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Building production-ready AI agents using LangChain requires robust error handling, provider redundancy, and seamless tool execution. In this guide, we will explore how to set up an advanced LangChain agent with fallback models using ChatGoogleGenerativeAI , Sarvam AI, and LiteLLM to ensure high availability and zero downtime. Why Implement Model Fallbacks in LangChain Agents? When deploying AI automation workflows in production, relying on a single Large Language Model (LLM) introduces single-point-of-failure risks—such as rate limits (e.g., token per minute caps), transient network errors, or sudden API outages. By implementing middleware-based fallbacks, your application can automatically switch to secondary providers like Llama 3 or Sarvam-105B without interrupting user experience. Prerequisites and Environment Setup Before writing your agent code, make sure you have the necessary API keys configured as environment variables. This ensures your code remai...