langchain-prod-checklist（LangChain prod checklist 命令行）

LangChain Production Checklist

Contents

• Overview
• Prerequisites
• Instructions
• Output
• Error Handling
• Examples
• Resources

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

Instructions

1. Configuration & Secrets

1. All API keys in secrets manager (not env vars in code)
2. Environment-specific configurations separated
3. Configuration validation on startup with pydantic_settings.BaseSettings

2. Error Handling & Resilience

1. Retry logic with exponential backoff
2. Fallback models: primary.with_fallbacks([fallback])
3. Circuit breaker for cascading failures

3. Observability

1. Structured logging, Prometheus metrics, LangSmith tracing
2. Alerting rules for error rate and latency

4. Performance

1. Redis caching for repeated queries
2. Connection pooling, timeout limits, batch processing

5. Security

1. Input validation (length limits, sanitization)
2. Rate limiting per user/IP, audit logging

6. Testing

1. Unit tests for all chains, integration tests with mock LLMs
2. Load tests and chaos engineering

7. Deployment

1. Health check endpoint, graceful shutdown, rolling deployment
2. Rollback procedure documented

8. Cost Management

1. Token counting, usage alerts, budget limits

See detailed implementation for code examples and deployment validation script.

Output

• Validated production configuration
• Health check endpoint
• Pre-deployment validation script
• Cost estimation utilities

Error Handling

Examples

Basic usage: Apply langchain prod checklist to a standard project setup with default configuration options.

Advanced scenario: Customize langchain prod checklist for production environments with multiple constraints and team-specific requirements.

Resources

• LangChain Production Guide
• LangSmith
• Twelve-Factor App

Next Steps

After launch, use langchain-observability for monitoring.