Zero to agent

Why you need this

You want an agent that can actually do things — run a command, read and write files — without hand-wiring a toolset first. The ten built-in tools ship on by default, so three lines gives you a working agent. You bring only a model endpoint; toolnexus is a library, not a runtime, so it drops into whatever control flow you already have.

The three-liner: a toolkit with the ten built-in tools, a client pointed at any OpenAI- or Anthropic-style endpoint, and one run. API keys are read from the environment (OPENROUTER_API_KEY, then OPENAI_API_KEY / ANTHROPIC_API_KEY) and never printed.

JavaScript

import { createToolkit, createClient } from "toolnexus"

const tk = await createToolkit()                       // 10 built-ins, on by default
const agent = createClient({
  baseUrl: "https://openrouter.ai/api/v1", style: "openai",
  model: "openai/gpt-4o-mini", apiKey: process.env.OPENROUTER_API_KEY,
})
const res = await agent.run("List the files here, then read the largest one.", tk)
console.log(res.text)

Python

from toolnexus import create_toolkit, create_client

tk = await create_toolkit()                            # 10 built-ins, on by default
agent = create_client(
    base_url="https://openrouter.ai/api/v1", style="openai",
    model="openai/gpt-4o-mini",
)
res = await agent.run("List the files here, then read the largest one.", tk)
print(res.text)

Go

ctx := context.Background()
tk, _ := toolnexus.CreateToolkit(ctx, toolnexus.Options{})   // 10 built-ins
agent := toolnexus.CreateClient(toolnexus.ClientOptions{
    BaseURL: "https://openrouter.ai/api/v1", Style: toolnexus.StyleOpenAI,
    Model: "openai/gpt-4o-mini", APIKey: os.Getenv("OPENROUTER_API_KEY"),
})
res, _ := agent.Run(ctx, "List the files here, then read the largest one.", tk)
fmt.Println(res.Text)

Java

Toolkit tk = Toolkit.create(new Toolkit.Options());   // 10 built-ins, on by default
LlmClient agent = LlmClient.create(new LlmClient.Options()
        .baseUrl("https://openrouter.ai/api/v1")
        .style("openai")
        .model("openai/gpt-4o-mini"));
System.out.println(agent.run("List the files here, then read the largest one.", tk).text);

C#

await using var tk = await Toolkit.CreateAsync(new Toolkit.Options());   // 10 built-ins
var agent = LlmClient.Create(new LlmClient.Options {
    BaseUrl = "https://openrouter.ai/api/v1", Style = "openai",
    Model = "openai/gpt-4o-mini",
});
var res = await agent.RunAsync("List the files here, then read the largest one.", tk);
Console.WriteLine(res.Text);

Elixir

{:ok, tk} = Toolnexus.create_toolkit()                 # 10 built-ins, on by default
client = Toolnexus.Client.create(
  base_url: "https://openrouter.ai/api/v1", style: "openai",
  model: "openai/gpt-4o-mini", api_key: System.get_env("OPENROUTER_API_KEY")
)
res = Toolnexus.Client.run(client, "List the files here, then read the largest one.", tk)
IO.puts(res.text)

Next: MCP servers.