open-agent

A thin, vendor-agnostic wrapper around the Vercel AI SDK that gives any LLM Claude-style coding agent capabilities — sandboxed bash, read, write, edit, glob, and grep — in a few lines of code.

No framework lock-in. No opinions on prompts or orchestration. Just plug in your model, point it at a Daytona sandbox, and let it work.

How it relates to the Claude Agent SDK

Anthropic's Claude Agent SDK gives you the full Claude Code experience — built-in tools, agent loop, context management, hooks, subagents, MCP, and sessions — but it's tied to Claude models via the Anthropic API.

open-agent takes the same core idea (give an LLM coding tools that run in a sandbox) and makes it work with any model through the Vercel AI SDK. It's a much thinner layer: no hooks, no subagents, no sessions — just tools, a sandbox, and a generate/stream loop. If you want the full-featured Claude agent experience, use the Claude Agent SDK. If you want the same tool pattern with any model and minimal abstraction, use this.

Install

npm install @dillion-ai/open-agent ai

ai is a peer dependency (>= 6.0.0).

Quick start

import { createOpenRouter } from "@openrouter/ai-sdk-provider";
import { createAgent } from "@dillion-ai/open-agent";

const openrouter = createOpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY!,
});

const agent = createAgent({
  model: openrouter("minimax/minimax-m2.5:exacto"),
  instructions: "You are a coding assistant.",
  tools: ["bash", "read", "write", "edit", "glob", "grep"],
  sandbox: { apiKey: process.env.DAYTONA_API_KEY },
});

const result = await agent.generate({
  prompt: "Create a hello world TypeScript file and run it",
});

console.log(result.text);
await agent.destroy();

Swap the model string for any provider — OpenAI, Google, Mistral, open-source via OpenRouter — everything else stays the same.

What this gives you

This SDK is intentionally small. It provides the wiring between the AI SDK's ToolLoopAgent and a cloud sandbox, and nothing more:

• Any model — anything the Vercel AI SDK supports works out of the box
• Claude-style tools — the same bash, read, write, edit, glob, grep pattern, running inside a Daytona sandbox
• Sandboxed execution — code runs in an isolated cloud environment, not on your machine
• File I/O — upload files in, download results out
• Streaming — first-class streaming support via the AI SDK
• Skills — optional markdown instruction files for domain-specific workflows (e.g. Excel generation)

The SDK doesn't impose prompt templates, memory systems, RAG pipelines, or agent architectures. You bring those.

Tools

Skills

Skills are markdown files that get injected into the system prompt. They teach the agent domain-specific workflows without requiring code changes. The SDK ships a built-in xlsx skill; you can add your own.

const agent = createAgent({
  model,
  tools: ["bash", "read", "write", "edit"],
  skills: {
    builtins: ["xlsx"],
    custom: ["./my-skills/"],
  },
  sandbox: { apiKey: process.env.DAYTONA_API_KEY },
});

File upload & download

await agent.uploadFiles([
  { path: "/home/daytona/data.csv", content: csvString },
]);

await agent.generate({ prompt: "Analyze data.csv and save results to output.xlsx" });

const file = await agent.downloadFile("/home/daytona/output.xlsx");
fs.writeFileSync("output.xlsx", file);

Streaming

const stream = await agent.stream({
  prompt: "Write a Python fibonacci generator and test it",
});

for await (const chunk of stream.textStream) {
  process.stdout.write(chunk);
}

Configuration

createAgent({
  model,                    // Any Vercel AI SDK LanguageModel
  instructions: "",         // System prompt — yours to define
  tools: [],                // Which built-in tools to enable
  sandbox: {                // Daytona sandbox config
    apiKey: "",
    apiUrl: "",             //   Custom API URL (optional)
    target: "",             //   Target region (optional)
    instance: sandbox,      //   Reuse an existing Sandbox (optional)
  },
  skills: {                 // Optional
    builtins: true,         //   true | false | string[]
    custom: [],             //   Paths to .md files or directories
  },
  maxSteps: 30,             // Max tool-use loop iterations
  cwd: "/home/daytona",    // Working directory in the sandbox
});

Examples

See examples/:

• basic.ts — minimal usage
• streaming.ts — real-time streaming
• xlsx-report.ts — Excel financial report from CSV
• chart-generation.ts — matplotlib charts to PNG
• upload-and-analyze.ts — upload, analyze, download spreadsheets
• custom-skills.ts — custom skill files

License

MIT