LangChain Advanced Patterns: Building Production-Grade AI Systems

A developer outlines advanced LangChain patterns for building production-grade AI systems, including memory management, routing, error handling, caching, and monitoring. The post provides code examples for scaling agents with conversation buffers, sequential chains, retry logic, and vector stores.

You've built a simple agent. Now scale it. Production LangChain systems require: python from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory agent = initialize agent tools, llm, memory=memory, agent type="conversational" python from langchain.memory import ConversationSummaryMemory memory = ConversationSummaryMemory llm=OpenAI , buffer="Current conversation summarized" python from langchain.chains import SequentialChain chain = SequentialChain chains= chain1, chain2, chain3 , verbose=True router template = """Given the input, route to: analysis, coding, or research Input: {input} Route:""" router = llm chain.run router template if "coding" in router: result = coding agent.run input python from tenacity import retry, stop after attempt @retry stop=stop after attempt 3 def safe agent run query : return agent.run query try: result = safe agent run query except Exception as e: logger.error f"Agent failed: {e}" result = fallback response python from langchain.cache import RedisCache import redis redis client = redis.Redis.from url "redis://localhost" langchain.llm cache = RedisCache redis client=redis client python results = agent.run q for q in queries Better: Use async import asyncio results = await asyncio.gather async agent q for q in queries python import logging from datetime import datetime class AgentLogger: def log run self, query, response, duration : logging.info f"Query: {query}" logging.info f"Response: {response}" logging.info f"Duration: {duration}s" Track metrics self.track metric "agent latency", duration self.track metric "token usage", count tokens response python from langchain.vectorstores import Pinecone from langchain.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings vector store = Pinecone.from documents docs, embeddings retriever = vector store.as retriever agent with retrieval = RetrievalQA.from chain type llm=llm, retriever=retriever python AWS Lambda def lambda handler event, context : query = event 'query' result = agent.run query return {'statusCode': 200, 'body': result} python def test agent accuracy : test cases = "query1", "expected output1" , "query2", "expected output2" for query, expected in test cases: result = agent.run query assert verify correctness result, expected ✅ Error handling for all tool calls ✅ Logging for debugging ✅ Monitoring & alerting ✅ Rate limiting ✅ Input validation ✅ Output sanitization ✅ Cost tracking ✅ Performance metrics ✅ Rollback procedures ✅ Security hardening Issue 1 : Token limits exceeded → Solution: Summarize long conversations Issue 2 : Tool calls fail silently → Solution: Add explicit error messages Issue 3 : Costs spiral out of control → Solution: Implement token budgets Issue 4 : Model drift over time → Solution: Regular monitoring & retraining LangChain in enterprise = structured, monitored, optimized. You now have the patterns to build production systems. What LangChain patterns are you using?