AI Agent Tools - Python Utility Library for AI Agents

📖 Overview

This library provides ready-to-use Python functions that AI agents can leverage to perform various tasks including file operations, text analysis, data transformation, memory management, and validation.

⚡ Quick Start

Installation

Method 1: Clone from GitHub

git clone https://github.com/cerbug45/ai-agent-tools.git
cd ai-agent-tools

Method 2: Direct Download

wget https://raw.githubusercontent.com/cerbug45/ai-agent-tools/main/ai_agent_tools.py

Method 3: Copy-Paste

Simply copy the ai_agent_tools.py file into your project directory.

Requirements

• Python 3.7 or higher
• No external dependencies (uses only standard library)

🛠️ Available Tools

1. FileTools - File Operations

Operations for reading, writing, and managing files.

Available Methods:

from ai_agent_tools import FileTools

# Read a file
content = FileTools.read_file("path/to/file.txt")

# Write to a file
FileTools.write_file("path/to/file.txt", "Hello World!")

# List files in directory
files = FileTools.list_files(".", extension=".py")

# Check if file exists
exists = FileTools.file_exists("path/to/file.txt")

Use Cases:

• Reading configuration files
• Saving agent outputs
• Listing available resources
• Checking file existence before operations

---

2. TextTools - Text Processing

Extract information and process text data.

Available Methods:

from ai_agent_tools import TextTools

text = "Contact: [email protected], phone: 0532 123 45 67"

# Extract emails
emails = TextTools.extract_emails(text)
# Output: ['[email protected]']

# Extract URLs
urls = TextTools.extract_urls("Visit https://example.com")
# Output: ['https://example.com']

# Extract phone numbers
phones = TextTools.extract_phone_numbers(text)
# Output: ['0532 123 45 67']

# Count words
count = TextTools.word_count("Hello world from AI")
# Output: 4

# Summarize text
summary = TextTools.summarize_text("Long text here...", max_length=50)

# Clean whitespace
clean = TextTools.clean_whitespace("Too   many    spaces")
# Output: "Too many spaces"

Use Cases:

• Extracting contact information from documents
• Cleaning and formatting text
• Text summarization
• Data extraction from unstructured text

---

3. DataTools - Data Transformation

Convert between different data formats.

Available Methods:

from ai_agent_tools import DataTools

# Save data as JSON
data = {"name": "Alice", "age": 30}
DataTools.save_json(data, "output.json")

# Load JSON file
loaded_data = DataTools.load_json("output.json")

# Convert CSV text to dictionary list
csv_text = """name,age,city
Alice,30,New York
Bob,25,London"""
data_list = DataTools.csv_to_dict(csv_text)
# Output: [{'name': 'Alice', 'age': '30', 'city': 'New York'}, ...]

# Convert dictionary list to CSV
data = [
    {"name": "Alice", "age": 30},
    {"name": "Bob", "age": 25}
]
csv = DataTools.dict_to_csv(data)

Use Cases:

• Saving structured data
• Converting between formats
• Processing API responses
• Generating reports

---

4. UtilityTools - General Utilities

Helper functions for common operations.

Available Methods:

from ai_agent_tools import UtilityTools

# Get current timestamp
timestamp = UtilityTools.get_timestamp()
# Output: "2026-02-15 14:30:25"

# Generate unique ID from text
id = UtilityTools.generate_id("user_john_doe")
# Output: "a3f5b2c1"

# Calculate percentage
percent = UtilityTools.calculate_percentage(25, 100)
# Output: 25.0

# Safe division (no divide by zero error)
result = UtilityTools.safe_divide(10, 0, default=0.0)
# Output: 0.0

Use Cases:

• Timestamping events
• Generating unique identifiers
• Safe mathematical operations
• Data analysis calculations

---

5. MemoryTools - Memory Management

Store and retrieve data during agent execution.

Available Methods:

from ai_agent_tools import MemoryTools

# Initialize memory
memory = MemoryTools()

# Store a value
memory.store("user_name", "Alice")
memory.store("session_id", "abc123")

# Retrieve a value
name = memory.retrieve("user_name")
# Output: "Alice"

# List all keys
keys = memory.list_keys()
# Output: ["user_name", "session_id"]

# Delete a value
memory.delete("session_id")

# Clear all memory
memory.clear()

Use Cases:

• Maintaining conversation context
• Storing intermediate results
• Session management
• Caching computed values

---

6. ValidationTools - Data Validation

Validate different types of data.

Available Methods:

from ai_agent_tools import ValidationTools

# Validate email
is_valid = ValidationTools.is_valid_email("[email protected]")
# Output: True

# Validate URL
is_valid = ValidationTools.is_valid_url("https://example.com")
# Output: True

# Validate phone number (Turkish format)
is_valid = ValidationTools.is_valid_phone("0532 123 45 67")
# Output: True

Use Cases:

• Input validation
• Data quality checks
• Form validation
• Pre-processing data

---

💡 Complete Usage Example

from ai_agent_tools import (
    FileTools, TextTools, DataTools, 
    UtilityTools, MemoryTools, ValidationTools
)

# Initialize memory for session
memory = MemoryTools()

# Read input file
text = FileTools.read_file("contacts.txt")

# Extract information
emails = TextTools.extract_emails(text)
phones = TextTools.extract_phone_numbers(text)

# Validate extracted data
valid_emails = [e for e in emails if ValidationTools.is_valid_email(e)]
valid_phones = [p for p in phones if ValidationTools.is_valid_phone(p)]

# Create structured data
contacts = []
for i, (email, phone) in enumerate(zip(valid_emails, valid_phones)):
    contact = {
        "id": UtilityTools.generate_id(f"contact_{i}"),
        "email": email,
        "phone": phone,
        "timestamp": UtilityTools.get_timestamp()
    }
    contacts.append(contact)

# Save results
DataTools.save_json(contacts, "output/contacts.json")

# Store in memory
memory.store("total_contacts", len(contacts))
memory.store("last_processed", UtilityTools.get_timestamp())

print(f"Processed {len(contacts)} contacts")
print(f"Saved to: output/contacts.json")

🎯 Best Practices

1. Error Handling

Always wrap file operations in try-except blocks:

try:
    content = FileTools.read_file("data.txt")
    # Process content
except Exception as e:
    print(f"Error reading file: {e}")

2. Memory Management

Clear memory when no longer needed:

memory = MemoryTools()
# ... use memory ...
memory.clear()  # Clean up

3. Data Validation

Always validate data before processing:

if ValidationTools.is_valid_email(email):
    # Process email
    pass
else:
    print(f"Invalid email: {email}")

4. Path Handling

Use absolute paths or ensure working directory is correct:

import os

base_dir = os.path.dirname(__file__)
filepath = os.path.join(base_dir, "data", "file.txt")
content = FileTools.read_file(filepath)

🔧 Advanced Usage

Chaining Operations

# Read -> Process -> Validate -> Save pipeline
text = FileTools.read_file("input.txt")
cleaned = TextTools.clean_whitespace(text)
emails = TextTools.extract_emails(cleaned)
valid = [e for e in emails if ValidationTools.is_valid_email(e)]
DataTools.save_json({"emails": valid}, "output.json")

Creating Custom Workflows

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