prompts

name	prompts
user-prompt	Version my prompts with LangWatch
description	Version and manage your agent's prompts with LangWatch Prompts CLI. Use for both onboarding (set up prompt versioning for an entire codebase) and targeted operations (version a specific prompt, create a new prompt version). Supports Python and TypeScript.
license	MIT
compatibility	Works with Claude Code and similar coding agents. The `langwatch` CLI is the only interface.

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 scenarios 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 direct the user to upgrade at https://app.langwatch.ai/settings/subscription.
• Do NOT delete existing resources to make room, and do NOT reuse a scenario set to cram in more tests.

If LANGWATCH_ENDPOINT is set in .env, the user is self-hosted — direct them to {LANGWATCH_ENDPOINT}/settings/license instead

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

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 is easily confused with 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. If it is missing, ask the user for it (they can mint a key for a specific project at https://app.langwatch.ai/authorize). Do NOT run langwatch login to pick a project, and never default to a personal project. If LANGWATCH_ENDPOINT is set, they are 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 — 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 — it will be rejected
• Do NOT ask for JSON in the prompt text when output must be structured — use a response_format block