langchain-agents

Build LLM applications with agents using LangChain by creating ReAct agents that reason and call external tools to automate complex tasks. This framework supports advanced patterns like Plan-and-Execute for robust task automation.

RAG in LangChain is a retrieval-augmented generation pipeline that answers questions from custom documents. It connects your document knowledge base to the LLM to provide accurate, context-aware responses for user queries.

Switch between different LLM providers like OpenAI, Anthropic, and Google in LangChain using its LLM provider agnosticism feature. This allows you to easily change the underlying model without rewriting your entire application logic.

Manage conversation memory for a chatbot in LangChain using its robust conversational memory management capabilities. This ensures your LLM-powered application maintains context across multiple turns for coherent interactions.

Automate complex tasks using the ReAct pattern in LangChain by developing agents that can reason through steps and use tool-calling capabilities. This approach allows the LLM to dynamically decide which tools to use to solve problems.

LangChain supports building a customer support chatbot that accesses a knowledge base of product documentation. It uses RAG pipelines to retrieve accurate information and answer user queries effectively without hallucinating.