Api Ai Langchain

LangChain.js Patterns

> Quick Guide: Use LangChain.js (v1.x) to build composable LLM applications. Use LCEL (prompt.pipe(model).pipe(parser)) for all chain composition -- never use legacy LLMChain. Use withStructuredOutput(zodSchema) for typed responses. Use createAgent() (LangGraph-backed) for agentic workflows -- AgentExecutor is legacy. All @langchain/* packages must share the same @langchain/core version or you get cryptic type errors at runtime.

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CRITICAL: Before Using This Skill

> All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST use LCEL pipe composition (prompt.pipe(model).pipe(parser)) for all chains -- never use legacy LLMChain, ConversationChain, or SequentialChain)

(You MUST ensure all @langchain/* packages depend on the same version of @langchain/core -- version mismatches cause cryptic runtime errors)

(You MUST use withStructuredOutput(zodSchema) for structured LLM responses -- never manually parse JSON from completion text)

(You MUST use createAgent() from langchain for new agent code -- AgentExecutor and createToolCallingAgent are legacy patterns)

(You MUST never hardcode API keys -- use environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.))

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Auto-detection: LangChain, langchain, @langchain/core, @langchain/openai, @langchain/anthropic, @langchain/google-genai, ChatOpenAI, ChatAnthropic, ChatPromptTemplate, StringOutputParser, RunnableSequence, pipe, withStructuredOutput, createAgent, createToolCallingAgent, AgentExecutor, tool, DynamicStructuredTool, RecursiveCharacterTextSplitter, MemoryVectorStore, OpenAIEmbeddings, LCEL, LangSmith, LANGCHAINTRACINGV2

When to use:

• Building LLM applications that compose prompts, models, and output parsers into chains
• Creating agentic workflows where models decide which tools to call
• Implementing RAG pipelines with document loading, splitting, embedding, and retrieval
• Needing structured output from LLMs with type-safe Zod schema validation
• Streaming LLM responses token-by-token to users
• Switching between LLM providers (OpenAI, Anthropic, Google) with a unified interface
• Tracing and debugging LLM applications with LangSmith

Key patterns covered:

• Chat model initialization and provider switching (ChatOpenAI, ChatAnthropic, ChatGoogleGenerativeAI)
• LCEL chain composition with .pipe() and RunnableSequence
• Prompt templates (ChatPromptTemplate, MessagesPlaceholder)
• Structured output with withStructuredOutput() and Zod schemas
• Tool definition with tool() function and Zod schemas
• Agent creation with createAgent() (LangGraph-backed)
• RAG pipelines: document loaders, text splitters, vector stores, retrievers
• Streaming from chains, models, and agents
• LangSmith tracing setup

When NOT to use:

• You only call one LLM provider and want the thinnest wrapper -- use the provider's SDK directly
• You need React-specific chat UI hooks (useChat, useCompletion) -- use a framework-integrated AI SDK
• You want a simple single-call completion with no chaining -- a direct SDK call is simpler
• You need real-time bidirectional communication -- LangChain does not cover WebSocket/Realtime APIs

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Examples Index

• [Core: Setup, LCEL & Chat Models](examples/core.md) -- Package installation, chat model init, LCEL chains, prompt templates, output parsers
• [Structured Output & Tools](examples/structured-output-tools.md) -- withStructuredOutput, tool definition, binding tools to models
• [Agents](examples/agents.md) -- createAgent, tool-calling agents, chat history, streaming agents
• [RAG Pipelines](examples/rag.md) -- Document loaders, text splitters, vector stores, retrieval chains
• [Streaming](examples/streaming.md) -- Model streaming, chain streaming, agent streaming
• [Quick API Reference](reference.md) -- Package map, import paths, environment variables, model IDs

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Philosophy

LangChain.js provides a composable framework for building LLM-powered applications. Its core abstraction is the Runnable -- any component that takes an input and produces an output. Runnables compose via LCEL (.pipe()) to form chains, and every Runnable supports .invoke(), .stream(), .batch() uniformly.

Core principles:

1. Composability via LCEL -- Chains are built by piping Runnables: prompt.pipe(model).pipe(parser). Each step is independently testable and replaceable. Legacy chain classes (LLMChain, ConversationChain) are deprecated.
2. Provider-agnostic models -- Chat models (ChatOpenAI, ChatAnthropic, ChatGoogleGenerativeAI) share a common interface. Swap providers by changing one import and model name. Use initChatModel() for runtime provider selection.
3. Type-safe structured output -- model.withStructuredOutput(zodSchema) constrains LLM responses to your schema. No manual JSON parsing.
4. Split package architecture -- @langchain/core holds abstractions, provider packages (@langchain/openai, @langchain/anthropic) hold implementations, langchain holds higher-level composables. All must share the same @langchain/core version.
5. Observability built in -- Set LANGCHAIN_TRACING_V2=true and every chain/agent/tool call is traced to LangSmith automatically.

When to use LangChain:

• You need to compose multi-step LLM workflows (prompt -> model -> parser -> next step)
• You want to swap LLM providers without rewriting business logic
• You need agent-style tool calling with automatic routing
• You need RAG with document loading, chunking, embedding, and retrieval
• You want built-in tracing and evaluation via LangSmith

When NOT to use:

• Single-provider, single-call use cases -- the provider SDK is simpler and has less overhead
• You want full control over HTTP requests -- LangChain abstracts the transport layer
• Extremely latency-sensitive applications where the abstraction overhead matters

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Core Patterns

Pattern 1: Chat Model Initialization

Initialize chat models from any provider. They all share the same interface.

import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({
  model: "gpt-4o",
  temperature: 0,
});

const response = await model.invoke("Explain TypeScript generics.");
console.log(response.text);

Why good: Explicit model name, temperature set for determinism, .text accessor for content

// BAD: Hardcoded API key, no model specified
import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({ apiKey: "sk-1234..." });

Why bad: Hardcoded API key is a security risk, missing model name uses unpredictable defaults

Provider Switching

import { ChatAnthropic } from "@langchain/anthropic";
const model = new ChatAnthropic({ model: "claude-sonnet-4-20250514" });

// Or use initChatModel for runtime provider selection
import { initChatModel } from "langchain";
const model = await initChatModel("openai:gpt-4o", { temperature: 0 });

See: [examples/core.md](examples/core.md) for full provider examples and configuration options

---

Pattern 2: LCEL Chain Composition

Compose chains using .pipe(). Every component is a Runnable.

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const prompt = ChatPromptTemplate.fromTemplate(
  "Summarize this in one sentence: {text}",
);
const model = new ChatOpenAI({ model: "gpt-4o" });
const parser = new StringOutputParser();

const chain = prompt.pipe(model).pipe(parser);
const result = await chain.invoke({ text: "LangChain is a framework..." });
// result is a plain string

Why good: Each step is independently testable, streaming propagates through the entire chain, swapping model is one line change

// BAD: Legacy LLMChain (deprecated)
import { LLMChain } from "langchain/chains";
const chain = new LLMChain({ llm: model, prompt });

Why bad: LLMChain is deprecated, does not support streaming propagation, harder to compose

See: [examples/core.md](examples/core.md) for RunnableSequence.from(), RunnablePassthrough, RunnableParallel

---

Pattern 3: Structured Output with Zod

Use withStructuredOutput() for type-safe LLM responses.

import { ChatOpenAI } from "@langchain/openai";
import { z } from "zod";

const MovieSchema = z.object({
  title: z.string().describe("The movie title"),
  year: z.number().describe("Release year"),
  genres: z.array(z.string()).describe("List of genres"),
});

const structuredModel = new ChatOpenAI({
  model: "gpt-4o",
}).withStructuredOutput(MovieSchema);
const movie = await structuredModel.invoke("Tell me about Inception.");
// movie is typed: { title: string; year: number; genres: string[] }

Why good: Output is validated against schema, fully typed, no manual JSON parsing

// BAD: Manual JSON parsing from completion text
const response = await model.invoke("Return JSON with title and year...");
const data = JSON.parse(response.text); // Fragile, untyped, can throw

Why bad: No schema validation, untyped result, model may return malformed JSON

See: [examples/structured-output-tools.md](examples/structured-output-tools.md) for complex schemas and edge cases

---

Pattern 4: Tool Definition

Define tools with the tool() function and Zod schemas. Use snake_case for tool names.

import { tool } from "@langchain/core/tools";
import { z } from "zod";

const getWeather = tool(
  async ({ location }) => {
    // Call real weather API here
    return `Weather in ${location}: 22C, sunny`;
  },
  {
    name: "get_weather",
    description: "Get current weather for a city",
    schema: z.object({
      location: z.string().describe("City name, e.g. 'San Francisco'"),
    }),
  },
);

Why good: Zod schema validates input, .describe() guides model's argument generation, snake_case name avoids provider compatibility issues

// BAD: Using DynamicStructuredTool (verbose, legacy pattern)
import { DynamicStructuredTool } from "@langchain/core/tools";
const tool = new DynamicStructuredTool({
  name: "getWeather",        // camelCase breaks some providers
  description: "...",
  schema: z.object({ ... }),
  func: async (input) => { ... },
});

Why bad: DynamicStructuredTool is verbose compared to tool(), camelCase name causes issues with some providers

See: [examples/structured-output-tools.md](examples/structured-output-tools.md) for binding tools to models and handling tool calls

---

Pattern 5: Agents with createAgent()

Use createAgent() for agentic workflows. It is backed by LangGraph and handles tool calling loops automatically.

import { createAgent } from "langchain";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const search = tool(async ({ query }) => `Results for: ${query}`, {
  name: "search",
  description: "Search for information",
  schema: z.object({ query: z.string() }),
});

const agent = createAgent({
  model: "openai:gpt-4o",
  tools: [search],
  systemPrompt: "You are a helpful research assistant.",
});

const stream = await agent.stream({
  messages: [{ role: "user", content: "Find info about LangChain" }],
});
for await (const step of stream) {
  console.log(step.messages.at(-1));
}

Why good: createAgent handles the tool-call loop, supports streaming, manages state via LangGraph

// BAD: Legacy AgentExecutor pattern
import { AgentExecutor, createToolCallingAgent } from "langchain/agents";
const agent = createToolCallingAgent({ llm, tools, prompt });
const executor = new AgentExecutor({ agent, tools });

Why bad: AgentExecutor is legacy, does not integrate with LangGraph state management, less composable

See: [examples/agents.md](examples/agents.md) for chat history, custom state, and middleware patterns

---

Pattern 6: RAG Pipeline

Load documents, split into chunks, embed, store in a vector store, and retrieve.

import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import { OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory";

const CHUNK_SIZE = 1000;
const CHUNK_OVERLAP = 200;

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: CHUNK_SIZE,
  chunkOverlap: CHUNK_OVERLAP,
});
const chunks = await splitter.splitDocuments(docs);

const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" });
const vectorStore = new MemoryVectorStore(embeddings);
await vectorStore.addDocuments(chunks);

// Retrieve
const results = await vectorStore.similaritySearch("query", 3);

Why good: Named constants for chunk parameters, explicit embedding model, MemoryVectorStore for prototyping

See: [examples/rag.md](examples/rag.md) for full RAG chains, agent-based RAG, and production vector stores

---

Pattern 7: Streaming

All Runnables support .stream(). Streaming propagates through LCEL chains.

const chain = prompt.pipe(model).pipe(parser);

const stream = await chain.stream({ text: "Explain quantum computing." });
for await (const chunk of stream) {
  process.stdout.write(chunk);
}

Why good: Streaming propagates through the entire chain, progressive output for better UX

// BAD: Collecting all output then displaying
const result = await chain.invoke({ text: "..." });
console.log(result); // User waits for full response

Why bad: User waits for full generation before seeing anything, bad UX for long responses

See: [examples/streaming.md](examples/streaming.md) for model streaming, stream events, agent streaming

---

Pattern 8: LangSmith Tracing

Enable tracing by setting environment variables. No code changes needed.

LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=lsv2_...
LANGCHAIN_PROJECT=my-project
# Recommended for non-serverless environments:
LANGCHAIN_CALLBACKS_BACKGROUND=true

Why good: Zero-code setup, traces every chain/model/tool invocation, LANGCHAIN_CALLBACKS_BACKGROUND=true reduces latency in long-running processes

See: [reference.md](reference.md) for all environment variables

---

Decision Framework

When to Use LangChain vs Direct SDK

Do you need multi-step LLM workflows (prompt -> model -> parser -> ...)?
+-- YES -> Use LangChain (LCEL chains)
+-- NO -> Do you need to swap between LLM providers?
    +-- YES -> Use LangChain (unified chat model interface)
    +-- NO -> Do you need RAG or agent tool calling?
        +-- YES -> Use LangChain
        +-- NO -> Use the provider SDK directly (simpler, fewer deps)

Which Chat Model Class

Which provider?
+-- OpenAI -> ChatOpenAI from @langchain/openai
+-- Anthropic -> ChatAnthropic from @langchain/anthropic
+-- Google -> ChatGoogleGenerativeAI from @langchain/google-genai
+-- Runtime selection -> initChatModel("provider:model") from langchain
+-- Other -> Check @langchain/community

LCEL vs createAgent

Does the model need to autonomously decide when to call tools?
+-- YES -> createAgent() (handles tool-call loops, state management)
+-- NO -> Is it a fixed sequence of steps?
    +-- YES -> LCEL chain (prompt.pipe(model).pipe(parser))
    +-- NO -> RunnableSequence.from() with branching

Legacy Chain vs LCEL

Are you writing new code?
+-- YES -> ALWAYS use LCEL (.pipe()) -- never legacy chains
+-- NO -> Is the existing code using LLMChain/ConversationChain?
    +-- YES -> Migrate to LCEL when touching the code
    +-- NO -> Keep as-is if it works

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RED FLAGS

High Priority Issues:

• Using legacy chains (LLMChain, `Conv

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: agents-inc
• Source: agents-inc/skills
• License: MIT

Install and usage instructions live in the source repository linked above.