pydantic-ai-claude-code 0.5.9

Use local Claude Code CLI as a Pydantic AI model provider with full support for structured responses, tools, and streaming

Pydantic AI Claude Code

Use your local Claude Code CLI as a Pydantic AI model provider.

This package provides a Pydantic AI-compatible model implementation that wraps the local Claude CLI, enabling you to use Claude locally with all Pydantic AI features including structured responses, tool calling, streaming, and multi-turn conversations.

Features

• Full Pydantic AI Compatibility: Drop-in replacement for any Pydantic AI model
• Structured Responses: Get validated, typed responses using Pydantic models
• Custom Tool Calling: Use your own Python functions as tools
• True Streaming: Real-time response streaming via Claude CLI's stream-json mode
• Local Execution: All processing happens locally on your machine
• Session Persistence: Maintain conversation context across multiple requests
• Configurable: Fine-tune permissions, working directories, and tool access

Installation

# Using uv (recommended)
uv add pydantic-ai-claude-code

# Using pip
pip install pydantic-ai-claude-code

Prerequisites: You must have Claude Code CLI installed and authenticated on your system.

Quick Start

Basic Usage (String Format - Simplest!)

import pydantic_ai_claude_code  # Register the provider

from pydantic_ai import Agent

# Just use the string format - easiest way!
agent = Agent('claude-code:sonnet')

# Run a simple query
result = agent.run_sync("What is 2+2?")
print(result.output)  # Output: 4

Structured Responses

import pydantic_ai_claude_code

from pydantic import BaseModel
from pydantic_ai import Agent

class Analysis(BaseModel):
    complexity: int  # 1-10
    maintainability: str
    suggestions: list[str]

# String format works with all Pydantic AI features
agent = Agent('claude-code:sonnet', output_type=Analysis)

result = agent.run_sync("Analyze this code: def foo(): pass")
print(f"Complexity: {result.output.complexity}")

Custom Tools

import pydantic_ai_claude_code

from pydantic_ai import Agent

def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"Weather in {city}: Sunny, 22°C"

agent = Agent(
    'claude-code:sonnet',
    tools=[get_weather],
)

result = agent.run_sync("What's the weather in Paris?")
print(result.output)

Streaming

import pydantic_ai_claude_code

from pydantic_ai import Agent

agent = Agent('claude-code:sonnet')

async with agent.run_stream('Write a haiku about code') as result:
    async for text in result.stream_text():
        print(text, end='', flush=True)

Advanced Configuration

When you need fine-grained control, use the explicit model and provider:

from pydantic_ai import Agent
from pydantic_ai_claude_code import ClaudeCodeModel, ClaudeCodeProvider

# Custom provider with specific settings
provider = ClaudeCodeProvider(
    working_directory="/path/to/project",
    allowed_tools=["Read", "Edit", "Grep"],  # Restrict tool access
    permission_mode="acceptEdits",
    use_temp_workspace=False,  # Use specific directory instead of /tmp
)

model = ClaudeCodeModel("sonnet", provider=provider)
agent = Agent(model)

result = agent.run_sync("Analyze the codebase structure")

Temporary Workspace

from pydantic_ai_claude_code import ClaudeCodeModel, ClaudeCodeProvider

# Create isolated workspace that auto-cleans
with ClaudeCodeProvider(use_temp_workspace=True) as provider:
    model = ClaudeCodeModel("sonnet", provider=provider)
    agent = Agent(model)
    result = agent.run_sync("Create a test file")
# Workspace automatically cleaned up

Logging

The package uses Python's standard logging module. To enable debug logging in your application:

import logging

# Enable debug logging for pydantic-ai-claude-code
logging.basicConfig(level=logging.DEBUG)
logging.getLogger('pydantic_ai_claude_code').setLevel(logging.DEBUG)

# Or just for specific components
logging.getLogger('pydantic_ai_claude_code.model').setLevel(logging.DEBUG)
logging.getLogger('pydantic_ai_claude_code.utils').setLevel(logging.INFO)

This will log:

• Model initialization and configuration
• CLI command execution and responses
• Message formatting and conversion
• Tool call parsing and execution
• Structured output handling
• Streaming events and completion

Available Models

• claude-code:sonnet - Claude 3.5 Sonnet (default, recommended)
• claude-code:opus - Claude 3 Opus (most capable)
• claude-code:haiku - Claude 3.5 Haiku (fastest)

Or use full model names like claude-code:claude-sonnet-4-5-20250929

Integration with Existing Projects

Replace your current LLM calls with Claude Code:

Before:

agent = Agent('openai:gpt-4o')
# or
agent = Agent('anthropic:claude-3-5-sonnet-latest')

After:

import pydantic_ai_claude_code  # Add this import

agent = Agent('claude-code:sonnet')  # Change this line

Everything else stays the same! All your tools, structured outputs, dependencies, and streaming code works identically.

Key Differences from Cloud Providers

Aspect	Cloud Providers	Claude Code Local
Execution	Remote API calls	Local on your machine
Cost	Per-token pricing	Uses Claude desktop subscription
Data Privacy	Data sent to cloud	Data stays local
Speed	Network latency	Local execution
API Key	Required	Not needed (uses local auth)

Examples

See the examples/ directory for more demonstrations:

• basic_example.py - Simple queries and usage tracking
• structured_example.py - Structured output with Pydantic models
• async_example.py - Async/await usage patterns
• advanced_example.py - Custom provider configurations
• tools_and_streaming.py - Custom tools and streaming responses

License

MIT License