Skip to main content

A minimalistic framework for building reliable AI agents

Project description

SmartPup Logo

SmartPup

A minimalistic framework for building reliable AI agents (pups) that do exactly what you tell them to do, without extra magic.

Why?

The existing agent frameworks feel too complex and bloated. Maybe they are right for some use cases, but my feeble brain needed something simpler. All this talk of "agents" is tricky because you can't really rely on an LLM to do what you want it to do. Not yet anyway. So I really want is not smart agents that will figure things out, but simply smart functions - that I can call with a system prompt and a bunch of tools and they will reliably do the one thing they are supposed to do. And if they can't - they will fail, ideally explaining why.

So less like superintelligent "agents" and more like well trained puppies. You tell them what to do and they do it, bringing back exactly what you needed in the form you needed. Maybe in the future there is some pavlovian conditioning and training. But for now they are just like your average well trained dog: excitable, reliable, and not very smart.

Features

  • Simple, predictable agents that do run off and do one thing at a time and if they can't - they don't invent shit. They fail clearly and explain why.
  • Uses OpenRouter to support multiple LLM providers (OpenAI, Anthropic, Google, etc.)
  • Built-in tool system with auto-discovery
  • No conversation history - each request is independent
  • Optional memory system for persistence when needed (built as a tool)
  • Uses Pydantic for type safety

Installation

pip install smartpup

Quick Start

import asyncio
from smartpup import Pup, ToolRegistry

async def main():
    # Initialize tools
    registry = ToolRegistry()
    registry.discover_tools()
    
    # Create a weather pup
    weather_pup = Pup(
        system_prompt="You are a weather assistant. Check the weather and report it as a short poem.",
        tools=registry.get_tools(["get_current_weather"])
    )

    # Get weather
    response = await weather_pup.run("What's the weather in Amsterdam?")
    print(response)

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

Environment Setup

Create a .env file:

OPENROUTER_API_KEY=your-api-key
OPENROUTER_BASE_URL=https://openrouter.ai/api/v1

Built-in Tools

  • Weather: Get current weather for any location
  • DateTime: Get current date/time in various formats
  • Memory: Simple key-value storage for persistence
  • Translate: Text translation between languages

Creating Custom Tools

from smartpup import BaseTool

class CalculatorTool(BaseTool):
    name = "calculator"
    description = "Perform basic arithmetic operations"
    
    async def execute(
        self,
        operation: str,
        a: float,
        b: float
    ) -> str:
        """
        Perform basic arithmetic
        
        Args:
            operation: One of 'add', 'subtract', 'multiply', 'divide'
            a: First number
            b: Second number
        """
        if operation == "add":
            return f"Result: {a + b}"
        # ... other operations ...

# Register and use the tool
registry = ToolRegistry()
registry.register_tool(CalculatorTool)

Examples

Check the examples directory for more usage examples:

  • Basic weather reporting
  • Translation service
  • Memory usage
  • Custom calculator tool

Configuration

Optional global configuration:

from smartpup import configure

configure(
    default_model="openai/gpt-4o-mini",  # Default LLM to use
    max_iterations=10,                    # Max tool call iterations
    memory_file="memory.json"            # For memory tools. alternatively you can set the environment variable MEMORY_FILE
)

Development

For development:

git clone https://github.com/georgestrakhov/smartpup.git
cd smartpup
pip install -e ".[dev]"
pytest  # Run tests

Philosophy

SmartPup is built on these principles:

  1. One Task at a Time: Each pup does one specific thing
  2. No Magic: Clear, predictable behavior without hidden complexity
  3. Tool-First: Tools provide clear interfaces for agent capabilities
  4. Independent Requests: No conversation history or context bleeding

License

MIT License - see LICENSE for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

smartpup-0.1.0.tar.gz (275.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

smartpup-0.1.0-py3-none-any.whl (21.1 kB view details)

Uploaded Python 3

File details

Details for the file smartpup-0.1.0.tar.gz.

File metadata

  • Download URL: smartpup-0.1.0.tar.gz
  • Upload date:
  • Size: 275.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for smartpup-0.1.0.tar.gz
Algorithm Hash digest
SHA256 391c7986254bf87cda40f02ab881c47961fa94b404ea00bd38c23e5b3fa1ff90
MD5 0b8e6396ee81e97c541f24203e457e3d
BLAKE2b-256 f28fa8d465e97ad912b9abe8793d50a6bc176e6e1d64ba96bf470d3053053b3a

See more details on using hashes here.

File details

Details for the file smartpup-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: smartpup-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 21.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for smartpup-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 76921a7fa1edcd19618587b994d464f1dda85dba36e2e98d4dbe56895e88f558
MD5 4784422ab73a80368378c5dc09f5e40c
BLAKE2b-256 0525ddb942575b3bd674c3eccccd7f9e2b01700334eccaa41c77b878b8349621

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page