langchain-js

langchain-js is a cursor rule for Cursor from sanjeed5/awesome-cursor-rules-mdc. It costs 3,839 tokens per session, scanned A, original, CC0-1.0.

A set of JavaScript and TypeScript rules for LangChain, a framework for connecting language models with prompts, tools, data, and agents. It covers modular design, typing, monitoring, and testing.

In plain words
What is it for?
Use it to organize models, prompts, tools, chains, agents, and memory, and to compose them with LangChain Expression Language.
Why use it?
It helps prevent AI application code from becoming a single tangled module and makes its behavior easier to test and observe.

Cursor rule for Cursor

Written for Cursor: a Cursor rule (.mdc).

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is import { getStockPriceTool } from "../tools/getStockPrice";.

Good fit Use it to organize models, prompts, tools, chains, agents, and memory, and to compose them with LangChain Expression Language.

Compare 6 cursor rules from other repositories ↓
About the project

awesome-cursor-rules-mdc is a generator that creates Cursor MDC rule files from structured library information, using semantic search and language models to gather and organize guidance. Developers use it to produce reusable rules for libraries in Cursor, and the catalogue includes 200 of those rules.

sanjeed5/awesome-cursor-rules-mdc · 3,570 stars · on GitHub

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/sanjeed5/awesome-cursor-rules-mdc
agentmods
npx agentmods add rules/sanjeed5/awesome-cursor-rules-mdc/langchain-js

Made for: Cursor.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for langchain-js

README.md
[![agentmods](https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/langchain-js.svg)](https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/langchain-js)
Your own site
<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/langchain-js"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/langchain-js.svg" alt="Measured on agentmods" height="20"></a>
Per session 3,839 This file is loaded in full into every session.
When invoked 3,839 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.03839 $0.03839
Opus 5 $0.01920 $0.01920
Sonnet 5 $0.00768 $0.00768
Haiku 4.5 $0.00384 $0.00384

Measured 5d ago against content hash a3222686804f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

langchain-js scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

rules-mdc/langchain-js.mdc · 520 lines

How it starts

The opening of the file, as written. The whole thing — 520 lines — stays where its author put it; the contents beside it link to each section on GitHub.

langchain-js Best Practices

Building LLM-powered applications with langchain-js requires a disciplined approach to ensure maintainability, performance, and reliability. This guide outlines our team's definitive best practices.

1. Code Organization and Structure

Always prioritize modularity and explicit typing. Break down complex logic into small, reusable components.

1.1. Modular Components & Single Responsibility

Each LangChain component (Agent, Tool, Chain, Model) should reside in its own file or a dedicated module, adhering to the Single Responsibility Principle.

BAD: Monolithic file

// src/agent.js
import { ChatOpenAI } from "@langchain/openai";
import { createAgent, tool } from "langchain";
import { z } from "zod";

const getStockPrice = tool(async ({ ticker }) => { /* ... */ }, { /* ... */ });
const getNews = tool(async ({ query }) => { /* ... */ }, { /* ... */ });

const model = new ChatOpenAI({ temperature: 0.7 });
const agent = createAgent({
  model,
  tools: [getStockPrice, getNews],
});

export async function runFinancialAgent(input) {
  return agent.invoke(input);
}

GOOD: Modular, reusable components

// src/tools/getStockPrice.ts
import { tool } from "langchain";
import { z } from "zod";

export const getStockPriceTool = tool(
  async ({ ticker }: { ticker: string }) => {
    // Simulate API call
    if (ticker === "AAPL") return "$170.00";
    return "Price not found.";
  },
  {
    name: "get_stock_price",
    description: "Get the current stock price for a given ticker symbol.",
    schema: z.object({
      ticker: z.string().describe("The stock ticker symbol (e.g., AAPL)"),
    }),
  }
);

// src/tools/getNews.ts
import { tool } from "langchain";
import { z } from "zod";

export const getNewsTool = tool(
  async ({ query }: { query: string }) => {
    // Simulate API call
    return `Latest news for ${query}: Market is up!`;
  },
  {
    name: "get_news",
    description: "Get the latest news for a given query.",
    schema: z.object({
      query: z.string().describe("The news query"),
    }),
  }
);

// src/agents/financialAgent.ts
import { ChatOpenAI } from "@langchain/openai";
import { createAgent } from "langchain";
import { getStockPriceTool } from "../tools/getStockPrice";
import { getNewsTool } from "../tools/getNews";

const model = new ChatOpenAI({ temperature: 0.7 });

export const financialAgent = createAgent({
  model,
  tools: [getStockPriceTool, getNewsTool],
});

// src/index.ts (or wherever the agent is invoked)
import { financialAgent } from "./agents/financialAgent";

async function main() {
  const result = await financialAgent.invoke({
    messages: [{ role: "user", content: "What's the price of AAPL and the latest market news?" }],
  });
  console.log(result);
}

main();

Read the full file on GitHub · 520 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 520 lines · 3,839 tokens per session scan A a3222686804f

Subscribe to this mod's changes

langchain-js is a cursor rule published in the GitHub repository sanjeed5/awesome-cursor-rules-mdc (3,570 stars, last pushed 3mo ago), licensed CC0-1.0. It adds 3,839 tokens to every session, about $0.0192 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.