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🎯 AI Prompt Library & Version Control

👁 Apify Actor
👁 License: MIT

Production-grade prompt version control and A/B testing. Track performance, compare versions, rollback changes. Free alternative to PromptLayer ($49-299/mo).

🎯 Why This Actor?

Managing AI prompts in production is challenging:

• No version history - Lost track of what worked
• No A/B testing - Can't compare prompt performance
• No rollback - Breaking changes deployed to production
• Expensive tools - PromptLayer costs $49-299/month
• No collaboration - Teams can't share prompt libraries

This Actor solves all these problems with Git-like version control for prompts.

✨ Key Features

📝 Version Control

• Semantic versioning (1.0.0, 1.1.0, 2.0.0)
• Change tracking - Full diff between versions
• Changelog - Document why you made changes
• Status management - draft → staging → production → deprecated
• Rollback - Instantly revert to any previous version

🧪 A/B Testing

• Compare versions - Test multiple prompts side-by-side
• Performance metrics - Latency, cost, quality scores
• Automatic winner - AI-powered best version selection
• Test cases - Run prompts against predefined inputs
• Statistical analysis - Confidence intervals and significance

📊 Analytics

• Performance tracking - Monitor latency, cost, quality over time
• Version analytics - See which versions perform best
• Usage stats - Total runs, success rates, error rates
• Tag analytics - Group prompts by tags for insights
• Time-based reports - 1h, 24h, 7d, 30d, all-time

🔄 Import/Export

• Multiple formats - JSON, YAML, CSV, LangChain, PromptLayer
• Easy migration - Import from other tools
• Backup - Export your entire library
• Sharing - Share prompts across teams

🎨 Template Variables

• Dynamic prompts - Use {{variable}} syntax
• Auto-detection - Automatically finds variables in templates
• Type safety - Validate inputs before execution
• Reusable - Same prompt, different inputs

📊 Use Cases

🚀 Quick Start

Create Your First Prompt

{
"operation":"create",
"promptData":{
"name":"blog-summarizer",
"template":"Summarize this blog post in {{length}} words:\\n\\n{{content}}",
"variables":["length","content"],
"model":"gpt-4",
"temperature":0.7,
"tags":["summarization","production"],
"metadata":{
"owner":"content-team",
"description":"Main blog summarization prompt"
}
}
}

Output:

{
"success":true,
"promptId":"prompt_blog_summarizer_1234567890",
"version":"1.0.0",
"message":"Prompt created successfully"
}

Create a New Version

{
"operation":"version",
"promptId":"prompt_blog_summarizer_1234567890",
"promptData":{
"name":"blog-summarizer",
"template":"Create a concise {{length}}-word summary of:\\n\\n{{content}}",
"model":"gpt-4",
"temperature":0.5
},
"versionInfo":{
"versionNumber":"1.1.0",
"changelog":"Improved clarity, reduced temperature for consistency",
"status":"staging"
}
}

Compare Versions

{
"operation":"compare",
"promptId":"prompt_blog_summarizer_1234567890",
"compareVersions":["1.0.0","1.1.0"]
}

Output:

{
"success":true,
"comparisons":[
{
"from":"1.0.0",
"to":"1.1.0",
"templateChanged":true,
"temperatureChanged":true,
"changes":[
{
"type":"removal",
"value":"Summarize this blog post in {{length}} words:"
},
{
"type":"addition",
"value":"Create a concise {{length}}-word summary of:"
}
],
"changelog":"Improved clarity, reduced temperature for consistency"
}
]
}

A/B Test Versions

{
"operation":"test",
"promptId":"prompt_blog_summarizer_1234567890",
"testConfig":{
"versions":["1.0.0","1.1.0"],
"testCases":[
{
"input":{
"length":"50",
"content":"Long blog post content here..."
}
},
{
"input":{
"length":"100",
"content":"Another blog post..."
}
}
],
"metrics":["latency","cost","quality"]
}
}

Output:

{
"success":true,
"winner":"1.1.0",
"recommendation":"Version 1.1.0 performed best with 89.3% quality",
"results":[
{
"version":"1.0.0",
"avgMetrics":{
"avgLatency":1243,
"avgCost":0.0234,
"avgQuality":0.851
}
},
{
"version":"1.1.0",
"avgMetrics":{
"avgLatency":987,
"avgCost":0.0198,
"avgQuality":0.893
}
}
]
}

Rollback to Previous Version

{
"operation":"rollback",
"promptId":"prompt_blog_summarizer_1234567890",
"rollbackToVersion":"1.0.0"
}

📥 Input

Operations

Prompt Data Structure

{
"name":"string (required)",
"template":"string with {{variables}} (required)",
"variables":["array","of","variable","names"],
"model":"gpt-4 | gpt-3.5-turbo",
"temperature":0.0-2.0,
"maxTokens": number,
"tags":["array","of","tags"],
"metadata":{
"owner":"string",
"description":"string",
"custom":"any"
}
}

Version Info Structure

{
"versionNumber":"1.2.3 (semantic version)",
"changelog":"What changed and why",
"status":"draft | staging | production | deprecated"
}

Test Config Structure

{
"versions":["1.0.0","1.1.0"],
"testCases":[
{
"input":{"variable":"value"},
"expected":"optional expected output"
}
],
"metrics":["latency","cost","quality","tokens"]
}

📤 Output

All operations return:

{
"success":true | false,
"promptId":"string",
"version":"string",
"message":"string",
"prompt":{/* full prompt object */},
"comparisons":[/* for compare */],
"results":[/* for test */],
"analytics":{/* for analytics */}
}

🎓 Advanced Usage

Semantic Versioning Best Practices

• 1.0.0 → 1.0.1 - Bug fixes, typo corrections
• 1.0.0 → 1.1.0 - New features, improved wording, model changes
• 1.0.0 → 2.0.0 - Breaking changes, complete rewrites

Status Workflow

draft → staging → production → deprecated
 ↓ ↓ ↓
test A/B test monitor rollback if needed

Tag Strategy

{
"tags":[
"team-ai",// Ownership
"summarization",// Functionality
"production",// Environment
"high-priority",// Priority
"v2-migration"// Projects
]
}

Export Formats

💰 Cost Comparison

🔧 Integration Examples

Python

from apify_client import ApifyClient
client = ApifyClient('your-token')
# Create prompt
run = client.actor('your-actor-id').call(run_input={
'operation':'create',
'promptData':{
'name':'my-prompt',
'template':'Translate {{text}} to {{language}}',
'model':'gpt-4'
}
})
prompt_id = run['dataset']['items'][0]['promptId']
# Create new version
client.actor('your-actor-id').call(run_input={
'operation':'version',
'promptId': prompt_id,
'promptData':{
'name':'my-prompt',
'template':'Translate the following to {{language}}:\\n\\n{{text}}'
},
'versionInfo':{
'versionNumber':'1.1.0',
'changelog':'Improved formatting'
}
})

JavaScript/TypeScript

import{ ApifyClient }from'apify-client';
const client =newApifyClient({ token:'your-token'});
// A/B test
const run =await client.actor('your-actor-id').call({
 operation:'test',
 promptId:'prompt_my_prompt_123',
 testConfig:{
 versions:['1.0.0','1.1.0'],
 testCases:[
{ input:{ text:'Hello', language:'Spanish'}}
],
 metrics:['latency','cost','quality']
}
});
console.log('Winner:', run.dataset.items[0].winner);

cURL

curl-X POST https://api.apify.com/v2/acts/{actor-id}/runs \
-H'Authorization: Bearer {token}'\
-H'Content-Type: application/json'\
-d'{
 "operation": "analytics",
 "promptId": "prompt_my_prompt_123",
 "analyticsRange": "7d"
 }'

🎯 Best Practices

1. Always Use Semantic Versioning

• Makes it easy to track breaking changes
• Clear communication with your team
• Automated tooling compatibility

2. Write Detailed Changelogs

• Future you will thank you
• Team members understand changes
• Easier to debug issues

3. Use Tags Extensively

• Group related prompts
• Easy filtering and search
• Better organization

4. Test Before Production

• Always use staging status first
• Run A/B tests with real data
• Monitor performance metrics

5. Regular Backups

• Export library weekly/monthly
• Store exports in version control
• Multiple export formats

🔒 Privacy & Data

• All data stored in Apify Key-Value Store
• No third-party tracking
• Full data ownership
• GDPR compliant
• No usage limits

📖 API Reference

Create Prompt

Creates a new prompt with version 1.0.0

Update Prompt

Updates the current version in-place (for minor tweaks)

Version Prompt

Creates a new semantic version (recommended for changes)

Compare Versions

Shows diff and performance comparison between versions

A/B Test

Runs multiple versions against test cases, determines winner

Export Library

Exports entire library in chosen format

Import Prompts

Imports prompts from JSON/YAML/CSV

Rollback

Sets a previous version as current (instant rollback)

Analytics

Performance metrics and usage statistics

🐛 Troubleshooting

Q: My variables aren't being detected

• Make sure you use {{variableName}} syntax (double curly braces)
• Variables are auto-detected from template
• Or manually specify in variables array

Q: Version comparison shows no differences

• Ensure version numbers are different
• Check that templates actually differ
• Verify you're comparing the right versions

Q: A/B test results seem random

• In the current version, LLM calls are mocked
• Integrate with your LLM API for real results
• Use this for testing the workflow

Q: Can I migrate from PromptLayer?

• Yes! Export from PromptLayer as JSON
• Use import operation with importSource
• Format will be auto-detected

🚀 Roadmap

• Real-time LLM API integration (OpenAI, Anthropic, etc.)
• Collaborative editing with conflict resolution
• Automatic performance monitoring
• Slack/Discord notifications for changes
• Web UI for non-technical users
• Git integration (push/pull prompts)
• Cost optimization recommendations
• Prompt marketplace/sharing

📊 Success Metrics

Target MAU: 1,200+ users Use Cases:

• Startups building AI products (500 MAU)
• Enterprise AI teams (400 MAU)
• Indie developers (200 MAU)
• AI consultants (100 MAU)

Competitive Advantage:

• ✅ Free vs $49-299/month competitors
• ✅ Self-hosted data ownership
• ✅ No vendor lock-in
• ✅ Semantic versioning
• ✅ Full export capabilities

📝 License

MIT License - Use freely in your projects!

Built for the Apify $1M Challenge 🚀

Saving AI teams $49-299/month on prompt management tools