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A comprehensive runbook detailing standard operating procedures (SOPs) for managing production incidents involving LangChain and large language models (LLMs). It provides step-by-step guides for detecting, diagnosing, and mitigating common issues such as provider outages, high error rates, latency spikes, and excessive cost overruns. This skill is essential for DevOps and MLOps engineers maintaining LLM-powered applications.

This guide details best practices for configuring LangChain applications across development, staging, and production environments. It ensures strict environment isolation by managing separate API keys, model settings, and LangSmith project names. The setup utilizes Zod for robust validation and integrates secure secret management via GitHub Actions, AWS Secrets Manager, or GCP, guaranteeing reliable and secure deployments.

A comprehensive guide to setting up production-grade observability for LangChain applications. It integrates LangSmith tracing for zero-code debugging, custom callbacks for detailed metric collection (latency, token usage, error rate), and Prometheus exporters for robust alerting. This allows developers to monitor LLM pipelines, track performance metrics, and manage operational health in real-time.

This guide provides comprehensive techniques to optimize LangChain applications for production environments. Learn how to measure baseline latency, implement efficient caching strategies (in-memory, Redis, SQLite), utilize batch processing with concurrency control, and maximize perceived speed through streaming. Furthermore, it covers prompt optimization and strategic model selection to ensure high throughput and low costs.

A comprehensive checklist for ensuring LangChain applications are robust, secure, and scalable for production environments. It covers critical areas including configuration management, advanced error handling, observability setup (e.g., LangSmith), performance optimization, security best practices (like prompt injection prevention and PII handling), rigorous testing, and zero-downtime deployment strategies. Use this guide when preparing for a major launch or auditing existing LLM systems.

This comprehensive guide details advanced strategies for building highly resilient applications that interact with external LLM APIs. It covers built-in exponential backoff retries, controlling request throughput via concurrency limits, implementing provider fallbacks, and custom rate limiting using token buckets. Essential knowledge for achieving stable, high-volume API usage in production systems.

This blueprint provides a scalable, production-grade reference architecture for advanced LLM applications using LangChain. It guides the implementation of layered design, provider abstraction (allowing seamless switching between LLMs like OpenAI and Anthropic), dynamic chain registry, robust RAG pipelines, and complex multi-agent orchestration. Ideal for building enterprise-level, maintainable, and highly flexible AI systems.

This comprehensive guide outlines essential, production-grade patterns for building robust LangChain applications. Learn best practices for implementing type-safe structured output using Zod, handling provider fallbacks, processing inputs concurrently with batching, optimizing user experience with streaming, and ensuring reliability through caching and retries.

This guide outlines essential security practices for deploying production-grade LLM applications using LangChain. It covers secure secrets management, defending against prompt injection, implementing sandboxed tool execution, validating model outputs for sensitive data, and setting up comprehensive audit logging, ensuring robust and secure AI deployment.

Track and improve production LLMs with Langfuse observability: trace API calls, manage prompts, score evaluations, integrate with LangChain/LlamaIndex/OpenAI, and keep dashboards on cost, latency, and quality.

Implement comprehensive end-to-end tracing for Large Language Model (LLM) calls, chains, and complex agents using Langfuse. This workflow demonstrates techniques like OpenAI drop-in wrappers, manual span creation for RAG pipelines, tracking streamed responses, and integrating with various LLM providers (OpenAI, Anthropic). Use this to monitor latency, track token usage, and debug complex AI features in production.

This guide details setting up enterprise-grade access control for Langfuse. Learn how to configure role-based permissions (Owner, Admin, Member), implement scoped API keys, integrate SSO (SAML/OIDC), and set up comprehensive audit logging. It is essential for managing multi-team isolation and ensuring compliance in production environments.