Logging Patterns - LangChain in Production

Learn advanced logging strategies for LangChain applications, including structured logs, log aggregation, and analysis

📝 Logging Patterns Overview

Logging is essential for debugging, monitoring, and compliance. This guide covers structured logging, aggregation, analysis, and best practices for production systems.

---

🏛️ Structured Logging

• Use JSON or key-value formats for logs
• Include request IDs, timestamps, and context
• Log errors, warnings, and performance data

---

🧑‍💻 Log Aggregation & Analysis

• Integrate with log aggregators (ELK, Azure Monitor, AWS CloudWatch)
• Analyze logs for trends, anomalies, and incidents
• Automate log rotation and retention

import logging
import json

logger = logging.getLogger("langchain")
logger.setLevel(logging.INFO)

# Structured log example
def log_event(event, context):
    log_entry = {
        "event": event,
        "context": context
    }
    logger.info(json.dumps(log_entry))

---

🧩 Example: FastAPI Logging Middleware

from fastapi import FastAPI, Request
import logging
import json

app = FastAPI()
logger = logging.getLogger("langchain")

@app.middleware("http")
async def logging_middleware(request: Request, call_next):
    logger.info(json.dumps({"path": str(request.url), "method": request.method}))
    response = await call_next(request)
    return response

---

🔗 Next Steps

• Monitoring Patterns
• Callback Patterns

---

Key Logging Takeaways:

• Use structured logs for clarity
• Aggregate and analyze logs for insights
• Automate log rotation and retention
• Continuously improve logging coverage