langchain-fundamentals

Define LangChain tools using the `@tool` decorator in Python or the `tool()` function in TypeScript. These methods allow you to wrap custom functions, enabling your agent to execute specific automated tasks like web searches or calculations.

State persistence for LangChain agents works by utilizing checkpointers and thread IDs. This mechanism ensures conversation memory is maintained across interactions, allowing the agent to remember previous context and continue multi-turn dialogues accurately.

The best way to get typed, validated responses from an LLM agent is to use structured output generation. This feature forces the agent to return responses formatted to specific typed schemas, ensuring data consistency for downstream application logic.

Yes, LangChain middleware supports custom error handling for agent workflows. Middleware allows you to implement advanced control flows, intercepting runtime exceptions and applying alternative logic or fallback mechanisms during tool execution.

Yes, you can integrate web search and calculation tools into a single LangChain agent. By defining multiple custom tools and attaching them to the agent, it can orchestrate complex task automation, search the web, and perform calculations simultaneously.