promptschema

Typed, versioned prompts for LLMs. Stop hardcoding AI prompts as strings. Define them as contracts.

npm install @dailybot/promptschema    # TypeScript / JavaScript
pip install promptschema              # Python

The problem

Every LLM project ends up with code like this:

// ❌ What most codebases look like today
const prompt = `You are an e-commerce assistant.
Order: ${order}
Language: ${lang}
${total > 100 ? "Offer 10% discount." : ""}
`
const result = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: prompt }]
})

No types. No validation. No version history. If lang is missing, it silently breaks at runtime. Nobody knows what version of this prompt is in production.

The solution

// ✅ With promptschema (TypeScript)
import { definePrompt, z } from '@dailybot/promptschema'

const orderPrompt = definePrompt({
  name:    'order-assistant',
  version: '1.0.0',
  model:   'openai/gpt-4o',
  input: z.object({
    order: z.string(),
    lang:  z.enum(['es', 'en']),
    total: z.number().positive(),
  }),
  template: (i) => `
    You are an e-commerce assistant.
    Order: ${i.order}, Language: ${i.lang}
    ${i.total > 100 ? 'Offer 10% discount.' : ''}
  `
})

const result = await orderPrompt.run({ order: 'Dress #204', lang: 'en', total: 149 })

# ✅ With promptschema (Python)
from promptschema import define_prompt
from pydantic import BaseModel
from typing import Literal

@define_prompt(name='order-assistant', version='1.0.0', model='openai/gpt-4o')
class OrderPrompt(BaseModel):
    order: str
    lang:  Literal['es', 'en']
    total: float

    def template(self) -> str:
        discount = 'Offer 10% discount.' if self.total > 100 else ''
        return f"""
            You are an e-commerce assistant.
            Order: {self.order}, Language: {self.lang}
            {discount}
        """

result = await OrderPrompt(order='Dress #204', lang='en', total=149).arun()

Type-safe. Validated at build time. Version tracked.

Load from registry

Define prompts in one language, load them in another — from the same registry:

// TypeScript — load a prompt defined anywhere (TS or Python)
import { loadFromRegistry } from '@dailybot/promptschema'

const prompt = loadFromRegistry('order-assistant')
// prompt.name    → 'order-assistant'
// prompt.version → '2.0.0'
// prompt.model   → 'openai/gpt-4o'

const validated = prompt.validate({ order: 'Dress #204', lang: 'en', total: 149 })
const result = await prompt.run({ order: 'Dress #204', lang: 'en', total: 149 })

# Python — same registry, same prompt, same validation
from promptschema import load_from_registry

OrderPrompt = load_from_registry("order-assistant")
instance = OrderPrompt(order="Dress #204", lang="en", total=149)
result = await instance.arun()

The registry stores JSON Schema, so both languages reconstruct identical validation from a single source of truth.

Install

# TypeScript / JavaScript
npm install @dailybot/promptschema

# Python
pip install promptschema[openai]       # OpenAI
pip install promptschema[anthropic]    # Anthropic
pip install promptschema[all]          # All providers

Requires Node >= 18 or Python >= 3.10.

Features

• 🔒 Type-safe — Zod (TS) and Pydantic (Python) schemas for every prompt input
• 🔖 Versioned — semantic versioning with automatic change detection
• 🔍 Diffable — readable diffs between prompt versions
• ⚡ Any model — OpenAI, Anthropic, Gemini, Ollama, or your own adapter
• 🌍 Dual — identical API in TypeScript and Python, shared registry
• 🔄 Cross-language — define in TS, load in Python (or vice versa) via loadFromRegistry
• 🪶 Lightweight — zero runtime dependencies beyond Zod/Pydantic

CLI

npx promptschema init                          # Create registry
npx promptschema status                        # Show sync state
npx promptschema bump order-assistant          # Bump version
npx promptschema diff order-assistant 1.0.0 2.0.0  # Show diff
npx promptschema validate                      # CI gate (exit 1 if unsynced)
npx promptschema list                          # List all prompts
npx promptschema history order-assistant       # Version timeline

The same commands work with Python: promptschema status, promptschema bump, etc.

Why promptschema?

| | Raw strings | LangChain | promptschema | |---|---|---|---| | Type-safe inputs | ❌ | ⚠️ partial | ✅ | | Build-time validation | ❌ | ❌ | ✅ | | Semantic versioning | ❌ | ❌ | ✅ | | Prompt diff (readable) | ❌ | ❌ | ✅ | | Works with any model | ✅ | ✅ | ✅ | | TypeScript + Python | ✅ | ✅ | ✅ | | Zero vendor lock-in | ✅ | ⚠️ partial | ✅ | | Bundle size | 0kb | ~2MB | ~12kb |

Custom adapters

Register your own LLM provider in a few lines:

import { registerAdapter } from '@dailybot/promptschema'

registerAdapter('my-provider', {
  name: 'my-provider',
  async call({ model, prompt, temperature, maxTokens }) {
    const response = await myLLMClient.generate({ model, prompt })
    return {
      text: response.text,
      promptTokens: response.usage.input,
      completionTokens: response.usage.output,
      totalTokens: response.usage.total,
      estimatedCost: 0,
    }
  },
})

Contributing

Contributions are welcome! Please open an issue first to discuss what you'd like to change.

git clone https://github.com/DailybotHQ/promptschema
cd promptschema
pnpm install
pnpm test

License

MIT