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: john@example.com, phone: 0532 123 45 67"
# Extract emails
emails = TextTools.extract_emails(text)
# Output: ['john@example.com']
# 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("user@example.com")
# 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
class DataProcessor:
def __init__(self):
self.memory = MemoryTools()
def process_document(self, filepath):
# Read
text = FileTools.read_file(filepath)
# Extract
emails = TextTools.extract_emails(text)
urls = TextTools.extract_urls(text)
# Store results
self.memory.store("emails", emails)
self.memory.store("urls", urls)
# Generate report
report = {
"timestamp": UtilityTools.get_timestamp(),
"file": filepath,
"emails_found": len(emails),
"urls_found": len(urls)
}
return report
📦 Integration with AI Agents
Example: LangChain Integration
from langchain.tools import Tool
from ai_agent_tools import FileTools, TextTools
def create_file_reader_tool():
return Tool(
name="ReadFile",
func=FileTools.read_file,
description="Read contents of a file"
)
def create_email_extractor_tool():
return Tool(
name="ExtractEmails",
func=TextTools.extract_emails,
description="Extract email addresses from text"
)
tools = [create_file_reader_tool(), create_email_extractor_tool()]
Example: OpenAI Function Calling
tools = [
{
"type": "function",
"function": {
"name": "read_file",
"description": "Read a file and return its contents",
"parameters": {
"type": "object",
"properties": {
"filepath": {
"type": "string",
"description": "Path to the file"
}
},
"required": ["filepath"]
}
}
}
]
# In your agent loop
def execute_function(name, arguments):
if name == "read_file":
return FileTools.read_file(arguments["filepath"])
🧪 Testing
Run the built-in test suite:
python ai_agent_tools.py
Expected output:
=== AI Ajanları İçin Araçlar Kütüphanesi ===
1. Dosya Araçları:
Okunan içerik: Merhaba AI Ajanı!
2. Metin Araçları:
Bulunan emailler: ['ali@example.com']
Bulunan telefonlar: ['0532 123 45 67']
3. Veri Araçları:
CSV çıktısı:
isim,yaş
Ali,25
Ayşe,30
...
✓ Tüm araçlar test edildi!
🤝 Contributing
Contributions are welcome! To contribute:
- Fork the repository
- Create a feature branch:
git checkout -b feature/new-tool
- Commit your changes:
git commit -am 'Add new tool'
- Push to the branch:
git push origin feature/new-tool
- Submit a pull request
📝 License
This project is open source and available under the MIT License.
👤 Author
GitHub: @cerbug45
🐛 Issues & Support
Found a bug or need help? Please open an issue on GitHub:
https://github.com/cerbug45/ai-agent-tools/issues
📚 Additional Resources
🔄 Version History
v1.0.0 (2026-02-15)
- Initial release
- 6 tool categories
- 25+ utility functions
- Full documentation
- Test suite included
Happy Coding! 🚀
