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langchain-superserve adapts a Superserve sandbox to the Deep Agents sandbox protocol. Because Deep Agents compiles to a LangGraph graph, the same backend works across LangChain, LangGraph, and Deep Agents — agent shell commands and file operations run inside an isolated Firecracker microVM instead of on your machine.

Setup

Requires Python 3.11+. langchain-anthropic ships as a base dependency of Deep Agents, so the Anthropic model used below is already installed. See API keys to create a Superserve API key.

Deep Agents SDK

Launch a sandbox from a python-equipped template, wrap it in SuperserveSandbox, and pass it as the backend to create_deep_agent(...) — the agent’s shell and file operations then run inside the sandbox:
SuperserveSandbox synthesizes ls/read/edit/glob with in-sandbox python3, so launch from a python-equipped image — superserve/python-3.11, superserve/python-ml, or superserve/code-interpreter. execute, grep, and file upload/download work on any image, including the minimal superserve/base.

Deep Agents Code

Installing langchain-superserve also registers a superserve sandbox provider for Deep Agents Code (dcode), the terminal coding agent built on the Deep Agents SDK:
The provider defaults to the superserve/python-3.11 template; override it with the SUPERSERVE_TEMPLATE environment variable.

Superserve extras

Beyond the standard Deep Agents backend, Superserve sandboxes support pause()/resume() for idle cost savings and proxy-brokered secret binding (Sandbox.create(secrets=...)) so real credentials never enter the sandbox. Manage these through the Superserve SDK on the sandbox you pass to SuperserveSandbox.

Resources

langchain-superserve on GitHub

Browse the source, tests, and release notes.

langchain-superserve on PyPI

Install the package and check released versions.