langfuse-prompt-migration

Langfuse Prompt Migration

Migrate hardcoded prompts to Langfuse for version control, A/B testing, and deployment-free iteration.

Prerequisites

Verify credentials before starting:

echo $LANGFUSE_PUBLIC_KEY   # pk-...
echo $LANGFUSE_SECRET_KEY   # sk-...
echo $LANGFUSE_HOST         # https://cloud.langfuse.com or self-hosted

If not set, ask user to configure them first.

Migration Flow

1. Scan codebase for prompts
2. Analyze templating compatibility
3. Propose structure (names, subprompts, variables)
4. User approves
5. Create prompts in Langfuse
6. Refactor code to use get_prompt()
7. Link prompts to traces (if tracing enabled)
8. Verify application works

Step 1: Find Prompts

Search for these patterns:

Framework	Look for
OpenAI	messages=[{"role": "system", "content": "..."}]
Anthropic	system="..."
LangChain	ChatPromptTemplate, SystemMessage
Vercel AI	system: "...", prompt: "..."
Raw	Multi-line strings near LLM calls

Step 2: Check Templating Compatibility

CRITICAL: Langfuse only supports simple {{variable}} substitution. No conditionals, loops, or filters.

Template Feature	Langfuse Native	Action
{{variable}}	✅	Direct migration
{var} / ${var}	⚠️	Convert to {{var}}
{% if %} / {% for %}	❌	Move logic to code
{{ var | filter }}	❌	Apply filter in code

Decision Tree

Contains {% if %}, {% for %}, or filters?
├─ No → Direct migration
└─ Yes → Choose:
    ├─ Option A (RECOMMENDED): Move logic to code, pass pre-computed values
    └─ Option B: Store raw template, compile client-side with Jinja2
        └─ ⚠️ Loses: Playground preview, UI experiments

Simplifying Complex Templates

Conditionals → Pre-compute in code:

# Instead of {% if user.is_premium %}...{% endif %} in prompt
# Use {{tier_message}} and compute value in code before compile()

Loops → Pre-format in code:

# Instead of {% for tool in tools %}...{% endfor %} in prompt
# Use {{tools_list}} and format the list in code before compile()

For external templating details, fetch: https://langfuse.com/faq/all/using-external-templating-libraries

Step 3: Propose Structure

Naming Conventions

Rule	Example	Bad
Lowercase, hyphenated	chat-assistant	ChatAssistant_v2
Feature-based	document-summarizer	prompt1
Hierarchical for related	support/triage	supportTriage
Prefix subprompts with _	_base-personality	shared-personality

Identify Subprompts

Extract when:

• Same text in 2+ prompts
• Represents distinct component (personality, safety rules, format)
• Would need to change together

Variable Extraction

Make Variable	Keep Hardcoded
User-specific ({{user_name}})	Output format instructions
Dynamic content ({{context}})	Safety guardrails
Per-request ({{query}})	Persona/personality
Environment-specific ({{company_name}})	Static examples

Step 4: Present Plan to User

Format:

Found N prompts across M files:

src/chat.py:
  - System prompt (47 lines) → 'chat-assistant'

src/support/triage.py:
  - Triage prompt (34 lines) → 'support/triage'
    ⚠️ Contains {% if %} - will simplify

Subprompts to extract:
  - '_base-personality' - used by: chat-assistant, support/triage

Variables to add:
  - {{user_name}} - hardcoded in 2 prompts

Proceed?

Step 5: Create Prompts in Langfuse

Use langfuse.create_prompt() with:

• name: Your chosen name
• prompt: Template text (or message array for chat type)
• type: "text" or "chat"
• labels: ["production"] (they're already live)
• config: Optional model settings

Labeling strategy:

• production → All migrated prompts
• staging → Add later for testing
• latest → Auto-applied by Langfuse

For full API: fetch https://langfuse.com/docs/prompts/get-started

Step 6: Refactor Code

Replace hardcoded prompts with:

prompt = langfuse.get_prompt("name", label="production")
messages = prompt.compile(var1=value1, var2=value2)

Key points:

• Always use label="production" (not latest) for stability
• Call .compile() to substitute variables
• For chat prompts, result is message array ready for API

For SDK examples (Python/JS/TS): fetch https://langfuse.com/docs/prompts/get-started

Step 7: Link Prompts to Traces

If codebase uses Langfuse tracing, link prompts so you can see which version produced each response.

Detect Existing Tracing

Look for:

• @observe() decorators
• langfuse.trace() calls
• from langfuse.openai import openai (instrumented client)

Link Methods

Setup	How to Link
@observe() decorator	langfuse_context.update_current_observation(prompt=prompt)
Manual tracing	trace.generation(prompt=prompt, ...)
OpenAI integration	openai.chat.completions.create(..., langfuse_prompt=prompt)

Verify in UI

1. Go to Traces → select a trace
2. Click on Generation
3. Check Prompt field shows name and version

For tracing details: fetch https://langfuse.com/docs/prompts/get-started#link-with-langfuse-tracing

Step 8: Verify Migration

Checklist

• All prompts created with production label
• Code fetches with label="production"
• Variables compile without errors
• Subprompts resolve correctly
• Application behavior unchanged
• Generations show linked prompt in UI (if tracing)

Common Issues

Issue	Solution
PromptNotFoundError	Check name spelling
Variables not replaced	Use {{var}} not {var}, call .compile()
Subprompt not resolved	Must exist with same label
Old prompt cached	Restart app

Out of Scope

• Prompt engineering (writing better prompts)
• Evaluation setup
• A/B testing workflow
• Non-LLM string templates