Strands Agents Tools is a community-driven project that provides a powerful set of tools for your agents to use. 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
• 🧠 Memory - Store user and agent memories across agent runs to provide personalized experiences with both Mem0, Amazon Bedrock Knowledge Bases, Elasticsearch, and MongoDB Atlas
• 🕸️ Web Infrastructure - Perform web searches, extract page content, and crawl websites with Tavily and Exa-powered tools
• 🌐 HTTP Client - Make API requests with comprehensive authentication support
• 💬 Slack Client - Real-time Slack events, message processing, and Slack API access
• 🐍 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
• 🎥 Video Processing - Use models and agents to generate dynamic videos
• 🎙️ Audio Output - Enable models to generate audio and speak
• 🔄 Environment Management - Handle environment variables safely
• 📝 Journaling - Create and manage structured logs and journals
• ⏱️ Task Scheduling - Schedule and manage cron jobs
• 🧠 Advanced Reasoning - Tools for complex thinking and reasoning capabilities
• 🐝 Swarm Intelligence - Coordinate multiple AI agents for parallel problem solving with shared memory
• 🤖 Agent as Tool - Create nested agent instances with model switching support for multi-model workflows and specialized sub-tasks
• 🔗 Multi-Agent Graph - Create and manage deterministic DAG-based multi-agent pipelines with output propagation and per-node model configuration
• 🔌 Dynamic MCP Client - ⚠️ Dynamically connect to external MCP servers and load remote tools (use with caution - see security warnings)
• 🔄 Multiple tools in Parallel - Call multiple other tools at the same time in parallel with Batch Tool
• 🔍 Browser Tool - Tool giving an agent access to perform automated actions on a browser (chromium)
• 📈 Diagram - Create AWS cloud diagrams, basic diagrams, or UML diagrams using python libraries
• 📰 RSS Feed Manager - Subscribe, fetch, and process RSS feeds with content filtering and persistent storage
• 🖱️ Computer Tool - Automate desktop actions including mouse movements, keyboard input, screenshots, and application management

📦 Installation

Quick Install

pip install strands-agents-tools

To install the dependencies for optional tools:

pip install strands-agents-tools[mem0_memory, use_browser, rss, use_computer]

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
a2a_client	provider = A2AClientToolProvider(known_agent_urls=["http://localhost:9000"]); agent = Agent(tools=provider.tools)	Discover and communicate with A2A-compliant agents, send messages between agents
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
tavily_search	agent.tool.tavily_search(query="What is artificial intelligence?", search_depth="advanced")	Real-time web search optimized for AI agents with a variety of custom parameters
tavily_extract	agent.tool.tavily_extract(urls=["www.tavily.com"], extract_depth="advanced")	Extract clean, structured content from web pages with advanced processing and noise removal
tavily_crawl	agent.tool.tavily_crawl(url="www.tavily.com", max_depth=2, instructions="Find API docs")	Crawl websites intelligently starting from a base URL with filtering and extraction
tavily_map	agent.tool.tavily_map(url="www.tavily.com", max_depth=2, instructions="Find all pages")	Map website structure and discover URLs starting from a base URL without content extraction
exa_search	agent.tool.exa_search(query="Best project management tools", text=True)	Intelligent web search with auto mode (default) for optimal results, plus fast and deep search modes
exa_get_contents	agent.tool.exa_get_contents(urls=["https://example.com/article"], text=True, summary={"query": "key points"})	Extract full content and summaries from specific URLs with live crawling fallback
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
code_interpreter	code_interpreter = AgentCoreCodeInterpreter(region="us-west-2"); agent = Agent(tools=[code_interpreter.code_interpreter])	Execute code in isolated sandbox environments with multi-language support (Python, JavaScript, TypeScript), persistent sessions, and file operations
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 with optional metadata
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
agent_core_memory	agent.tool.agent_core_memory(action="record", content="Hello, I like vegetarian food")	Store and retrieve memories with Amazon Bedrock Agent Core Memory service
mem0_memory	agent.tool.mem0_memory(action="store", content="Remember I like to play tennis", user_id="alex")	Store user and agent memories across agent runs to provide personalized experience
bright_data	agent.tool.bright_data(action="scrape_as_markdown", url="https://example.com")	Web scraping, search queries, screenshot capture, and structured data extraction from websites and different data feeds
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_stability	agent.tool.generate_image_stability(prompt="A tranquil pool")	Creating images using Stability AI models
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
sleep	agent.tool.sleep(seconds=5)	Pause execution for the specified number of seconds, interruptible with SIGINT (Ctrl+C)
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
graph	agent.tool.graph(action="create", graph_id="pipeline", topology={"nodes": [...], "edges": [...]})	Create and manage deterministic DAG-based multi-agent graphs using Strands SDK Graph implementation with per-node model configuration
cron*	agent.tool.cron(action="schedule", name="task", schedule="0 * * * *", command="backup.sh")	Schedule and manage recurring tasks with cron job syntax **Does not work on Windows
slack	agent.tool.slack(action="post_message", channel="general", text="Hello team!")	Interact with Slack workspace for messaging and monitoring
speak	agent.tool.speak(text="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
handoff_to_user	agent.tool.handoff_to_user(message="Please confirm action", breakout_of_loop=False)	Hand off control to user for confirmation, input, or complete task handoff
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
use_agent	agent.tool.use_agent(prompt="Analyze this code", system_prompt="You are a code analyst.", model_provider="bedrock")	Create nested agent instances with model switching, multi-model workflows, cost optimization, and specialized sub-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
mcp_client	agent.tool.mcp_client(action="connect", connection_id="my_server", transport="stdio", command="python", args=["server.py"])	⚠️ SECURITY WARNING: Dynamically connect to external MCP servers via stdio, sse, or streamable_http, list tools, and call remote tools. This can pose security risks as agents may connect to malicious servers. Use with caution in production.
batch	agent.tool.batch(invocations=[{"name": "current_time", "arguments": {"timezone": "Europe/London"}}, {"name": "stop", "arguments": {}}])	Call multiple other tools in parallel.
browser	browser = LocalChromiumBrowser(); agent = Agent(tools=[browser.browser])	Web scraping, automated testing, form filling, web automation tasks
diagram	agent.tool.diagram(diagram_type="cloud", nodes=[{"id": "s3", "type": "S3"}], edges=[])	Create AWS cloud architecture diagrams, network diagrams, graphs, and UML diagrams (all 14 types)
rss	agent.tool.rss(action="subscribe", url="https://example.com/feed.xml", feed_id="tech_news")	Manage RSS feeds: subscribe, fetch, read, search, and update content from various sources
use_computer	agent.tool.use_computer(action="click", x=100, y=200, app_name="Chrome")	Desktop automation, GUI interaction, screen capture
search_video	agent.tool.search_video(query="people discussing AI")	Semantic video search using TwelveLabs' Marengo model
chat_video	agent.tool.chat_video(prompt="What are the main topics?", video_id="video_123")	Interactive video analysis using TwelveLabs' Pegasus model
mongodb_memory	agent.tool.mongodb_memory(action="record", content="User prefers vegetarian pizza", connection_string="mongodb+srv://...", database_name="memories")	Store and retrieve memories using MongoDB Atlas with semantic search via AWS Bedrock Titan embeddings
elasticsearch_memory	agent.tool.elasticsearch_memory(action="record", content="User prefers dark mode", cloud_id="...", api_key="...")	Store and retrieve memories using Elasticsearch with semantic search via AWS Bedrock Titan embeddings

* These tools do not work on windows

💻 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")

Dynamic MCP Client Integration

⚠️ SECURITY WARNING: The Dynamic MCP Client allows agents to autonomously connect to external MCP servers and load remote tools at runtime. This poses significant security risks as agents can potentially connect to malicious servers and execute untrusted code. Use with extreme caution in production environments.

This tool is different from the static MCP server implementation in the Strands SDK (see MCP Tools Documentation) which uses pre-configured, trusted MCP servers.

from strands import Agent
from strands_tools import mcp_client

agent = Agent(tools=[mcp_client])

# Connect to a custom MCP server via stdio
agent.tool.mcp_client(
    action="connect",
    connection_id="my_tools",
    transport="stdio",
    command="python",
    args=["my_mcp_server.py"]
)

# List available tools on the server
tools = agent.tool.mcp_client(
    action="list_tools",
    connection_id="my_tools"
)

# Call a tool from the MCP server
result = agent.tool.mcp_client(
    action="call_tool",
    connection_id="my_tools",
    tool_name="calculate",
    tool_args={"x": 10, "y": 20}
)

# Connect to a SSE-based server
agent.tool.mcp_client(
    action="connect",
    connection_id="web_server",
    transport="sse",
    server_url="http://localhost:8080/sse"
)

# Connect to a streamable HTTP server
agent.tool.mcp_client(
    action="connect",
    connection_id="http_server",
    transport="streamable_http",
    server_url="https://api.example.com/mcp",
    headers={"Authorization": "Bearer token"},
    timeout=60
)

# Load MCP tools into agent's registry for direct access
# ⚠️ WARNING: This loads external tools directly into the agent
agent.tool.mcp_client(
    action="load_tools",
    connection_id="my_tools"
)
# Now you can call MCP tools directly as: agent.tool.calculate(x=10, y=20)

Shell Commands

Note: shell does not work on Windows.

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"
)

# Convert HTML webpages to markdown for better readability
response = agent.tool.http_request(
    method="GET",
    url="https://example.com/article",
    convert_to_markdown=True
)

Tavily Search, Extract, Crawl, and Map

from strands import Agent
from strands_tools.tavily import (
    tavily_search, tavily_extract, tavily_crawl, tavily_map
)

# For async usage, call the corresponding *_async function with await.
# Synchronous usage 
agent = Agent(tools=[tavily_search, tavily_extract, tavily_crawl, tavily_map])

# Real-time web search
result = agent.tool.tavily_search(
    query="Latest developments in renewable energy",
    search_depth="advanced",
    topic="news",
    max_results=10,
    include_raw_content=True
)

# Extract content from multiple URLs
result = agent.tool.tavily_extract(
    urls=["www.tavily.com", "www.apple.com"],
    extract_depth="advanced",
    format="markdown"
)

# Advanced crawl with instructions and filtering
result = agent.tool.tavily_crawl(
    url="www.tavily.com",
    max_depth=2,
    limit=50,
    instructions="Find all API documentation and developer guides",
    extract_depth="advanced",
    include_images=True
)

# Basic website mapping
result = agent.tool.tavily_map(url="www.tavily.com")

Exa Search and Contents

from strands import Agent
from strands_tools.exa import exa_search, exa_get_contents

agent = Agent(tools=[exa_search, exa_get_contents])

# Basic search (auto mode is default and recommended)
result = agent.tool.exa_search(
    query="Best project management software",
    text=True
)

# Company-specific search when needed
result = agent.tool.exa_search(
    query="Anthropic AI safety research",
    category="company",
    include_domains=["anthropic.com"],
    num_results=5,
    summary={"query": "key research areas and findings"}
)

# News search with date filtering
result = agent.tool.exa_search(
    query="AI regulation policy updates",
    category="news",
    start_published_date="2024-01-01T00:00:00.000Z",
    text=True
)

# Get detailed content from specific URLs
result = agent.tool.exa_get_contents(
    urls=[
        "https://example.com/blog-post",
        "https://github.com/microsoft/semantic-kernel"
    ],
    text={"maxCharacters": 5000, "includeHtmlTags": False},
    summary={
        "query": "main points and practical applications"
    },
    subpages=2,
    extras={"links": 5, "imageLinks": 2}
)

# Structured summary with JSON schema
result = agent.tool.exa_get_contents(
    urls=["https://example.com/article"],
    summary={
        "query": "main findings and recommendations",
        "schema": {
            "type": "object",
            "properties": {
                "main_points": {"type": "string", "description": "Key points from the article"},
                "recommendations": {"type": "string", "description": "Suggested actions or advice"},
                "conclusion": {"type": "string", "description": "Overall conclusion"},
                "relevance": {"type": "string", "description": "Why this matters"}
            },
            "required": ["main_points", "conclusion"]
        }
    }
)

Python Code Execution

Note: python_repl does not work on Windows.

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()
""")

Code Interpreter

from strands import Agent
from strands_tools.code_interpreter import AgentCoreCodeInterpreter

# Create the code interpreter tool
bedrock_agent_core_code_interpreter = AgentCoreCodeInterpreter(region="us-west-2")
agent = Agent(tools=[bedrock_agent_core_code_interpreter.code_interpreter])

# Create a session
agent.tool.code_interpreter({
    "action": {
        "type": "initSession",
        "description": "Data analysis session",
        "session_name": "analysis-session"
    }
})

# Execute Python code
agent.tool.code_interpreter({
    "action": {
        "type": "executeCode",
        "session_name": "analysis-session",
        "code": "print('Hello from sandbox!')",
        "language": "python"
    }
})

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"
)

Use AWS

from strands import Agent
from strands_tools import use_aws

agent = Agent(tools=[use_aws])

# List S3 buckets
result = agent.tool.use_aws(
    service_name="s3",
    operation_name="list_buckets",
    parameters={},
    region="us-east-1",
    label="List all S3 buckets"
)

# Get the contents of a specific S3 bucket
result = agent.tool.use_aws(
    service_name="s3",
    operation_name="list_objects_v2",
    parameters={"Bucket": "example-bucket"},  # Replace with your actual bucket name
    region="us-east-1",
    label="List objects in a specific S3 bucket"
)

# Get the list of EC2 subnets
result = agent.tool.use_aws(
    service_name="ec2",
    operation_name="describe_subnets",
    parameters={},
    region="us-east-1",
    label="List all subnets"
)

Retrieve Tool

from strands import Agent
from strands_tools import retrieve

agent = Agent(tools=[retrieve])

# Basic retrieval without metadata
result = agent.tool.retrieve(
    text="What is artificial intelligence?"
)

# Retrieval with metadata enabled
result = agent.tool.retrieve(
    text="What are the latest developments in machine learning?",
    enableMetadata=True
)

# Using environment variable to set default metadata behavior
# Set RETRIEVE_ENABLE_METADATA_DEFAULT=true in your environment
result = agent.tool.retrieve(
    text="Tell me about cloud computing"
    # enableMetadata will default to the environment variable value
)

Batch Tool

import os
import sys

from strands import Agent
from strands_tools import batch, http_request, use_aws

# Example usage of the batch with http_request and use_aws tools
agent = Agent(tools=[batch, http_request, use_aws])

result = agent.tool.batch(
    invocations=[
        {"name": "http_request", "arguments": {"method": "GET", "url": "https://api.ipify.org?format=json"}},
        {
            "name": "use_aws",
            "arguments": {
                "service_name": "s3",
                "operation_name": "list_buckets",
                "parameters": {},
                "region": "us-east-1",
                "label": "List S3 Buckets"
            }
        },
    ]
)

Video Tools

from strands import Agent
from strands_tools import search_video, chat_video

agent = Agent(tools=[search_video, chat_video])

# Search for video content using natural language
result = agent.tool.search_video(
    query="people discussing AI technology",
    threshold="high",
    group_by="video",
    page_limit=5
)

# Chat with existing video (no index_id needed)
result = agent.tool.chat_video(
    prompt="What are the main topics discussed in this video?",
    video_id="existing-video-id"
)

# Chat with new video file (index_id required for upload)
result = agent.tool.chat_video(
    prompt="Describe what happens in this video",
    video_path="/path/to/video.mp4",
    index_id="your-index-id"  # or set TWELVELABS_PEGASUS_INDEX_ID env var
)

AgentCore Memory

from strands import Agent
from strands_tools.agent_core_memory import AgentCoreMemoryToolProvider


provider = AgentCoreMemoryToolProvider(
    memory_id="memory-123abc",  # Required
    actor_id="user-456",        # Required
    session_id="session-789",   # Required
    namespace="default",        # Required
    region="us-west-2"          # Optional, defaults to us-west-2
)

agent = Agent(tools=provider.tools)

# Create a new memory
result = agent.tool.agent_core_memory(
    action="record",
    content="I am allergic to shellfish"
)

# Search for relevant memories
result = agent.tool.agent_core_memory(
    action="retrieve",
    query="user preferences"
)

# List all memories
result = agent.tool.agent_core_memory(
    action="list"
)

# Get a specific memory by ID
result = agent.tool.agent_core_memory(
    action="get",
    memory_record_id="mr-12345"
)

Browser

from strands import Agent
from strands_tools.browser import LocalChromiumBrowser

# Create browser tool
browser = LocalChromiumBrowser()
agent = Agent(tools=[browser.browser])

# Simple navigation
result = agent.tool.browser({
    "action": {
        "type": "navigate",
        "url": "https://example.com"
    }
})

# Initialize a session first
result = agent.tool.browser({
    "action": {
        "type": "initSession",
        "session_name": "main-session",
        "description": "Web automation session"
    }
})

Handoff to User

from strands import Agent
from strands_tools import handoff_to_user

agent = Agent(tools=[handoff_to_user])

# Request user confirmation and continue
response = agent.tool.handoff_to_user(
    message="I need your approval to proceed with deleting these files. Type 'yes' to confirm.",
    breakout_of_loop=False
)

# Complete handoff to user (stops agent execution)
agent.tool.handoff_to_user(
    message="Task completed. Please review the results and take any necessary follow-up actions.",
    breakout_of_loop=True
)

Use Agent (Agent as Tool)

from strands import Agent
from strands_tools import use_agent

agent = Agent(tools=[use_agent])

# Basic usage - inherits parent agent's model
result = agent.tool.use_agent(
    prompt="Tell me about the advantages of tool-building in AI agents",
    system_prompt="You are a helpful AI assistant specializing in AI development concepts."
)

# Use a different model provider for specialized tasks
result = agent.tool.use_agent(
    prompt="Calculate 2 + 2 and explain the result",
    system_prompt="You are a helpful math assistant.",
    model_provider="bedrock",
    model_settings={
        "model_id": "us.anthropic.claude-sonnet-4-20250514-v1:0"
    },
    tools=["calculator"]
)

# Use environment variables to determine model
import os
os.environ["STRANDS_PROVIDER"] = "ollama"
os.environ["STRANDS_MODEL_ID"] = "qwen3:4b"
result = agent.tool.use_agent(
    prompt="Analyze this code",
    system_prompt="You are a code review assistant.",
    model_provider="env"
)

# Custom model configuration with specific parameters
result = agent.tool.use_agent(
    prompt="Write a creative story",
    system_prompt="You are a creative writing assistant.",
    model_provider="github",
    model_settings={
        "model_id": "openai/o4-mini",
        "params": {"temperature": 1, "max_tokens": 4000}
    }
)

A2A Client

from strands import Agent
from strands_tools.a2a_client import A2AClientToolProvider

# Initialize the A2A client provider with known agent URLs
provider = A2AClientToolProvider(known_agent_urls=["http://localhost:9000"])
agent = Agent(tools=provider.tools)

# Use natural language to interact with A2A agents
response = agent("discover available agents and send a greeting message")

# The agent will automatically use the available tools:
# - discover_agent(url) to find agents
# - list_discovered_agents() to see all discovered agents
# - send_message(message_text, target_agent_url) to communicate

Diagram

from strands import Agent
from strands_tools import diagram

agent = Agent(tools=[diagram])

# Create an AWS cloud architecture diagram
result = agent.tool.diagram(
    diagram_type="cloud",
    nodes=[
        {"id": "users", "type": "Users", "label": "End Users"},
        {"id": "cloudfront", "type": "CloudFront", "label": "CDN"},
        {"id": "s3", "type": "S3", "label": "Static Assets"},
        {"id": "api", "type": "APIGateway", "label": "API Gateway"},
        {"id": "lambda", "type": "Lambda", "label": "Backend Service"}
    ],
    edges=[
        {"from": "users", "to": "cloudfront"},
        {"from": "cloudfront", "to": "s3"},
        {"from": "users", "to": "api"},
        {"from": "api", "to": "lambda"}
    ],
    title="Web Application Architecture"
)

# Create a UML class diagram
result = agent.tool.diagram(
    diagram_type="class",
    elements=[
        {
            "name": "User",
            "attributes": ["+id: int", "-name: string", "#email: string"],
            "methods": ["+login(): bool", "+logout(): void"]
        },
        {
            "name": "Order",
            "attributes": ["+id: int", "-items: List", "-total: float"],
            "methods": ["+addItem(item): void", "+calculateTotal(): float"]
        }
    ],
    relationships=[
        {"from": "User", "to": "Order", "type": "association", "multiplicity": "1..*"}
    ],
    title="E-commerce Domain Model"
)

RSS Feed Management

from strands import Agent
from strands_tools import rss

agent = Agent(tools=[rss])

# Subscribe to a feed
result = agent.tool.rss(
    action="subscribe",
    url="https://news.example.com/rss/technology"
)

# List all subscribed feeds
feeds = agent.tool.rss(action="list")

# Read entries from a specific feed
entries = agent.tool.rss(
    action="read",
    feed_id="news_example_com_technology",
    max_entries=5,
    include_content=True
)

# Search across all feeds
search_results = agent.tool.rss(
    action="search",
    query="machine learning",
    max_entries=10
)

# Fetch feed content without subscribing
latest_news = agent.tool.rss(
    action="fetch",
    url="https://blog.example.org/feed",
    max_entries=3
)

Use Computer

from strands import Agent
from strands_tools import use_computer

agent = Agent(tools=[use_computer])

# Find mouse position
result = agent.tool.use_computer(action="mouse_position")

# Automate adding text
result = agent.tool.use_computer(action="type", text="Hello, world!", app_name="Notepad")

# Analyze current computer screen
result = agent.tool.use_computer(action="analyze_screen")

result = agent.tool.use_computer(action="open_app", app_name="Calculator")
result = agent.tool.use_computer(action="close_app", app_name="Calendar")

result = agent.tool.use_computer(
    action="hotkey",
    hotkey_str="command+ctrl+f",  # For macOS
    app_name="Chrome"
)

Graph (Multi-Agent DAG)

Create deterministic DAG-based multi-agent pipelines where agents are nodes with dependency relationships. Unlike agent_graph (which uses persistent message-passing), graph uses task-based execution with output propagation.

from strands import Agent
from strands_tools.graph import graph

agent = Agent(tools=[graph])

# Create a multi-agent research pipeline
result = agent.tool.graph(
    action="create",
    graph_id="research_pipeline",
    topology={
        "nodes": [
            {
                "id": "researcher",
                "role": "researcher",
                "system_prompt": "You research topics thoroughly.",
                "model_provider": "bedrock",
                "model_settings": {"model_id": "us.anthropic.claude-sonnet-4-20250514-v1:0"}
            },
            {
                "id": "analyst",
                "role": "analyst",
                "system_prompt": "You analyze research data.",
                "model_provider": "bedrock",
                "model_settings": {"model_id": "us.anthropic.claude-3-5-haiku-20241022-v1:0"}
            },
            {
                "id": "reporter",
                "role": "reporter",
                "system_prompt": "You create comprehensive reports.",
                "tools": ["file_write", "editor"]
            }
        ],
        "edges": [
            {"from": "researcher", "to": "analyst"},
            {"from": "analyst", "to": "reporter"}
        ],
        "entry_points": ["researcher"]
    }
)

# Execute a task through the graph
result = agent.tool.graph(
    action="execute",
    graph_id="research_pipeline",
    task="Research and analyze the impact of AI on healthcare"
)

# Get graph status
result = agent.tool.graph(action="status", graph_id="research_pipeline")

# List all graphs
result = agent.tool.graph(action="list")

# Delete a graph
result = agent.tool.graph(action="delete", graph_id="research_pipeline")

Elasticsearch Memory

Note: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.

from strands import Agent
from strands_tools.elasticsearch_memory import elasticsearch_memory

# Create agent with direct tool usage
agent = Agent(tools=[elasticsearch_memory])

# Store a memory with semantic embeddings
result = agent.tool.elasticsearch_memory(
    action="record",
    content="User prefers vegetarian pizza with extra cheese",
    metadata={"category": "food_preferences", "type": "dietary"},
    cloud_id="your-elasticsearch-cloud-id",
    api_key="your-api-key",
    index_name="memories",
    namespace="user_123"
)

# Search memories using semantic similarity (vector search)
result = agent.tool.elasticsearch_memory(
    action="retrieve",
    query="food preferences and dietary restrictions",
    max_results=5,
    cloud_id="your-elasticsearch-cloud-id",
    api_key="your-api-key",
    index_name="memories",
    namespace="user_123"
)

# Use configuration dictionary for cleaner code
config = {
    "cloud_id": "your-elasticsearch-cloud-id",
    "api_key": "your-api-key",
    "index_name": "memories",
    "namespace": "user_123"
}

# List all memories with pagination
result = agent.tool.elasticsearch_memory(
    action="list",
    max_results=10,
    **config
)

# Get specific memory by ID
result = agent.tool.elasticsearch_memory(
    action="get",
    memory_id="mem_1234567890_abcd1234",
    **config
)

# Delete a memory
result = agent.tool.elasticsearch_memory(
    action="delete",
    memory_id="mem_1234567890_abcd1234",
    **config
)

# Use Elasticsearch Serverless (URL-based connection)
result = agent.tool.elasticsearch_memory(
    action="record",
    content="User prefers vegetarian pizza",
    es_url="https://your-serverless-cluster.es.region.aws.elastic.cloud:443",
    api_key="your-api-key",
    index_name="memories",
    namespace="user_123"
)

MongoDB Atlas Memory

Note: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.

from strands import Agent
from strands_tools.mongodb_memory import mongodb_memory

# Create agent with direct tool usage
agent = Agent(tools=[mongodb_memory])

# Store a memory with semantic embeddings
result = agent.tool.mongodb_memory(
    action="record",
    content="User prefers vegetarian pizza with extra cheese",
    metadata={"category": "food_preferences", "type": "dietary"},
    connection_string="mongodb+srv://username:password@cluster0.mongodb.net/?retryWrites=true&w=majority",
    database_name="memories",
    collection_name="user_memories",
    namespace="user_123"
)

# Search memories using semantic similarity (vector search)
result = agent.tool.mongodb_memory(
    action="retrieve",
    query="food preferences and dietary restrictions",
    max_results=5,
    connection_string="mongodb+srv://username:password@cluster0.mongodb.net/?retryWrites=true&w=majority",
    database_name="memories",
    collection_name="user_memories",
    namespace="user_123"
)

# Use configuration dictionary for cleaner code
config = {
    "connection_string": "mongodb+srv://username:password@cluster0.mongodb.net/?retryWrites=true&w=majority",
    "database_name": "memories",
    "collection_name": "user_memories",
    "namespace": "user_123"
}

# List all memories with pagination
result = agent.tool.mongodb_memory(
    action="list",
    max_results=10,
    **config
)

# Get specific memory by ID
result = agent.tool.mongodb_memory(
    action="get",
    memory_id="mem_1234567890_abcd1234",
    **config
)

# Delete a memory
result = agent.tool.mongodb_memory(
    action="delete",
    memory_id="mem_1234567890_abcd1234",
    **config
)

# Use environment variables for connection
# Set MONGODB_ATLAS_CLUSTER_URI in your environment
result = agent.tool.mongodb_memory(
    action="record",
    content="User prefers vegetarian pizza",
    database_name="memories",
    collection_name="user_memories",
    namespace="user_123"
)

🌍 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
BYPASS_TOOL_CONSENT	Bypass consent for tool invocation, set to "true" to enable	false	All tools that require consent (e.g. shell, file_write, python_repl)
STRANDS_TOOL_CONSOLE_MODE	Enable rich UI for tools, set to "enabled" to enable	disabled	All tools that have optional rich UI
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

Sleep Tool

Environment Variable	Description	Default
MAX_SLEEP_SECONDS	Maximum allowed sleep duration in seconds	300

Tavily Search, Extract, Crawl, and Map Tools

Environment Variable	Description	Default
TAVILY_API_KEY	Tavily API key (required for all Tavily functionality)	None

• Visit https://www.tavily.com/ to create a free account and API key.

Exa Search and Contents Tools

Environment Variable	Description	Default
EXA_API_KEY	Exa API key (required for all Exa functionality)	None

• Visit https://dashboard.exa.ai/api-keys to create a free account and API key.

Mem0 Memory Tool

The Mem0 Memory Tool supports three different backend configurations:

1. Mem0 Platform:

   • Uses the Mem0 Platform API for memory management
   • Requires a Mem0 API key

2. OpenSearch (Recommended for AWS environments):

   • Uses OpenSearch as the vector store backend
   • Requires AWS credentials and OpenSearch configuration

3. FAISS (Default for local development):

   • Uses FAISS as the local vector store backend
   • Requires faiss-cpu package for local vector storage

4. Neptune Analytics (Optional Graph backend for search enhancement):

   • Uses Neptune Analytics as the graph store backend to enhance memory recall.
   • Requires AWS credentials and Neptune Analytics configuration

   # Configure your Neptune Analytics graph ID in the .env file:
   export NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER=sample-graph-id
   
   # Configure your Neptune Analytics graph ID in Python code:
   import os
   os.environ['NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER'] = "g-sample-graph-id"
   
   

Environment Variable	Description	Default	Required For
MEM0_API_KEY	Mem0 Platform API key	None	Mem0 Platform
OPENSEARCH_HOST	OpenSearch Host URL	None	OpenSearch
AWS_REGION	AWS Region for OpenSearch	us-west-2	OpenSearch
NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER	Neptune Analytics Graph Identifier	None	Neptune Analytics
DEV	Enable development mode (bypasses confirmations)	false	All modes
MEM0_LLM_PROVIDER	LLM provider for memory processing	aws_bedrock	All modes
MEM0_LLM_MODEL	LLM model for memory processing	anthropic.claude-3-5-haiku-20241022-v1:0	All modes
MEM0_LLM_TEMPERATURE	LLM temperature (0.0-2.0)	0.1	All modes
MEM0_LLM_MAX_TOKENS	LLM maximum tokens	2000	All modes
MEM0_EMBEDDER_PROVIDER	Embedder provider for vector embeddings	aws_bedrock	All modes
MEM0_EMBEDDER_MODEL	Embedder model for vector embeddings	amazon.titan-embed-text-v2:0	All modes

Note:

• If MEM0_API_KEY is set, the tool will use the Mem0 Platform
• If OPENSEARCH_HOST is set, the tool will use OpenSearch
• If neither is set, the tool will default to FAISS (requires faiss-cpu package)
• If NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER is set, the tool will configure Neptune Analytics as graph store to enhance memory search
• LLM configuration applies to all backend modes and allows customization of the language model used for memory processing

Bright Data Tool

Environment Variable	Description	Default
BRIGHTDATA_API_KEY	Bright Data API Key	None
BRIGHTDATA_ZONE	Bright Data Web Unlocker Zone	web_unlocker1

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
PYTHON_REPL_INTERACTIVE	Whether to enable interactive PTY mode	None
PYTHON_REPL_RESET_STATE	Whether to reset the REPL state before execution	None
PYTHON_REPL_PERSISTENCE_DIR	Set Directory for python_repl tool to write state file	None

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
EDITOR_DISABLE_BACKUP	Skip creating .bak backup files during edit operations	false

Environment Tool

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

HTTP Request Tool

Environment Variable	Description	Default
STRANDS_HTTP_ALLOW_INSECURE_SSL	Allow disabling SSL certificate verification via verify_ssl parameter	false

Dynamic MCP Client Tool

Environment Variable	Description	Default
STRANDS_MCP_TIMEOUT	Default timeout in seconds for MCP operations	30.0

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

Browser Tool

Environment Variable	Description	Default
STRANDS_DEFAULT_WAIT_TIME	Default setting for wait time with actions	1
STRANDS_BROWSER_MAX_RETRIES	Default number of retries to perform when an action fails	3
STRANDS_BROWSER_RETRY_DELAY	Default retry delay time for retry mechanisms	1
STRANDS_BROWSER_SCREENSHOTS_DIR	Default directory where screenshots will be saved	screenshots
STRANDS_BROWSER_USER_DATA_DIR	Default directory where data for reloading a browser instance is stored	~/.browser_automation
STRANDS_BROWSER_HEADLESS	Default headless setting for launching browsers	false
STRANDS_BROWSER_WIDTH	Default width of the browser	1280
STRANDS_BROWSER_HEIGHT	Default height of the browser	800

RSS Tool

Environment Variable	Description	Default
STRANDS_RSS_MAX_ENTRIES	Default setting for maximum number of entries per feed	100
STRANDS_RSS_UPDATE_INTERVAL	Default amount of time between updating rss feeds in minutes	60
STRANDS_RSS_STORAGE_PATH	Default storage path where rss feeds are stored locally	strands_rss_feeds (this may vary based on your system)

Retrieve Tool

Environment Variable	Description	Default
RETRIEVE_ENABLE_METADATA_DEFAULT	Default setting for enabling metadata in retrieve tool responses	false

Use Agent Tool

Environment Variable	Description	Default
STRANDS_PROVIDER	Default model provider when using model_provider="env"	ollama
STRANDS_MODEL_ID	Default model identifier for environment-based model selection	None
STRANDS_MAX_TOKENS	Maximum tokens for the nested agent model	None
STRANDS_TEMPERATURE	Sampling temperature for the nested agent model	None

Elasticsearch Memory Tool

Environment Variable	Description	Default
ELASTICSEARCH_CLOUD_ID	Elasticsearch Cloud ID for connection	None
ELASTICSEARCH_URL	Elasticsearch URL for serverless connection	None
ELASTICSEARCH_API_KEY	Elasticsearch API key for authentication	None
ELASTICSEARCH_INDEX_NAME	Elasticsearch index name for memory storage	strands_memory
ELASTICSEARCH_NAMESPACE	Namespace for memory isolation	default
ELASTICSEARCH_EMBEDDING_MODEL	Amazon Bedrock model for embeddings	amazon.titan-embed-text-v2:0
AWS_REGION	AWS region for Bedrock embedding service	us-west-2

Note: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.

Graph Tool

The graph tool uses the same model provider environment variables as use_agent for per-node model configuration. No additional environment variables are required.

Video Tools

Environment Variable	Description	Default
TWELVELABS_API_KEY	TwelveLabs API key for video analysis	None
TWELVELABS_MARENGO_INDEX_ID	Default index ID for search_video tool	None
TWELVELABS_PEGASUS_INDEX_ID	Default index ID for chat_video tool	None

MongoDB Atlas Memory Tool

Environment Variable	Description	Default
MONGODB_ATLAS_CLUSTER_URI	MongoDB Atlas connection string	None
MONGODB_DEFAULT_DATABASE	Default database name for MongoDB operations	memories
MONGODB_DEFAULT_COLLECTION	Default collection name for MongoDB operations	user_memories
MONGODB_DEFAULT_NAMESPACE	Default namespace for memory isolation	default
MONGODB_DEFAULT_MAX_RESULTS	Default maximum results for list operations	50
MONGODB_DEFAULT_MIN_SCORE	Default minimum relevance score for filtering results	0.4

Note: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.

Contributing ❤️

This is a community-driven project, powered by passionate developers like you. We enthusiastically welcome contributions from everyone, regardless of experience level—your unique perspective is valuable to us!

How to Get Started?

1. Find your first opportunity: If you're new to the project, explore our labeled "good first issues" for beginner-friendly tasks.
2. Understand our workflow: Review our Contributing Guide to learn about our development setup, coding standards, and pull request process.
3. Make your impact: Contributions come in many forms—fixing bugs, enhancing documentation, improving performance, adding features, writing tests, or refining the user experience.
4. Submit your work: When you're ready, submit a well-documented pull request, and our maintainers will provide feedback to help get your changes merged.

Your questions, insights, and ideas are always welcome!

Together, we're building something meaningful that impacts real users. We look forward to collaborating with you!

License

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

Security

See CONTRIBUTING for more information.