Prompts CLI

Automated setup available. Copy the prompts skill prompt into your coding agent to set up prompt versioning automatically.

The langwatch prompt command provides dependency management for AI prompts as plain YAML files, enabling you to version prompts locally with Git while synchronizing with the LangWatch platform for testing, evaluation, and team collaboration.

Installation

Install the CLI globally:

npm install -g langwatch

Authenticate:

langwatch login

Quick Start

1. Initialize Your Project

Create a new prompts project:

langwatch prompt init

This will create the following structure to manage your prompts:

├── prompts # directory to create your prompts
│   └── .materialized # where remote fetched prompts will be stored
├── prompts.json # prompt dependencies
└── prompts-lock.json # lock file

2. Add Your First Prompt

Create a local prompt:

langwatch prompt create my-summarizer

Or add an existing remote prompt dependency:

langwatch prompt add agent/customer-service

Your newly created or fetched prompt will be on a yaml file in the prompts/ directory, and look like this:

model: openai/gpt-5.5
messages:
  - role: system
    content: You are a helpful assistant.
  - role: user
    content: "{{input}}"

3. Synchronize

Sync all prompts (fetch remote, push local changes):

langwatch prompt sync

Go to app.langwatch.ai to see your new synced prompts.

Using the Prompts

Once you’ve created your prompts, you can use them in your application code. The LangWatch SDK provides a simple interface to fetch and compile prompts with dynamic variables.

Python
TypeScript

import langwatch
from litellm import completion

# Get the latest prompt by handle
prompt = langwatch.prompts.get("my-summarizer")

# Compile prompt with variables
compiled_prompt = prompt.compile(
    user_input="Summarize this article for me...",
    context="Article content here"
)

# Use with LiteLLM (supports all major providers)
response = completion(
    model=prompt.model,
    messages=compiled_prompt.messages,
    temperature=prompt.temperature,
    max_tokens=prompt.max_tokens
)

print(response.choices[0].message.content)

import { LangWatch } from "langwatch";
import { openai } from "@ai-sdk/openai";
import { generateText } from "ai";

const langwatch = new LangWatch({
  apiKey: process.env.LANGWATCH_API_KEY
});

// Get the latest prompt by handle
const prompt = await langwatch.prompts.get("my-summarizer");

// Compile prompt with variables
const compiledPrompt = prompt.compile({
  user_input: "Summarize this article for me...",
  context: "Article content here"
});

// Use with Vercel AI SDK
const result = await generateText({
  model: openai(prompt.model.replace("openai/", "")),
  messages: compiledPrompt.messages,
  temperature: prompt.temperature,
  maxTokens: prompt.maxTokens
});

console.log(result.text);

Working with Local and Remote Prompts

The SDK loads prompts dynamically at runtime, so you don’t need to worry about whether they’re local or remote:

Local prompts: Fetched directly from your LangWatch project
Remote prompts: Also fetched from LangWatch after being synced from the materialized files

For production deployments, the CLI materializes prompts locally so you have a complete snapshot of all dependencies. The SDK can automatically use these materialized prompts for guaranteed availability in offline or air-gapped environments. However, when online, the SDK always fetches the latest version from the server unless you specify a version.Learn more about guaranteed availability and offline deployments.

Loading Specific Versions

You can also load specific prompt versions instead of always using the latest:

# Get a specific version
prompt = langwatch.prompts.get("my-summarizer", version=5)

// Get a specific version
const prompt = await langwatch.prompts.get("my-summarizer", { version: 5 });

Prompt Variables

Prompts use {{variable_name}} syntax for dynamic content. When you compile a prompt, provide all required variables:

my-prompt.prompt.yaml

messages:
  - role: system
    content: |
      You are a {{role}} assistant.
      User: {{user_name}}
      Context: {{context}}
  - role: user
    content: "{{user_input}}"

Then compile with all variables:

compiled = prompt.compile(
    role="customer support",
    user_name="John Doe",
    context="Premium user since 2020",
    user_input="I need help with my account"
)

const compiled = prompt.compile({
  role: "customer support",
  user_name: "John Doe",
  context: "Premium user since 2020",
  user_input: "I need help with my account"
});

The compiled prompt contains fully rendered messages ready to send to your LLM provider, with all variables replaced and the correct model configuration applied.

Core Concepts

Dependency Management

The CLI uses two configuration files: prompts.json - Declares your prompt dependencies:

{
  "prompts": {
    "agent/customer-service": "latest",
    "shared/guidelines": "5",
    "my-local-prompt": "file:./prompts/my-local-prompt.prompt.yaml"
  }
}

prompts-lock.json - Tracks resolved versions and materialized file paths:

{
  "lockfileVersion": 1,
  "prompts": {
    "agent/customer-service": {
      "version": 12,
      "versionId": "prompt_version_scRQwSRMIyJvoSxTqP2nR",
      "materialized": "prompts/.materialized/agent/customer-service.prompt.yaml"
    }
  }
}

Local vs Remote Prompts

Remote Prompts (agent/customer-service@latest)

Pulled from LangWatch platform
Fetched and materialized locally in ./prompts/.materialized/
Read-only locally

Local Prompts (file:./prompts/my-prompt.prompt.yaml)

Stored as local YAML files
Version controlled with Git
Pushed to platform during sync for sharing and evaluation

YAML Format

Prompts files end with .prompt.yaml extension and follow this format:

model: openai/gpt-5.5
modelParameters:
  max_tokens: 1000
messages:
  - role: system
    content: You are a helpful assistant specializing in customer service.
  - role: user
    content: |
      Please help the customer with their inquiry:

      {{customer_message}}

This is the same structure as GitHub Prompts.

Commands Reference

`langwatch prompt init`

Initialize a new prompts project in the current directory.

langwatch prompt init

`langwatch prompt add <spec> [localFile]`

Add a new prompt dependency and immediately fetch/materialize it.

# Add remote prompt
langwatch prompt add shared/guidelines@latest

# Add specific version
langwatch prompt add agent/support@5

# Add local file as dependency
langwatch prompt add my-prompt ./prompts/my-prompt.prompt.yaml

Arguments:

<spec> - Prompt specification (name@version or name for latest)
[localFile] - Optional path to local YAML file to add

Behavior:

Updates prompts.json with new dependency
Fetches prompt from server and materializes locally
Updates prompts-lock.json with resolved version

`langwatch prompt remove <name>`

Remove a prompt dependency and clean up associated files.

langwatch prompt remove agent/support

Behavior:

Removes entry from prompts.json
Removes entry from prompts-lock.json
Deletes materialized file
For local prompts: deletes source file and warns about server state

`langwatch prompt create <name>`

Create a new local prompt file with default content.

langwatch prompt create my-new-prompt

Behavior:

Creates ./prompts/<name>.prompt.yaml with template content
Automatically adds to prompts.json as file: dependency
Updates prompts-lock.json

`langwatch prompt sync`

Synchronize all prompts between local files and the server. This runs both pull and push in sequence.

langwatch prompt sync

Behavior:

Fetches remote prompts if new versions available
Pushes local prompt changes to server
Handles conflict resolution interactively
Cleans up orphaned materialized files
Reports what was synced

Conflict Resolution: When local and remote versions have both changed:

Conflict detected for my-prompt:
Local changes: Updated system message
Remote changes: Added temperature parameter

Choose resolution:
  l) Use local version (will create new version on server)
  r) Use remote version (will overwrite local file)
  a) Abort sync for this prompt

`langwatch prompt pull`

Pull remote prompts from the server and materialize them locally. Does not push any local changes.

langwatch prompt pull

Behavior:

Fetches remote prompts if new versions are available
Materializes them into prompts/.materialized/
Updates prompts-lock.json with resolved versions
Cleans up orphaned materialized files
Does not push local prompts to the server

Use this when you want to update your local materialized files without pushing any local prompt changes, for example during CI/CD deployments or after a teammate updates a shared prompt.

`langwatch prompt push`

Push local prompts to the server. Does not pull any remote changes.

langwatch prompt push

Behavior:

Pushes local prompt changes (those declared as file: dependencies) to the server
Handles conflict resolution interactively if remote has diverged
Updates prompts-lock.json with new version info
Does not fetch or update remote prompts locally

Use this when you want to publish your local prompt changes without fetching remote updates.

`langwatch prompt list`

Display current prompt dependencies and their status.

langwatch prompt list

CI/CD Integration

Integrate prompt materialization into your deployment pipeline:

.github/workflows/deploy.yml

name: Deploy with Prompts

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Setup Node.js
        uses: actions/setup-node@v4
        with:
          node-version: "18"

      - name: Install LangWatch CLI
        run: npm install -g langwatch

      - name: Materialize prompts
        env:
          LANGWATCH_API_KEY: ${{ secrets.LANGWATCH_API_KEY }}
        run: langwatch prompt pull

      - name: Build application
        run: npm run build

      - name: Deploy with materialized prompts
        run: |
          # Deploy application including prompts/.materialized/
          # Your deployment commands here

Workflows

Team Collaboration

Setup:

One team member initializes project with langwatch prompt init
Commit prompts.json and prompts-lock.json to Git
Add prompts/.materialized to .gitignore
Team members run langwatch prompt pull after pulling

Adding Shared Prompts:

# Add organization prompts
langwatch prompt add company/brand-guidelines@latest
langwatch prompt add legal/privacy-policy@^2

# Sync to materialize
langwatch prompt sync

Creating Local Prompts:

# Create and develop locally
langwatch prompt create feature/user-onboarding

# Edit the file
vim prompts/feature/user-onboarding.prompt.yaml

# Push to platform for evaluation
langwatch prompt push

Version Management

Pinning Versions:

{
  "prompts": {
    "critical/system-prompt": "5", // Exact version
    "experimental/new-feature": "latest" // Auto-update
  }
}

Upgrading Dependencies:

# Edit prompts.json to change versions
# Then sync to fetch new versions
langwatch prompt sync

Rolling Back:

# Restore from Git
git checkout prompts.json prompts-lock.json

# Re-sync to match committed state
langwatch prompt sync

Development Workflow:

# Create new feature prompt
langwatch prompt create features/new-capability

# Edit and test locally
# When ready, sync to platform
langwatch prompt sync

# Platform team can now evaluate and provide feedback

Coding Assistant Integration

Since prompts are just YAML files, you refer to them directly from other tools or coding assistants.

Cursor Integration

Reference prompts in a .cursor/rules/*.mdc file:

---
description:
globs:
alwaysApply: true
---

@/prompts/.materialized/company/java-code-guidelines.prompt.yaml

Cloud Code Integration

Include prompt content in cloud development environments by referencing the YAML files in the prompts/.materialized directory.

Reference: structured outputs

For prompts that must return strict JSON, add a response_format block alongside messages in the .prompt.yaml:

response_format:
  name: product_category
  schema:
    type: object
    properties:
      category: { type: string }
      reasoning: { type: string }
    required: [category, reasoning]
    additionalProperties: false

response_format round-trips through push and pull: the schema you author locally comes back identical after editing on the platform.

Get Started

Agent Simulations

Observability

Evaluations

Prompt Management

Platform

Examples & Cookbooks

Help

Installation

Quick Start

1. Initialize Your Project

2. Add Your First Prompt

3. Synchronize

Using the Prompts

Working with Local and Remote Prompts

Loading Specific Versions

Prompt Variables

Core Concepts

Dependency Management

Local vs Remote Prompts

YAML Format

Commands Reference

`langwatch prompt init`

`langwatch prompt add <spec> [localFile]`

`langwatch prompt remove <name>`

`langwatch prompt create <name>`

`langwatch prompt sync`

`langwatch prompt pull`

`langwatch prompt push`

`langwatch prompt list`

CI/CD Integration

Workflows

Team Collaboration

Version Management

Coding Assistant Integration

Cursor Integration

Cloud Code Integration

Reference: structured outputs

​Installation

​Quick Start

​1. Initialize Your Project

​2. Add Your First Prompt

​3. Synchronize

​Using the Prompts

​Working with Local and Remote Prompts

​Loading Specific Versions

​Prompt Variables

​Core Concepts

​Dependency Management

​Local vs Remote Prompts

​YAML Format

​Commands Reference

​langwatch prompt init

​langwatch prompt add <spec> [localFile]

​langwatch prompt remove <name>

​langwatch prompt create <name>

​langwatch prompt sync

​langwatch prompt pull

​langwatch prompt push

​langwatch prompt list

​CI/CD Integration

​Workflows

​Team Collaboration

​Version Management

​Coding Assistant Integration

​Cursor Integration

​Cloud Code Integration

​Reference: structured outputs

Installation

Quick Start

1. Initialize Your Project

2. Add Your First Prompt

3. Synchronize

Using the Prompts

Working with Local and Remote Prompts

Loading Specific Versions

Prompt Variables

Core Concepts

Dependency Management

Local vs Remote Prompts

YAML Format

Commands Reference

`langwatch prompt init`

`langwatch prompt add <spec> [localFile]`

`langwatch prompt remove <name>`

`langwatch prompt create <name>`

`langwatch prompt sync`

`langwatch prompt pull`

`langwatch prompt push`

`langwatch prompt list`

CI/CD Integration

Workflows

Team Collaboration

Version Management

Coding Assistant Integration

Cursor Integration

Cloud Code Integration

Reference: structured outputs