Amazon Bedrock Code Interpreter connector

With the Amazon Bedrock Code Interpreter outbound connector, you can execute Python code in a secure Amazon Bedrock AgentCore Code Interpreter sandbox from your BPMN process.

Prerequisites

To use the Amazon Bedrock Code Interpreter connector, you need the following:

• An AWS account with an access key and secret key, or a configured default credentials chain.
• IAM permissions to execute the following actions on the bedrock-agentcore service:
  • StartCodeInterpreterSession
  • InvokeCodeInterpreter
  • StopCodeInterpreterSession
• The AgentCore Code Interpreter service must be available in your selected AWS region.

Learn more about the AgentCore Code Interpreter in the official documentation.

note

Use Camunda secrets to store credentials and avoid exposing sensitive information directly from the process. Refer to managing secrets to learn more.

Create an Amazon Bedrock Code Interpreter connector task

You can apply a connector to a task or event via the append menu. For example:

• From the canvas: Select an element and click the Change element icon to change an existing element, or use the append feature to add a new element to the diagram.
• From the properties panel: Navigate to the Template section and click Select.
• From the side palette: Click the Create element icon.

After you have applied a connector to your element, follow the configuration steps or see using connectors to learn more.

Authentication

To authenticate, choose one of the methods from the Authentication dropdown. The supported options are:

• Use Credentials if you have a valid pair of access and secret keys provided by your AWS account administrator.

note

This option is applicable for both SaaS and Self-Managed users.

• Use Default Credentials Chain if your system is configured as an implicit authentication mechanism, such as role-based authentication, credentials supplied via environment variables, or files on target host. This approach uses the Default Credential Provider Chain to resolve required credentials.

note

This option is applicable only for Self-Managed or hybrid distributions.

For more information on authentication and security in Amazon Bedrock, see Amazon Bedrock security and privacy.

Configuration

In the Region field, enter the AWS region where Amazon Bedrock AgentCore Code Interpreter is available. For example, us-east-1.

Action

The Amazon Bedrock Code Interpreter connector supports the following action.

Code execution

Parameters

Parameter	Required	Description
Code	Yes	The Python code to execute in the sandbox. Supports FEEL expressions.
Session timeout (seconds)	No	How long the sandbox session stays alive, in seconds (60–28,800). Defaults to 300.

note

The session timeout controls how long the sandbox environment remains available, not how long the code takes to execute. Code execution typically completes in seconds.

The sandbox environment includes common Python libraries such as matplotlib, pandas, numpy, and csv. Network access is not available in the sandbox.

Response

The connector returns the following fields:

Field	Description
stdout	The standard output printed by the code.
stderr	Any error output produced during execution.
exitCode	The process exit code. A value of 0 indicates successful execution.
executionTimeMs	The execution duration in milliseconds.
files	A list of Camunda documents for any files generated by the code.

File handling

Any files created by the code during execution are automatically detected and returned as Camunda documents in the files field. This includes images (for example, charts saved with plt.savefig()), CSV files, text files, and other output files.

note

A maximum of 10 files are retrieved per execution, with a total size limit of 10 MB. Files exceeding these limits are skipped, and a warning is logged.

Output mapping

1. Use Result Variable to store the response in a process variable. For example, codeResult.
2. Use Result Expression to map specific fields from the response into process variables.

For example, to extract the output and generated files:

= {
  output: stdout,
  files: files
}

Example: Run a calculation

Code:

total = sum([12000, 8500, 15000, 9200])
print(f"Total claims: ${total:,}")

Response:

{
  "stdout": "Total claims: $44,700\n",
  "stderr": "",
  "exitCode": 0,
  "executionTimeMs": 0.05,
  "files": []
}

Example: Generate a chart

Code:

import matplotlib.pyplot as plt
plt.bar(["Q1", "Q2", "Q3", "Q4"], [12000, 8500, 15000, 9200])
plt.title("Claims by Quarter")
plt.ylabel("Amount ($)")
plt.savefig("chart.png")
print("Chart saved")

Response:

{
  "stdout": "Chart saved\n",
  "stderr": "",
  "exitCode": 0,
  "executionTimeMs": 1.67,
  "files": [
    {
      "storeId": "in-memory",
      "documentId": "851505b2-61bd-4717-9c86-07678b67f033",
      "metadata": {
        "contentType": "image/png",
        "size": 14734,
        "fileName": "chart.png"
      },
      "camunda.document.type": "camunda"
    }
  ]
}

Example: Generate a CSV report

Code:

import csv
with open("claims.csv", "w", newline="") as f:
    w = csv.writer(f)
    w.writerow(["ClaimID", "Amount", "Status"])
    w.writerows([["C001", 45000, "Rejected"], ["C002", 12000, "Approved"]])
print("Report created")

Response:

{
  "stdout": "Report created\n",
  "stderr": "",
  "exitCode": 0,
  "executionTimeMs": 0.05,
  "files": [
    {
      "storeId": "in-memory",
      "documentId": "c209e3a4-cefa-4622-adc1-88c92bf06bb2",
      "metadata": {
        "contentType": "text/csv",
        "size": 80,
        "fileName": "claims.csv"
      },
      "camunda.document.type": "camunda"
    }
  ]
}