AWS Bedrock AgentCore Tools

AWS Bedrock AgentCore Tools

This module provides tools for interacting with AWS Bedrock AgentCore's browser and code interpreter sandbox tools.

Installation

(Optional) To run the examples below, first install:

pip install llama-index llama-index-llms-bedrock-converse

Install the main tools package:

pip install llama-index-tools-aws-bedrock-agentcore

Toolspecs

Browser

The Bedrock AgentCore Browser toolspec provides a set of tools for interacting with web browsers in a secure sandbox environment. It enables your LlamaIndex agents to navigate websites, extract content, click elements, and more.

Included tools:

• navigate_browser: Navigate to a URL
• click_element: Click on an element using CSS selectors
• extract_text: Extract all text from the current webpage
• extract_hyperlinks: Extract all hyperlinks from the current webpage
• get_elements: Get elements matching a CSS selector
• navigate_back: Navigate to the previous page
• current_webpage: Get information about the current webpage

Example usage:

import asyncio
from llama_index.llms.bedrock_converse import BedrockConverse
from llama_index.tools.aws_bedrock_agentcore import AgentCoreBrowserToolSpec
from llama_index.core.agent.workflow import FunctionAgent

import nest_asyncio

nest_asyncio.apply()  # In case of existing loop (ex. in JupyterLab)


async def main():
    tool_spec = AgentCoreBrowserToolSpec(region="us-west-2")
    tools = tool_spec.to_tool_list()

    llm = BedrockConverse(
        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
        region_name="us-west-2",
    )

    agent = FunctionAgent(
        tools=tools,
        llm=llm,
    )

    task = "Go to https://news.ycombinator.com/ and tell me the titles of the top 5 posts."

    response = await agent.run(task)
    print(str(response))

    await tool_spec.cleanup()


if __name__ == "__main__":
    asyncio.run(main())

Code Interpreter

The Bedrock AgentCore code_interpreter toolspec provides a set of tools interacting with a secure code interpreter sandbox environment. It enables your LlamaIndex agents to execute code, run shell commands, manage files, and perform computational task.

Included tools:

• execute_code: Run code in various languages (primarily Python)
• execute_command: Run shell commands
• read_files: Read content of files in the environment
• list_files: List files in directories
• delete_files: Remove files from the environment
• write_files: Create or update files
• start_command: Start long-running commands asynchronously
• get_task: Check status of async tasks
• stop_task: Stop running tasks

Example usage:

import asyncio
from llama_index.llms.bedrock_converse import BedrockConverse
from llama_index.tools.aws_bedrock_agentcore import (
    AgentCoreCodeInterpreterToolSpec,
)
from llama_index.core.agent.workflow import FunctionAgent

import nest_asyncio

nest_asyncio.apply()  # In case of existing loop (ex. in JupyterLab)


async def main():
    tool_spec = AgentCoreCodeInterpreterToolSpec(region="us-west-2")
    tools = tool_spec.to_tool_list()

    llm = BedrockConverse(
        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
        region_name="us-west-2",
    )

    agent = FunctionAgent(
        tools=tools,
        llm=llm,
    )

    code_task = "Write a Python function that calculates the factorial of a number and test it."

    code_response = await agent.run(code_task)
    print(str(code_response))

    command_task = "Use terminal CLI commands to: 1) Show the environment's Python version. 2) Show me the list of Python package currently installed in the environment."

    command_response = await agent.run(command_task)
    print(str(command_response))

    await tool_spec.cleanup()


if __name__ == "__main__":
    asyncio.run(main())