--- title: LangChain product: vercel url: /docs/sandbox/ecosystem/langchain canonical_url: "https://vercel.com/docs/sandbox/ecosystem/langchain" last_updated: 2026-08-04 type: tutorial prerequisites: - /docs/sandbox/ecosystem - /docs/sandbox related: - /docs/sandbox - /docs/ai-gateway - /docs/cli - /docs/ai-gateway/authentication-and-byok/authentication - /docs/sandbox/concepts/authentication summary: Give a LangChain agent a tool that executes model-generated code in an isolated Vercel Sandbox, with models served by AI Gateway. install_vercel_plugin: npx plugins add vercel/vercel-plugin --- # LangChain This guide shows how to give a [LangChain](https://js.langchain.com) agent a tool that executes code in [Vercel Sandbox](/docs/sandbox), so model-generated code runs in an isolated microVM instead of on your host. Model requests route through [AI Gateway](/docs/ai-gateway). ## Related pages > **For AI agents:** Follow these links to understand how this page connects to the rest of the Vercel ecosystem. For the full cross-link map (inbound, outbound, prerequisites, and semantic neighbors), see the .graph.md link below. - [Run Python code securely with AI SDK and Vercel Sandbox](https://vercel.com/kb/guide/python-ai-sdk-vercel-sandbox?from=related) — Add an \\`executeCode\\` tool to your AI SDK agent with the \\`ai-sdk-tool-code-execution\\` package to run Python 3.13 insi - [Building an agent with OpenAI Agents SDK and Vercel Sandbox](https://vercel.com/kb/guide/building-an-agent-with-openai-agents-sdk-and-vercel-sandbox?from=related) — Learn how to build an agent with with OpenAI Agents SDK and Vercel Sandbox - [Safely running AI generated code in your Next.js application](https://vercel.com/kb/guide/running-ai-generated-code-sandbox?from=related) — How to execute untrusted, AI‑generated code from a Next.js app using Vercel Sandbox, an isolated, ephemeral environment. - [How to execute AI-generated code safely with Vercel Sandbox](https://vercel.com/kb/guide/how-to-execute-ai-generated-code-safely?from=related) — Learn how to run code generated by AI models in an isolated sandbox environment. - [Building an AI chatbot with Next.js, Langchain, and OpenAI](https://vercel.com/kb/guide/nextjs-langchain-vercel-ai?from=related) — Dive into the world of LangChain.js and Next.js with our detailed guide. Learn how to set up a chatbot, structure output - [LangChain](https://vercel.com/docs/ai-gateway/ecosystem/framework-integrations/langchain?from=related) — Learn how to integrate Vercel AI Gateway with LangChain to access multiple AI models through a unified interface - [sitemap.md](https://vercel.com/docs/sitemap.md?from=related) — Learn about sitemap.md on Vercel. Full cross-link map for this page: [/docs/sandbox/ecosystem/langchain.graph.md](/docs/sandbox/ecosystem/langchain.graph.md) > **💡 Note:** You need a [Vercel account](https://vercel.com/signup), [Vercel > CLI](/docs/cli), Node.js 22 or later, and an [AI Gateway API > key](/docs/ai-gateway/authentication-and-byok/authentication). ## Getting started - ### Create a new project Create a new directory for your project and initialize it: ```bash filename="terminal" mkdir langchain-sandbox cd langchain-sandbox npm init -y ``` - ### Install dependencies Install LangChain, the Anthropic and OpenAI provider packages, and the Sandbox SDK: ```bash pnpm i langchain @langchain/core @langchain/anthropic @langchain/openai @vercel/sandbox zod ``` ```bash yarn i langchain @langchain/core @langchain/anthropic @langchain/openai @vercel/sandbox zod ``` ```bash npm i langchain @langchain/core @langchain/anthropic @langchain/openai @vercel/sandbox zod ``` ```bash bun i langchain @langchain/core @langchain/anthropic @langchain/openai @vercel/sandbox zod ``` - ### Configure authentication Link the project to Vercel and pull your development environment. This creates a `.env.local` file with a `VERCEL_OIDC_TOKEN`, which authenticates the Sandbox SDK: ```bash filename="terminal" vercel link vercel env pull ``` Create an AI Gateway API key, then add it to `.env.local`: ```bash filename=".env.local" AI_GATEWAY_API_KEY=your_ai_gateway_api_key ``` > **💡 Note:** Outside Vercel and local development, authenticate the Sandbox SDK with an > [access token](/docs/sandbox/concepts/authentication#access-tokens) by setting > `VERCEL_TOKEN`, `VERCEL_TEAM_ID`, and `VERCEL_PROJECT_ID`. - ### Create your agent Create a new file called `index.ts` with the following code: ```typescript filename="index.ts" import * as z from 'zod'; import { createAgent, tool } from 'langchain'; import { ChatAnthropic } from '@langchain/anthropic'; import { Sandbox } from '@vercel/sandbox'; const sandbox = await Sandbox.create({ timeout: 300_000 }); try { const runCode = tool( async ({ code }) => { const result = await sandbox.runCommand('node', ['-e', code]); return result.exitCode === 0 ? await result.stdout() : `Command failed: ${await result.stderr()}`; }, { name: 'run_code', description: 'Run Node.js code in an isolated Vercel Sandbox and return its output.', schema: z.object({ code: z.string().describe('Node.js code to execute'), }), }, ); const model = new ChatAnthropic({ model: 'anthropic/claude-opus-5', apiKey: process.env.AI_GATEWAY_API_KEY, anthropicApiUrl: 'https://ai-gateway.vercel.sh', }); const agent = createAgent({ model, tools: [runCode] }); const result = await agent.invoke({ messages: [ { role: 'user', content: "Compute the SHA-256 hex digest of the exact string 'vercel sandbox' with Node.js, then report the digest.", }, ], }); console.log(result.messages.at(-1)?.content); } finally { await sandbox.stop(); } ``` The `timeout` value gives the sandbox up to five minutes to finish the agent loop. The `finally` block stops it sooner when the loop finishes or throws. The following code: - Creates a sandbox that the agent reuses across tool calls - Defines a `run_code` tool that executes code with [`sandbox.runCommand()`](/docs/sandbox/sdk-reference#sandboxruncommand) - Points the Anthropic chat model at AI Gateway, authenticated with your Vercel credentials - Passes the tool to `createAgent`, which runs the tool-calling loop The example uses an Anthropic model. To use an OpenAI model instead, import `ChatOpenAI` from `@langchain/openai` and point it at the same gateway: ```typescript filename="index.ts" import { ChatOpenAI } from '@langchain/openai'; const model = new ChatOpenAI({ model: 'openai/gpt-5.6-sol', apiKey: process.env.AI_GATEWAY_API_KEY ?? process.env.VERCEL_OIDC_TOKEN, configuration: { baseURL: 'https://ai-gateway.vercel.sh/v1' }, }); ``` Any [AI Gateway model](/docs/ai-gateway/models-and-providers) works the same way. - ### Run the agent Run your agent with the environment variables from `.env.local`: ```bash filename="terminal" npx tsx --env-file=.env.local index.ts ``` The agent writes Node.js code, executes it in the sandbox, and reports the digest. --- [View full sitemap](/docs/sitemap)