llama-index-tools-aws-bedrock-agentcore 0.2.0

llama-index tools AWS Bedrock AgentCore integration

AWS Bedrock AgentCore Tools

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

Prerequisites

• AWS credentials configured via environment variables, AWS CLI profile, or IAM role
• IAM permissions for bedrock-agentcore:* actions (see the AgentCore documentation for details)
• Python 3.9+

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
• generate_live_view_url: Generate a presigned URL for human oversight of a browser session
• take_control: Take manual control of a browser session (disables automation)
• release_control: Release manual control (re-enables automation)

Lifecycle methods available for programmatic use (not exposed as agent tools):

• list_browsers, create_browser, delete_browser, get_browser

You can optionally pass a custom identifier for VPC-enabled browser resources:

tool_spec = AgentCoreBrowserToolSpec(
    region="us-west-2",
    identifier="my-custom-browser-id",
)

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
• upload_file: Upload a file with an optional semantic description
• upload_files: Upload multiple files at once
• install_packages: Install Python packages via pip
• download_file: Download a file from the sandbox
• download_files: Download multiple files from the sandbox
• clear_context: Clear all variable state in the Python execution context

Lifecycle methods available for programmatic use (not exposed as agent tools):

• list_code_interpreters, create_code_interpreter, delete_code_interpreter, get_code_interpreter

You can optionally pass a custom identifier for VPC-enabled code interpreter resources:

tool_spec = AgentCoreCodeInterpreterToolSpec(
    region="us-west-2",
    identifier="my-custom-interpreter-id",
)

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())

Example Notebooks

• Browser Tool Notebook
• Code Interpreter Tool Notebook