connectonion 0.2.5

A simple Python framework for creating AI agents with behavior tracking

🧅 ConnectOnion

A simple, elegant open-source framework for production-ready AI agents

📚 Documentation • 💬 Discord • ⭐ Star Us

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🌟 Philosophy: "Keep simple things simple, make complicated things possible"

This is the core principle that drives every design decision in ConnectOnion.

🎯 Living Our Philosophy

# Simple thing (2 lines) - Just works!
from connectonion import Agent
agent = Agent("assistant").input("Hello!")

# Complicated thing (still possible) - Production ready!
agent = Agent("production",
              model="gpt-5",                    # Latest models
              tools=[search, analyze, execute], # Your functions as tools
              system_prompt=company_prompt,     # Custom behavior
              max_iterations=10,                # Safety controls
              trust="prompt")                    # Multi-agent ready

✨ What Makes ConnectOnion Special

• 🎯 Simple API: Just one Agent class and your functions as tools
• 🚀 Production Ready: Battle-tested with GPT-5, Gemini 2.5, Claude Opus 4.1
• 🌍 Open Source: MIT licensed, community-driven development
• ⚡ No Boilerplate: Start building in 2 lines, not 200
• 🔧 Extensible: Scale from prototypes to production systems

🚀 Quick Start

Installation

pip install connectonion

Quickest Start - Use the CLI

# Create a new agent project with one command
co init

# Follow the prompts to set up your API key and run
cp .env.example .env  # Add your OpenAI API key
python agent.py

Manual Usage

import os  
from connectonion import Agent

# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "your-api-key-here"

# 1. Define tools as simple functions
def search(query: str) -> str:
    """Search for information."""
    return f"Found information about {query}"

def calculate(expression: str) -> float:
    """Perform mathematical calculations."""
    return eval(expression)  # Use safely in production

# 2. Create an agent with tools and personality
agent = Agent(
    name="my_assistant",
    system_prompt="You are a helpful and friendly assistant.",
    tools=[search, calculate]
    # max_iterations=10 is the default - agent will try up to 10 tool calls per task
)

# 3. Use the agent
result = agent.input("What is 25 * 4?")
print(result)  # Agent will use the calculate function

result = agent.input("Search for Python tutorials") 
print(result)  # Agent will use the search function

# 4. View behavior history (automatic!)
print(agent.history.summary())

🔧 Core Concepts

Agent

The main class that orchestrates LLM calls and tool usage. Each agent:

• Has a unique name for tracking purposes
• Can be given a custom personality via system_prompt
• Automatically converts functions to tools
• Records all behavior to JSON files

Function-Based Tools

NEW: Just write regular Python functions! ConnectOnion automatically converts them to tools:

def my_tool(param: str, optional_param: int = 10) -> str:
    """This docstring becomes the tool description."""
    return f"Processed {param} with value {optional_param}"

# Use it directly - no wrapping needed!
agent = Agent("assistant", tools=[my_tool])

Key features:

• Automatic Schema Generation: Type hints become OpenAI function schemas
• Docstring Integration: First line becomes tool description
• Parameter Handling: Supports required and optional parameters
• Type Conversion: Handles different return types automatically

System Prompts

Define your agent's personality and behavior with flexible input options:

# 1. Direct string prompt
agent = Agent(
    name="helpful_tutor",
    system_prompt="You are an enthusiastic teacher who loves to educate.",
    tools=[my_tools]
)

# 2. Load from file (any text file, no extension restrictions)
agent = Agent(
    name="support_agent",
    system_prompt="prompts/customer_support.md"  # Automatically loads file content
)

# 3. Using Path object
from pathlib import Path
agent = Agent(
    name="coder",
    system_prompt=Path("prompts") / "senior_developer.txt"
)

# 4. None for default prompt
agent = Agent("basic_agent")  # Uses default: "You are a helpful assistant..."

Example prompt file (prompts/customer_support.md):

# Customer Support Agent

You are a senior customer support specialist with expertise in:
- Empathetic communication
- Problem-solving
- Technical troubleshooting

## Guidelines
- Always acknowledge the customer's concern first
- Look for root causes, not just symptoms
- Provide clear, actionable solutions

Logging

Automatic logging of all agent activities including:

• User inputs and agent responses
• LLM calls with timing
• Tool executions with parameters and results
• Default storage in .co/logs/{name}.log (human-readable format)

🎯 Example Tools

You can still use the traditional Tool class approach, but the new functional approach is much simpler:

Traditional Tool Classes (Still Supported)

from connectonion.tools import Calculator, CurrentTime, ReadFile

agent = Agent("assistant", tools=[Calculator(), CurrentTime(), ReadFile()])

New Function-Based Approach (Recommended)

def calculate(expression: str) -> float:
    """Perform mathematical calculations."""
    return eval(expression)  # Use safely in production

def get_time(format: str = "%Y-%m-%d %H:%M:%S") -> str:
    """Get current date and time."""
    from datetime import datetime
    return datetime.now().strftime(format)

def read_file(filepath: str) -> str:
    """Read contents of a text file."""
    with open(filepath, 'r') as f:
        return f.read()

# Use them directly!
agent = Agent("assistant", tools=[calculate, get_time, read_file])

The function-based approach is simpler, more Pythonic, and easier to test!

🎨 CLI Templates

ConnectOnion CLI provides templates to get you started quickly:

# Basic agent with ConnectOnion knowledge
co init

# Conversational chat agent
co init --template chat

# Data analysis agent
co init --template data

# Web automation with Playwright
co init --template playwright

Each template includes:

• Pre-configured agent with relevant tools
• Customizable system prompt in prompt.md
• Environment configuration template
• Embedded ConnectOnion documentation

Learn more in the CLI Documentation and Templates Guide.

🔨 Creating Custom Tools

The simplest way is to use functions (recommended):

def weather(city: str) -> str:
    """Get current weather for a city."""
    # Your weather API logic here
    return f"Weather in {city}: Sunny, 22°C"

# That's it! Use it directly
agent = Agent(name="weather_agent", tools=[weather])

Or use the Tool class for more control:

from connectonion.tools import Tool

class WeatherTool(Tool):
    def __init__(self):
        super().__init__(
            name="weather",
            description="Get current weather for a city"
        )
    
    def run(self, city: str) -> str:
        return f"Weather in {city}: Sunny, 22°C"
    
    def get_parameters_schema(self):
        return {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name"}
            },
            "required": ["city"]
        }

agent = Agent(name="weather_agent", tools=[WeatherTool()])

📁 Project Structure

connectonion/
├── connectonion/
│   ├── __init__.py         # Main exports
│   ├── agent.py            # Agent class
│   ├── tools.py            # Tool interface and built-ins
│   ├── llm.py              # LLM interface and OpenAI implementation
│   ├── console.py          # Terminal output and logging
│   └── cli/                # CLI module
│       ├── main.py         # CLI commands
│       ├── docs.md         # Embedded documentation
│       └── templates/      # Agent templates
│           ├── basic_agent.py
│           ├── chat_agent.py
│           ├── data_agent.py
│           └── *.md        # Prompt templates
├── docs/                   # Documentation
│   ├── getting-started.md
│   ├── cli.md
│   ├── templates.md
│   └── ...
├── examples/
│   └── basic_example.py
├── tests/
│   └── test_agent.py
└── requirements.txt

🧪 Running Tests

python -m pytest tests/

Or run individual test files:

python -m unittest tests.test_agent

📊 Automatic Logging

All agent activities are automatically logged to:

.co/logs/{agent_name}.log  # Default location

Each log entry includes:

• Timestamp
• User input
• LLM calls with timing
• Tool executions with parameters and results
• Final responses

Control logging behavior:

# Default: logs to .co/logs/assistant.log
agent = Agent("assistant")

# Log to current directory
agent = Agent("assistant", log=True)  # → assistant.log

# Disable logging
agent = Agent("assistant", log=False)

# Custom log file
agent = Agent("assistant", log="my_logs/custom.log")

🔑 Configuration

OpenAI API Key

Set your API key via environment variable:

export OPENAI_API_KEY="your-api-key-here"

Or pass directly to agent:

agent = Agent(name="test", api_key="your-api-key-here")

Model Selection

agent = Agent(name="test", model="gpt-5")  # Default: gpt-5-mini

Iteration Control

Control how many tool calling iterations an agent can perform:

# Default: 10 iterations (good for most tasks)
agent = Agent(name="assistant", tools=[...])

# Complex tasks may need more iterations
research_agent = Agent(
    name="researcher", 
    tools=[search, analyze, summarize, write_file],
    max_iterations=25  # Allow more steps for complex workflows
)

# Simple agents can use fewer iterations for safety
calculator = Agent(
    name="calc", 
    tools=[calculate],
    max_iterations=5  # Prevent runaway calculations
)

# Per-request override for specific complex tasks
result = agent.input(
    "Analyze all project files and generate comprehensive report",
    max_iterations=50  # Override for this specific task
)

When an agent reaches its iteration limit, it returns:

"Task incomplete: Maximum iterations (10) reached."

Choosing the Right Limit:

• Simple tasks (1-3 tools): 5-10 iterations
• Standard workflows: 10-15 iterations (default: 10)
• Complex analysis: 20-30 iterations
• Research/multi-step: 30+ iterations

🛠️ Advanced Usage

Multiple Tool Calls

Agents can chain multiple tool calls automatically:

result = agent.input(
    "Calculate 15 * 8, then tell me what time you did this calculation"
)
# Agent will use calculator first, then current_time tool

Custom LLM Providers

from connectonion.llm import LLM

class CustomLLM(LLM):
    def complete(self, messages, tools=None):
        # Your custom LLM implementation
        pass

agent = Agent(name="test", llm=CustomLLM())

🚧 Current Limitations (MVP)

This is an MVP version with intentional limitations:

• Single LLM provider (OpenAI)
• Synchronous execution only
• JSON file storage only
• Basic error handling
• No multi-agent collaboration

🗺️ Future Roadmap

• Multiple LLM provider support (Anthropic, Local models)
• Async/await support
• Database storage options
• Advanced memory systems
• Multi-agent collaboration
• Web interface for behavior monitoring
• Plugin system for tools

🔗 Connect With Us

• 💬 Discord: Join our community - Get help, share ideas, meet other developers
• 📚 Documentation: docs.connectonion.com - Comprehensive guides and examples
• ⭐ GitHub: Star the repo - Show your support
• 🐛 Issues: Report bugs - We respond quickly

🤝 Contributing

We welcome contributions! ConnectOnion is open source and community-driven.

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

See our Contributing Guide for more details.

📄 License

MIT License - Use it anywhere, even commercially. See LICENSE file for details.

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💫 Remember

"Keep simple things simple, make complicated things possible"

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