Documentation Index
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Amazon Bedrock AgentCore Code Interpreter enables agents to execute code in secure, managed sandbox environments. Agents can run Python, JavaScript, and TypeScript code for calculations, data analysis, file manipulation, and visualizations.
Overview
Integration details
| Returns artifact | Native async | Return data | Pricing |
|---|
| ✅ | ✅ | Text, Files, Images | Pay-per-use (AWS) |
| Tool | Description |
|---|
execute_code | Run Python/JavaScript/TypeScript code with persistent state |
execute_command | Run shell commands in the environment |
read_files | Read content of files in the environment |
write_files | Create or update files |
list_files | List files in directories |
delete_files | Remove files from the environment |
upload_file | Upload files with semantic descriptions |
install_packages | Install Python packages |
start_command_execution | Start a long-running command asynchronously |
get_task | Check status of an async task by task_id |
stop_task | Stop a running async task by task_id |
Setup
The integration lives in the langchain-aws package, which wraps the bedrock-agentcore SDK.
pip install -U langchain-aws bedrock-agentcore
Credentials
You need AWS credentials configured with permissions for Bedrock AgentCore Code Interpreter. See the Amazon Bedrock AgentCore documentation for required IAM permissions.
It’s also helpful (but not needed) to set up LangSmith for best-in-class observability:
import os
os.environ["LANGSMITH_API_KEY"] = "your-api-key"
os.environ["LANGSMITH_TRACING"] = "true"
Instantiation
The toolkit is created using an async factory function:
from langchain_aws.tools import create_code_interpreter_toolkit
# Create toolkit and get tools (async)
toolkit, code_tools = await create_code_interpreter_toolkit(region="us-west-2")
Invocation
Get specific tools and invoke them:
# Get tools by name
tools_by_name = toolkit.get_tools_by_name()
# Execute Python code
result = tools_by_name["execute_code"].invoke({
"code": """
import numpy as np
data = [1, 2, 3, 4, 5]
print(f"Mean: {np.mean(data)}")
print(f"Sum: {np.sum(data)}")
""",
"language": "python"
})
print(result)
Use within an agent
import asyncio
from langchain.agents import create_react_agent
from langchain.chat_models import init_chat_model
from langchain_aws.tools import create_code_interpreter_toolkit
async def main():
# Create toolkit
toolkit, code_tools = await create_code_interpreter_toolkit(region="us-west-2")
# Initialize chat model
llm = init_chat_model(
"us.anthropic.claude-sonnet-4-20250514-v1:0",
model_provider="bedrock_converse",
)
# Create agent with code interpreter tools
agent = create_react_agent(
model=llm,
tools=code_tools,
)
# Create config with thread_id for session isolation
config = {"configurable": {"thread_id": "session-123"}}
# Run the agent
result = await agent.ainvoke(
{"messages": [{"role": "user", "content": "Calculate the factorial of 10"}]},
config=config
)
print(result["messages"][-1].content)
# Clean up when done
await toolkit.cleanup()
asyncio.run(main())
Thread-based session isolation
The toolkit supports multiple concurrent sessions via thread_id. Each thread maintains its own code interpreter session with isolated state:
# Different threads have isolated sessions
config_user1 = {"configurable": {"thread_id": "user-1"}}
config_user2 = {"configurable": {"thread_id": "user-2"}}
# Variables defined in user-1's session won't exist in user-2's session
await agent.ainvoke(
{"messages": [{"role": "user", "content": "Set x = 100"}]},
config=config_user1
)
await agent.ainvoke(
{"messages": [{"role": "user", "content": "What is x?"}]}, # x is undefined here
config=config_user2
)
Working with files
Write and read files
tools_by_name = toolkit.get_tools_by_name()
# Write a file
tools_by_name["write_files"].invoke({
"files": [{"path": "data.csv", "text": "name,value\nAlice,100\nBob,200"}]
})
# Read it back
content = tools_by_name["read_files"].invoke({"paths": ["data.csv"]})
print(content)
# List files in current directory
files = tools_by_name["list_files"].invoke({"directory_path": "."})
print(files)
Upload files with descriptions
tools_by_name["upload_file"].invoke({
"path": "sales.csv",
"content": "date,revenue,product\n2024-01-01,1000,Widget\n2024-01-02,1500,Gadget",
"description": "Sales data with columns: date, revenue, product_id"
})
Installing packages
tools_by_name["install_packages"].invoke({
"packages": ["pandas>=2.0", "matplotlib", "scikit-learn"],
"upgrade": False
})
Async task management
For long-running commands, you can start them asynchronously and check their status:
tools_by_name = toolkit.get_tools_by_name()
config = {"configurable": {"thread_id": "session-123"}}
# Start a long-running command asynchronously
result = tools_by_name["start_command_execution"].invoke(
{"command": "python long_running_script.py"},
config=config
)
# Returns a task_id
# Check task status
status = tools_by_name["get_task"].invoke(
{"task_id": "task-abc123"},
config=config
)
print(status)
# Stop a running task if needed
tools_by_name["stop_task"].invoke(
{"task_id": "task-abc123"},
config=config
)
Session cleanup
Always clean up sessions when done to release resources:
# Clean up all sessions
await toolkit.cleanup()
# Or clean up a specific thread's session
await toolkit.cleanup(thread_id="session-123")
API reference
For detailed documentation of all features and configurations, see:
