--- name: langchain-reference-architecture-d1ec487890e4 description: | Implement LangChain reference architecture patterns for production. Use when designing LangChain systems, implementing scalable patterns, or architecting enterprise LLM applications. Trigger with phrases like "langchain architecture", "langchain design", "langchain scalable", "langchain enterprise", "langchain patterns". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore --- # LangChain Reference Architecture ## Overview Production-ready architectural patterns for building scalable, maintainable LangChain applications. ## Prerequisites - Understanding of LangChain fundamentals - Experience with software architecture - Knowledge of cloud infrastructure ## Architecture Patterns ### Pattern 1: Layered Architecture ``` src/ ├── api/ # API layer (FastAPI/Flask) │ ├── __init__.py │ ├── routes/ │ │ ├── chat.py │ │ └── agents.py │ └── middleware/ │ ├── auth.py │ └── rate_limit.py ├── core/ # Business logic layer │ ├── __init__.py │ ├── chains/ │ │ ├── __init__.py │ │ ├── chat_chain.py │ │ └── rag_chain.py │ ├── agents/ │ │ ├── __init__.py │ │ └── research_agent.py │ └── tools/ │ ├── __init__.py │ └── search.py ├── infrastructure/ # Infrastructure layer │ ├── __init__.py │ ├── llm/ │ │ ├── __init__.py │ │ └── provider.py │ ├── vectorstore/ │ │ └── pinecone.py │ └── cache/ │ └── redis.py ├── config/ # Configuration │ ├── __init__.py │ └── settings.py └── main.py ``` ### Pattern 2: Provider Abstraction ```python # infrastructure/llm/provider.py from abc import ABC, abstractmethod from langchain_core.language_models import BaseChatModel from langchain_openai import ChatOpenAI from langchain_anthropic import ChatAnthropic class LLMProvider(ABC): """Abstract LLM provider.""" @abstractmethod def get_chat_model(self, **kwargs) -> BaseChatModel: pass class OpenAIProvider(LLMProvider): def get_chat_model(self, model: str = "gpt-4o-mini", **kwargs) -> BaseChatModel: return ChatOpenAI(model=model, **kwargs) class AnthropicProvider(LLMProvider): def get_chat_model(self, model: str = "claude-3-5-sonnet-20241022", **kwargs) -> BaseChatModel: return ChatAnthropic(model=model, **kwargs) class LLMFactory: """Factory for creating LLM instances.""" _providers = { "openai": OpenAIProvider(), "anthropic": AnthropicProvider(), } @classmethod def create(cls, provider: str = "openai", **kwargs) -> BaseChatModel: if provider not in cls._providers: raise ValueError(f"Unknown provider: {provider}") return cls._providers[provider].get_chat_model(**kwargs) # Usage llm = LLMFactory.create("openai", model="gpt-4o-mini") ``` ### Pattern 3: Chain Registry ```python # core/chains/__init__.py from typing import Dict, Type from langchain_core.runnables import Runnable class ChainRegistry: """Registry for managing chains.""" _chains: Dict[str, Runnable] = {} @classmethod def register(cls, name: str, chain: Runnable) -> None: cls._chains[name] = chain @classmethod def get(cls, name: str) -> Runnable: if name not in cls._chains: raise ValueError(f"Chain '{name}' not found") return cls._chains[name] @classmethod def list_chains(cls) -> list: return list(cls._chains.keys()) # Register chains at startup from core.chains.chat_chain import create_chat_chain from core.chains.rag_chain import create_rag_chain ChainRegistry.register("chat", create_chat_chain()) ChainRegistry.register("rag", create_rag_chain()) # Usage in API @app.post("/invoke/{chain_name}") async def invoke_chain(chain_name: str, request: InvokeRequest): chain = ChainRegistry.get(chain_name) return await chain.ainvoke(request.input) ``` ### Pattern 4: RAG Architecture ```python # core/chains/rag_chain.py from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_pinecone import PineconeVectorStore def create_rag_chain( llm: BaseChatModel = None, vectorstore: VectorStore = None ) -> Runnable: """Create a RAG chain with retrieval.""" llm = llm or ChatOpenAI(model="gpt-4o-mini") vectorstore = vectorstore or PineconeVectorStore.from_existing_index( index_name="knowledge-base", embedding=OpenAIEmbeddings() ) retriever = vectorstore.as_retriever( search_type="similarity", search_kwargs={"k": 5} ) prompt = ChatPromptTemplate.from_template(""" Answer the question based on the following context: Context: {context} Question: {question} Answer: """) def format_docs(docs): return "\n\n".join(doc.page_content for doc in docs) chain = ( {"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt | llm ) return chain ``` ### Pattern 5: Multi-Agent System ```python # core/agents/orchestrator.py from langchain_core.runnables import RunnableLambda from typing import Dict, Any class AgentOrchestrator: """Orchestrate multiple specialized agents.""" def __init__(self): self.agents = {} self.router = None def register_agent(self, name: str, agent: Runnable) -> None: self.agents[name] = agent def set_router(self, router: Runnable) -> None: """Set routing logic for agent selection.""" self.router = router async def route_and_execute(self, input_data: Dict[str, Any]) -> Any: """Route input to appropriate agent and execute.""" # Determine which agent to use agent_name = await self.router.ainvoke(input_data) if agent_name not in self.agents: raise ValueError(f"Agent '{agent_name}' not found") # Execute with selected agent agent = self.agents[agent_name] return await agent.ainvoke(input_data) # Setup orchestrator = AgentOrchestrator() orchestrator.register_agent("research", research_agent) orchestrator.register_agent("coding", coding_agent) orchestrator.register_agent("general", general_agent) # Router uses LLM to classify request router_prompt = ChatPromptTemplate.from_template(""" Classify this request into one of: research, coding, general Request: {input} Classification: """) orchestrator.set_router(router_prompt | llm | StrOutputParser()) ``` ### Pattern 6: Configuration-Driven Design ```python # config/settings.py from pydantic_settings import BaseSettings from pydantic import Field class LLMSettings(BaseSettings): provider: str = "openai" model: str = "gpt-4o-mini" temperature: float = 0.7 max_tokens: int = 4096 max_retries: int = 3 class VectorStoreSettings(BaseSettings): provider: str = "pinecone" index_name: str = "default" embedding_model: str = "text-embedding-3-small" class Settings(BaseSettings): llm: LLMSettings = Field(default_factory=LLMSettings) vectorstore: VectorStoreSettings = Field(default_factory=VectorStoreSettings) redis_url: str = "redis://localhost:6379" log_level: str = "INFO" class Config: env_file = ".env" env_nested_delimiter = "__" settings = Settings() # Usage llm = LLMFactory.create( settings.llm.provider, model=settings.llm.model, temperature=settings.llm.temperature ) ``` ## Architecture Diagram ``` ┌─────────────────┐ │ API Gateway │ └────────┬────────┘ │ ┌──────────────┼──────────────┐ │ │ │ ┌─────────▼─────┐ ┌──────▼──────┐ ┌─────▼─────────┐ │ Chat Chain │ │ RAG Chain │ │ Agent System │ └───────┬───────┘ └──────┬──────┘ └───────┬───────┘ │ │ │ └────────────────┼────────────────┘ │ ┌──────────────┼──────────────┐ │ │ │ ┌─────────▼─────┐ ┌──────▼──────┐ ┌─────▼─────────┐ │ LLM Provider │ │ VectorStore │ │ Cache │ └───────────────┘ └─────────────┘ └───────────────┘ ``` ## Output - Layered architecture with clear separation - Provider abstraction for LLM flexibility - Chain registry for runtime management - Multi-agent orchestration pattern ## Resources - [LangChain Architecture Guide](https://python.langchain.com/docs/concepts/architecture/) - [Clean Architecture](https://blog.cleancoder.com/uncle-bob/2012/08/13/the-clean-architecture.html) - [Domain-Driven Design](https://martinfowler.com/bliki/DomainDrivenDesign.html) ## Next Steps Use `langchain-multi-env-setup` for environment management. --- Source attribution: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/langchain-pack/skills/langchain-reference-architecture/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License (with OpenAI/Anthropic Rider) Copyright (c) 2026 Jeffrey Emanuel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: ADDITIONAL RIDER / RESTRICTION (OpenAI / Anthropic): This rider is part of the "conditions" of this License. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: Dicklesworthstone Original project: https://github.com/Dicklesworthstone/pi_agent_rust/blob/f28fc46ee0db8e710d95b8b55592cd38d60f2b26/tests/ext_conformance/artifacts/plugins-community/plugins/saas-packs/langchain-pack/skills/langchain-reference-architecture/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management Roles: Backend Developer, Data Scientist, ML / AI Engineer, Product Manager, Technical Product Manager Specialties: API, Backend Tools: Langchain Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: a4340e2e89dd20d891b14f16d93efb6c1a7ba020294f4ee96081fcde6029e80b