agents-sdk-models 0.0.10

Model adapters for OpenAI Agents SDK

Agents SDK Models 🤖🔌

A collection of model adapters for OpenAI Agents SDK, allowing you to use various LLM providers with a unified interface! 🚀

🌟 Features

• 🔄 Unified Interface: Use the same OpenAI Agents SDK interface with multiple model providers
• 🧩 Multiple Models: Support for Ollama, Google Gemini, and Anthropic Claude
• 📊 Structured Output: All models support structured output using Pydantic models

🛠️ Installation

From PyPI (Recommended)

# Install from PyPI
pip install agents-sdk-models

# For examples with structured output
pip install agents-sdk-models[examples]

From Source

# Clone the repository
git clone https://github.com/kitfactory/agents-sdk-models.git
cd agents-sdk-models

# Create and activate a virtual environment
python -m venv .venv
.venv\Scripts\activate  # Windows
source .venv/bin/activate  # Linux/Mac

# Install the package in development mode
pip install -e .

🚀 Quick Start

LlmModel (Example: OpenAI)

import asyncio
import os
from agents import Agent, Runner
from agents_sdk_models import LlmModel

async def main():
    # Get API key from environment variable (if needed)
    # api_key = os.environ.get("OPENAI_API_KEY") # Uncomment if using OpenAI, Google, or Anthropic

    # Initialize the LlmModel model (Example uses OpenAI's gpt-4o-mini)
    model = LlmModel(
        provider="openai",  # Can be "openai", "google", "anthropic", or "ollama"
        model="gpt-4o-mini", # Specify the model name for the chosen provider
        temperature=0.7,
        # api_key=api_key # Uncomment and provide API key if needed
    )

    # Create an agent with the model
    agent = Agent(
        name="Assistant",
        instructions="You are a helpful assistant.",
        model=model
    )

    # Run the agent
    response = await Runner.run(agent, "What is your name and what can you do?")
    print(response.final_output)

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

Ollama

import asyncio
from agents import Agent, Runner
from agents_sdk_models import OllamaModel

async def main():
    # Initialize the Ollama model
    model = OllamaModel(
        model="llama3",  # or any other model available in your Ollama instance
        temperature=0.7
    )
    
    # Create an agent with the model
    agent = Agent(
        name="Assistant",
        instructions="You are a helpful assistant.",
        model=model
    )
    
    # Run the agent
    response = await Runner.run(agent, "What is your name and what can you do?")
    print(response.final_output)

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

Google Gemini

import asyncio
import os
from agents import Agent, Runner
from agents_sdk_models import GeminiModel

async def main():
    # Get API key from environment variable
    api_key = os.environ.get("GOOGLE_API_KEY")
    
    # Initialize the Gemini model
    model = GeminiModel(
        model="gemini-1.5-pro",
        temperature=0.7,
        api_key=api_key
    )
    
    # Create an agent with the model
    agent = Agent(
        name="Assistant",
        instructions="You are a helpful assistant.",
        model=model
    )
    
    # Run the agent
    response = await Runner.run(agent, "What is your name and what can you do?")
    print(response.final_output)

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

Anthropic Claude

import asyncio
import os
from agents import Agent, Runner
from agents_sdk_models import ClaudeModel

async def main():
    # Get API key from environment variable
    api_key = os.environ.get("ANTHROPIC_API_KEY")
    
    # Initialize the Claude model
    model = ClaudeModel(
        model="claude-3-sonnet-20240229",
        temperature=0.7,
        api_key=api_key,
        thinking=True  # Enable thinking for complex reasoning
    )
    
    # Create an agent with the model
    agent = Agent(
        name="Assistant",
        instructions="You are a helpful assistant.",
        model=model
    )
    
    # Run the agent
    response = await Runner.run(agent, "What is your name and what can you do?")
    print(response.final_output)

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

📊 Structured Output

All models support structured output using Pydantic models:

from pydantic import BaseModel
from typing import List

class WeatherInfo(BaseModel):
    location: str
    temperature: float
    condition: str
    recommendation: str

class WeatherReport(BaseModel):
    report_date: str
    locations: List[WeatherInfo]

# Create an agent with structured output
agent = Agent(
    name="Weather Reporter",
    model=model,
    instructions="You are a helpful weather reporter.",
    output_type=WeatherReport
)

# Get structured response
response = await Runner.run(agent, "What's the weather like in Tokyo, Osaka, and Sapporo?")
weather_report = response.final_output  # This is a WeatherReport object

🔧 Supported Environments

• Operating Systems: Windows, macOS, Linux
• Python Version: 3.9+
• Dependencies:
  • openai>=1.73.0
  • openai-agents==0.0.9
  • pydantic>=2.10, <3 (for examples with structured output)

📝 License

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

🙏 Acknowledgements

• OpenAI Agents SDK
• Ollama
• Google Gemini
• Anthropic Claude