Hataraku

An autonomous coding agent and SDK for building AI-powered tools. The name "Hataraku" (働く) means "to work" in Japanese.


Description

Hataraku is a powerful toolkit that enables the creation of AI-powered development tools and autonomous coding agents. It provides a flexible SDK and CLI for building intelligent development workflows, code analysis, and automation tasks.

Key Features

- 🤖 Autonomous coding agent capabilities
- 🛠️ Extensible SDK for building AI-powered tools
- 📦 Support for multiple AI providers (OpenRouter, Claude, Amazon Bedrock)
- 🧠 AWS Bedrock Knowledge Base integration for RAG applications
- 🔄 Workflow automation and parallel task execution
- 📊 Schema validation and structured tasks
- 🧰 Built-in tool integration system
- 🔗 Model Context Protocol (MCP) support
- 🔄 Extends the powerful AI SDK from Vercel.

Installation

``bash


Using npm

npm install -g hataraku

Using yarn

yarn global add hataraku

Using pnpm

pnpm global add hataraku
`

Quick Start

$3

`typescript
// Import the SDK
import { createAgent, createTask } from 'hataraku';
import { z } from 'zod';

// Bring in any ai-sdk provider https://sdk.vercel.ai/providers/ai-sdk-providers
import { createOpenRouter } from "@openrouter/ai-sdk-provider";

// Create an agent using Claude via OpenRouter
// You can pass API key directly or use environment variable
const openrouter = createOpenRouter({
apiKey: 'YOUR_OPENROUTER_API_KEY',
});
const model = openrouter.chatModel('anthropic/claude-3.5-sonnet');

const agent = createAgent({
name: 'MyAgent',
description: 'A helpful assistant',
role: 'You are a helpful assistant that provides accurate information.',
model: model
});

// Run a one-off task
const result = await agent.task('Create a hello world function');

// Create a simple reusable task with schema validation
const task = createTask({
name: 'HelloWorld',
description: 'Say Hello to the user',
agent: agent,
inputSchema: z.object({ name: z.string() }),
task: ({name}) => Say hello to ${name} in a friendly manner
});

// Execute the task
const result = await task.run({name: 'Hataraku'});
console.log(result);
`

$3

First, install the CLI globally:

`bash
npm install -g hataraku
`

Initialize a new project:

`bash
hataraku init my-project
cd my-project
`

Run a task using the CLI:

`bash


Run a predefined task

hataraku task run hello-world

Run with custom input

hataraku task run hello-world --input '{"prompt": "Write a function that calculates factorial"}'

Run with streaming output

hataraku task run hello-world --stream
`

Configure providers and explore available commands:

`bash


Configure a provider

hataraku provider configure openrouter

List all available commands

hataraku --help
`

$3

Hataraku's output can be enhanced using Glow, a terminal-based markdown viewer that makes the output more readable and visually appealing.

#### Installing Glow

`bash


macOS

brew install glow

Ubuntu/Debian

sudo apt-get update && sudo apt-get install glow

Windows with Chocolatey

choco install glow
`

#### Using Glow with Hataraku

Create a function in your shell configuration file (.bashrc, .zshrc, etc.):

`bash


Alias for Hataraku

alias h="hataraku"

Function to pipe Hataraku output to Glow

hd() {
hataraku "$@" | glow -
}
`

Now you can use the hd command to run Hataraku with enhanced output:

For more details, see the Glow Integration Guide.

API Overview

Hataraku provides several core components:

- Task: Create and execute AI-powered tasks
- Agent: Build autonomous coding agents
- Workflow: Orchestrate complex multi-step operations
- Tools: Integrate custom capabilities and external services

For detailed API documentation, see the Types Documentation.

Documentation

- Agent Documentation - Learn about autonomous agents
- CLI Reference - Available CLI commands and options
- API Reference - Complete API reference
- Configuration Guide - Configuration options
- Providers - Supported AI providers
- Knowledge Base - AWS Bedrock Knowledge Base integration
- Tools - Built-in tools and extensions
- Architecture - System architecture
- Troubleshooting - Solving common issues
- Glow Integration - Using Glow to enhance Hataraku output

Examples

The package includes various examples in the /examples` directory demonstrating different features:

- Basic task execution
- Streaming responses
- Schema validation
- Multi-step workflows
- Tool integration
- Thread management

These examples are available for reference in the repository and can be examined to understand different use cases and implementation patterns.

See the examples README for more details.

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

MIT License - see the LICENSE file for details.

Support

- GitHub Issues: Report bugs or request features
- Documentation: See the docs directory for detailed guides