Best sandbox for LangChain and LangGraph agents in 2026

LangChain and LangGraph make any Python function a tool, so the best sandbox is a whole Linux machine behind four functions.

Runtime's sandbox_tools(sbx) gives a LangChain agent or a LangGraph ToolNode a shell and a filesystem in a Firecracker microVM, and a Deep Agent gets the same machine as its backend. Each LangGraph thread can keep its own sandbox between turns, paused at no compute cost. Ten thousand four-minute graph runs a month, each busy for 40 CPU-seconds, cost $22.78 on Runtime against $110.40 on Daytona and $158.64 on Modal, at rates checked 23 September to 2 October 2026.

Which sandboxes work with LangChain?

LangChain's docs, read on 1 October 2026, offer three kinds:

Runtime is also a Deep Agents backend: RuntimeSandbox(sbx) from withruntime.deepagents implements BaseSandbox, so execute, grep and edit_file all act on the microVM (Deep Agents in a sandbox).

What should a LangGraph agent's sandbox do?

• Follow the thread. Name the sandbox after the LangGraph thread_id with Sandbox.getOrCreate, and the next turn finds the same files, installed packages and running processes, woken from pause on the first tool call.
• Branch with the graph. When a graph explores two approaches, sbx.fork() copies the running sandbox, memory included, so each branch starts from the same state instead of replaying its setup.
• Run what the model writes, not just Python. An in-process interpreter stops at Python; a microVM runs npm test, cargo build or a database.
• Keep the host out of it. A tool that calls subprocess runs on the machine serving your graph. A sandbox tool sends the command to a machine with its own kernel.

What do LangGraph runs cost on each sandbox?

Ten thousand runs a month, each four minutes on a 2 vCPU, 4 GiB sandbox and busy for 40 CPU-seconds, at each provider's published rates:

Runs on Runtime, recorded 1 October 2026

The same four functions serve both frameworks:

Pythonfrom langchain.agents import create_agentfrom langchain_core.tools import toolfrom withruntime import Sandboxfrom withruntime.tools import sandbox_toolswith Sandbox.create() as sbx:    agent = create_agent("claude-sonnet-4-6", tools=[tool(f) for f in sandbox_tools(sbx)])    result = agent.invoke({"messages": [{"role": "user", "content": "Fit a line to (1,2), (2,4.1), (3,6.2) and give the slope."}]})    print(result["messages"][-1].content)

We ran create_agent from LangChain 1.4.3, and a LangGraph 1.2.12 graph with a ToolNode and tools_condition, each with a scripted chat model in place of Claude, so the tool calls were fixed and the frameworks ran them against live sandboxes:

textLangChain create_agent  runtime_write_file slope.py     Wrote 79 bytes to /workspace/slope.py  runtime_exec python3 slope.py   exit_code 0, stdout "2.1"LangGraph ToolNode  runtime_write_file fizzbuzz.py  Wrote 85 bytes to /workspace/fizzbuzz.py  runtime_exec python3 fizzbuzz.py    exit_code 0, stdout "1 2 Fizz 4 Buzz Fizz 7 8 Fizz Buzz 11 Fizz 13 14 FizzBuzz"

The slope came from NumPy, already in the image. The graph wiring and the per-thread sandbox are in LangGraph in a sandbox, and the agent's options in LangChain in a sandbox.

When might another sandbox fit better?

• LangSmith's own sandboxes. A team running everything inside LangSmith may prefer the sandboxes listed beside its tracing and deployments.

Sources

Checked 1 October 2026.

• LangChain tool integrations: the code interpreter integrations
• Deep Agents sandboxes: the sandbox backend providers
• Each provider's published rates, checked 23 September to 2 October 2026, as the pricing guide lists them
• The recorded runs: Runtime's Python SDK with LangChain 1.4.3 and LangGraph 1.2.12 on Python 3.12, against production on 1 October 2026