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What You’ll Learn

  • Defining a @tool with langchain_core.tools that executes Python in a Declaw sandbox
  • Creating a LangGraph ReAct agent with create_react_agent
  • Demo mode that exercises the sandbox tool directly without needing an OpenAI key

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Prerequisites

  • Declaw instance running and DECLAW_API_KEY / DECLAW_DOMAIN set
  • OPENAI_API_KEY (optional — the example runs in demo mode without it)
pip install declaw python-dotenv langgraph langchain-openai
This example is available in Python. TypeScript support coming soon.

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Code Walkthrough

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1. Define the Declaw sandbox tool

Wrap Sandbox.create() in a LangChain @tool. The agent receives the function’s docstring as the tool description, so make it clear: 
from langchain_core.tools import tool
from declaw import Sandbox

@tool
def execute_python(code: str) -> str:
    """Execute Python code in a secure Declaw sandbox."""
    sbx = Sandbox.create(template="python", timeout=300)
    try:
        sbx.files.write("/tmp/code.py", code)
        result = sbx.commands.run("python3 /tmp/code.py", timeout=30)
        return (
            f"stdout: {result.stdout}\n"
            f"stderr: {result.stderr}\n"
            f"exit_code: {result.exit_code}"
        )
    finally:
        sbx.kill()
Each call to execute_python spins up a fresh sandbox, runs the code, and destroys the sandbox. Sandboxes are fully isolated — code from one call cannot affect another.

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2. Create a LangGraph ReAct agent

from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
agent = create_react_agent(llm, [execute_python])

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3. Run the agent

prompt = "Write Python code to compute the first 10 Fibonacci numbers and print them."
result = agent.invoke({"messages": [("user", prompt)]})

for msg in result["messages"]:
    role = getattr(msg, "type", "unknown")
    content = getattr(msg, "content", "")
    if content:
        print(f"\n[{role}] {content}")
The agent will reason about the task, call execute_python with the generated code, receive the sandbox output, and formulate a final answer.

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4. Demo mode (no API key needed)

Run the sandbox tool directly without the LangGraph agent: 
from declaw import Sandbox

code = """\
a, b = 0, 1
fibs = []
for _ in range(10):
    fibs.append(a)
    a, b = b, a + b
print("Fibonacci:", fibs)
"""

sbx = Sandbox.create(template="python", timeout=300)
try:
    sbx.files.write("/tmp/code.py", code)
    result = sbx.commands.run("python3 /tmp/code.py", timeout=30)
    print(result.stdout)
finally:
    sbx.kill()

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Expected Output

In demo mode: 
=======================================================
LangGraph + Declaw Sandbox Example
=======================================================
No OPENAI_API_KEY found -- running demo mode.

--- Tool Definition ---
from langchain_core.tools import tool
from declaw import Sandbox

@tool
def execute_python(code: str) -> str:
    """Execute Python code in a secure Declaw sandbox."""
    ...

--- Running Code Directly in Declaw Sandbox ---
Code:
a, b = 0, 1
fibs = []
...

stdout: Fibonacci: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
stderr:
exit_code: 0

Sandbox cleaned up.

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Done!
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