Build tool-calling agents and RAG pipelines with LangChain.
npx skills add https://github.com/qcmuu/AI-Research-Skills --skill langchain-qcmuu
Please help me install this Agent Skill. Skill: langchain Source: https://github.com/qcmuu/AI-Research-Skills/tree/main/14-agents/langchain Command: npx skills add https://github.com/qcmuu/AI-Research-Skills --skill langchain-qcmuu
LangChain helps you design and ship LLM-powered applications that can reliably call tools and answer questions over your own documents, instead of producing responses without context.
Ask the AI to build a tool-calling LangChain agent that performs retrieval-augmented question answering over a provided document set.
High-intent search queries and answers about installing and using this skill.
To build a RAG pipeline, you load documents, chunk them with splitters, embed the chunks, store them in vector stores, and use retriever chains to generate answers with source context. This grounds responses in your specific data.
Yes, you can create ReAct-style and tool-calling agents that plan, invoke tools, and synthesize results. This enables agents to execute structured workflows and reliably answer complex user queries.
Yes, LangChain provides provider-agnostic LLM integration. You can swap model providers like OpenAI, Anthropic, or Google while keeping your application logic stable across different model implementations.
You can debug and trace LLM application execution using LangSmith tracing. This provides observability for your application, helping you monitor streaming execution, memory support, and improve overall reliability.
The best way to maintain context in multi-turn conversations is by using built-in memory support. This allows your LLM application to retain conversation history and stream outputs for a better user experience.
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