Hataraku

Hataraku

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

[](https://badge.fury.io/js/hataraku) [](https://opensource.org/licenses/MIT)

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

# Using npm
npm install -g hataraku

# Using yarn
yarn global add hataraku

# Using pnpm
pnpm global add hataraku

Quick Start

SDK Usage

// 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)

CLI Usage

First, install the CLI globally:

npm install -g hataraku

Initialize a new project:

hataraku init my-project
cd my-project

Run a task using the CLI:

# 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:

# Configure a provider
hataraku provider configure openrouter

# List all available commands
hataraku --help

Enhancing Output with Glow

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

Installing Glow

# 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.):

# 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](docs/glow-guide.md).

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](docs/types.md).

Documentation

• [Agent Documentation](docs/agent.md) - Learn about autonomous agents
• [CLI Reference](docs/cli.md) - Available CLI commands and options
• [API Reference](docs/api-reference.md) - Complete API reference
• [Configuration Guide](docs/configuration.md) - Configuration options
• [Providers](docs/providers.md) - Supported AI providers
• [Knowledge Base](docs/knowledge-base.md) - AWS Bedrock Knowledge Base integration
• [Tools](docs/tools.md) - Built-in tools and extensions
• [Architecture](docs/architecture.md) - System architecture
• [Troubleshooting](docs/troubleshooting.md) - Solving common issues
• [Glow Integration](docs/glow-guide.md) - 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](examples/README.md) for more details.

Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

License

MIT License - see the [LICENSE](LICENSE) file for details.

Support

• GitHub Issues: Report bugs or request features
• Documentation: See the [docs](./docs) directory for detailed guides

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: turlockmike
• Source: turlockmike/hataraku
• License: MIT

Install and usage instructions live in the source repository linked above.