Version Your Prompts with LangWatch Prompts CLI

Determine Scope

If the user's request is general ("set up prompt versioning", "version my prompts"):

• Read the full codebase to find all hardcoded prompt strings
• Study git history to understand what changed and why: focus on agent behavior changes, prompt tweaks, bug fixes. Read commit messages for context.
• Set up the Prompts CLI and create managed prompts for each hardcoded prompt
• Update all application code to use langwatch.prompts.get()

If the user's request is specific ("version this prompt", "create a new prompt version"):

• Focus on the specific prompt
• Create or update the managed prompt
• Update the relevant code to use langwatch.prompts.get()

Plan Limits

LangWatch's free plan has limits on prompts, scenarios, evaluators, experiments, and datasets. When you hit a limit, the API returns "Free plan limit of N reached..." with an upgrade link.

How to handle:

• Work within the limits. If 3 resources of the relevant type are allowed, create 3 meaningful ones, not 10.
• Make every creation count: each one should demonstrate clear value.
• Show what works FIRST. If you hit a limit, summarize what was accomplished and note that upgrading the plan raises it. Point to the subscription settings on the platform, or to the license settings if LANGWATCH_ENDPOINT is set (self-hosted).
• Do NOT delete existing resources to make room or repurpose an existing resource to evade the limit.

Step 1: Read the Prompts CLI Docs

Use langwatch docs <path> to read documentation as Markdown. Some useful entry points:

langwatch docs                                    # Docs index
langwatch docs integration/python/guide           # Python integration
langwatch docs integration/typescript/guide       # TypeScript integration
langwatch docs prompt-management/cli              # Prompts CLI
langwatch scenario-docs                           # Scenario docs index

Discover commands with langwatch --help and langwatch <subcommand> --help. List and get commands accept --format json for machine-readable output. Read the docs first instead of guessing SDK APIs or CLI flags.

If no shell is available, fetch the same Markdown over plain HTTP. Append .md to any docs path (e.g. https://langwatch.ai/docs/integration/python/guide.md). Index: https://langwatch.ai/docs/llms.txt. Scenario index: https://langwatch.ai/scenario/llms.txt

If anything fails or confuses you while following this skill (broken commands, docs that do not match reality, errors you had to work around), ask the user for permission and run npx langwatch report --user-approved with a --title and --summary (or --session <transcript.jsonl>) to send it to the LangWatch team, and it directly shapes what gets fixed. No login or API key needed. Nothing is sent without --user-approved, and --dry-run prints the exact payload without sending anything. The title, summary and transcript are scrubbed locally first, by pattern: secrets and API keys, plus email addresses, phone numbers, card numbers and public IPv4 addresses. Anything no pattern matches is sent as written, including a contact address passed with --email, so preview with --dry-run when the session touched sensitive data. npx langwatch report --help explains the options.

Projects and API keys: target a real project, not a personal one.

LangWatch has two kinds of project:

• Team / shared projects: real projects inside an organization. Evaluations, experiments, prompts, datasets, simulations and instrumentation must always target one of these.
• Personal projects: a private "My Workspace" scratch space tied to a single user. Never send a user's evaluations, experiments or production traces here: it is for personal exploration only, and you can mistake it for a real project.

And two ways to authenticate:

• A project API key in .env (LANGWATCH_API_KEY): the credential everything in these skills uses. It is scoped to one real project. This is the default; prefer it unless the user explicitly asks for something else.
• langwatch login --device (AI-tools / SSO): a personal device session for wrapping coding assistants (langwatch claude, langwatch codex, …). It is NOT for evaluations, prompts, datasets, scenarios or SDK instrumentation, and it points at a personal workspace. Do not run it to set up the work in these skills.

So for anything in these skills: make sure LANGWATCH_API_KEY for a real, shared project is in the project's .env. Check whether the variable is already set there before you ask for a new key, and let the CLI read the value: never print, copy or send it. Do NOT run langwatch login to pick a project, and never default to a personal project. If LANGWATCH_ENDPOINT is set, the user is self-hosted: use that endpoint instead of app.langwatch.ai.

Then specifically read the Prompts CLI guide:

langwatch docs prompt-management/cli

CRITICAL: Do NOT guess how to use the Prompts CLI. Read the docs first.

Step 2: Initialize Prompts in the Project

langwatch prompt init

Creates a prompts.json config and a prompts/ directory in the project root.

Step 3: Create a Managed Prompt for Each Hardcoded Prompt

Scan the codebase for hardcoded prompt strings (system messages, instructions). For each:

langwatch prompt create <name>

Edit the generated .prompt.yaml file to match the original prompt content.

Model: keep the generated model on a current model (the latest OpenAI generation is openai/gpt-5.5). Never downgrade a new prompt to a legacy model like gpt-4o-mini.

Temperature: the gpt-5 family rejects a custom temperature, so do not add modelParameters.temperature for those models. create omits it on purpose.

Structured outputs: if the prompt must return strict JSON, add a response_format block instead of asking for JSON in prose:

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

response_format round-trips losslessly through sync/pull. See langwatch docs prompt-management/cli for the full format.

Step 4: Update Application Code

Replace every hardcoded prompt string with a call to langwatch.prompts.get().

Python (BAD → GOOD):

agent = Agent(instructions="You are a helpful assistant.")

import langwatch
prompt = langwatch.prompts.get("my-agent")
agent = Agent(instructions=prompt.compile().messages[0]["content"])

TypeScript (BAD → GOOD):

const systemPrompt = "You are a helpful assistant.";

const langwatch = new LangWatch();
const prompt = await langwatch.prompts.get("my-agent");

CRITICAL: Do NOT wrap langwatch.prompts.get() in a try/catch with a hardcoded fallback string. The whole point of prompt versioning is that prompts are managed externally. A fallback defeats this by silently reverting to a stale hardcoded copy.

Step 5: Sync to the Platform

langwatch prompt sync

Step 6: Tag Versions for Deployment

Three built-in tags: latest (auto-assigned), production, staging. Update code to fetch by tag:

prompt = langwatch.prompts.get("my-agent", tag="production")

const prompt = await langwatch.prompts.get("my-agent", { tag: "production" });

Assign tags via the CLI (or the Deploy dialog in the LangWatch UI):

langwatch prompt tag assign my-agent production

For canary or blue/green deployments, create custom tags with langwatch prompt tag create.

Step 7: Verify

Run langwatch prompt list to confirm everything synced, or open the Prompts section in the LangWatch app.

Common Mistakes

• Do NOT hardcode prompts. Always fetch via langwatch.prompts.get()
• Do NOT add a hardcoded fallback string in a try/catch; that silently defeats versioning
• Do NOT manually edit prompts.json. Use the CLI
• Do NOT skip langwatch prompt sync after creating prompts
• Prefer the current flagship (openai/gpt-5.5). Pick an older model like gpt-4o-mini only when intentionally optimizing for cost or latency
• Do NOT set modelParameters.temperature on a gpt-5-family model; the family rejects it
• Do NOT ask for JSON in the prompt text when output must be structured. Use a response_format block