500-AI-Agents-Projects Documentation

1. Project Overview & Quickstart (ashishpatel26/500-AI-Agents-Projects) Copy Chapter

File: README.md Copy Chapter

500+ AI Agent Projects & Use Cases

What is this? Copy Chapter

A curated collection of 500+ AI agent projects — production examples, tutorials, and working code spanning every major framework (LangGraph, CrewAI, AutoGen, Agno) and industry (Healthcare, Finance, Education, Cybersecurity, and more).

Who it's for:

• 🧑‍💻 Developers building their first or next AI agent
• 🔬 Researchers surveying the agent landscape
• 🏢 Teams evaluating frameworks for production use
• 🎓 Students learning agent architectures from real examples

⚡ Quick Start Copy Chapter

Pick a framework and run an agent in under 5 minutes:

# Clone the repo
git clone https://github.com/ashishpatel26/500-AI-Agents-Projects.git
cd 500-AI-Agents-Projects

# Run any agent from the agents/ directory
cd agents/01-web-research-agent
pip install -r requirements.txt
cp .env.example .env        # add your API key
python agent.py

All agents in agents/ are self-contained with their own requirements.txt and .env.example. No monorepo setup needed.

🗺️ Navigation Guide Copy Chapter

📊 Framework Comparison Copy Chapter

Choosing a framework? Here's when to use each:

Quick decision guide:

• Just starting out → Agno or CrewAI
• Need stateful graphs + RAG → LangGraph
• Building code-writing / research agents → AutoGen
• Enterprise document pipelines → LlamaIndex

🏭 Industry Use Cases Copy Chapter

🔧 Browse by Framework Copy Chapter

CrewAI Copy Section

Role-based multi-agent framework. Great for business automation.

AutoGen Copy Section

Microsoft's framework for code generation, execution, and multi-agent research.

Code Generation, Execution, and Debugging

Multi-Agent Collaboration

Sequential Multi-Agent Chats

Nested Chats

Tools

Multimodal Agents

Observability & Evaluation

Agno Copy Section

Lightweight, fast agent framework. Best for single-agent tools and rapid prototyping.

LangGraph Copy Section

State-machine framework for complex, stateful agent workflows and RAG pipelines.

🤝 Contributing Copy Chapter

Contributions are welcome! 🎉 This repo grows through community contributions.

Ways to contribute:

1. Add a working agent — create a folder in agents/ with runnable code
2. Add an external link — add a row to the industry or framework tables
3. Fix a broken link — open an issue or PR
4. Improve documentation — fix typos, add context, improve examples

To contribute:

1. Fork the repository
2. Create a branch: feat/agent-name or fix/description
3. Add your changes following the Contributing Guidelines
4. Open a PR using the PR template

See CONTRIBUTION.md for full requirements (metadata.yaml, requirements.txt, etc.).

Star History Copy Chapter

_{Regenerated weekly from the GitHub API by .github/workflows/star-history.yml. Click the chart for the interactive version.}

📜 License Copy Chapter

This repository is licensed under the MIT License. See the LICENSE file for more information.

File: crewai_mcp_course/README.md Copy Chapter

CrewAI with FastMCP Server Integration Course

This course teaches beginners how to use CrewAI with FastMCP server access through step-by-step programming examples.

Course Overview Copy Chapter

This course is designed for beginner developers with basic Python knowledge who want to learn how to integrate CrewAI agents with FastMCP servers. The course covers fundamental concepts, practical implementation, and advanced patterns for building intelligent agent workflows.

Lessons Copy Chapter

Lesson 1: Setting up CrewAI with MCP Server Access Copy Section

• Install required packages
• Set up environment variables
• Create a basic CrewAI agent
• Execute simple tasks

graph TD
    A[Install Packages] --> B[Set Environment Variables]
    B --> C[Create CrewAI Agent]
    C --> D[Execute Task]

Lesson 2: Integrating MCP Server with CrewAI Copy Section

• Create custom tools for MCP server access
• Configure authentication and connection settings
• Use MCP server data in agent tasks
• Handle errors and exceptions

graph TD
    A[Create FastMCP Tool] --> B[Configure Authentication]
    B --> C[Create Agent with Tool]
    C --> D[Execute Task with MCP Data]
    D --> E[Handle Response]

Lesson 3: Advanced CrewAI Patterns with MCP Server Copy Section

• Implement multi-agent workflows
• Use hierarchical processes
• Share data between agents through the MCP server
• Store and retrieve research findings
• Implement quality assurance processes

graph TD
    A[Researcher Agent] -->|Query| B(FastMCP Server)
    B -->|Return Data| A
    A -->|Share Findings| C[Writer Agent]
    C -->|Create Report| D[Reviewer Agent]
    D -->|Provide Feedback| C
    C -->|Final Report| E[Output]

Getting Started Copy Chapter

Using pip (traditional method) Copy Section

1. Install the required packages for the lesson you want to run:

pip install -r lesson_01/requirements.txt
pip install -r lesson_02/requirements.txt
pip install -r lesson_03/requirements.txt

2. Set up your environment variables:

export OPENAI_API_KEY=your-openai-api-key
export MCP_SERVER_URL=http://localhost:8000

3. Run the examples:

python lesson_01/agent.py
python lesson_02/agent.py --topic "AI agents in healthcare"

# Lesson 03 includes a matching FastMCP server and CrewAI wrappers.
python lesson_03/agent.py
# Optional: inspect/run the FastMCP server separately:
python lesson_03/mcp_server.py

Using uv (recommended modern method) Copy Section

uv is a fast Python package installer and resolver. To use uv:

1. Install uv:

pip install uv

2. Create and activate a virtual environment:

uv venv
source .venv/Scripts/activate

3. Install dependencies:

uv pip install -r lesson_01/requirements.txt
uv pip install -r lesson_02/requirements.txt
uv pip install -r lesson_03/requirements.txt

4. Set up your environment variables:

export OPENAI_API_KEY=your-openai-api-key
export MCP_SERVER_URL=http://localhost:8000

5. Run the examples:

python lesson_01/agent.py
python lesson_02/agent.py --topic "AI agents in healthcare"

python lesson_03/agent.py
# Optional: inspect/run the FastMCP server separately:
python lesson_03/mcp_server.py

Requirements Copy Chapter

• Python 3.8+
• CrewAI library
• FastMCP library
• OpenAI API key
• FastMCP server example for Lesson 03

Course Structure Copy Chapter

Each lesson includes:

• A lesson folder with agent.py, requirements.txt, and .env.example
• Clear objectives and expected outcomes
• Step-by-step implementation
• Best practices for error handling and security

Next Steps Copy Chapter

After completing this course, you should be able to:

• Create and configure CrewAI agents
• Integrate MCP servers with agent workflows
• Build complex multi-agent systems
• Implement data sharing between agents
• Design robust error handling for production systems

2. Official Technical Reference & Guides (ashishpatel26/ashishpatel26.github.io) Copy Chapter

500+ AI Agent Projects & Use Cases

What is this? Copy Chapter

A curated collection of 500+ AI agent projects — production examples, tutorials, and working code spanning every major framework (LangGraph, CrewAI, AutoGen, Agno) and industry (Healthcare, Finance, Education, Cybersecurity, and more).

Who it's for:

• 🧑‍💻 Developers building their first or next AI agent
• 🔬 Researchers surveying the agent landscape
• 🏢 Teams evaluating frameworks for production use
• 🎓 Students learning agent architectures from real examples

⚡ Quick Start Copy Chapter

Pick a framework and run an agent in under 5 minutes:

# Clone the repo
git clone https://github.com/ashishpatel26/500-AI-Agents-Projects.git
cd 500-AI-Agents-Projects

# Run any agent from the agents/ directory
cd agents/01-web-research-agent
pip install -r requirements.txt
cp .env.example .env        # add your API key
python agent.py

All agents in agents/ are self-contained with their own requirements.txt and .env.example. No monorepo setup needed.

🗺️ Navigation Guide Copy Chapter

📊 Framework Comparison Copy Chapter

Choosing a framework? Here's when to use each:

Quick decision guide:

• Just starting out → Agno or CrewAI
• Need stateful graphs + RAG → LangGraph
• Building code-writing / research agents → AutoGen
• Enterprise document pipelines → LlamaIndex

🏭 Industry Use Cases Copy Chapter

🔧 Browse by Framework Copy Chapter

CrewAI Copy Section

Role-based multi-agent framework. Great for business automation.

AutoGen Copy Section

Microsoft's framework for code generation, execution, and multi-agent research.

Code Generation, Execution, and Debugging

Multi-Agent Collaboration

Sequential Multi-Agent Chats

Nested Chats

Tools

Multimodal Agents

Observability & Evaluation

Agno Copy Section

Lightweight, fast agent framework. Best for single-agent tools and rapid prototyping.

LangGraph Copy Section

State-machine framework for complex, stateful agent workflows and RAG pipelines.

🤝 Contributing Copy Chapter

Contributions are welcome! 🎉 This repo grows through community contributions.

Ways to contribute:

1. Add a working agent — create a folder in agents/ with runnable code
2. Add an external link — add a row to the industry or framework tables
3. Fix a broken link — open an issue or PR
4. Improve documentation — fix typos, add context, improve examples

To contribute:

1. Fork the repository
2. Create a branch: feat/agent-name or fix/description
3. Add your changes following the Contributing Guidelines
4. Open a PR using the PR template

See CONTRIBUTION.md for full requirements (metadata.yaml, requirements.txt, etc.).

Star History Copy Chapter

📜 License Copy Chapter

This repository is licensed under the MIT License. See the LICENSE file for more information.