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langchain-workflow-builder

Builds LangChain LLM applications with chains, agents, memory, tools, and RAG pipelines.

2|Updated Jun 5, 2026
Search Tags:#typescript#OpenAI#langchain#llm#rag#agents#chains
One-click install
npx skills add https://github.com/sathishssj3/NexVR-Engine --skill langchain-workflow-builder-sathishssj3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-workflow-builder
Source: https://github.com/sathishssj3/NexVR-Engine/tree/main/.agents/skills/langchain-workflow-builder
Command: npx skills add https://github.com/sathishssj3/NexVR-Engine --skill langchain-workflow-builder-sathishssj3

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, @langchain/openai, @langchain/community, @langchain/pinecone, @pinecone-database/pinecone, zod, bottleneck.

What problem does it solve? Building LLM-powered applications from scratch requires wiring together prompts, models, memory, tools, and retrieval logic, which is error-prone and time-consuming without a structured approach. ## Core Features & Use Cases - Chain Composition: Build simple, sequential, and branching chains using LangChain Expression Language (LCEL) with prompt templates and output parsers. - Agents & Tools: Create ReAct or OpenAI Functions agents with custom structured tools validated by Zod schemas, plus conversation memory (buffer, window, summary, Redis-persisted). - RAG Pipelines: Load documents (PDF, text, JSON), split them into chunks, embed them into Pinecone vector stores, and build retrieval or conversational RAG chains. - Use Case: A developer needs a customer-support chatbot that answers questions from company documentation. Use this Skill to set up a conversational RAG chain with history-aware retrieval, Redis-backed memory, and streaming responses. ## Quick Start Ask the AI to build a LangChain RAG pipeline that loads PDF documents, stores embeddings in Pinecone, and answers questions with conversation memory.

Frequently Asked Questions about langchain-workflow-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a RAG pipeline with LangChain?▼

Load documents with DirectoryLoader, split them using RecursiveCharacterTextSplitter, embed chunks into a Pinecone vector store with OpenAIEmbeddings, then combine a retriever with a stuff-documents chain via createRetrievalChain to answer questions from context.

How to create a LangChain agent with custom tools?▼

Define tools with DynamicStructuredTool and Zod schemas for input validation, then pass them to createReactAgent or createOpenAIFunctionsAgent. Wrap the agent in an AgentExecutor with maxIterations to control reasoning loops.

What memory type should I use for LangChain conversations?▼

BufferMemory keeps full history, BufferWindowMemory retains only the last k exchanges, and ConversationSummaryMemory compresses history to save tokens. For multi-session persistence, use BufferMemory backed by RedisChatMessageHistory.

Does LangChain support streaming responses?▼

Yes, chains built with LCEL support the stream method, which yields output chunks asynchronously. You can also pass callbacks with handleLLMNewToken to process tokens as they arrive from the model.

Why does my LangChain agent loop without finishing?▼

Agents loop when tool descriptions are unclear or the model cannot decide on a final answer. Set maxIterations on the AgentExecutor, write precise tool descriptions, and ensure tools return concise string outputs.

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