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Add migration samples from Anthropic code execution to AgentCore Code Interpreter
Includes 7 working examples covering Converse API, InvokeModel API, Strands SDK, high-level CodeInterpreter API, Claude Code in a sandbox, and prompt caching. All scripts tested against a live AWS account. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎snippets/migration-from-code-execution-tool-to-agentcore-code-interpreter/00_anthropic_code_execution.py‎

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"""
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00 - Anthropic Code Execution Tool (BEFORE - Reference)
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This is the ORIGINAL Anthropic approach. One API call — Claude decides to execute
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code, runs it in a managed container, and returns the answer inline.
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No agentic loop. No tool-use handling. No session management.
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This is what we are migrating FROM.
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Requirements:
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pip install anthropic
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Usage:
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export ANTHROPIC_API_KEY=your_key
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python 00_anthropic_code_execution.py
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"""
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import anthropic
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client = anthropic.Anthropic()
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# One API call — Claude handles everything server-side
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response = client.beta.messages.create(
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model="claude-sonnet-4-5-20250929",
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betas=["code-execution-2025-08-25"],
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max_tokens=4096,
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messages=[{
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"role": "user",
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"content": "Calculate the mean and standard deviation of "
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"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
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}],
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tools=[{
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"type": "code_execution_20250825",
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"name": "code_execution"
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}]
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)
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# Results are inline — no loop needed
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for block in response.content:
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if block.type == "text":
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print(block.text)
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elif block.type == "code_execution_tool_result":
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print(f"[Code Output] {block.content}")

‎snippets/migration-from-code-execution-tool-to-agentcore-code-interpreter/01_converse_api_with_code_interpreter.py‎

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"""
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01 - Bedrock Converse API + AgentCore Code Interpreter
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This is the custom agentic loop approach using the Converse API.
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It wires together two AWS services:
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- Bedrock Runtime (Converse API) for Claude
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- AgentCore Code Interpreter for sandboxed code execution
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The loop: Claude → tool_use? → execute code → return result → Claude → final answer.
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Requirements:
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pip install boto3 bedrock-agentcore
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Usage:
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# Configure AWS credentials (IAM, SSO, or environment variables)
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python 01_converse_api_with_code_interpreter.py
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"""
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import boto3
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# --- Configuration ---
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REGION = "us-west-2"
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MODEL_ID = "global.anthropic.claude-sonnet-4-6"
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# --- AWS Clients ---
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bedrock = boto3.client("bedrock-runtime", region_name=REGION)
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agentcore = boto3.client("bedrock-agentcore", region_name=REGION)
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# --- Tool definition for Claude ---
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tool_config = {
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"tools": [{
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"toolSpec": {
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"name": "execute_python",
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"description": (
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"Execute Python code in a secure sandbox. "
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"Use this for calculations, data analysis, or to verify results. "
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"Common libraries like numpy, pandas, and matplotlib are available."
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),
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"inputSchema": {
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"json": {
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"type": "object",
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"properties": {
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"code": {
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"type": "string",
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"description": "Python code to execute"
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}
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},
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"required": ["code"]
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}
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}
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}
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}]
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}
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def execute_code(code: str, session_id: str) -> str:
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"""Execute code via AgentCore and return the output."""
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response = agentcore.invoke_code_interpreter(
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codeInterpreterIdentifier="aws.codeinterpreter.v1",
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sessionId=session_id,
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name="executeCode",
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arguments={"language": "python", "code": code}
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)
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parts = []
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for event in response["stream"]:
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if "result" in event:
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for item in event["result"].get("content", []):
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if item["type"] == "text":
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parts.append(item["text"])
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return "\n".join(parts)
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def converse_with_code_execution(user_message: str) -> str:
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"""Full agentic loop: Converse API + AgentCore Code Interpreter."""
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# Start a code interpreter session
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session = agentcore.start_code_interpreter_session(
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codeInterpreterIdentifier="aws.codeinterpreter.v1",
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name="converse-session",
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sessionTimeoutSeconds=900
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)
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session_id = session["sessionId"]
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print(f"[Session started: {session_id}]")
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messages = [{"role": "user", "content": [{"text": user_message}]}]
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try:
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while True:
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# Step 1: Call Claude via Converse API
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response = bedrock.converse(
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modelId=MODEL_ID,
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messages=messages,
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toolConfig=tool_config,
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inferenceConfig={"maxTokens": 4096}
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)
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assistant_msg = response["output"]["message"]
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messages.append(assistant_msg)
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# Step 2: If Claude is done (no tool request), return the answer
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if response["stopReason"] != "tool_use":
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break
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# Step 3: Execute any requested tools
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tool_results = []
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for block in assistant_msg["content"]:
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if "toolUse" in block:
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tool = block["toolUse"]
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print(f"[Executing code...]")
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try:
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result = execute_code(tool["input"]["code"], session_id)
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print(f"[Code output: {result[:100]}...]")
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tool_results.append({
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"toolResult": {
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"toolUseId": tool["toolUseId"],
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"content": [{"text": result}],
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"status": "success"
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}
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})
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except Exception as e:
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tool_results.append({
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"toolResult": {
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"toolUseId": tool["toolUseId"],
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"content": [{"text": str(e)}],
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"status": "error"
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}
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})
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# Step 4: Send tool results back to Claude and loop
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messages.append({"role": "user", "content": tool_results})
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# Extract final text response
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return "\n".join(
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b["text"] for b in assistant_msg["content"] if "text" in b
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)
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finally:
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# Always clean up the session
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agentcore.stop_code_interpreter_session(
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codeInterpreterIdentifier="aws.codeinterpreter.v1",
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sessionId=session_id
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)
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print(f"[Session stopped: {session_id}]")
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# --- Run ---
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if __name__ == "__main__":
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answer = converse_with_code_execution(
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"Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
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)
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print("\n--- Final Answer ---")
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print(answer)

‎snippets/migration-from-code-execution-tool-to-agentcore-code-interpreter/02_invoke_model_with_code_interpreter.py‎

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"""
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02 - Bedrock InvokeModel API + AgentCore Code Interpreter
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This approach uses the InvokeModel API with the native Anthropic Messages format.
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The request body is nearly identical to what you'd send to api.anthropic.com,
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making it the easiest path to minimize changes to existing Anthropic code.
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The agentic loop is the same as the Converse API version, but the request/response
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format matches the native Anthropic format.
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Requirements:
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pip install boto3 bedrock-agentcore
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Usage:
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# Configure AWS credentials (IAM, SSO, or environment variables)
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python 02_invoke_model_with_code_interpreter.py
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"""
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import boto3
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import json
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# --- Configuration ---
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REGION = "us-west-2"
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MODEL_ID = "global.anthropic.claude-sonnet-4-6"
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# --- AWS Clients ---
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bedrock = boto3.client("bedrock-runtime", region_name=REGION)
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agentcore = boto3.client("bedrock-agentcore", region_name=REGION)
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# --- Tool definition (native Anthropic format) ---
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TOOLS = [{
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"name": "execute_python",
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"description": (
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"Execute Python code in a secure sandbox. "
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"Use this for calculations, data analysis, or to verify results. "
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"Common libraries like numpy, pandas, and matplotlib are available."
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),
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"input_schema": {
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"type": "object",
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"properties": {
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"code": {
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"type": "string",
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"description": "Python code to execute"
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}
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},
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"required": ["code"]
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}
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}]
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def execute_code(code: str, session_id: str) -> str:
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"""Execute code via AgentCore and return the output."""
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response = agentcore.invoke_code_interpreter(
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codeInterpreterIdentifier="aws.codeinterpreter.v1",
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sessionId=session_id,
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name="executeCode",
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arguments={"language": "python", "code": code}
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)
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parts = []
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for event in response["stream"]:
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if "result" in event:
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for item in event["result"].get("content", []):
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if item["type"] == "text":
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parts.append(item["text"])
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return "\n".join(parts)
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def invoke_model_with_code_execution(user_message: str) -> str:
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"""Full agentic loop: InvokeModel API + AgentCore Code Interpreter."""
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# Start a code interpreter session
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session = agentcore.start_code_interpreter_session(
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codeInterpreterIdentifier="aws.codeinterpreter.v1",
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name="invoke-model-session",
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sessionTimeoutSeconds=900
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)
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session_id = session["sessionId"]
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print(f"[Session started: {session_id}]")
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messages = [{"role": "user", "content": [{"type": "text", "text": user_message}]}]
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try:
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while True:
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# Step 1: Call Claude via InvokeModel (native Anthropic format)
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request_body = {
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"anthropic_version": "bedrock-2023-05-31",
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"max_tokens": 4096,
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"messages": messages,
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"tools": TOOLS
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}
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response = bedrock.invoke_model(
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modelId=MODEL_ID,
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body=json.dumps(request_body),
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contentType="application/json",
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accept="application/json"
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)
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result = json.loads(response["body"].read())
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# Add assistant response to conversation
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messages.append({"role": "assistant", "content": result["content"]})
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# Step 2: If Claude is done (no tool request), return the answer
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if result["stop_reason"] != "tool_use":
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break
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# Step 3: Execute any requested tools
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tool_results = []
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for block in result["content"]:
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if block["type"] == "tool_use":
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print(f"[Executing code...]")
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try:
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output = execute_code(block["input"]["code"], session_id)
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print(f"[Code output: {output[:100]}...]")
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tool_results.append({
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"type": "tool_result",
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"tool_use_id": block["id"],
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"content": [{"type": "text", "text": output}]
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})
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except Exception as e:
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tool_results.append({
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"type": "tool_result",
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"tool_use_id": block["id"],
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"content": [{"type": "text", "text": str(e)}],
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"is_error": True
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})
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# Step 4: Send tool results back to Claude and loop
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messages.append({"role": "user", "content": tool_results})
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# Extract final text response
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return "\n".join(
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b["text"] for b in result["content"] if b["type"] == "text"
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)
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finally:
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# Always clean up the session
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agentcore.stop_code_interpreter_session(
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codeInterpreterIdentifier="aws.codeinterpreter.v1",
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sessionId=session_id
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)
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print(f"[Session stopped: {session_id}]")
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# --- Run ---
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if __name__ == "__main__":
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answer = invoke_model_with_code_execution(
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"Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
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)
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print("\n--- Final Answer ---")
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print(answer)

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