LangChain Reference Architecture
Implements a battle-tested, 5-layer production architecture for LangChain and LangGraph services in Python.
This skill provides a standardized, modular structure for AI applications, isolating HTTP routes, business logic, vendor integrations, configuration, and domain models. It addresses critical production issues like multi-tenant data isolation, persistent conversation history, and centralized LLM configuration. By enforcing a strict downward dependency flow, it ensures that LangChain services remain maintainable, testable, and secure as they scale from prototype to production-grade deployments.
주요 기능
01Centralized LLM Factory with version-safe defaults and provider-agnostic interfaces
02Environment-specific checkpointer selection for robust conversation persistence
03Per-request tenant scoping for retrievers and tools to prevent data leakage
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05Standardized 5-layer directory layout (App, Services, Adapters, Config, Domain)
06Pydantic-validated configuration with SecretStr for secure environment management
사용 사례
01Bootstrapping a new production-grade LangChain or LangGraph service with FastAPI
02Refactoring a disorganized AI codebase into a maintainable, layered architecture
03Implementing strict multi-tenancy and data isolation in LLM-powered applications