---
title: Amazon Bedrock AgentCore Code Interpreter Tools
 | Developer Documentation
---

This notebook demonstrates how to use the [Amazon Bedrock AgentCore](https://aws.amazon.com/bedrock/agentcore/) Code Interpreter tools with LlamaIndex. The toolkit provides secure, sandboxed code execution, file management, and package installation in a remote environment.

## Prerequisites

- An AWS account with Amazon Bedrock AgentCore access
- Configured AWS credentials (via `~/.aws/credentials`, environment variables, or IAM role)
- Required IAM permissions: `bedrock-agentcore:InvokeCodeInterpreter`

## Install Dependencies

```
%pip install -q llama-index llama-index-tools-aws-bedrock-agentcore llama-index-llms-bedrock-converse
```

## Setup

Create an `AgentCoreCodeInterpreterToolSpec` instance. A sandboxed code interpreter session is started lazily on first tool use.

```
from llama_index.tools.aws_bedrock_agentcore import (
    AgentCoreCodeInterpreterToolSpec,
)


tool_spec = AgentCoreCodeInterpreterToolSpec(region="us-west-2")
tools = tool_spec.to_tool_list()


print(f"Available tools: {[t.metadata.name for t in tools]}")
```

## Direct Tool Usage

You can call the tool spec methods directly without an agent. This is useful for scripting or when you want precise control.

### Execute Code

```
result = tool_spec.execute_code("print('Hello from AgentCore!')")
print(result)
```

```
result = tool_spec.execute_code(
    """
import math
print(f"Factorial of 10: {math.factorial(10)}")
print(f"Pi to 15 digits: {math.pi:.15f}")
"""
)
print(result)
```

### File Operations

```
# Write a file to the sandbox
result = tool_spec.write_files(
    [{"path": "hello.txt", "text": "Hello from the code interpreter sandbox!"}]
)
print(result)


# List files
result = tool_spec.list_files()
print(result)


# Read the file back
result = tool_spec.read_files(["hello.txt"])
print(result)
```

### Upload and Download Files

```
# Upload a file with a semantic description
csv_content = (
    "Name,Age,City\nAlice,30,Seattle\nBob,25,Portland\nCharlie,35,Denver"
)
result = tool_spec.upload_file(
    path="people.csv",
    content=csv_content,
    description="CSV file with name, age, and city data for three people",
)
print(result)


# Download it back
result = tool_spec.download_file("people.csv")
print(result)
```

### Install Packages

```
# Install a package and verify it's available
result = tool_spec.install_packages(["sympy"])
print(result)


result = tool_spec.execute_code(
    """
from sympy import symbols, expand
x, y = symbols('x y')
expr = expand((x + y) ** 3)
print(f"(x + y)^3 = {expr}")
"""
)
print(result)
```

## Agent Integration

Combine the code interpreter tools with a `FunctionAgent` and `BedrockConverse` LLM to let the agent autonomously write and execute code.

```
from llama_index.llms.bedrock_converse import BedrockConverse
from llama_index.core.agent.workflow import FunctionAgent


llm = BedrockConverse(model="us.anthropic.claude-sonnet-4-20250514-v1:0")


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

### Data Analysis Task

Ask the agent to create a CSV dataset, analyze it with pandas, and report findings.

```
response = await agent.run(
    "Create a CSV file with 20 rows of sample sales data (date, product, quantity, price). "
    "Then use pandas to calculate total revenue by product and show the top 3."
)
print(str(response))
```

### Plot Generation

Ask the agent to generate a matplotlib chart. Each `agent.run()` call is independent, so the task must be self-contained.

```
response = await agent.run(
    "Generate a matplotlib bar chart comparing the populations of Tokyo, Delhi, Shanghai, "
    "São Paulo, and Mexico City. Save the chart as 'populations.png'."
)
print(str(response))
```

## Cleanup

Always clean up code interpreter sessions when done to release resources.

```
await tool_spec.cleanup()
print("Code interpreter sessions cleaned up.")
```

## References

- [Amazon Bedrock AgentCore Documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html)
- [llama-index-tools-aws-bedrock-agentcore on PyPI](https://pypi.org/project/llama-index-tools-aws-bedrock-agentcore/)