Tune - AI chat in text file

Tune is a handy extension for Visual Studio Code to chat with LLM in text file.

Demo

Setup

install tune-sdk

npm install -g tune-sdk

tune-sdk init

edit ~/.tune/.env file and add OPENAI_KEY and other keys

Template Language

@myprompt     include file
@image        include image
@path/to/file include file at path
@gpt-4.1      connect model
@shell        connect tool
@@prompt      include file recursively

@{ name with whitespaces } - include file with whitespaces
@{ image | resize 512 }    - modify with processors
@{ largefile | tail 100 }  - modify with processors
@{| sh tree }              - insert generated content with processors

Extend with Middlewares

Extend Tune with middlewares:

tune-fs - connect tools & files from local filesystem
tune-models - connect llm models from Anthropic/OpenAI/Gemini/Openrouter/Mistral/Groq
tune-basic-toolset - basic tools like read file, write file, shell etc.
tune-s3 - read/write files from s3

For example:

cd ~/.tune 
npm install tune-models

Edit default.ctx.js and add middlewares

const models = require('tune-models')

module.exports = [
    ...
    models({
        default: "gpt-5-mini"
    })
    ...
]

Edit .env file and add provider's keys

OPENAI_KEY="<openai_key>"
ANTHROPIC_KEY="<anthropic_key>"

Use it in chat

system: 
@gemini-2.5-pro @openai_imgen

user: 
draw a stickman with talking bubble "Hello world"

assistant: 
tool_call: openai_imgen {"filename":"stickman_hello_world.png"}
a simple stickman drawing with a talking bubble saying 'Hello world'

tool_result: 
image generated

CLI

# install tune globally
npm install -g tune-sdk

# append user message to newchat.chat run and save
tune-sdk --user "hi how are you?" --filename newchat.chat  --save

# start new chat with system prompt and initial user message 
# print result to console
tune-sdk --system "You are Groot" --user "Hi how are you?"

#set context variable
tune-sdk --set-test "hello" --user "@test" --system "You are echo you print everythting back"

Javascript SDK

npm install tune-sdk

const tune = require("tune-sdk");
const sonnet = require("./sonnet.llm.js");

require('dotenv').config();

async function main() {
  const ctx = tune.makeContext({
    echo: "You are echo, you print everything back",
    OPENROUTER_KEY: process.env.OPENROUTER_KEY,
    "default": {
      type: "llm",
      exec: sonnet
    }
  })

  const text = "s: @echo\nu: hello world";
  const messages = await tune.text2run(text, ctx)
  console.log(tune.msg2text(messages))
  // a: hello world
}
main()

tune

Ilya Ovdin