langchain-architecture

To build LangChain agents with dynamic tool usage, implement patterns like ReAct, OpenAI Functions, or Structured Chat. These architectures enable autonomous agents to decide which tools to invoke during multi-step workflows.

To preserve context across sessions in LangChain, configure memory options within your chains. Memory components maintain conversational history, allowing LLM applications to reference previous interactions and sustain multi-step workflows.

LangChain handles document processing and retrieval by using loaders to ingest data, vector stores to embed it, and retrievers to fetch relevant context. This integration grounds LLM responses in external documentation.

Yes, production deployments require knowledge of LangChain concepts and patterns. You must apply testing, caching, batching, and performance patterns to ensure your LLM applications are modular, scalable, and observable.

Yes, you can route LLM requests to different chains based on input by implementing a RouterChain. This architecture dynamically directs the workflow to specialized LLMChains or TransformChains based on the query context.

You should use instrumented callbacks in LangChain applications to achieve observability. Callbacks monitor execution steps within agents and chains, which is essential for debugging complex multi-step workflows and ensuring production readiness.