安装
创建沙盒后端
请参考 沙盒指南 了解使用方法、文件操作和生命周期细节。与深度智能体一起使用
清理
完成操作后请始终停止解释器以释放资源。 另见:沙盒。官方原文:src/oss/python/integrations/sandboxes/aws.mdx · 非官方翻译,译文由 AI 生成,以英文原文为准。
Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
使用 LangChain Python 集成 AgentCoreSandbox 沙盒后端。
pip install langchain-agentcore-codeinterpreter
uv add langchain-agentcore-codeinterpreter
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)
interpreter.stop()
from bedrock_agentcore.tools.code_interpreter_client import CodeInterpreter
from langchain_agentcore_codeinterpreter import AgentCoreSandbox
from langchain_anthropic import ChatAnthropic
from deepagents import create_deep_agent
interpreter = CodeInterpreter(region="us-west-2")
interpreter.start()
backend = AgentCoreSandbox(interpreter=interpreter)
agent = create_deep_agent(
model=ChatAnthropic(model="claude-sonnet-4-6"),
system_prompt="You are a coding assistant with sandbox access.",
backend=backend,
)
try:
result = agent.invoke(
{"messages": [{"role": "user", "content": "Write and run a Python script"}]}
)
finally:
interpreter.stop()
官方原文:src/oss/python/integrations/sandboxes/aws.mdx · 非官方翻译,译文由 AI 生成,以英文原文为准。