langchain-architecture

Build autonomous AI agents with LangGraph by designing multi-step workflows that enable LLMs to execute decision-making and actions. This Skill implements agent creation using LangGraph APIs for complex, autonomous task execution.

Manage conversational state in LangChain applications by implementing robust memory systems for context retention. This Skill provides state management patterns to maintain conversation history across multi-step LLM workflows.

Connect LLMs with external APIs and data sources by integrating external tools into your agent workflows. This Skill handles tool integration to allow autonomous agents to interact with external systems like order management platforms.

Yes, LangChain 0.1+ supports designing RAG pipelines for document question answering. This Skill configures RAG workflows to integrate LLMs with external data sources and retrieve answers from provided documents.

Production deployment patterns for LLM applications involve applying best practices for scalable and maintainable architectures. This Skill implements workflow orchestration and state management to ensure robust production deployment.

Use LangGraph over standard LangChain chains when building complex, multi-step LLM processes requiring advanced state management. This Skill implements workflow orchestration using LangGraph APIs for autonomous agent workflows.