Agenty

A Pythonic framework for building AI agents and LLM pipelines, powered by pydantic-ai. The framework emphasizes simplicity and maintainability without sacrificing power, making it an ideal choice for rapid prototyping.

📚 Documentation

Agenty provides a clean, type-safe interface for creating:

• Conversational AI agents with structured inputs and outputs
• LLM pipelines
• Complex agent interactions with minimal boilerplate

Key Features

• Built on pydantic-ai for type validation
• Automatic conversation history management
• Intuitive Pythonic interfaces

The framework is currently only officially tested with the OpenAI API (through a proxy such as LiteLLM/OpenRouter) although theoretically it supports all the models supported by pydantic-ai.

Installation

pip install agenty

Or with uv:

uv add agenty

Quick Preview

Here's a simple example to get started:

import asyncio
from agenty import Agent
from pydantic_ai.models.openai import OpenAIModel

async def main():
    agent = Agent(
        model=OpenAIModel(
            "gpt-4o",
            api_key="your-api-key"
        ),
        system_prompt="You are a helpful and friendly AI assistant."
    )

    response = await agent.run("Hello, how are you?")
    print(response)

asyncio.run(main())

In most cases, to build a custom AI agent, you'll want to create your own class that inherits from Agent. The below is functionally equivalent to the above code (and is the recommended way to use this framework)

import asyncio
from agenty import Agent
from pydantic_ai.models.openai import OpenAIModel

class Assistant(Agent):
    model = OpenAIModel("gpt-4o", api_key="your-api-key")
    system_prompt = "You are a helpful and friendly AI assistant."

async def main():
    agent = Assistant()
    response = await agent.run("Hello, how are you?")
    print(response)

asyncio.run(main())

Tools

Add capabilities to your agents with simple decorators:

class WeatherAgent(Agent):
    system_prompt = "You help users check the weather."

    def __init__(self, location: str):
        super().__init__()
        self.location = location
        self.temperature = 72  # Simulated temperature

    @tool
    def get_temperature(self) -> float:
        """Get the current temperature."""
        return self.temperature

    @tool
    def get_location(self) -> str:
        """Get the configured location."""
        return self.location

Structured I/O

Define type-safe inputs and outputs for predictable behavior:

from agenty import Agent
from agenty.types import BaseIO

class User(BaseIO):
    name: str
    age: int
    hobbies: List[str]

class UserExtractor(Agent[str, User]):
    input_schema = str
    output_schema = User
    system_prompt = "Extract user information from the text"

Agent Pipelines

Chain multiple agents together for complex workflows:

class TextCleaner(Agent[str, str]):
    model = OpenAIModel("gpt-4o-mini", api_key="your-api-key")
    system_prompt = "Clean and format the input text"

class SentimentAnalyzer(Agent[str, str]):
    model = OpenAIModel("gpt-4o-mini", api_key="your-api-key")
    system_prompt = "Analyze the sentiment of the text"

# Create and use the pipeline
pipeline = TextCleaner() | SentimentAnalyzer()
result = await pipeline.run("This is my input text!")

Templates

Create dynamic prompts with Jinja templates:

class DynamicGreeter(Agent):
    system_prompt = """
    You are a greeter who:
    - Speaks in a {{TONE}} tone
    - Gives {{LENGTH}} responses
    """
    TONE: str = "friendly"
    LENGTH: str = "concise"

Hooks

Transform inputs and outputs with hooks:

class MyAgent(Agent[str, str]):
    @hook.input
    def add_prefix(self, input: str) -> str:
        return f"prefix_{input}"
        
    @hook.output 
    def add_suffix(self, output: str) -> str:
        return f"{output}_suffix"

📚 Like what you see? Read the Documentation to learn more!

Requirements

• Python >= 3.12

License

MIT License - see the LICENSE file for details.

Author

Jonathan Chun (@jonchun)