LangChain Reference Architecture

Contents

• Overview

• Prerequisites

• Instructions

• Output

• Error Handling

• Examples

• Resources

Overview

Production-ready architectural patterns for building scalable, maintainable LangChain applications including layered architecture, provider abstraction, chain registry, RAG, and multi-agent orchestration.

Prerequisites

• Understanding of LangChain fundamentals

• Experience with software architecture

• Knowledge of cloud infrastructure

Instructions

Step 1: Adopt Layered Architecture

Organize code into API, core (business logic), infrastructure, and config layers. Each layer has clear responsibilities and dependencies flow inward.

Step 2: Implement Provider Abstraction

Use an LLMFactory with abstract LLMProvider classes to decouple from specific LLM vendors (OpenAI, Anthropic, etc.).

Step 3: Set Up Chain Registry

Create a ChainRegistry to manage named chains at runtime, enabling dynamic chain selection via API endpoints.

Step 4: Build RAG Architecture

Combine a retriever (vector store) with an LLM chain using RunnablePassthrough for context injection.

Step 5: Orchestrate Multi-Agent Systems

Use an AgentOrchestrator with LLM-based routing to dispatch requests to specialized agents.

Step 6: Use Configuration-Driven Design

Leverage pydantic_settings.BaseSettings for validated, environment-driven configuration.

See detailed implementation for complete code patterns and architecture diagrams.

Output

• Layered architecture with clear separation

• Provider abstraction for LLM flexibility

• Chain registry for runtime management

• Multi-agent orchestration pattern

Error Handling

Issue Cause Solution

Circular dependencies Wrong layering Separate concerns strictly by layer

Provider not found Missing registration Check LLMFactory provider map

Chain not found Unregistered chain Register chains at startup

Examples

Basic usage: Apply langchain reference architecture to a standard project setup with default configuration options.

Advanced scenario: Customize langchain reference architecture for production environments with multiple constraints and team-specific requirements.

Resources

• LangChain Architecture Guide

• Clean Architecture

• Domain-Driven Design

Next Steps

Use langchain-multi-env-setup for environment management.