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Here we’ll build a CLI-based AI coding agent that can execute bash commands to help you with development tasks.

Prerequisites

• Bun runtime installed
• Ollama running locally with a model
  • qwen3.5:35b-a3b-coding-nvfp4 is the newest hotness
• Node.js basics familiarity

The Code

Here’s the complete implementation in index.ts:

#!/usr/bin/env bun

import { createOpenAI } from "@ai-sdk/openai";
import { generateText, isLoopFinished, tool, zodSchema } from "ai";
import type { ModelMessage } from "ai";
import { z } from "zod";
import { spawnSync } from "child_process";
import * as readline from "readline";

/** CONSTANTS */
const WORKDIR = process.cwd();
const MODEL = "qwen3.5:35b-a3b-coding-nvfp4";
const BLOCKED_COMMANDS = ["rm -rf /", "sudo", "shutdown", "reboot", "> /dev/"];

/** API */
const ollama = createOpenAI({
  baseURL: "http://localhost:11434/v1",
  apiKey: "ollama",
});

/** TOOLS */
const runBash = (command: string): string => {
  if (BLOCKED_COMMANDS.some((c) => command.includes(c))) {
    return "Error: Danger Will Robinson!!!";
  }
  try {
    const result = spawnSync("sh", ["-c", command], {
      cwd: WORKDIR,
      encoding: "utf8",
      timeout: 120000
    });
    return (result.stdout + result.stderr).trim().slice(0, 50000) || "";

  } catch (e) {
    return `Error ${e}`;
  }
};

const TOOLS = {
  bash: tool({
    description: "Run a shell command",
    inputSchema: zodSchema(z.object({ command: z.string() })),
    execute: async ({ command }: { command: string; }) => {
      const output = runBash(command);
      return output;
    }
  })
};


/** AGENT LOOP */

const agentLoop = async (messages: ModelMessage[]): Promise<string> => {
  const { text } = await generateText({
    model: ollama.chat(MODEL),
    system: `You are a coding agents at ${WORKDIR}. Use bash to solve tasks.  Act, dont explain.`,
    messages,
    tools: TOOLS,
    stopWhen: isLoopFinished(),
  });
  return text;
};

/** INTERFACE */

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout
});
const history: ModelMessage[] = [];

const prompt = (): void => {
  rl.question(" input >> ", async (query) => {
    history.push({ role: "user", content: query });
    const reply = await agentLoop(history);
    history.push({ role: "assistant", content: reply });
    if (reply) console.log(reply);
    console.log();
    prompt();
  });
};

prompt();

How It Works

1. API Configuration

The code creates an OpenAI-compatible client pointing to your local Ollama instance:

const ollama = createOpenAI({
  baseURL: "http://localhost:11434/v1",
  apiKey: "ollama",
});

2. Tool Definition

We define a bash tool that the model can call to execute shell commands. The tool uses Zod schema validation to ensure proper input:

const TOOLS = {
  bash: tool({
    description: "Run a shell command",
    inputSchema: zodSchema(z.object({ command: z.string() })),
    execute: async ({ command }) => runBash(command)
  })
};

3. Security Measures

A blocklist prevents dangerous commands from executing:

const BLOCKED_COMMANDS = ["rm -rf /", "sudo", "shutdown", "reboot", "> /dev/"];

4. Agent Loop

The agentLoop function uses generateText with stopWhen: isLoopFinished() to automatically handle tool execution cycles. The model will keep calling tools until it completes the task without needing more input.

const agentLoop = async (messages: ModelMessage[]): Promise<string> => {
  const { text } = await generateText({
    model: ollama.chat(MODEL),
    system: `You are a coding agents at ${WORKDIR}. Use bash to solve tasks.  Act, dont explain.`,
    messages,
    tools: TOOLS,
    stopWhen: isLoopFinished(),
  });
  return text;
};

5. Interactive CLI

The readline interface provides a simple interactive prompt that maintains conversation history:

const prompt = (): void => {
  rl.question(" input >> ", async (query) => {
    history.push({ role: "user", content: query });
    const reply = await agentLoop(history);
    // ...
  });
};

Running the Agent

1. Ensure Ollama is running with your chosen model
2. Run the script:

bun run index.ts

3. Enter your task at the prompt. For example:

input >> Create a new file called hello.txt with the content "Hello World"

The agent will execute the necessary bash commands to complete your task.

Key Concepts

• Tool Execution: Vercel AI SDK’s tool function allows models to execute code and return results
• Loop Detection: isLoopFinished() automatically detects when the model has completed its task
• Message History: Maintaining conversation context helps the agent understand ongoing tasks
• Zod Integration: Schema validation ensures tools receive correct input types

This implementation provides a foundation for building more sophisticated AI coding assistants!