micsapp LangChain Reference Architecture
Contents
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
Next Steps
Use langchain-multi-env-setup for environment management.