agente 0.3.2

A very simple Python framework for building AI Agents

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

Install the package:

pip install agente

For running the examples with Gradio UI:

pip install agente[examples]

Note: The frameworks works on top of litellm, so you need to set your provider API key in the environment variables.

Quick Start

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

import os 
os.environ["OPENAI_API_KEY"] = "your_api_key" #load your provider API key
from agente.core.base import BaseAgent


class SimpleAgent(BaseAgent):
    agent_name: str = "SimpleAgent"
    system_prompt: str = "You are a helpful AI assistant."
    silent: bool = True #while running the agent, it will not print execution logs
    completion_kwargs: dict = {
        "model": "gpt-4.1-mini",
        "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 = await agent.run()

# by default the response have litellm format
print(responses[0].choices[0].message.content)

Using agente response format

# Add a message
agent.add_message(role = "user", content =  "Another one, please.")

# Now with agente response format
responses = await agent.run(output_format = "agente")

print(responses[0].content)

To access the conversation history

conversation_history = agent.conv_history

print(conversation_history.model_dump())

To access the logs

# Get the logs
logs = agent.log_calls
print(logs)

logs = agent.log_completions
print(logs)

Advanced Usage

Adding Tools

Agents can be enhanced with tools using the @function_tool decorator. The decorator will automatically generate a tool schema for the function based on the docstring and the Annotated type hints.

import os
os.environ["OPENAI_API_KEY"] = "your_api_key" #load your provider API key
from agente.core.base import BaseAgent
from agente.core.decorators import function_tool
from typing import Annotated

class AddAgent(BaseAgent):
    agent_name: str = "add_agent"

    @function_tool
    async def calculate_sum(self, a: Annotated[int,"The first number"], b: Annotated[int,"The second number"]) -> int:
        """Calculate the sum of two numbers."""
        return a + b

agent = AddAgent()
agent.completion_kwargs['model'] = 'gpt-4.1-mini'
agent.add_message(role = "user", content = "How much is 10 + 10?")
responses = await agent.run()
print(responses[-1].choices[0].message.content)

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 task_completed method that returns the result of the task.

import os
os.environ["OPENAI_API_KEY"] = "your_api_key" #load your provider API key
from agente.core.base import BaseAgent,BaseTaskAgent
from agente.core.decorators import function_tool,agent_tool
import random

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 task_completed(self,joke:Annotated[str,"The joke to return"]):
        """To be used as a tool to complete the task."""
        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:Annotated[str,"The topic of the joke"]):
        """Tool to get a 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 = await example_agent.run()
print(responses[-1].choices[0].message.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.