https://github.com/cheolwanpark/claude-agent-toolkit

Python framework for building agents using claude-code-sdk with programmable tools
https://github.com/cheolwanpark/claude-agent-toolkit
agent agentic-ai agentic-ai-development agentic-workflow agents ai ai-agents claude claude-ai claude-code claude-code-sdk fastmcp mcp python
Last synced: 9 months ago
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Python framework for building agents using claude-code-sdk with programmable tools
Host: GitHub
URL: https://github.com/cheolwanpark/claude-agent-toolkit
Owner: cheolwanpark
License: mit
Created: 2025-08-31T09:36:59.000Z (10 months ago)
Default Branch: main
Last Pushed: 2025-09-01T14:00:36.000Z (10 months ago)
Last Synced: 2025-09-01T15:08:58.661Z (10 months ago)
Topics: agent, agentic-ai, agentic-ai-development, agentic-workflow, agents, ai, ai-agents, claude, claude-ai, claude-code, claude-code-sdk, fastmcp, mcp, python
Language: Python
Homepage:
Size: 205 KB
Stars: 2
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

          # Claude Code Agent Toolkit (claude-agent-toolkit)

A Python framework for building Claude Code agents with custom tools, designed to leverage Claude Code's advanced reasoning capabilities with your subscription token. The framework provides Docker-isolated environments where Claude Code can orchestrate custom MCP tools for production workflows.

## Key Features

- **Claude Code Integration** - Leverage Claude Code's advanced reasoning with your existing subscription token

- **Docker Isolation** - Complete isolation of agent execution environment with Claude Code CLI

- **Advanced State Management** - JSON patch-based state management with conflict resolution and automatic retries  

- **CPU-bound Operations** - Support for CPU-intensive operations with process pools and parallel execution

- **Multi-tool Coordination** - Claude Code orchestrates multiple tools in complex workflows

- **Production Ready** - Build scalable agents using Claude Code's capabilities with custom tool integration

## Architecture

### Core Components

- **Agent Framework** (`src/claude_agent_toolkit/agent/`) - Docker-isolated Agent class that runs Claude Code with MCP tool support

- **MCP Tool Framework** (`src/claude_agent_toolkit/tool/`) - BaseTool class for creating custom MCP tools with state management

- **Example Tools** (`src/examples/`) - Demonstration tools showing practical agent development patterns

- **Docker Environment** (`src/docker/`) - Isolated environment with Claude Code CLI and dependencies

## Quick Start

### Prerequisites

- **Python 3.12+** with `uv` package manager

- **Docker Desktop** (must be running)

- **Claude Code OAuth Token** - Get from [Claude Code](https://claude.ai/code)

### Installation

```bash

# Using pip

pip install claude-agent-toolkit

# Using uv

uv add claude-agent-toolkit

# Using poetry

poetry add claude-agent-toolkit

# Set your OAuth token

export CLAUDE_CODE_OAUTH_TOKEN='your-token-here'

```

### Run the Demo

```bash

# Clone the repository for examples

git clone https://github.com/cheolwanpark/claude-agent-toolkit.git

cd claude-agent-toolkit

# Start Docker Desktop first, then run the verification demo

# Run calculator example:

cd src/examples/calculator && python main.py

# Run weather example:

cd src/examples/weather && python main.py

```

This will run demonstration examples:

1. **Calculator Demo** - Shows stateful mathematical operations and problem solving

2. **Weather Demo** - Demonstrates external API integration with real-time data

## Tool Development

### Creating Custom Tools

Create tools by inheriting from `BaseTool` and using the `@tool()` decorator:

```python

from claude_agent_toolkit import BaseTool, tool

class MyTool(BaseTool):

    def __init__(self):

        super().__init__()

        self.state = {"counter": 0}

    

    @tool(description="Increment counter and return new value")

    async def increment(self) -> dict:

        self.state["counter"] += 1

        return {"value": self.state["counter"]}

    

    @tool(description="Heavy computation", cpu_bound=True)  

    def compute_heavy(self, data: str) -> dict:

        # CPU-intensive operation runs in process pool

        import time

        time.sleep(2)  # Simulate heavy computation

        return {"processed": f"Heavy result for {data}"}

```

### Using Tools with Agents

```python

from claude_agent_toolkit import Agent, ConnectionError, ExecutionError

try:

    # Create and start tool

    my_tool = MyTool().run(workers=2)

    # New pattern (recommended) - cleaner initialization

    agent = Agent(

        system_prompt="You are a helpful assistant specialized in calculations",

        tools=[my_tool]

    )

    # Traditional pattern - still supported

    # agent = Agent()

    # agent.connect(my_tool)

    # Run agent with prompt (verbose=True shows detailed message processing)

    result = await agent.run(

        "Please increment the counter twice and tell me the result",

        verbose=True  # Shows detailed Claude Code interaction logs

    )

    print(f"Success: {result['success']}")

    print(f"Response: {result['response']}")

    # Verify tool was actually called

    print(f"Tool state: {my_tool.state}")

    

except ConnectionError as e:

    print(f"Connection issue: {e}")

    # Handle Docker, network, or port binding problems

    

except ExecutionError as e:

    print(f"Execution failed: {e}")

    # Handle agent execution or tool failures

```

## Model Selection

Choose the right Claude model for your agent's needs:

### Available Models

- **"haiku"** - Fast and efficient for simple tasks

- **"sonnet"** - Balanced performance (good default choice)

- **"opus"** - Most capable for complex reasoning

### Usage Examples

```python

from claude_agent_toolkit import Agent

# Use fast Haiku model for simple tasks

weather_agent = Agent(

    system_prompt="You are a weather assistant",

    tools=[weather_tool],

    model="haiku"  # Fast, efficient for simple weather queries

)

# Use Sonnet for general-purpose tasks

general_agent = Agent(

    system_prompt="You are a helpful assistant", 

    tools=[calculator_tool, weather_tool],

    model="sonnet"  # Balanced performance

)

# Use Opus for complex analysis

analysis_agent = Agent(

    system_prompt="You are a data analyst",

    tools=[analysis_tool],

    model="opus"  # Maximum reasoning capability

)

# Override model for specific queries

result = await weather_agent.run(

    "Complex weather pattern analysis for next month",

    model="opus"  # Use more capable model for this specific task

)

# Full model IDs also work

agent = Agent(model="claude-3-5-haiku-20241022")

```

### When to Use Each Model

- **Haiku**: Simple queries, basic operations, fast responses needed

- **Sonnet**: General purpose tasks, good balance of speed and capability

- **Opus**: Complex reasoning, detailed analysis, maximum quality needed

## Why Claude Code Agents?

Unlike generic agent frameworks, this toolkit specifically leverages Claude Code's unique capabilities:

1. **Advanced Reasoning** - Use Claude Code's sophisticated decision-making in your agents

2. **Existing Subscription** - Build production agents with your current Claude Code subscription

3. **Stateful Workflows** - Claude Code builds context across multiple tool interactions

4. **Intelligent Orchestration** - Claude Code decides which tools to use and when

5. **Production Infrastructure** - Leverage Claude's robust infrastructure for your agents

### Example: Intelligent Workflow

```python

# Claude Code analyzes data with one tool, then decides to process it with another

# The agent maintains context and makes intelligent decisions about tool usage

# Your tools provide capabilities, Claude Code provides the intelligence

```

## API Reference

### Agent Class

```python

class Agent:

    def __init__(                                          # Initialize agent

        self,

        oauth_token: Optional[str] = None,                 # Your Claude Code token

        system_prompt: Optional[str] = None,               # Custom agent behavior

        tools: Optional[List[BaseTool]] = None,            # Tools to connect automatically

        model: Optional[Union[Literal["opus", "sonnet", "haiku"], str]] = None  # Model selection

    )

    def connect(self, tool: BaseTool) -> 'Agent'           # Connect custom tools  

    async def run(                                         # Run Claude Code with tools

        self,

        prompt: str,                                       # Instruction for Claude

        verbose: bool = False,                             # Show detailed processing logs

        model: Optional[Union[Literal["opus", "sonnet", "haiku"], str]] = None  # Override model

    ) -> Dict[str, Any]

```

### BaseTool Class  

```python

class BaseTool:

    def __init__(self)

    def run(self, host="127.0.0.1", port=None, *, workers=None) -> 'BaseTool'

    @property def connection_url(self) -> str

    @property def state(self) -> Any  # Mutable state dictionary

```

### @tool() Decorator

```python

@tool(

    name: Optional[str] = None,           # Tool method name

    description: str = "",               # Method description  

    cpu_bound: bool = False,             # Use process pool

    timeout_s: int = 60,                 # Timeout for CPU-bound operations

    conflict_policy: str = "retry",      # How to handle state conflicts

    max_retries: int = 16                # Max retry attempts

)

```

### Exception Classes

Claude Agent Toolkit provides specific exception types for clear error handling:

```python

# Import exception classes

from claude_agent_toolkit import (

    ClaudeAgentError,     # Base exception for all library errors

    ConfigurationError,   # Missing OAuth tokens, invalid configuration

    ConnectionError,      # Docker, network, port binding failures  

    ExecutionError,       # Agent execution, tool failures, timeouts

    StateError           # Tool lifecycle, state management issues

)

# Exception hierarchy

ClaudeAgentError

├── ConfigurationError    # Configuration issues

├── ConnectionError       # Network/service connectivity

├── ExecutionError       # Runtime execution failures

└── StateError          # State management problems

```

**When to catch each exception:**

- **ConfigurationError**: Handle setup issues, missing tokens, invalid configs

- **ConnectionError**: Handle Docker, network, and port binding failures

- **ExecutionError**: Handle runtime failures, timeouts, tool execution issues  

- **StateError**: Handle tool lifecycle violations, state conflicts

- **ClaudeAgentError**: Catch all library errors with a single handler

## Development Workflow

### 1. Start Docker Desktop

Required for agent execution - must be running before creating Claude Code agents.

### 2. Set OAuth Token  

```bash

export CLAUDE_CODE_OAUTH_TOKEN='your-token-here'

```

### 3. Create Custom Tools

Inherit from `BaseTool` and implement `@tool` methods that extend Claude Code's capabilities.

### 4. Build Your Agent  

Use the examples in `src/examples/` to see demonstrations or create custom agent scripts.

### 5. Deploy to Production

Use your Claude Code subscription to run agents at scale with custom tool integration.

## Dependencies

### Runtime Dependencies

- `docker>=7.1.0` - Docker container management

- `fastmcp>=2.11.3` - MCP server framework

- `httpx>=0.28.1` - HTTP client for health checks

- `jsonpatch>=1.33` - State management with JSON patches  

- `uvicorn>=0.35.0` - ASGI server for MCP HTTP endpoints

### Docker Environment  

- Python 3.11 with Claude Code SDK

- Node.js 20 with Claude Code CLI

- Non-root user execution for security

## Error Handling

Claude Agent Toolkit uses specific exception types to help you handle errors gracefully:

```python

from claude_agent_toolkit import (

    Agent, BaseTool, tool,

    ClaudeAgentError, ConfigurationError, ConnectionError,

    ExecutionError, StateError

)

# Handle specific error types

try:

    agent = Agent(

        oauth_token="your-token",

        tools=[MyTool().run()]

    )

    result = await agent.run("Process my request")

    

except ConfigurationError as e:

    print(f"Configuration issue: {e}")

    # Handle missing OAuth token, invalid tool config

    

except ConnectionError as e:

    print(f"Connection failed: {e}")

    # Handle Docker, network, port binding issues

    

except ExecutionError as e:

    print(f"Execution failed: {e}")

    # Handle agent execution, tool failures, timeouts

    

except StateError as e:

    print(f"State management issue: {e}")

    # Handle tool lifecycle, state conflicts

    

except ClaudeAgentError as e:

    print(f"Library error: {e}")

    # Catch all library errors

```

## Troubleshooting

### Common Issues

**ConfigurationError: "OAuth token required"**

```python

# Set environment variable

export CLAUDE_CODE_OAUTH_TOKEN='your-token-here'

# Or pass directly to Agent

agent = Agent(oauth_token='your-token-here')

```

**ConnectionError: "Cannot connect to Docker"**

- Ensure Docker Desktop is running

- Check Docker daemon is accessible

- Linux: `sudo systemctl start docker`

**ConnectionError: "Port binding failed"**

```python

# Let tools auto-select available ports

tool = MyTool().run()  # Auto-selects port

# Or specify different port

tool = MyTool().run(port=9000)

```

**StateError: "Tool is not running"**

```python

# Start tool before accessing properties

tool = MyTool()

tool.run()  # Start the tool

url = tool.connection_url  # Now accessible

```

**ExecutionError: "Operation timed out"**

```python

# Increase timeout for CPU-bound operations

@tool(cpu_bound=True, timeout_s=300)  # 5 minute timeout

async def heavy_computation(self):

    pass

```

### Debug Mode

```python

from claude_agent_toolkit import set_logging, LogLevel

# Enable detailed logging

set_logging(LogLevel.DEBUG, show_time=True, show_level=True)

# Run with verbose output

result = await agent.run("your prompt", verbose=True)

```

## Contributing

1. Create custom tools for different Claude Code agent use cases

2. Add new agent development patterns and templates

3. Improve Docker image efficiency and security

4. Enhance state management and conflict resolution

5. Add support for additional MCP server types

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Related Projects

- [Claude Code](https://claude.ai/code) - Official Claude Code interface (required for this framework)

- [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) - Protocol for AI-tool integration

- [FastMCP](https://github.com/jlowin/fastmcp) - Fast MCP server implementation
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