LangChain Advanced Patterns: Building Production-Grade AI Systems

Beyond Basic LangChain You've built a simple agent. Now scale it. Production LangChain systems require: Memory management Error handling Performance optimization Monitoring & observability Memory Patterns Conversation Memory from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() agent = initialize_agent( tools, llm, memory=memory, agent_type="conversational" ) Enter fullscreen mode Exit fullscreen mode Summary Memory (for long conversations) from langchain.memory import ConversationSummaryMemory memory = ConversationSummaryMemory( llm=OpenAI(), buffer="Current conversation summarized" ) Enter fullscreen mode Exit fullscreen mode Tool Chains & Sequences Sequential Chain from langchain.chains import SequentialChain chain = SequentialChain( chains=[chain1, chain2, chain3], verbose=True ) Enter fullscreen mode Exit fullscreen mode Conditional Routing 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) Enter fullscreen mode Exit fullscreen mode Error Handling & Retry Logic 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() Enter fullscreen mode Exit fullscreen mode Performance Optimization Caching from langchain.cache import RedisCache import redis redis_client = redis.Redis.from_url("redis://localhost") langchain.llm_cache = RedisCache(redis_client=redis_client) Enter fullscreen mode Exit fullscreen mode Batch Processing results = [agent.run(q) for q in queries] # Better: Use async import asyncio results = await asyncio.gather(*[async_agent(q) for q in queries]) Enter fullscreen mode Exit fullscreen mode Monitoring & Observability 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)) Enter fullscreen mode Exit fullscreen mode Integration with Vector Stores 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 ) Enter fullscreen mode Exit fullscreen mode Deployment Strategies Local + Cloud Hybrid Local cache for frequently used data Cloud for complex reasoning Best of both worlds Serverless Deployment # AWS Lambda def lambda_handler(event, context): query = event['query'] result = agent.run(query) return {'statusCode': 200, 'body': result} Enter fullscreen mode Exit fullscreen mode Testing Your Agent 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) Enter fullscreen mode Exit fullscreen mode Production Checklist ✅ 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 Common Production Issues 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 The Enterprise Path LangChain in enterprise = structured, monitored, optimized. You now have the patterns to build production systems. What LangChain patterns are you using?