Keep your agent. Give it a sandbox.

TypeScriptimport { jsonSchema, tool } from "ai";import type { Sandbox } from "withruntime";export function runtimeTool(sandbox: Sandbox) {  return tool({    description:      "Run a shell command in the application's Runtime sandbox and get exitCode, stdout and stderr.",    inputSchema: jsonSchema<{ command: string }>({      type: "object",      additionalProperties: false,      properties: { command: { type: "string", description: "Runs under bash -c." } },      required: ["command"],    }),    execute: ({ command }) => sandbox.exec(command),  });}

Pass { runtime: runtimeTool(sbx) } as the call's tools.

TypeScriptimport { z } from "zod";import { tool } from "@openai/agents";import type { Sandbox } from "withruntime";export function runtimeTool(sandbox: Sandbox) {  return tool({    name: "runtime_exec",    description:      "Run a shell command in the application's Runtime sandbox and get exitCode, stdout and stderr.",    parameters: z.object({ command: z.string().min(1) }),    execute: async ({ command }) => JSON.stringify(await sandbox.exec(command)),  });}

Add runtimeTool(sbx) to the agent's tools.

TypeScriptimport { createSdkMcpServer, tool } from "@anthropic-ai/claude-agent-sdk";import { z } from "zod";import type { Sandbox } from "withruntime";export function runtimeServer(sandbox: Sandbox) {  return createSdkMcpServer({    name: "runtime",    version: "0.2.0",    tools: [      tool(        "runtime_exec",        "Run a shell command in the application's Runtime sandbox and get exitCode, stdout and stderr.",        { command: z.string().min(1) },        async ({ command }) => ({          content: [{ type: "text", text: JSON.stringify(await sandbox.exec(command)) }],        }),      ),    ],  });}/** The same tool in a Messages API loop: run sandbox.exec(input.command). */export const messagesTool = {  name: "runtime_exec",  description:    "Run a shell command in the application's Runtime sandbox and get exitCode, stdout and stderr.",  input_schema: {    type: "object" as const,    properties: { command: { type: "string" } },    required: ["command"],    additionalProperties: false,  },};

Pass runtimeServer(sbx) as mcpServers.runtime and allow only mcp__runtime__runtime_exec.

Pythonfrom langchain_core.tools import toolfrom withruntime import Sandboxdef runtime_tool(sandbox: Sandbox):    @tool    def runtime_exec(command: str) -> dict:        """Run a shell command in the application's Runtime sandbox; returns exitCode, stdout and stderr."""        return sandbox.exec(command).to_dict()    return runtime_exec

Pass runtime_tool(sbx) to create_agent or a LangGraph ToolNode.