smolantic-ai 0.1.17

A Python framework leveraging pydantic-ai to build specialized AI agents for multi-step task processing, tool calling, and code generation/execution

Smolantic AI

Smolantic AI is a Python framework leveraging pydantic-ai to build specialized AI agents for multi-step task processing, tool calling, and code generation/execution. It provides structured agent types with memory management and planning capabilities.

Features

• Modular Agent Design: Hierarchical agent system with BaseAgent as foundation, supporting specialized agents (MultistepAgent, CodeAgent).
• Structured Planning & Execution: Agents follow defined steps for planning, execution, and error handling.
• Tool Integration: Easily integrate and use custom or pre-built tools with specialized agents.
• Code Generation & Execution: Generate and safely execute Python code using CodeAgent with configurable executors (local, Docker, E2B).
• Configuration: Flexible configuration via environment variables or .env files using pydantic-settings.
• Extensible Models: Uses Pydantic models for clear data structures (Messages, Actions, Memory).

Agent Architecture

The framework uses a hierarchical agent system:

BaseAgent
├── MultistepAgent
└── CodeAgent

• BaseAgent: Provides core functionality for agent initialization, tool management, and result processing
• MultistepAgent: Specialized for multi-step task execution with planning capabilities
• CodeAgent: Specialized for code generation and execution with configurable executors

Installation

Production Installation

You can install Smolantic AI directly from PyPI:

pip install smolantic-ai

Development Installation

For development purposes, you can install the package in editable mode:

1. Clone the repository:

   git clone https://github.com/esragoth/smolantic_ai.git
   cd smolantic_ai
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Install the package in editable mode:

   pip install -e .
   

5. For development with testing tools:

   pip install -e ".[dev]"
   

Environment Setup

This project requires various API keys for Large Language Models (LLMs) and external tools used by the prebuilt agents.

1. Create a .env file in the root directory of the project by copying the example file:

   cp .env.example .env
   

2. Edit the .env file and add your actual API keys and credentials. The required variables are listed in .env.example and include:

   • LLM Keys: OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY (provide keys for the models you intend to use)
   • Tool Keys/URLs:
     • WEATHERAPI_API_KEY (from WeatherAPI.com)
     • IPGEOLOCATION_API_KEY (from ipgeolocation.io)
     • EXCHANGERATE_API_KEY (from e.g., exchangeratesapi.io)
     • API_NINJA_API_KEY (from api-ninjas.com - note potential free tier limits)
     • JINA_API_KEY (from Jina AI, for the reader tool)
     • BRIGHTDATA_PROXY_URL (Full proxy URL including credentials, e.g., from Bright Data, for the search tool)

The application uses pydantic-settings to load these variables from the .env file. Some tool-specific keys are loaded directly using os.getenv within the tool functions in src/smolantic_ai/prebuilt_tools.py.

Usage

Here are basic examples of how to use the agents:

BaseAgent (Direct Usage):

import asyncio
from pydantic_ai import Tool
from smolantic_ai import BaseAgent
from smolantic_ai.models import Message, MessageRole

# Define a simple tool
def get_weather(city: str) -> str:
    """Gets the weather for a city."""
    # Replace with actual API call
    return f"The weather in {city} is sunny."

weather_tool = Tool(
    name="get_weather",
    description="Get the current weather for a specific city",
    function=get_weather,
)

async def run_base_agent():
    agent = BaseAgent(tools=[weather_tool])
    result = await agent.run("What's the weather like in London?")
    print(f"Agent Result: {result}")

asyncio.run(run_base_agent())

CodeAgent:

import asyncio
from smolantic_ai import CodeAgent
from smolantic_ai.models import CodeResult

async def run_code_agent():
    # Create a CodeAgent with specific configuration
    agent = CodeAgent(
        authorized_imports=["math", "numpy"],  # Allow specific imports
        executor_type="local",  # Use local Python executor
        max_steps=10,  # Limit execution steps
        planning_interval=3  # Replan every 3 steps
    )
    
    # Run the agent with a task
    result = await agent.run(
        "Write a Python function to calculate the area of a circle given its radius."
    )
    
    # Handle the result
    if isinstance(result, CodeResult):
        print(f"Generated Code:\n{result.code}")
        print(f"Execution Output:\n{result.result}")
        if result.error:
            print(f"Error: {result.error}")
            print(f"Explanation: {result.explanation}")
            print(f"Execution Logs:\n{result.execution_logs}")

asyncio.run(run_code_agent())

MultistepAgent:

import asyncio
from smolantic_ai import MultistepAgent
from smolantic_ai.models import MultistepAgentResult

async def run_multistep_agent():
    # Create a MultistepAgent with specific configuration
    agent = MultistepAgent(
        max_steps=20,  # Maximum number of steps
        planning_interval=5,  # Replan every 5 steps
        logger_name="custom_logger"  # Custom logger name
    )
    
    # Run the agent with a complex task
    result = await agent.run(
        "Research the capital of France and then find its population."
    )
    
    # Handle the result
    if isinstance(result, MultistepAgentResult):
        print(f"Final Answer: {result.result}")
        print("\nSteps Taken:")
        for i, step in enumerate(result.steps, 1):
            print(f"\nStep {i}:")
            print(f"Thought: {step.input_messages[0].content if step.input_messages else 'N/A'}")
            if step.tool_calls:
                print("Actions:")
                for tool_call in step.tool_calls:
                    print(f"  - {tool_call['name']}({tool_call['args']})")
            if step.tool_outputs:
                print("Observations:")
                for output in step.tool_outputs:
                    print(f"  - {output['name']}: {output['output']}")

asyncio.run(run_multistep_agent())

For more detailed examples, see:

• examples/multistep_agent_generic.py - Generic multistep agent usage
• examples/multistep_agent_numbers.py - Number processing example
• examples/code_agent_basic.py - Basic code generation and execution
• examples/code_agent_advanced.py - Advanced code agent features

Development

To set up the development environment:

1. Clone the repository (git clone ...)
2. Create and activate a virtual environment (python -m venv venv, source venv/bin/activate)
3. Install development dependencies:

   pip install -r requirements.txt
   pip install -e ".[dev]" # Installs test dependencies
   

Testing

Run tests using:

pytest

License

MIT License