Adding Pyton REPL to Tool

In this guide, we’ll extend a LangChain-based agent with Python REPL functionality. By combining the Tavily Search Results tool for information lookup with a Python REPL tool for on-the-fly calculations, your agent can handle both data retrieval and computation seamlessly.

Make sure you have Python 3.8+ installed and the necessary packages (langchain, langchain-experimental, langchain-openai, langchain-community) available in your environment.

Prerequisites

Tavily API key stored in the TAVILY_API_KEY environment variable
Access to OpenAI’s Chat API via langchain-openai

Without a valid TAVILY_API_KEY, the search tool will fail to retrieve tournament data. Use export TAVILY_API_KEY=your_api_key to configure it.

1. Import Modules and Initialize Tools

from langchain_core.prompts.chat import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_experimental.tools import PythonREPLTool

from langchain.agents import create_tool_calling_agent
from langchain.agents import AgentExecutor

import os

# Set your Tavily API key
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")

search = TavilySearchResults()
python_repl = PythonREPLTool()

2. Build the Chat Prompt Template

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Think step by step before responding."),
    ("placeholder", "{chat_history}"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])

3. Initialize the Agent Executor

llm = ChatOpenAI()
tools = [search, python_repl]
message_history = ChatMessageHistory()

agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

4. Example Queries

Below are common examples demonstrating search and computation capabilities.

Tool	Use Case	Initialization
search	Fetch tournament data	`TavilySearchResults()`
python_repl	Execute Python code	`PythonREPLTool()`

Tournament Schedule

response = agent_executor.invoke({"input": "When is the ICC Men's T20 World Cup scheduled?"})
print(response["output"])

Hosting Countries

response = agent_executor.invoke(
    {"input": "Which countries are hosting?"}, 
    config={"configurable": {"$session_id": "session"}}
)
print(response["output"])

Date Calculation

response = agent_executor.invoke({
    "input": "Today's date is May 1st, 2024. How many days before the first match starts?"
})
print(response["output"])

Under the Hood: Python REPL Execution

# Code executed in the Python REPL environment
from datetime import datetime

start_date = datetime(2024, 6, 1)
today_date = datetime(2024, 5, 1)
days_until_start = (start_date - today_date).days
print(days_until_start)

This code prints:

And the agent returns:

There are 31 days left before the first match of the ICC Men’s T20 World Cup 2024 starts on June 1, 2024.

By combining the Tavily search tool for tournament data and the Python REPL tool for on-the-fly math, the agent can answer complex queries accurately and interactively.

Links and References

Stay tuned for more demos and advanced capabilities in upcoming articles!

Introduction

Overview of LangChain

Key Components of LangChain

Interacting with LLMs

Tips, Tricks, and Resources

Introduction to LCEL

Adding Memory to LLM Apps

Performing Retrieval

Implementing Chains

Using Tools

Building Agents

Conclusion

Building Blocks of LLM Apps

Adding Pyton REPL to Tool

Prerequisites

1. Import Modules and Initialize Tools

2. Build the Chat Prompt Template

3. Initialize the Agent Executor

4. Example Queries

Under the Hood: Python REPL Execution

Links and References

Watch Video

Practice Lab

​Prerequisites

​1. Import Modules and Initialize Tools

​2. Build the Chat Prompt Template

​3. Initialize the Agent Executor

​4. Example Queries

​Under the Hood: Python REPL Execution

​Links and References

Watch Video

Practice Lab

Prerequisites

1. Import Modules and Initialize Tools

2. Build the Chat Prompt Template

3. Initialize the Agent Executor

4. Example Queries

Under the Hood: Python REPL Execution

Links and References