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"
)

Summary Memory (for long conversations)

from langchain.memory import ConversationSummaryMemory

memory = ConversationSummaryMemory(
    llm=OpenAI(),
    buffer="Current conversation summarized"
)

Tool Chains & Sequences

Sequential Chain

from langchain.chains import SequentialChain

chain = SequentialChain(
    chains=[chain1, chain2, chain3],
    verbose=True
)

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)

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()

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)

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])

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))

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
)

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}

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)

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.

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What LangChain patterns are you using?