rizerphe / anthropic-tools

Generate Anthropic Claude tool use schemas based on type annotations and function docstrings.

Anthropic tools

The anthropic-tools library simplifies the usage of Anthropics’s tool use feature. It abstracts away the complexity of parsing function signatures and docstrings by providing developers with a clean and intuitive interface. It's a near-clone of my openai-functions library that does the same with OpenAI.

Installation

You can install anthropic-tools from PyPI using pip:

pip install anthropic-tools

Usage

1. Import the necessary modules and provide your API key:

import enum
import anthropic
from anthropic_tools import Conversation

client = anthropic.Anthropic(
    api_key="<YOUR_API_KEY>",
)

2. Create a Conversation instance:

conversation = Conversation(client)

3. Define your tools using the @conversation.add_tool decorator:

class Unit(enum.Enum):
    FAHRENHEIT = "fahrenheit"
    CELSIUS = "celsius"

@conversation.add_tool()
def get_current_weather(location: str, unit: Unit = Unit.FAHRENHEIT) -> dict:
    """Get the current weather in a given location.

    Args:
        location (str): The city and state, e.g., San Francisco, CA
        unit (Unit): The unit to use, e.g., fahrenheit or celsius
    """
    return {
        "location": location,
        "temperature": "72",
        "unit": unit.value,
        "forecast": ["sunny", "windy"],
    }

4. Ask the AI a question:

response = conversation.ask("What's the weather in San Francisco?")
# Should return three messages, the last one's content being something like:
# The current weather in San Francisco is 72 degrees Fahrenheit and it is sunny and windy.

You can read more about how to use Conversation here.

More barebones use - just schema generation and result parsing:

from anthropic_tools import ToolWrapper

wrapper = ToolWrapper(get_current_weather)
schema = wrapper.schema
result = wrapper({"location": "San Francisco, CA"})

Or you could use skills.

How it Works

anthropic-tools takes care of the following tasks:

• Parsing the function signatures (with type annotations) and docstrings.
• Sending the conversation and tool descriptions to Anthropic Claude.
• Deciding whether to call a tool based on the model's response.
• Calling the appropriate function with the provided arguments.
• Updating the conversation with the tool response.
• Repeating the process until the model generates a user-facing message.

This abstraction allows developers to focus on defining their functions and adding user messages without worrying about the details of tool use.

Note

Please note that anthropic-tools is an unofficial project not maintained by Anthropic. Use it at your discretion.