openagentkit 0.1.0a1

An open-source framework for building and deploying AI agents.

OpenAgentKit

A comprehensive open-source toolkit for building agentic applications. OpenAgentKit provides a unified interface to work with various LLM providers, tools, and agent frameworks.

WARNING: Everything here is still in development, expect many bugs and unsupported features, please feel free to contribute!

Features

• Unified LLM Interface: Consistent API across multiple LLM providers
• Generator-based event stream: Event-driven processing using a generator
• Async Support: Built-in asynchronous processing for high-performance applications
• Tool Integration: Pre-built tools for common agent tasks
• Extensible Architecture: Easily add custom models, tools, and handlers
• Type Safety: Comprehensive typing support with Pydantic models

Installation

pip install openagentkit==0.1.0a1

Quick Start

from openagentkit.modules.openai import OpenAIExecutor
from openagentkit.core.utils.tool_wrapper import tool
from pydantic import BaseModel
from typing import Annotated
import openai
import os

# Define a tool
@tool # Wrap the function in a tool decorator to automatically create a schema
def get_weather(city: str):
    """Get the weather of a city"""

    # Actual implementation here...
    # ...

    return f"Weather in {city}: sunny, 20°C, feels like 22°C, humidity: 50%"

# Initialize OpenAI client
client = openai.OpenAI(
    api_key=os.getenv("OPENAI_API_KEY"),
)

# Initialize LLM service
executor = OpenAIExecutor(
    client=client,
    model="gpt-4o-mini",
    system_message="""
    You are a helpful assistant that can answer questions and help with tasks.
    You are also able to use tools to get information.
    """,
    tools=[get_weather],
    temperature=0.5,
    max_tokens=100,
    top_p=1.0,
)

generator = executor.execute(
    messages=[
        {"role": "user", "content": "What's the weather like in New York?"}
    ]
)

for response in generator:
    print(response.content)

Supported Integrations

• LLM Providers:

  • OpenAI
  • SmallestAI
  • Azure OpenAI (via OpenAI integration)
  • More coming soon!

• Tools (Mostly for prototyping purposes):

  • Weather information (Requires WEATHERAPI_API_KEY)
  • Search capabilities (Requires TAVILY_API_KEY)

Architecture

OpenAgentKit is built with a modular architecture:

• Interfaces: Abstract base classes defining the contract for all implementations
• Models: Pydantic models for type-safe data handling
• Modules: Implementation of various services and integrations
• Handlers: Processors for tools and other extensions
• Utils: Helper functions and utilities

Advanced Usage

Asynchronous Processing

from openagentkit.modules.openai import AsyncOpenAIExecutor
from openagentkit.core.utils.tool_wrapper import tool
from pydantic import BaseModel
from typing import Annotated
import asyncio
import openai
import os

# Define a tool
@tool # Wrap the function in a tool decorator to automatically create a schema
def get_weather(city: str):
    """Get the weather of a city"""

    # Actual implementation here...
    # ...

    return f"Weather in {city}: sunny, 20°C, feels like 22°C, humidity: 50%"

# Initialize OpenAI client
client = openai.AsyncOpenAI(
    api_key=os.getenv("OPENAI_API_KEY"),
)

async def main():
    # Initialize LLM service
    executor = AsyncOpenAIExecutor(
        client=client,
        model="gpt-4o-mini",
        system_message="""
        You are a helpful assistant that can answer questions and help with tasks.
        You are also able to use tools to get information.
        """,
        tools=[get_weather],
        temperature=0.5,
        max_tokens=100,
        top_p=1.0,
    )

    generator = executor.execute(
        messages=[
            {"role": "user", "content": "What's the weather like in New York?"}
        ]
    )

    async for response in generator:
        print(response.content)

if __name__ == "__main__":
    asyncio.run(main())

Custom Tool Integration

from openagentkit.core.utils.tool_wrapper import tool
from pydantic import BaseModel
from typing import Annotated

# Define a tool
@tool # Wrap the function in a tool decorator to automatically create a schema
def get_weather(city: str):
    """Get the weather of a city""" # Always try to add pydoc in the function for better comprehension by LLM 

    # Actual implementation here...
    # ...

    return f"Weather in {city}: sunny, 20°C, feels like 22°C, humidity: 50%"

# Get the tool schema
print(get_weather.schema)

# Run the tool like any other function
weather_response = get_weather("Hanoi")
print(weather_response) 

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.