# Laurigates Claude Plugins Langchain Development > LangChain Development - Skill: `tomevault-io/laurigates-claude-plugins-langchain-development` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/laurigates-claude-plugins-langchain-development` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/laurigates-claude-plugins-langchain-development/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/laurigates-claude-plugins-langchain-development --- # LangChain Development ## When to Use | Scenario | Use this skill | Alternative | |----------|---------------|-------------| | Working with chat models (OpenAI, Anthropic, etc.) | Yes | - | | Building LCEL chains (prompt | model | parser) | Yes | - | | Implementing RAG with document loaders and vector stores | Yes | - | | Defining and binding custom tools | Yes | - | | Using prompt templates and few-shot prompting | Yes | - | | Building stateful graph-based agent workflows | No | `langgraph-agents` for LangGraph state machines | | Need hierarchical agent orchestration with planning | No | `deep-agents` for Deep Agents library | | Scaffolding a brand-new LangChain project | No | `/langchain:init` to generate project boilerplate | | Simple one-off API call without LangChain framework | No | Direct SDK usage (`@anthropic-ai/sdk`, `openai`) | ## Core Expertise LangChain JS/TS is a framework for building LLM applications: - Unified interface across model providers (OpenAI, Anthropic, Google, etc.) - Composable chains and agents - Built-in tool integration - RAG (Retrieval-Augmented Generation) support - LangSmith observability integration ## Installation ### Package Manager Setup ```bash # Core package npm install langchain # or pnpm add langchain # or bun add langchain # Model provider packages (install what you need) npm install @langchain/openai npm install @langchain/anthropic npm install @langchain/google-genai # Common integrations npm install @langchain/community # Community integrations npm install @langchain/textsplitters # Document splitting ``` ### TypeScript Configuration ```json { "compilerOptions": { "target": "ES2020", "module": "NodeNext", "moduleResolution": "NodeNext", "esModuleInterop": true, "strict": true } } ``` ## Chat Models ### Basic Usage ```typescript import { ChatOpenAI } from "@langchain/openai"; import { ChatAnthropic } from "@langchain/anthropic"; import { HumanMessage, SystemMessage } from "@langchain/core/messages"; // OpenAI const openai = new ChatOpenAI({ model: "gpt-4o", temperature: 0, }); // Anthropic const anthropic = new ChatAnthropic({ model: "claude-haiku", temperature: 0, }); // Invoke with messages const response = await openai.invoke([ new SystemMessage("You are a helpful assistant."), new HumanMessage("Hello!"), ]); ``` ### Streaming ```typescript const stream = await openai.stream([new HumanMessage("Tell me a story")]); for await (const chunk of stream) { process.stdout.write(chunk.content as string); } ``` ### Structured Output ```typescript import { z } from "zod"; const schema = z.object({ name: z.string().describe("The name"), age: z.number().describe("The age"), }); const structuredLlm = openai.withStructuredOutput(schema); const result = await structuredLlm.invoke("John is 30 years old"); // { name: "John", age: 30 } ``` ## Prompt Templates ### Basic Templates ```typescript import { ChatPromptTemplate } from "@langchain/core/prompts"; const prompt = ChatPromptTemplate.fromMessages([ ["system", "You are a {role}."], ["human", "{input}"], ]); const formatted = await prompt.invoke({ role: "helpful assistant", input: "Hello!", }); ``` ### Few-Shot Prompts ```typescript import { FewShotChatMessagePromptTemplate } from "@langchain/core/prompts"; const examples = [ { input: "2+2", output: "4" }, { input: "3+3", output: "6" }, ]; const fewShotPrompt = new FewShotChatMessagePromptTemplate({ examplePrompt: ChatPromptTemplate.fromMessages([ ["human", "{input}"], ["ai", "{output}"], ]), examples, inputVariables: ["input"], }); ``` ## Chains (LCEL) ### Basic Chain ```typescript import { ChatOpenAI } from "@langchain/openai"; import { ChatPromptTemplate } from "@langchain/core/prompts"; import { StringOutputParser } from "@langchain/core/output_parsers"; const prompt = ChatPromptTemplate.fromTemplate("Tell me a joke about {topic}"); const model = new ChatOpenAI(); const parser = new StringOutputParser(); // Chain with pipe operator const chain = prompt.pipe(model).pipe(parser); const result = await chain.invoke({ topic: "programming" }); ``` ### Parallel Chains ```typescript import { RunnableParallel } from "@langchain/core/runnables"; const parallel = RunnableParallel.from({ joke: jokeChain, poem: poemChain, }); const results = await parallel.invoke({ topic: "cats" }); // { joke: "...", poem: "..." } ``` ### Branching ```typescript import { RunnableBranch } from "@langchain/core/runnables"; const branch = RunnableBranch.from([ [(x) => x.type === "math", mathChain], [(x) => x.type === "code", codeChain], defaultChain, // Fallback ]); ``` ## Tools ### Defining Tools ```typescript import { tool } from "@langchain/core/tools"; import { z } from "zod"; const calculatorTool = tool( async ({ a, b, operation }) => { switch (operation) { case "add": return String(a + b); case "subtract": return String(a - b); case "multiply": return String(a * b); case "divide": return String(a / b); } }, { name: "calculator", description: "Performs basic arithmetic", schema: z.object({ a: z.number(), b: z.number(), operation: z.enum(["add", "subtract", "multiply", "divide"]), }), }, ); ``` ### Tool Binding ```typescript const modelWithTools = model.bindTools([calculatorTool]); const response = await modelWithTools.invoke("What is 25 * 4?"); // Check for tool calls if (response.tool_calls?.length) { const toolCall = response.tool_calls[0]; const result = await calculatorTool.invoke(toolCall.args); } ``` ## RAG (Retrieval-Augmented Generation) ### Document Loading ```typescript import { TextLoader } from "langchain/document_loaders/fs/text"; import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf"; import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters"; // Load documents const loader = new TextLoader("./data/document.txt"); const docs = await loader.load(); // Split into chunks const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 200, }); const splitDocs = await splitter.splitDocuments(docs); ``` ### Vector Store ```typescript import { MemoryVectorStore } from "langchain/vectorstores/memory"; import { OpenAIEmbeddings } from "@langchain/openai"; const embeddings = new OpenAIEmbeddings(); const vectorStore = await MemoryVectorStore.fromDocuments( splitDocs, embeddings, ); // Search const results = await vectorStore.similaritySearch("query", 4); ``` ### RAG Chain ```typescript import { createRetrievalChain } from "langchain/chains/retrieval"; import { createStuffDocumentsChain } from "langchain/chains/combine_documents"; const retriever = vectorStore.asRetriever({ k: 4 }); const combineDocsChain = await createStuffDocumentsChain({ llm: model, prompt: ChatPromptTemplate.fromTemplate(` Answer based on this context: {context} Question: {input} `), }); const ragChain = await createRetrievalChain({ retriever, combineDocsChain, }); const response = await ragChain.invoke({ input: "What is the document about?", }); ``` ## Agents (ReAct) ### Basic Agent ```typescript import { createReactAgent } from "@langchain/langgraph/prebuilt"; const agent = createReactAgent({ llm: model, tools: [calculatorTool, searchTool], }); const result = await agent.invoke({ messages: [{ role: "user", content: "Calculate 25 * 4" }], }); ``` ## Agentic Optimizations | Context | Command/Pattern | | --------------- | -------------------------------------- | | Quick test | `npx tsx --test src/**/*.test.ts` | | Type check | `npx tsc --noEmit` | | Debug traces | Set `LANGCHAIN_TRACING_V2=true` | | Reduce tokens | Use `StringOutputParser` for text-only | | Stream output | Use `.stream()` instead of `.invoke()` | | Batch requests | Use `.batch([inputs])` for parallel | | Cache responses | Use `InMemoryCache` for repeated calls | ## Quick Reference ### Environment Variables | Variable | Description | | ---------------------- | ------------------------ | | `OPENAI_API_KEY` | OpenAI API key | | `ANTHROPIC_API_KEY` | Anthropic API key | | `LANGCHAIN_TRACING_V2` | Enable LangSmith tracing | | `LANGCHAIN_API_KEY` | LangSmith API key | | `LANGCHAIN_PROJECT` | LangSmith project name | ### Common Imports | Import | Package | | -------------------- | -------------------------------- | | `ChatOpenAI` | `@langchain/openai` | | `ChatAnthropic` | `@langchain/anthropic` | | `ChatPromptTemplate` | `@langchain/core/prompts` | | `StringOutputParser` | `@langchain/core/output_parsers` | | `tool` | `@langchain/core/tools` | | `RunnableSequence` | `@langchain/core/runnables` | ### Key Packages | Package | Purpose | | ---------------------- | ---------------------- | | `langchain` | Core framework | | `@langchain/core` | Base abstractions | | `@langchain/openai` | OpenAI integration | | `@langchain/anthropic` | Anthropic integration | | `@langchain/community` | Community integrations | | `@langchain/langgraph` | Graph-based agents | --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/laurigates) — claim your Tome and manage your conversions.