strands-agents-tools 0.2.21
A collection of specialized tools for Strands Agents
License: Apache-2.0
Requires Python: >=3.10

<div align="center">
  <div>
    <a href="https://strandsagents.com">
      <img src="https://strandsagents.com/latest/assets/logo-github.svg" alt="Strands Agents" width="55px" height="105px">
    </a>
  </div>

  <h1>
    Strands Agents Tools
  </h1>

  <h2>
    A model-driven approach to building AI agents in just a few lines of code.
  </h2>

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    <a href="https://python.org"><img alt="Python versions" src="https://img.shields.io/pypi/pyversions/strands-agents-tools"/></a>
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  <p>
    <a href="https://strandsagents.com/">Documentation</a>
    ◆ <a href="https://github.com/strands-agents/samples">Samples</a>
    ◆ <a href="https://github.com/strands-agents/sdk-python">Python SDK</a>
    ◆ <a href="https://github.com/strands-agents/tools">Tools</a>
    ◆ <a href="https://github.com/strands-agents/agent-builder">Agent Builder</a>
    ◆ <a href="https://github.com/strands-agents/mcp-server">MCP Server</a>
  </p>
</div>

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
- 🔌 **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

```bash
pip install strands-agents-tools
```

To install the dependencies for optional tools:

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

### Development Install

```bash
# 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) that combines neural and keyword search for optimal results |
| 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 |
| cron* | `agent.tool.cron(action="schedule", name="task", schedule="0 * * * *", command="backup.sh")` | Schedule and manage recurring tasks with cron job syntax <br> **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 |
| 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 |

\* *These tools do not work on windows*

## 💻 Usage Examples

### File Operations

```python
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](https://github.com/strands-agents/docs/blob/main/docs/user-guide/concepts/tools/mcp-tools.md)) which uses pre-configured, trusted MCP servers.

```python
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.*

```python
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

```python
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

```python
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

```python
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.*

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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

```python
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
```python
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
```python
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

```python
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
)
```

### A2A Client

```python
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

```python
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

```python
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

```python
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"
)
```

### Elasticsearch Memory

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

```python
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.

```python
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 embedd