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.
- 📁 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
pip install strands-agents-tools# 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 installBelow 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 |
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")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)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"
)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()
""")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"
)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).
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 |
| 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 |
| Environment Variable | Description | Default |
|---|---|---|
| DEFAULT_TIMEZONE | Default timezone for current_time tool | UTC |
| Environment Variable | Description | Default |
|---|---|---|
| OPENSEARCH_HOST | OpenSearch Host URL | None |
| 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 |
| 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 |
| Environment Variable | Description | Default |
|---|---|---|
| PYTHON_REPL_BINARY_MAX_LEN | Maximum length for binary content before truncation | 100 |
| Environment Variable | Description | Default |
|---|---|---|
| SHELL_DEFAULT_TIMEOUT | Default timeout in seconds for shell commands | 900 |
| 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 |
| 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 |
| 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 Variable | Description | Default |
|---|---|---|
| ENV_VARS_MASKED_DEFAULT | Default setting for masking sensitive values | true |
| 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 |
- Python 3.10+
- pip
- git
- gh
# Run all tests
hatch run test
# Run specific test file
hatch run test -- tests/test_specific.py# Run all linters
hatch run lintsrc/strands_tools/- Core tools implementationtests/- Test suiteexamples/- Example scripts and notebooksdocs/- Documentation
We welcome contributions from the community! Please see our Contributing Guide for more details on how to get involved.
This project adheres to the Python Community Code of Conduct. By participating, you are expected to uphold this code.
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.
- Documentation: Strands Agents Docs
- Issues: GitHub Issues
- Discussions: GitHub Discussions
Made with ❤️ by the Strands Team