Amazon Bedrock AgentCore Code Interpreter sandbox integration for Deep Agents
Project description
langchain-agentcore-codeinterpreter
Amazon Bedrock AgentCore Code Interpreter sandbox integration for Deep Agents.
This package provides AgentCoreSandbox — a SandboxBackendProtocol implementation that wraps AgentCore's Code Interpreter, a secure, isolated MicroVM environment for executing code. The caller manages the interpreter lifecycle (start() / stop()); the sandbox backend handles command execution and file operations.
Note: For the LangChain
BaseToolintegration (used withcreate_react_agentand LangGraph agents), seelangchain-aws[tools]. This package is specifically for the Deep AgentsBaseSandboxprotocol.
Prerequisites
1. AWS credentials configured via one of the following methods:
# Option 1: Long-lived IAM credentials
export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION="us-west-2"
# Option 2: Temporary credentials (IAM roles, SSO, STS AssumeRole)
export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_SESSION_TOKEN="your-session-token"
export AWS_REGION="us-west-2"
# Option 3: AWS CLI profile (picks up ~/.aws/credentials + ~/.aws/config)
aws configure
# or for SSO:
aws configure sso
aws sso login --profile your-profile
Any method supported by the boto3 credential chain works, including EC2 instance profiles, ECS task roles, and environment variables.
2. IAM permissions — your credentials must allow bedrock-agentcore:InvokeCodeInterpreter (or the equivalent action for your region). See the AgentCore Code Interpreter docs for the required IAM policy.
3. Region availability — Code Interpreter is available in select AWS regions. us-west-2 is a safe default. Pass the region to CodeInterpreter(region=...).
Quick Install
pip install langchain-agentcore-codeinterpreter
Usage
Standalone
from bedrock_agentcore.tools.code_interpreter_client import CodeInterpreter
from langchain_agentcore_codeinterpreter import AgentCoreSandbox
interpreter = CodeInterpreter(region="us-west-2")
interpreter.start()
backend = AgentCoreSandbox(interpreter=interpreter)
result = backend.execute("echo hello")
print(result.output) # "hello"
interpreter.stop()
With Deep Agents
from bedrock_agentcore.tools.code_interpreter_client import CodeInterpreter
from deepagents import create_deep_agent
from langchain_agentcore_codeinterpreter import AgentCoreSandbox
from langchain_aws import ChatBedrockConverse
interpreter = CodeInterpreter(region="us-west-2")
interpreter.start()
model = ChatBedrockConverse(
model="us.anthropic.claude-sonnet-4-6",
region_name="us-west-2",
)
backend = AgentCoreSandbox(interpreter=interpreter)
agent = create_deep_agent(
model=model,
backend=backend,
system_prompt="You are a coding assistant with sandbox access.",
)
try:
result = agent.invoke(
{
"messages": [
{"role": "user", "content": "Create and run a hello world script"}
]
}
)
print(result["messages"][-1].content)
finally:
interpreter.stop()
File operations
# Upload files
backend.upload_files([
("data.csv", b"name,value\nalice,42\nbob,17"),
("analyze.py", b"import csv\nprint('ready')"),
])
# Download files
results = backend.download_files(["data.csv"])
for r in results:
if r.content is not None:
print(f"{r.path}: {r.content.decode()}")
else:
print(f"Failed: {r.path}: {r.error}")
Session behavior
AgentCore sessions cannot be reconnected after interpreter.stop() is called. Each start() creates a fresh, isolated MicroVM. Sessions auto-expire after a configurable timeout (default 15 minutes, maximum 8 hours).
Contributing
See the langchain-aws contributing guide.
cd libs/agentcore-codeinterpreter
# Run unit tests (no network, no AWS credentials needed)
make tests
# Run linter
make lint
# Run integration tests (requires AWS credentials)
make integration_tests
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