LangChain Production Checklist

LangChain Production Checklist

Overview

Comprehensive checklist for deploying LangChain applications to production with reliability, security, and performance.

Prerequisites

• LangChain application developed and tested
• Infrastructure provisioned
• CI/​CD pipeline configured

Production Checklist

1. Configuration & Secrets

• All API keys in secrets manager (not env vars in code)
• Environment-specific configurations separated
• Fallback values for non-critical settings
• Configuration validation on startup

from pydantic_settings import BaseSettings
from pydantic import Field, SecretStr

class Settings(BaseSettings):
    """Validated configuration."""
    openai_api_key: SecretStr = Field(..., env="OPENAI_API_KEY")
    model_name: str = "gpt-4o-mini"
    max_retries: int = Field(default=3, ge=1, le=10)
    timeout_seconds: int = Field(default=30, ge=5, le=120)

    class Config:
        env_file = ".env"

settings = Settings()  # Validates on import

2. Error Handling & Resilience

• Retry logic with exponential backoff
• Fallback models configured
• Circuit breaker for cascading failures
• Graceful degradation strategy

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic

primary = ChatOpenAI(model="gpt-4o-mini", max_retries=3)
fallback = ChatAnthropic(model="claude-3-5-sonnet-20241022")

robust_llm = primary.with_fallbacks([fallback])

3. Observability

• Structured logging configured
• Metrics collection enabled
• Distributed tracing (LangSmith or OpenTelemetry)
• Alerting rules defined

import os

# LangSmith tracing
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = settings.langsmith_api_key
os.environ["LANGCHAIN_PROJECT"] = "production"

# Prometheus metrics
from prometheus_client import Counter, Histogram

llm_requests = Counter("langchain_llm_requests_total", "Total LLM requests")
llm_latency = Histogram("langchain_llm_latency_seconds", "LLM latency")

4. Performance

• Caching configured for repeated queries
• Connection pooling enabled
• Timeout limits set
• Batch processing for bulk operations

from langchain_core.globals import set_llm_cache
from langchain_community.cache import RedisCache
import redis

# Production caching with Redis
redis_client = redis.Redis.from_url(os.environ["REDIS_URL"])
set_llm_cache(RedisCache(redis_client))

5. Security

• Input validation implemented
• Output sanitization enabled
• Rate limiting per user/​IP
• Audit logging for all LLM calls

from langchain_core.runnables import RunnableLambda

def validate_input(input_data: dict) -> dict:
    """Validate and sanitize input."""
    user_input = input_data.get("input", "")
    if len(user_input) > 10000:
        raise ValueError("Input too long")
    return input_data

secure_chain = RunnableLambda(validate_input) | prompt | llm

6. Testing

• Unit tests for all chains
• Integration tests with mock LLMs
• Load tests completed
• Chaos engineering (failure injection)

# pytest.ini
[pytest]
markers =
    unit: Unit tests
    integration: Integration tests
    load: Load tests

7. Deployment

• Health check endpoint
• Graceful shutdown handling
• Rolling deployment strategy
• Rollback procedure documented

from fastapi import FastAPI
from contextlib import asynccontextmanager

@asynccontextmanager
async def lifespan(app: FastAPI):
    # Startup
    print("Warming up LLM connections...")
    yield
    # Shutdown
    print("Cleaning up...")

app = FastAPI(lifespan=lifespan)

@app.get("/​health")
async def health_check():
    return {"status": "healthy", "model": settings.model_name}

8. Cost Management

• Token counting implemented
• Usage alerts configured
• Cost allocation by tenant/​feature
• Budget limits enforced

import tiktoken

def estimate_cost(text: str, model: str = "gpt-4o-mini") -> float:
    """Estimate API cost for text."""
    encoding = tiktoken.encoding_for_model(model)
    tokens = len(encoding.encode(text))
    # Approximate pricing (check current rates)
    cost_per_1k = {"gpt-4o-mini": 0.00015, "gpt-4o": 0.005}
    return (tokens / 1000) * cost_per_1k.get(model, 0.001)

Deployment Validation Script

#!/​usr/​bin/​env python3
"""Pre-deployment validation script."""

def run_checks():
    checks = []

    # Check 1: API key configured
    try:
        settings = Settings()
        checks.append(("API Key", "PASS"))
    except Exception as e:
        checks.append(("API Key", f"FAIL: {e}"))

    # Check 2: LLM connectivity
    try:
        llm = ChatOpenAI(model="gpt-4o-mini")
        llm.invoke("test")
        checks.append(("LLM Connection", "PASS"))
    except Exception as e:
        checks.append(("LLM Connection", f"FAIL: {e}"))

    # Check 3: Cache connectivity
    try:
        redis_client.ping()
        checks.append(("Cache (Redis)", "PASS"))
    except Exception as e:
        checks.append(("Cache (Redis)", f"FAIL: {e}"))

    for name, status in checks:
        print(f"[{status}] {name}")

    return all("PASS" in status for _, status in checks)

if __name__ == "__main__":
    exit(0 if run_checks() else 1)

Resources

• LangChain Production Guide
• LangSmith
• Twelve-Factor App

Next Steps

After launch, use langchain-observability for monitoring.