octogen-sdk-langgraph 0.1.2

Octogen Python SDK built on langgraph

Octogen Python SDK built on LangGraph

A Python SDK for building LLM-powered shop agents using LangGraph and LangChain, designed to work with the Octogen platform.

Features

• Build conversational shop agents with LangGraph's state management
• Structured output parsing with Pydantic models
• Built-in recommendation expansion functionality
• Server deployment capabilities with FastAPI
• Integration with Octogen MCP tools for product discovery
• Streamlined agent creation through factory patterns

Installation

pip install octogen-sdk-langgraph

Requirements

• Python ≥ 3.12
• Dependencies:
  • langchain ≥ 0.3.25
  • langgraph ≥ 0.4.3
  • pydantic ≥ 2.11.4
  • octogen-api ≥ 0.1.0a4
  • structlog ≥ 25.3.0

Quick Start

from langchain_openai import ChatOpenAI
from octogen.shop_agent import ShopAgent, create_agent
from your_models import ResponseClass, HydratedResponseClass

# Define your recommendation expansion function
def expand_recommendations(response, messages):
    # Process and expand recommendations
    return json.dumps(expanded_response)

# Create a shop agent
async with create_agent(
    model=ChatOpenAI(model="gpt-4"),
    agent_name="MyShopAgent",
    response_class=ResponseClass,
    hydrated_response_class=HydratedResponseClass,
    rec_expansion_fn=expand_recommendations,
    tool_names=["agent_search_products", "enrich_product_image"],
    hub_prompt_id="your/hub/prompt_id",
) as agent:
    # Use the agent
    result = await agent.run("I'm looking for a new jacket")

Usage Examples

See the examples/ directory for complete implementations:

• examples/stylist/ - A personal shopping assistant
• examples/discovery/ - Product discovery agent
• examples/comparison/ - Product comparison tool

License

This project is licensed under the terms included in the LICENSE file.