Tune - chat with llm in a text file

Tune is a handy extension for Visual Studio Code and and plugin for Neovim and plugin for Sublime Text that lets you chat with large language models (LLMs) in a text file. With tune javascript sdk you can make apps and agents.

Demo

Setup

install tune-sdk

npm install -g tune-sdk

# create ~/.tune folder and install batteries
tune init

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

Template Language

user:
@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

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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
• tune-mcp - connect tools from mcp servers
• maik - fetch all you emails, and index them into sqlite database

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

tune "hi how are you?"

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

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

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

Javascript SDK

npm install tune-sdk

Tune core is middleware-based. A context resolves @name references into nodes like text, tool, llm, and processor.

const tune = require("tune-sdk")

async function main() {
  const ctx = tune.makeContext()

  ctx.use(async function middleware(name) {
    if (name === "file.txt") {
      return {
        type: "text",
        name: "file.txt",
        read: async () => fs.readFileSync("file.txt", "utf8")
      }
    }

    if (name === "readfile") {
      return {
        type: "tool",
        name: "readfile",
        schema: {
          type: "object",
          properties: {
            filename: { type: "string" }
          }
        },
        exec: async ({ filename }) => fs.readFileSync(filename, "utf8")
      }
    }

    if (name === "gpt-5") {
      return {
        type: "llm",
        name: "gpt-5",
        exec: async (payload) => ({
          url: "https://api.openai.com/v1/chat/completions",
          method: "POST",
          headers: {
            Authorization: `Bearer ${process.env.OPENAI_KEY}`,
            "Content-Type": "application/json"
          },
          body: JSON.stringify({
            model: "gpt-5",
            ...payload
          })
        })
      }
    }

    if (name === "tail") {
      return {
        type: "processor",
        name: "tail",
        exec: async (node, args) => {
          if (!node) return
          if (node.type !== "text") throw Error("tail can only modify text nodes")
          return {
            ...node,
            read: async () => {
              const content = await node.read()
              const n = parseInt(args.trim(), 10) || 20
              return content.split("\n").slice(-n).join("\n")
            }
          }
        }
      }
    }
  })

  const content = await ctx.file2run({
    system: "@gpt-5 @readfile",
    user: "can you read file.txt?",
    stream: false,
    response: "content"
  })

  console.log(content)
}

main()

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