Skip to main content

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

Project description

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",
    max_turns=10,
    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

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pydantic_ai_claude_code-0.2.2.tar.gz (16.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pydantic_ai_claude_code-0.2.2-py3-none-any.whl (22.3 kB view details)

Uploaded Python 3

File details

Details for the file pydantic_ai_claude_code-0.2.2.tar.gz.

File metadata

File hashes

Hashes for pydantic_ai_claude_code-0.2.2.tar.gz
Algorithm Hash digest
SHA256 1c105205372caf5dfda768b199f967dc541eb3cfcb5bd13892ebaaa6159ce286
MD5 d3c5d1ca24848e8530fc7150cabf5047
BLAKE2b-256 f39c95cc483789812ebc81f6000ae1978fcfd51f55ae3ef1d33d81af74ac2a1c

See more details on using hashes here.

File details

Details for the file pydantic_ai_claude_code-0.2.2-py3-none-any.whl.

File metadata

File hashes

Hashes for pydantic_ai_claude_code-0.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 61a5df22cf20269228f411a4e11b9ba98a84a473a3f43d58b819a564b694544f
MD5 1e941d6430448994e8718e2df4ffa431
BLAKE2b-256 7b7beab8ad61f7d7ff921611feaabdddac43368b8925f090ab5b62264a657602

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page