agente

A very simple Python framework for building AI Agents.

Overview

Agente is a Python framework that allows you to create AI agents just like you create Python classes and methods.

Each method can be converted to into a function calling tool using a simple decorator. This allow you to think the tools as regular class methods within the instante namespace of the agent.

Multi-agent orchestration is supported in an hierarchical way, starting from a main agent that can delegate tasks to specialized agents.

Features

• Simple agent creation and easily customizable
• Support for streaming responses
• Multi-agent orchestration (hierarchical)
• Autonomous agent tool that allows an agent to create its own tools (experimental)

Installation

Instal the required dependencies:

pip install -r requirements.txt

Install the package:

pip install agente

Quick Start

Here's a simple example of creating a conversational agent:

from agente.core.base import BaseAgent
from dotenv import load_dotenv

# Load environment variables (requires OpenAI API key)
load_dotenv()

class SimpleAgent(BaseAgent):
    agent_name: str = "SimpleAgent"
    system_prompt: str = "You are a helpful AI assistant."
    completion_kwargs: dict = {
        "model": "gpt-4",
        "stream": False,
        "temperature": 1.0,
        "max_tokens": 500,
    }

# Create agent instance
agent = SimpleAgent()

# Add a message
agent.add_message(role = "user", content =  "Tell me a joke about programming.")

# Run the agent and get responses
responses = [r async for r in agent.run()]

all_messasges = agent.conv_history.messages

# Print the last response
print(all_messasges[-1].content)

Advanced Usage

Adding Tools

Agents can be enhanced with tools using the @function_tool decorator:

from agente.core.base import BaseAgent
from agente.core.decorators import function_tool

class AddAgent(BaseAgent):
    agent_name: str = "add_agent"
    
    @function_tool
    async def calculate_sum(self, a: int, b: int) -> int:
        """Calculate the sum of two numbers.
        
        Args:
            a: The first number.
            b: The second number.        
        """
        return a + b

agent = AddAgent()
agent.add_message(role = "user", content = "How much is 10 + 10?")
responses = [r async for r in agent.run()]
print(agent.conv_history.messages[-1].content)


# Get the logs
call_logs = agent.log_calls
completions_logs = agent.logs_completions

Creating Multi-Agent Systems

You can create complex multi-agent systems where agents can call other agents using the @agent_tool decorator.

For now the framework was designed to work with a hierarchical structure, where a main agent can call other specialized agents that can call other agents and so on. These sub-agents must be TaskAgents that inherit from BaseTaskAgent and must have a complete_task method that returns the result of the task.

from agente.core.base import BaseAgent,BaseTaskAgent
from agente.core.decorators import function_tool,agent_tool
import random
from dotenv import load_dotenv
load_dotenv()

class JokeTeller(BaseTaskAgent):
    agent_name: str = "JokeTeller"
    system_prompt:str = "Your task is to write a funny joke."
    completion_kwargs: dict = {
        "model": "gpt-4o-mini",
        "stream": False,
    }

    @function_tool
    def complete_task(self,joke:str):
        """To be used as a tool to complete the task.

        Args:
            joke: The joke to return.
        """
        return joke



class MainAgent(BaseAgent):
    agent_name: str = "main_agent"
    
    @function_tool(next_tool = "get_joke") # To make sure the agent calls the get_joke tool we add the next_tool argument to force it.
    def random_topic(self):
        """Tool to get a random topic.
        """
        topics = ["programming","science","animals","food","sports"]
        topic = random.choice(topics)

        return topic


    @agent_tool()
    def get_joke(self,joke_topic:str):
        """Tool to get a joke.

        Args:
            joke_topic: The topic of the joke.
        """

        joke_agent = JokeTeller()
        joke_agent.add_message(role = "user", content = "Tell me a joke about " + joke_topic)
        return joke_agent
    
example_agent = MainAgent()
example_agent.add_message(role = "user", content = "Call the tool random_topic to get a random topic and then tell  me a joke about it")
responses = [r async for r in example_agent.run()]
print(example_agent.conv_history.messages[-1].content)

Examples

For more examples, check out the examples directory:

1. Simple Conversational Agent
2. Data Analysis Agent
3. Scientific Paper Research Agent
4. Autonomous Agent with Dynamic Tools

License

Apache License 2.0

Contributing

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