Upgrading from llm-exe 2.x to 3.x? Read the v2 to v3 migration guide before updating. It covers parser and output-boundary changes that may require code updates.
A package that provides simplified base components to make building and maintaining LLM-powered applications easier.
- Write functions powered by LLMs with easy-to-use building blocks.
- Pure JavaScript and TypeScript. Allows you to pass and infer types.
- Supercharge your prompts by using handlebars within prompt template.
- Support for text-based and chat-based prompts. (ChatGPT, Claude, Grok, Gemini, Bedrock, Ollama, etc)
- Call LLMs from different providers without changing your code. (OpenAI/Anthropic/xAI/Google/AWS Bedrock/Ollama/DeepSeek)
- Allow LLM's to call functions (or call other LLM executors).
- Not very opinionated. You have control on how you use it.
See full docs here: https://llm-exe.com
Install llm-exe using npm.
npm i llm-exe
ESM-first. CommonJS works too.
// ESM
import * as llmExe from "llm-exe";
// or specific modules
import { useLlm, createChatPrompt, createParser } from "llm-exe";
// CommonJS
const llmExe = require("llm-exe");import { useLlm, createChatPrompt, createParser, createLlmExecutor, defineSchema } from "llm-exe";
// Prompt
const prompt = createChatPrompt("You are a support agent. Help the user.");
prompt.addUserMessage("I need help with my order.");
// LLM
const llm = useLlm("openai.gpt-4o");
// Parser — schema uses JSON Schema (via defineSchema)
const schema = defineSchema({
type: "object",
properties: {
answer: { type: "string" },
action: { type: "string" },
},
required: ["answer", "action"],
} as const);
const parser = createParser("json", { schema });
// Executor
const executor = createLlmExecutor({ llm, prompt, parser });
await executor.execute({ input: "..." });const prompt = createChatPrompt(`
{{#if user.isFirstTime}}
Welcome!
{{else}}
Welcome back!
{{/if}}
`);createParser("string"); // pass-through, returns string
createParser("json", { schema }); // JSON with optional schema validation
createParser("boolean"); // parses a boolean token
createParser("number"); // extracts number from response
createParser("stringExtract", { enum: ["yes", "no"] }); // match one of the enum values
createParser("listToArray"); // newline-separated list → string[]
createParser("listToJson"); // key: value list → object (with optional schema)
createParser("listToKeyValue"); // key: value list → Array<{ key, value }>
createParser("markdownCodeBlock"); // single code block → { code, language }
createParser("markdownCodeBlocks"); // multiple code blocks → Array<{ code, language }>
createParser("replaceStringTemplate"); // handlebars-based output templatingconst parser = createCustomParser("MyUppercaseParser", (output, input) => {
return output.toUpperCase();
});Manage conversation history and structured data across LLM calls:
import { createState, createDialogue, createStateItem } from "llm-exe";
// Create a state container
const state = createState();
// Dialogues — store conversation history
const chat = state.createDialogue("chat");
chat.setUserMessage("Hi");
chat.setAssistantMessage("Hello!");
chat.getHistory(); // returns message array
// Standalone dialogue (without state)
const dialogue = createDialogue("chat");
dialogue.setUserMessage("Hi");
// Context items — typed values with get/set/reset
const intent = createStateItem("userIntent", "unknown");
state.createContextItem(intent);
intent.setValue("booking");
intent.getValue(); // "booking"
intent.resetValue(); // resets to "unknown"
// Attributes — simple key-value metadata
state.setAttribute("userId", "abc-123");
state.attributes["userId"]; // "abc-123"executor.on("onSuccess", console.log);
executor.on("onError", console.error);Below is simple example:
// 1. Use the model you want
const llm = useLlm("openai.gpt-4o");
// 2. Create a parameterized prompt
const instruction = `
You are a classifier. Given a user message, reply with the category it belongs to.
Pick from only the following options:
{{#each options}}- {{this}}
{{/each}}
Respond with only one of the options.`;
const prompt = createChatPrompt<{ options: string[]; input: string }>(
instruction
).addUserMessage("{{input}}"); // placeholder for message content
// 3. Create a parser that ensures a clean match
const parser = createParser("stringExtract", {
enum: ["billing", "support", "cancel", "unknown"],
});
// 4. Create the executor
const classifyMessage = createLlmExecutor({
llm,
prompt,
parser,
});
// 5. Pass in options and a message — like a real function!
// classifyMessage.execute is typed based on the prompt/parser!
const result = await classifyMessage.execute({
input: "Hi, I'm moving and no longer need this service.",
options: ["billing", "support", "cancel", "unknown"],
});
console.log(result); // => "cancel"