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@langchain/openai

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const require_rolldown_runtime = require('../_virtual/rolldown_runtime.cjs'); const __langchain_core_tools = require_rolldown_runtime.__toESM(require("@langchain/core/tools")); const zod_v4 = require_rolldown_runtime.__toESM(require("zod/v4")); //#region src/tools/shell.ts const ShellActionSchema = zod_v4.z.object({ commands: zod_v4.z.array(zod_v4.z.string()).describe("Array of shell commands to execute"), timeout_ms: zod_v4.z.number().optional().describe("Optional timeout in milliseconds for the commands"), max_output_length: zod_v4.z.number().optional().describe("Optional maximum number of characters to return from each command") }); const TOOL_NAME = "shell"; /** * Creates a Shell tool that allows models to run shell commands through your integration. * * The shell tool allows the model to interact with your local computer through a controlled * command-line interface. The model proposes shell commands; your integration executes them * and returns the outputs. This creates a simple plan-execute loop that lets models inspect * the system, run utilities, and gather data until they can finish the task. * * **Important**: The shell tool is available through the Responses API for use with `GPT-5.1`. * It is not available on other models, or via the Chat Completions API. * * **When to use**: * - **Automating filesystem or process diagnostics** – For example, "find the largest PDF * under ~/Documents" or "show running gunicorn processes." * - **Extending the model's capabilities** – Using built-in UNIX utilities, python runtime * and other CLIs in your environment. * - **Running multi-step build and test flows** – Chaining commands like `pip install` and `pytest`. * - **Complex agentic coding workflows** – Using other tools like `apply_patch` to complete * workflows that involve complex file operations. * * **How it works**: * The tool operates in a continuous loop: * 1. Model sends shell commands (`shell_call` with `commands` array) * 2. Your code executes the commands (can be concurrent) * 3. You return stdout, stderr, and outcome for each command * 4. Repeat until the task is complete * * **Security Warning**: Running arbitrary shell commands can be dangerous. * Always sandbox execution or add strict allow/deny-lists before forwarding * a command to the system shell. * * @see {@link https://platform.openai.com/docs/guides/tools-shell | OpenAI Shell Documentation} * @see {@link https://github.com/openai/codex | Codex CLI} for reference implementation. * * @param options - Configuration for the Shell tool * @returns A Shell tool that can be passed to `bindTools` * * @example * ```typescript * import { ChatOpenAI, tools } from "@langchain/openai"; * import { exec } from "child_process/promises"; * * const model = new ChatOpenAI({ model: "gpt-5.1" }); * * // With execute callback for automatic command handling * const shellTool = tools.shell({ * execute: async (action) => { * const outputs = await Promise.all( * action.commands.map(async (cmd) => { * try { * const { stdout, stderr } = await exec(cmd, { * timeout: action.timeout_ms ?? undefined, * }); * return { * stdout, * stderr, * outcome: { type: "exit" as const, exit_code: 0 }, * }; * } catch (error) { * const timedOut = error.killed && error.signal === "SIGTERM"; * return { * stdout: error.stdout ?? "", * stderr: error.stderr ?? String(error), * outcome: timedOut * ? { type: "timeout" as const } * : { type: "exit" as const, exit_code: error.code ?? 1 }, * }; * } * }) * ); * return { * output: outputs, * maxOutputLength: action.max_output_length, * }; * }, * }); * * const llmWithShell = model.bindTools([shellTool]); * const response = await llmWithShell.invoke( * "Find the largest PDF file in ~/Documents" * ); * ``` * * @example * ```typescript * // Full shell loop example * async function shellLoop(model, task) { * let response = await model.invoke(task, { * tools: [tools.shell({ execute: myExecutor })], * }); * * while (true) { * const shellCall = response.additional_kwargs.tool_outputs?.find( * (output) => output.type === "shell_call" * ); * * if (!shellCall) break; * * // Execute commands (with proper sandboxing!) * const result = await executeCommands(shellCall.action); * * // Send output back to model * response = await model.invoke([ * response, * { * type: "shell_call_output", * call_id: shellCall.call_id, * output: result.output, * max_output_length: result.maxOutputLength, * }, * ], { * tools: [tools.shell({ execute: myExecutor })], * }); * } * * return response; * } * ``` * * @remarks * - Only available through the Responses API (not Chat Completions) * - Designed for use with `gpt-5.1` model * - Commands are provided as an array of strings that can be executed concurrently * - Action includes: `commands`, `timeout_ms`, `max_output_length` * - Always sandbox or validate commands before execution * - The `timeout_ms` from the model is only a hint—enforce your own limits * - If `max_output_length` exists in the action, always pass it back in the output * - Many CLI tools return non-zero exit codes for warnings; still capture stdout/stderr */ function shell(options) { const executeWrapper = async (action) => { const result = await options.execute(action); return JSON.stringify({ output: result.output, max_output_length: result.maxOutputLength }); }; const shellTool = (0, __langchain_core_tools.tool)(executeWrapper, { name: TOOL_NAME, description: "Execute shell commands in a managed environment. Commands can be run concurrently.", schema: ShellActionSchema }); shellTool.extras = { ...shellTool.extras ?? {}, providerToolDefinition: { type: "shell" } }; return shellTool; } //#endregion exports.shell = shell; //# sourceMappingURL=shell.cjs.map