langchainkit 0.1.0

A Python toolkit for working with Large Language Models (LLMs) using LangChain

LangChainKit

A Python toolkit for working with Large Language Models (LLMs) using LangChain. It provides structured output parsing and multi-provider LLM access.

Features

• Multi-provider LLM access: Support for local/self-hosted, cloud API, and commercial LLM providers
• Structured output parsing: Force LLM outputs into Pydantic models with built-in retry logic
• Batch processing: Concurrent batch requests with configurable concurrency
• Built-in observability: Integration with Langfuse for tracking and monitoring
• Lazy initialization: Efficient resource management with lazy loading

Supported LLM Providers

Local/Self-hosted

• Qwen3 variants with vLLM

Cloud API

• DashScope Qwen3-235B

Commercial APIs

• DeepSeek
• OpenAI GPT-4o

Installation

pip install langchainkit

Quick Start

Basic Usage

from langchainkit import LocalLLM, ApiLLM, GeneralLLM

# Initialize different LLM providers
local_llm = LocalLLM(model="qwen3-32b", api_base="http://localhost:8000/v1")
api_llm = ApiLLM(model="qwen3-235b")
gpt_llm = GeneralLLM(model="gpt-4o")

# Use the LLMs
response = local_llm.invoke("Hello, world!")

Structured Output

from langchainkit import prompt_parsing
from pydantic import BaseModel

class Response(BaseModel):
    answer: str
    confidence: float

result = prompt_parsing(
    "What is the capital of France?",
    Response,
    llm=local_llm
)
print(result.answer)  # "Paris"
print(result.confidence)  # 0.95

Configuration

Set up your environment variables:

# For local vLLM instances
export LOCAL_VLLM_API_KEY="your-api-key"

# For cloud APIs
export DASHSCOPE_API_KEY="your-dashscope-key"
export DEEPSEEK_API_KEY="your-deepseek-key"
export OPENAI_API_KEY="your-openai-key"

Development

Installation from Source

git clone https://github.com/AInseven/langchainkit.git
cd langchainkit
pip install -e .

Running Tests

pip install -e ".[dev]"
pytest tests/

Code Quality

# Format code
black langchainkit/

# Sort imports
isort langchainkit/

# Lint code
flake8 langchainkit/

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• LangChain for the core framework
• vLLM for high-throughput LLM inference
• Langfuse for observability and monitoring