, and it includes a framework comparison table. Check each one's README to see which layout and examples suit your goal.

What if the sample agent will not run?

Check that Python and Git are installed, that you ran pip install -r requirements.txt inside the agent folder, and that your .env file has your API key. The README does not list other troubleshooting steps, so consult the linked project or CONTRIBUTION.md if problems continue.

New agent skills, repos and AI tools — every week

The best new skills and open-source projects, the fastest-growing repos, and one free prompt. Five minutes to read. Unsubscribe anytime.

Our verdict

Use this repo if you are a developer, student or researcher who wants to compare agent frameworks or study industry examples in code. Skip it if you need a finished product, a non-Python workflow, or entries with verified quality, and look at the other collections or the individual linked projects instead.

Install options & downloads