strands-agents-tools 0.1.3

A collection of specialized tools for Strands Agents

Agents Tools

Agents Tools: A comprehensive toolkit for building, extending, and deploying AI agents.

🚀 What is Agents Tools?

Agents Tools is an open-source Python library that provides a unified toolkit for developing autonomous AI agents. It bridges the gap between large language models and practical applications by offering ready-to-use tools for file operations, system execution, API interactions, mathematical operations, and more.

✨ Features

• 📁 File Operations - Read, write, and edit files with syntax highlighting and intelligent modifications
• 🖥️ Shell Integration - Execute and interact with shell commands securely
• 🧠 Mem0 Memory - Store user and agent memories across agent runs to provide personalized experience
• 🌐 HTTP Client - Make API requests with comprehensive authentication support
• 🐍 Python Execution - Run Python code snippets with state persistence, user confirmation for code execution, and safety features
• 🧮 Mathematical Tools - Perform advanced calculations with symbolic math capabilities
• ☁️ AWS Integration - Seamless access to AWS services
• 🖼️ Image Processing - Generate and process images for AI applications
• 🔄 Environment Management - Handle environment variables safely
• 📝 Journaling - Create and manage structured logs and journals
• 🧠 Advanced Reasoning - Tools for complex thinking and reasoning capabilities
• 🐝 Swarm Intelligence - Coordinate multiple AI agents for parallel problem solving with shared memory

📦 Installation

Quick Install

pip install strands-agents-tools

To install the dependencies for optional tools:

pip install strands-agents-tools[mem0_memory]

Development Install

# Clone the repository
git clone https://github.com/strands-agents/tools.git
cd tools

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Tools Overview

Below is a comprehensive table of all available tools, how to use them with an agent, and typical use cases:

Tool	Agent Usage	Use Case
file_read	agent.tool.file_read(path="path/to/file.txt")	Reading configuration files, parsing code files, loading datasets
file_write	agent.tool.file_write(path="path/to/file.txt", content="file content")	Writing results to files, creating new files, saving output data
editor	agent.tool.editor(command="view", path="path/to/file.py")	Advanced file operations like syntax highlighting, pattern replacement, and multi-file edits
shell	agent.tool.shell(command="ls -la")	Executing shell commands, interacting with the operating system, running scripts
http_request	agent.tool.http_request(method="GET", url="https://api.example.com/data")	Making API calls, fetching web data, sending data to external services
python_repl	agent.tool.python_repl(code="import pandas as pd\ndf = pd.read_csv('data.csv')\nprint(df.head())")	Running Python code snippets, data analysis, executing complex logic with user confirmation for security
calculator	agent.tool.calculator(expression="2 * sin(pi/4) + log(e**2)")	Performing mathematical operations, symbolic math, equation solving
use_aws	agent.tool.use_aws(service_name="s3", operation_name="list_buckets", parameters={}, region="us-west-2")	Interacting with AWS services, cloud resource management
retrieve	agent.tool.retrieve(text="What is STRANDS?")	Retrieving information from Amazon Bedrock Knowledge Bases
nova_reels	agent.tool.nova_reels(action="create", text="A cinematic shot of mountains", s3_bucket="my-bucket")	Create high-quality videos using Amazon Bedrock Nova Reel with configurable parameters via environment variables
mem0_memory	agent.tool.mem0_memory(action="store", content="Remember I like to tennis", user_id="alex")	Store user and agent memories across agent runs to provide personalized experience
memory	agent.tool.memory(action="retrieve", query="product features")	Store, retrieve, list, and manage documents in Amazon Bedrock Knowledge Bases with configurable parameters via environment variables
environment	agent.tool.environment(action="list", prefix="AWS_")	Managing environment variables, configuration management
generate_image	agent.tool.generate_image(prompt="A sunset over mountains")	Creating AI-generated images for various applications
image_reader	agent.tool.image_reader(image_path="path/to/image.jpg")	Processing and reading image files for AI analysis
journal	agent.tool.journal(action="write", content="Today's progress notes")	Creating structured logs, maintaining documentation
think	agent.tool.think(thought="Complex problem to analyze", cycle_count=3)	Advanced reasoning, multi-step thinking processes
load_tool	agent.tool.load_tool(path="path/to/custom_tool.py", name="custom_tool")	Dynamically loading custom tools and extensions
swarm	agent.tool.swarm(task="Analyze this problem", swarm_size=3, coordination_pattern="collaborative")	Coordinating multiple AI agents to solve complex problems through collective intelligence
current_time	agent.tool.current_time(timezone="US/Pacific")	Get the current time in ISO 8601 format for a specified timezone
agent_graph	agent.tool.agent_graph(agents=["agent1", "agent2"], connections=[{"from": "agent1", "to": "agent2"}])	Create and visualize agent relationship graphs for complex multi-agent systems
cron	agent.tool.cron(action="schedule", name="task", schedule="0 * * * *", command="backup.sh")	Schedule and manage recurring tasks with cron job syntax
slack	agent.tool.slack(action="post_message", channel="general", text="Hello team!")	Interact with Slack workspace for messaging and monitoring
speak	agent.tool.speak(message="Operation completed successfully", style="green", mode="polly")	Output status messages with rich formatting and optional text-to-speech
stop	agent.tool.stop(message="Process terminated by user request")	Gracefully terminate agent execution with custom message
use_llm	agent.tool.use_llm(prompt="Analyze this data", system_prompt="You are a data analyst")	Create nested AI loops with customized system prompts for specialized tasks
workflow	agent.tool.workflow(action="create", name="data_pipeline", steps=[{"tool": "file_read"}, {"tool": "python_repl"}])	Define, execute, and manage multi-step automated workflows

💻 Usage Examples

File Operations

from strands import Agent
from strands_tools import file_read, file_write, editor

agent = Agent(tools=[file_read, file_write, editor])

agent.tool.file_read(path="config.json")
agent.tool.file_write(path="output.txt", content="Hello, world!")
agent.tool.editor(command="view", path="script.py")

Shell Commands

from strands import Agent
from strands_tools import shell

agent = Agent(tools=[shell])

# Execute a single command
result = agent.tool.shell(command="ls -la")

# Execute a sequence of commands
results = agent.tool.shell(command=["mkdir -p test_dir", "cd test_dir", "touch test.txt"])

# Execute commands with error handling
agent.tool.shell(command="risky-command", ignore_errors=True)

HTTP Requests

from strands import Agent
from strands_tools import http_request

agent = Agent(tools=[http_request])

# Make a simple GET request
response = agent.tool.http_request(
    method="GET",
    url="https://api.example.com/data"
)

# POST request with authentication
response = agent.tool.http_request(
    method="POST",
    url="https://api.example.com/resource",
    headers={"Content-Type": "application/json"},
    body=json.dumps({"key": "value"}),
    auth_type="Bearer",
    auth_token="your_token_here"
)

Python Code Execution

from strands import Agent
from strands_tools import python_repl

agent = Agent(tools=[python_repl])

# Execute Python code with state persistence
result = agent.tool.python_repl(code="""
import pandas as pd

# Load and process data
data = pd.read_csv('data.csv')
processed = data.groupby('category').mean()

processed.head()
""")

Swarm Intelligence

from strands import Agent
from strands_tools import swarm

agent = Agent(tools=[swarm])

# Create a collaborative swarm of agents to tackle a complex problem
result = agent.tool.swarm(
    task="Generate creative solutions for reducing plastic waste in urban areas",
    swarm_size=5,
    coordination_pattern="collaborative"
)

# Create a competitive swarm for diverse solution generation
result = agent.tool.swarm(
    task="Design an innovative product for smart home automation",
    swarm_size=3,
    coordination_pattern="competitive"
)

# Hybrid approach combining collaboration and competition
result = agent.tool.swarm(
    task="Develop marketing strategies for a new sustainable fashion brand",
    swarm_size=4,
    coordination_pattern="hybrid"
)

🌍 Environment Variables Configuration

Agents Tools provides extensive customization through environment variables. This allows you to configure tool behavior without modifying code, making it ideal for different environments (development, testing, production).

Global Environment Variables

These variables affect multiple tools:

Environment Variable	Description	Default	Affected Tools
AWS_REGION	Default AWS region for AWS operations	us-west-2	use_aws, retrieve, generate_image, memory, nova_reels
AWS_PROFILE	AWS profile name to use from ~/.aws/credentials	default	use_aws, retrieve
LOG_LEVEL	Logging level (DEBUG, INFO, WARNING, ERROR)	INFO	All tools

Tool-Specific Environment Variables

Calculator Tool

Environment Variable	Description	Default
CALCULATOR_MODE	Default calculation mode	evaluate
CALCULATOR_PRECISION	Number of decimal places for results	10
CALCULATOR_SCIENTIFIC	Whether to use scientific notation for numbers	False
CALCULATOR_FORCE_NUMERIC	Force numeric evaluation of symbolic expressions	False
CALCULATOR_FORCE_SCIENTIFIC_THRESHOLD	Threshold for automatic scientific notation	1e21
CALCULATOR_DERIVE_ORDER	Default order for derivatives	1
CALCULATOR_SERIES_POINT	Default point for series expansion	0
CALCULATOR_SERIES_ORDER	Default order for series expansion	5

Current Time Tool

Environment Variable	Description	Default
DEFAULT_TIMEZONE	Default timezone for current_time tool	UTC

Mem0 Memory Tool

Environment Variable	Description	Default
OPENSEARCH_HOST	OpenSearch Host URL	None

Memory Tool

Environment Variable	Description	Default
MEMORY_DEFAULT_MAX_RESULTS	Default maximum results for list operations	50
MEMORY_DEFAULT_MIN_SCORE	Default minimum relevance score for filtering results	0.4

Nova Reels Tool

Environment Variable	Description	Default
NOVA_REEL_DEFAULT_SEED	Default seed for video generation	0
NOVA_REEL_DEFAULT_FPS	Default frames per second for generated videos	24
NOVA_REEL_DEFAULT_DIMENSION	Default video resolution in WIDTHxHEIGHT format	1280x720
NOVA_REEL_DEFAULT_MAX_RESULTS	Default maximum number of jobs to return for list action	10

Python REPL Tool

Environment Variable	Description	Default
PYTHON_REPL_BINARY_MAX_LEN	Maximum length for binary content before truncation	100

Shell Tool

Environment Variable	Description	Default
SHELL_DEFAULT_TIMEOUT	Default timeout in seconds for shell commands	900

Slack Tool

Environment Variable	Description	Default
SLACK_DEFAULT_EVENT_COUNT	Default number of events to retrieve	42
STRANDS_SLACK_AUTO_REPLY	Enable automatic replies to messages	false
STRANDS_SLACK_LISTEN_ONLY_TAG	Only process messages containing this tag	None

Speak Tool

Environment Variable	Description	Default
SPEAK_DEFAULT_STYLE	Default style for status messages	green
SPEAK_DEFAULT_MODE	Default speech mode (fast/polly)	fast
SPEAK_DEFAULT_VOICE_ID	Default Polly voice ID	Joanna
SPEAK_DEFAULT_OUTPUT_PATH	Default audio output path	speech_output.mp3
SPEAK_DEFAULT_PLAY_AUDIO	Whether to play audio by default	True

Editor Tool

Environment Variable	Description	Default
EDITOR_DIR_TREE_MAX_DEPTH	Maximum depth for directory tree visualization	2
EDITOR_DEFAULT_STYLE	Default style for output panels	default
EDITOR_DEFAULT_LANGUAGE	Default language for syntax highlighting	python

Environment Tool

Environment Variable	Description	Default
ENV_VARS_MASKED_DEFAULT	Default setting for masking sensitive values	true

File Read Tool

Environment Variable	Description	Default
FILE_READ_RECURSIVE_DEFAULT	Default setting for recursive file searching	true
FILE_READ_CONTEXT_LINES_DEFAULT	Default number of context lines around search matches	2
FILE_READ_START_LINE_DEFAULT	Default starting line number for lines mode	0
FILE_READ_CHUNK_OFFSET_DEFAULT	Default byte offset for chunk mode	0
FILE_READ_DIFF_TYPE_DEFAULT	Default diff type for file comparisons	unified
FILE_READ_USE_GIT_DEFAULT	Default setting for using git in time machine mode	true
FILE_READ_NUM_REVISIONS_DEFAULT	Default number of revisions to show in time machine mode	5

🛠️ Development

Prerequisites

• Python 3.10+
• pip
• git
• gh

Testing

# Run all tests
hatch run test

# Run specific test file
hatch run test -- tests/test_specific.py

Linting

# Run all linters
hatch run lint

📚 Project Structure

• src/strands_tools/ - Core tools implementation
• tests/ - Test suite
• examples/ - Example scripts and notebooks
• docs/ - Documentation

🤝 Contributing

We welcome contributions from the community! Please see our Contributing Guide for more details on how to get involved.

Code of Conduct

This project adheres to the Python Community Code of Conduct. By participating, you are expected to uphold this code.

📄 License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

📞 Support

• Documentation: Strands Agents Docs
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Made with ❤️ by the Strands Team