agentic-tools 0.2.1

A toolset for agentic workflows

agentic-tools

A python package that lets you setup agents with tools very easy

All you need is to add a docorator do the function that should be made accessable to the LLM.

from agentic_tools import ai_tool

def llm_can_not_use():
    pass 

@ai_tool
def llm_can_use():
    pass

Be awere that this will eat more input tokens, since you are adding context of the functions.

Context that gets added

• Function signarure, icludeing, arguments and type hints
• Docstring

install

pip install agentic-tools 

Agentic tools needs a LangChain object chat model.

build

git clone [e-mail]:AxelGard/agentic-tools.git
cd agentic-tools
python3 -m venv env 
source ./env/bin/activate
pip install -e .
# or 
# pip install -e .[dev] 
# if you want dev depenacies 

DEMO

you can check out my expriment notebook

or

suppose you have some functions that you want you LLM to be able to call. Agentic tools allows you to just add a decorator to it and then the agent will have access to those tools.

All LangChain models are supported, but for this demo I will use Ollama with llama3.1

install

install Ollma

curl -fsSL https://ollama.com/install.sh | sh

and then run ollama

ollama run llama3.1

install the python dependacnices

pip install agentic-tools langchain-ollama yfinance

Example

from langchain_ollama.chat_models import ChatOllama
from agentic_tools import ai_tool, Agent
import yfinance as yf


@ai_tool
def get_stock_price(symbol: str, period:str="1d") -> str:
    stock = yf.Ticker(symbol)
    price = stock.history(period=period)["Close"].iloc[0]
    return f"The stock price of {symbol} was ${price:.2f} {period} ago"

@ai_tool
def get_market_cap(symbol: str) -> str:
    stock = yf.Ticker(symbol)
    cap = stock.info.get("marketCap", None)
    if cap:
        return f"The market cap of {symbol} is ${cap:,}"
    return f"Market cap data for {symbol} is not available."

@ai_tool
def get_pe_ratio(symbol: str) -> str:
    """ Will return P/E ratrio of the company """
    stock = yf.Ticker(symbol)
    pe = stock.info.get("trailingPE", None)
    if pe:
        return f"The P/E ratio of {symbol} is {pe:.2f}"
    return f"P/E ratio data for {symbol} is not available."


llm = ChatOllama(model="llama3.1", temperature=0)
agent = Agent(llm_chat_model=llm)

query = "what was apples stock price a 5 days ago?"
print(f"{agent.query(question=query)}\n") 
# The stock price of AAPL was $210.16 5d ago

query="who are you?"
print(f"{agent.query(question=query)}\n") 
# I'm an AI assistant. I don't have a personal identity or emotions, but I can provide information and help with tasks to the best of my abilities. How can I assist you today?