langchain

To build a LangChain app with agents and RAG, compose prompt templates into chains, implement tool-using agents for multi-step execution, and feed retrieved document embeddings into the generation workflow.

Maintain conversational memory in LangChain by adding buffer, summary, or vector-store backed memory to your chains. This ensures the assistant retains relevant historical context across multi-turn interactions.

Implement retrieval-augmented generation pipelines in LangChain by using embeddings and retrievers to fetch relevant documents, then passing that retrieved context into the LLM generation step for grounded outputs.

Yes, you can create tool-using agents in LangChain that call external services and tools to complete multi-step tasks. This allows the LLM to autonomously select and execute tools based on the task requirements.

For reliable LangChain chains, use few-shot and chat prompt structures alongside structured output parsing patterns. These prompt engineering techniques ensure consistent and repeatable results across your LLM applications.

Yes, LangChain integrates with LangGraph for multi-step agent execution. The framework's best-practice guidance is aligned to LangGraph documentation, ensuring robust orchestration of complex agent workflows.