LlamaIndex Agent with Bedrock AgentCore Integration

Information	Details
Agent type	Synchronous
Agentic Framework	LlamaIndex
LLM model	OpenAI GPT-4o-mini
Components	AgentCore Runtime, Yahoo Finance Tools
Example complexity	Easy
SDK used	Amazon BedrockAgentCore Python SDK

This example demonstrates how to integrate a LlamaIndex agent with AWS Bedrock AgentCore, enabling you to deploy a financial assistant with tool-using capabilities as a managed service.

Prerequisites

Python 3.10+
uv - Fast Python package installer and resolver
AWS account with Bedrock access
OpenAI API key (for the model client)

Setup Instructions

1. Create a Python Environment with uv

# Install uv if you don't have it already
pip install uv

# Create and activate a virtual environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install Requirements

uv pip install -r requirements.txt

3. Understanding the Agent Code

The llama_agent_hello_world.py file contains a LlamaIndex agent with financial tools and basic math capabilities, integrated with Bedrock AgentCore:

import asyncio
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
from llama_index.tools.yahoo_finance import YahooFinanceToolSpec

# Define custom function tools
def multiply(a: float, b: float) -> float:
    """Multiply two numbers and returns the product"""
    return a * b

def add(a: float, b: float) -> float:
    """Add two numbers and returns the sum"""
    return a + b

# Add other predefined tools
finance_tools = YahooFinanceToolSpec().to_tool_list()
finance_tools.extend([multiply, add])

# Create an agent workflow with our tools
agent = FunctionAgent(
    tools=finance_tools,
    llm=OpenAI(model="gpt-4o-mini"),
    system_prompt="You are a helpful assistant.",
)

from bedrock_agentcore.runtime import BedrockAgentCoreApp
app = BedrockAgentCoreApp()

@app.entrypoint
async def main(payload):
    # Run the agent
    response = await agent.run(payload.get("prompt","What is the current stock price of AMZN?"))
    print(response)
    return response.response.content

# Run the agent
if __name__ == "__main__":
    app.run()

4. Configure and Launch with Bedrock AgentCore Toolkit

# Configure your agent for deployment
agentcore configure -e llama_agent_hello_world.py

# Deploy your agent with OpenAI API key
agentcore launch --env OPENAI_API_KEY=sk-...

5. Testing Your Agent

You can test your agent locally before deploying to the cloud:

# Launch locally with your OpenAI API key
agentcore launch -l --env OPENAI_API_KEY=sk-...

# Invoke the agent with a query
agentcore invoke -l '{"prompt":"Price of AMZN stock today"}'

For cloud deployment, remove the -l flag:

# Deploy to cloud
agentcore launch --env OPENAI_API_KEY=sk-...

# Invoke the deployed agent
agentcore invoke '{"prompt":"Price of AMZN stock today"}'

The agent will:

Process your financial query
Use the Yahoo Finance tools to fetch real-time stock data
Provide a response with the requested financial information
Perform calculations if needed using the math tools

How It Works

This agent uses LlamaIndex's agent framework to create a financial assistant that can:

Process natural language queries about stocks and financial data
Access real-time stock information through Yahoo Finance tools
Perform basic mathematical operations when needed
Provide comprehensive responses based on the data

The agent is wrapped with the Bedrock AgentCore framework, which handles:

Deployment to AWS
Scaling and management
Request/response handling
Environment variable management

README.md