🤖 AgentHub

🚀 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 — and lets you compose them into robust multi-agent Teams.

🗺️ At a Glance

• What you do: Install agents, customize with tools/knowledge, and compose a Team to solve complex goals
• What you get: High-level agent.solve() and Team().solve() APIs, isolation, auto-install, and monitoring
• Who it's for: Developers and builders who want pragmatic, composable AI systems without boilerplate

🎯 Core Abilities

AgentHub revolutionizes how you work with AI agents:

• 🧩 Compose Multi-Agent Systems: Build a Team() by plugging together pre-built or custom agents
• 🧠 Universal Intelligence: agent.solve() and Team().solve() for high-level, goal-driven execution
• 🔧 Customize Agents: Add custom tools and domain knowledge to any agent
• 🏪 Agent Marketplace: Discover and install agents from GitHub with one command
• 🔌 One-Line Integration: ah.load_agent("user/agent") - no complex setup required
• 🔒 Isolated Environments: No dependency conflicts between agents
• ⚡ Auto-Installation: Agents install automatically when needed
• 🎯 CLI Interface: Full command-line management and execution
• 📊 Comprehensive Monitoring: Full visibility into agent execution and performance

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 agenthub as ah
coding_agent = ah.load_agent("agentplug/coding-agent")
code = coding_agent.generate_code("neural network class")

🧩 Build Multi-Agent Teams (High-Level Flow)

Create complex systems by composing pre-built agents (research, analysis, data access, planning, quality control, ...) and your custom agents into a Team().

import agenthub as ah

# 1) Load pre-built agents from the marketplace (customize via load params)
research = ah.load_agent(
    "agentplug/research-agent",
    external_tools=["web_search"],            # attach your tools
    knowledge=["knowledge/base/"]             # attach your knowledge sources
)

analysis = ah.load_agent(
    "agentplug/analysis-agent",
    knowledge=["knowledge/base/"]
)

planner  = ah.load_agent("agentplug/planning-agent")
qc       = ah.load_agent("agentplug/quality-control-agent")

# 3) Compose into a Team
#    Team API preview — designed for simple, high-level orchestration
from agenthub import Team  # Coming soon

team = Team(name="ResearchPipeline", agents=[planner, research, analysis, qc])

# 4) High-level goal, single entry point
result = team.solve("Analyze the latest LLM papers and produce actionable insights")
print(result["result"])  # Unified output

Flow to build:

• Pick agents: research, analysis, data access, planning, quality control, ...
• Customize: pass external_tools=[...] and knowledge=[...] to ah.load_agent(...)
• Compose: add agents into a Team()
• Solve: call Team().solve(goal) to run the entire multi-agent pipeline

✨ Key Features

• 🧩 Team Composition (High-Level Logic): Create a Team() and plug agents together to build complex systems
• 🧠 Universal Solve Method: agent.solve() and Team().solve() - describe goals, not steps
• 🏪 Agent Marketplace: Discover and install agents from GitHub with one command
• 🔌 One-Line Integration: ah.load_agent("user/agent") - no complex setup required
• 🛠️ Custom Tools & Knowledge: Create tools with @tool, connect via run_resources(), and attach domain knowledge
• 🔒 Isolated Environments: No dependency conflicts between agents
• ⚡ Auto-Installation: Agents install automatically when needed
• 🎯 CLI Interface: Full command-line management and execution
• 📊 Comprehensive Monitoring: Full visibility into agent execution and performance

🚀 Quick Start

⚡ Install AgentHub

# Install AgentHub
pip install agenthub-sdk

# Verify installation
agenthub --version

🎯 Your First Agent (30 seconds)

import agenthub as ah

# 🪄 One line to load any agent
coding_agent = ah.load_agent("agentplug/coding-agent")

# 🧠 Universal solve method - AI automatically selects the best approach
result = coding_agent.solve("Create a Python function that calculates compound interest")
print(result["result"])

# ✅ Magic happens automatically:
# • GitHub repository cloned
# • Virtual environment created
# • Dependencies installed
# • Agent validated and ready
# • AI selects optimal method for your query

🧠 Universal Solve Method

Describe your goal in natural language — agent.solve() (and soon Team().solve()) selects the best internal method and executes the steps:

import agenthub as ah

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

# 🧠 AI automatically selects the best method for each query
code = coding_agent.solve("Create a neural network class")  # → generate_code()
review = coding_agent.solve("Review this code: def hello(): print('world')")  # → review_code()
explanation = coding_agent.solve("Explain what this function does")  # → explain_code()

# 📊 Analysis agent automatically chooses the right approach
insights = analysis_agent.solve("Analyze this customer feedback: 'Great app!'")  # → analyze_text()
data_analysis = analysis_agent.solve("Process sales_data.csv")  # → analyze_data()

# ✅ No need to know specific method names - just describe what you want!

🛠️ Custom Tools & Extensions

AgentHub makes it easy to extend agents with custom tools:

from agenthub.core.tools import tool, run_resources

# Create custom tools with @tool decorator
@tool(name="web_search", description="Search the web for information")
def web_search(query: str) -> str:
    """Search the web for information."""
    # Your custom implementation
    return f"Search results for: {query}"

@tool(name="database_query", description="Execute SQL query on database")
def database_query(sql: str) -> dict:
    """Execute SQL query on database."""
    # Your custom implementation
    return {"results": "..."}

# Start the tool server - this makes tools available to agents
if __name__ == "__main__":
    print("🚀 Starting tool server...")
    run_resources()  # This starts the MCP server

Using Tools with Agents:

import agenthub as ah

# Load agent with external tools (tools are now available after run_resources())
coding_agent = ah.load_agent(
    "agentplug/coding-agent",
    external_tools=["web_search", "database_query"]  # Connect to your custom tools
)
result = coding_agent.solve("Search for React best practices and create a component")
# ✅ Agent can now use your custom web_search tool!

🔗 Complete Tool Workflow Example

Here's the complete workflow for using custom tools with agents:

# 1. Define and start tools (run this first)
from agenthub.core.tools import tool, run_resources

@tool(name="web_search", description="Search the web for information")
def web_search(query: str) -> str:
    return f"Search results for: {query}"

if __name__ == "__main__":
    run_resources()  # Start MCP server

# 2. Use tools with agents (run this after starting tools)
import agenthub as ah

coding_agent = ah.load_agent(
    "agentplug/coding-agent",
    external_tools=["web_search"]  # Connect to your custom tool
)

result = coding_agent.solve("Search for Python best practices and create a function")
# ✅ Agent can now use your web_search tool!

🔒 Isolated Environments

Each agent runs in its own isolated environment:

# No dependency conflicts between agents
coding_agent = ah.load_agent("agentplug/coding-agent")      # Uses Python 3.11
data_agent = ah.load_agent("agentplug/data-agent")          # Uses Python 3.12
ml_agent = ah.load_agent("agentplug/ml-agent")             # Uses different packages

# All agents work independently without conflicts

💻 CLI Commands

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

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

# Execute agent method (multiple ways)
agenthub exec agentplug/scientific-paper-analyzer analyze_paper "research.pdf"
agenthub exec agentplug/scientific-paper-analyzer analyze_paper '{"file": "research.pdf"}'
agenthub exec agentplug/scientific-paper-analyzer analyze_paper --interactive

# Agent management commands
agenthub agent list                                    # List installed agents
agenthub agent status agentplug/scientific-paper-analyzer  # Check agent status
agenthub agent remove agentplug/scientific-paper-analyzer  # Remove an agent
agenthub agent backup agentplug/scientific-paper-analyzer  # Create backup
agenthub agent restore agentplug/scientific-paper-analyzer # Restore from backup
agenthub agent repair agentplug/scientific-paper-analyzer  # Repair broken agent
agenthub agent migrate agentplug/scientific-paper-analyzer # Migrate Python version
agenthub agent optimize agentplug/scientific-paper-analyzer # Optimize environment
agenthub agent analyze-deps agentplug/scientific-paper-analyzer # Analyze dependencies

# System validation
agenthub validate

🛠️ 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

agenthub 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 agenthub as ah
agent = ah.load_agent("your-username/my-coding-agent")
code = agent.generate_code("React component")

📚 Examples

🧠 Universal Solve Method (Recommended)

import agenthub as ah

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

# 🎯 AI automatically selects the best method for each query
code = coding_agent.solve("Create a React component for data table")
print(code["result"])

review = coding_agent.solve("Review this code: def hello(): print('world')")
print(review["result"])

insights = analysis_agent.solve("Analyze this customer feedback: 'Great app!'")
print(insights["result"])

🛠️ Direct Method Calls

import agenthub as ah

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

# Direct method calls (when you know the specific method)
code = coding_agent.generate_code("React component for data table")
print(code["result"])

review = coding_agent.review_code("def hello(): print('world')")
print(review["result"])

🧠 Universal Solve Method Benefits

• 🎯 No Method Learning: Just describe what you want
• 🤖 AI Method Selection: Automatically chooses the best approach
• 📝 Natural Language: Use plain English queries
• 🔄 Consistent Interface: Same pattern across all agents

🤝 Contributing

We welcome contributions! You can:

• Build and contribute new tools (via @tool and run_resources())
• Create new pre-built agents (publishable GitHub repos with agent.yaml)
• Enhance existing agents (add methods, improve prompts, optimize flows)
• Improve documentation and examples

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

• 🧰 Tools: Add new tools or improve existing ones
• 🤖 Pre-built Agents: Create and share agents for common tasks
• 🚀 Enhancements: Optimize agent methods, prompts, and pipelines
• 🐛 Bug Reports: Open an Issue
• 📖 Documentation: Improve guides and examples
• 🔧 Code: Fix bugs, add features
• 🎨 Design: UI/UX improvements
• 📊 Testing: Help improve test coverage

📞 Support & Community

💬 Get Help

Platform	Purpose	Link
💬 Discord	Live chat and support	Join Server
🐦 Twitter	Updates and announcements	@AgentHub
📧 Email	Business inquiries	agenthub@agentplug.net

🐛 Report Issues

• Bug Reports: GitHub Issues
• Feature Requests: GitHub Discussions
• Security Issues: agenthub@agentplug.net

📄 License

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

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