agentplug 0.1.2

AgentHub - The App Store for AI Agents

🤖 AgentHub

The "App Store for AI Agents" - Discover, install, and use AI agents with one-line simplicity

📖 Documentation • 🚀 Quick Start • 🤝 Contributing • 📧 Contact

🚀 What is AgentHub?

Transform weeks of AI agent integration into one line of code. AgentHub makes powerful AI agents as easy to use as installing a Python package.

Before AgentHub

# Traditional approach: 2-4 weeks setup
# 1. Find agent on GitHub
# 2. Clone repository
# 3. Read documentation
# 4. Install dependencies (version conflicts!)
# 5. Configure environment
# 6. Debug integration issues
# 7. Write wrapper code
# 8. Test and validate

With AgentHub

# One line, 30 seconds
import agentplug as ah
coding_agent = ah.load_agent("agentplug/coding-agent")
code = coding_agent.generate_code("neural network class")

✨ Key Features

• 🏪 Agent Marketplace: Discover and install agents from GitHub
• 🔌 One-Line Integration: ah.load_agent("user/agent")
• 🛠️ Custom Tools: Create and inject tools with @tool decorator
• 🔒 Isolated Environments: No dependency conflicts
• ⚡ Auto-Installation: Agents install automatically when needed
• 🎯 CLI Interface: Full command-line management

🚀 Quick Start

⚡ Install AgentHub

# Install AgentHub
pip install agentplug

# Verify installation
agentplug --version

🎯 Your First Agent (30 seconds)

import agentplug as ah

# 🪄 One line to load any agent
paper_analyzer = ah.load_agent("agentplug/scientific-paper-analyzer")

# 📄 Use the agent immediately
result = paper_analyzer.analyze_paper("research_paper.pdf")
print(f"📊 Summary: {result['summary'][:200]}...")

# ✅ Magic happens automatically:
# • GitHub repository cloned
# • Virtual environment created
# • Dependencies installed
# • Agent validated and ready

🛠️ Using Custom Tools

Create powerful agents with your own tools:

from agentplug.core.tools import tool, run_resources

@tool(name="web_search", description="Search the web for information")
def web_search(query: str, max_results: int = 10) -> list:
    """Search the web and return results."""
    # Your search implementation here
    return [f"Result {i+1} for '{query}'" for i in range(min(max_results, 3))]

@tool(name="data_analyzer", description="Analyze data patterns")
def data_analyzer(data: list, analysis_type: str = "basic") -> dict:
    """Analyze data and return insights."""
    return {
        "type": analysis_type,
        "count": len(data),
        "insights": f"Analyzed {len(data)} items"
    }

# 🚀 Start the tool server
if __name__ == "__main__":
    print("🔧 Starting tool server...")
    run_resources()  # Starts MCP server for tool execution

# 🤖 Use tools with agents (run in separate process/terminal)
import agentplug as ah

# Load agent with custom tools
agent = ah.load_agent("agentplug/analysis-agent", tools=["web_search", "data_analyzer"])

# Agent's AI decides when and how to use tools
result = agent.analyze("What are the latest AI trends?")
# Agent automatically uses web_search and data_analyzer as needed!

💻 CLI Commands

# List all agents
agentplug list

# Get agent information
agentplug info agentplug/scientific-paper-analyzer

# Install new agent
agentplug agent install agentplug/scientific-paper-analyzer

# Execute agent method
agentplug exec agentplug/scientific-paper-analyzer analyze_paper "research.pdf"

# Check agent status
agentplug agent status agentplug/scientific-paper-analyzer

# Remove an agent
agentplug agent remove agentplug/scientific-paper-analyzer

🛠️ Creating Your Own Agent

1. Create Agent Files

mkdir my-coding-agent
cd my-coding-agent/

Create agent.py:

class CodingAgent:
    def __init__(self):
        self.name = "Coding Agent"

    def generate_code(self, description: str) -> str:
        """Generate code based on description."""
        return f"# Generated code for: {description}\nprint('Hello, World!')"

    def review_code(self, code: str) -> str:
        """Review and improve code."""
        return f"Code review: {code} looks good!"

Create agent.yaml:

name: coding-agent
version: 1.0.0
description: AI agent for code generation and review
author: your-username
entry_point: agent.py:CodingAgent

2. Test Locally

agentplug exec ./my-coding-agent generate_code "hello world"

3. Publish to GitHub

git init
git add .
git commit -m "Initial agent release"
git remote add origin https://github.com/your-username/my-coding-agent.git
git push -u origin main

4. Share with the World!

# Anyone can now use your agent:
import agentplug as ah
agent = ah.load_agent("your-username/my-coding-agent")
code = agent.generate_code("React component")

📚 Examples

Code Generation Agent

import agentplug as ah

# Load coding agent
coding_agent = ah.load_agent("agentplug/coding-agent")

# Generate code
code = coding_agent.generate_code("React component for data table")
print(code)

# Review existing code
review = coding_agent.review_code("def hello(): print('world')")
print(review)

Data Analysis Agent

import agentplug as ah

# Load analysis agent with tools
data_agent = ah.load_agent("agentplug/analysis-agent", tools=["data_analyzer", "web_search"])

# Analyze data
insights = data_agent.analyze("sales_data.csv")
print(insights)

Scientific Paper Analyzer

import agentplug as ah

# Load paper analyzer
paper_agent = ah.load_agent("agentplug/scientific-paper-analyzer")

# Analyze research paper
result = paper_agent.analyze_paper("research.pdf")
print(f"Summary: {result['summary']}")
print(f"Key findings: {result['key_findings']}")

🎯 Available Agents

Agent	Description	Usage
agentplug/coding-agent	Generate and review code	ah.load_agent("agentplug/coding-agent")
agentplug/analysis-agent	Data analysis and insights	ah.load_agent("agentplug/analysis-agent")
agentplug/scientific-paper-analyzer	Analyze research papers	ah.load_agent("agentplug/scientific-paper-analyzer")

🤝 Contributing

We welcome contributions! Here's how to get started:

🚀 Development Setup

# 1. Fork and clone
git clone https://github.com/YOUR_USERNAME/agenthub.git
cd agenthub

# 2. Setup environment
python3.12 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -e ".[dev]"

# 3. Run tests
pytest tests/ -v

# 4. Make changes
git checkout -b feature/your-feature

🎯 Ways to Contribute

• 🐛 Bug Reports: Open an Issue
• 📖 Documentation: Improve guides and examples
• 🔧 Code: Fix bugs, add features
• 🎨 Design: UI/UX improvements
• 📊 Testing: Help improve test coverage

📊 Roadmap

✅ Phase 1: Foundation (Live!)

• ✅ Core SDK with one-line agent loading
• ✅ GitHub integration and auto-installation
• ✅ Environment isolation with UV
• ✅ CLI tools and validation engine

✅ Phase 2.5: Tool Injection (Live!)

• ✅ Tool registry with FastMCP integration
• ✅ @tool decorator for custom tools
• ✅ Agent tool assignment functionality
• ✅ Comprehensive testing suite

🚧 Phase 2: Developer Experience (In Progress)

• 🚧 Agent Studio visual development environment
• 🚧 Testing framework and validation suite
• 🚧 Marketplace UI for agent discovery
• 🚧 Analytics dashboard

📋 Phase 3: Ecosystem Growth (Planning)

• 📋 Multi-agent workflows
• 📋 AI-powered agent recommendations
• 📋 Mobile app for agent management
• 📋 Revenue sharing platform

📞 Support & Community

💬 Get Help

Platform	Purpose	Link
💬 Discord	Live chat and support	Join Server
🐦 Twitter	Updates and announcements	@AgentHub
📧 Email	Business inquiries	[e-mail]

🐛 Report Issues

• Bug Reports: GitHub Issues
• Feature Requests: GitHub Discussions
• Security Issues: [e-mail]

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🚀 AgentHub - Making AI agents as easy as pip install

One line. Infinite possibilities.