🎯 AI Prompt Library & Version Control

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

Use Case	Configuration
Production Prompts	Create → Test → Version (1.0.0) → Status: production
A/B Testing	Version 1.0.0 vs 1.1.0 → Run test → Deploy winner
Rollback	Production broken → Rollback to previous version → Fix
Team Collaboration	Tags: team-ai, team-backend → Export/import → Share
Cost Optimization	Compare versions → Track cost metrics → Choose cheapest

🚀 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

Operation	Description	Required Fields
create	Create new prompt	promptData
update	Update current version	promptId, promptData
version	Create new version	promptId, promptData, versionInfo
compare	Compare versions	promptId, compareVersions (array)
test	A/B test versions	promptId, testConfig
export	Export library	exportFormat
import	Import prompts	importSource
rollback	Rollback to version	promptId, rollbackToVersion
analytics	Get performance stats	promptId, analyticsRange

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

Format	Best For
json	Backup, full fidelity
yaml	Human-readable, git commits
langchain	LangChain integration
promptlayer	Migration from PromptLayer
csv	Excel, data analysis

💰 Cost Comparison

Feature	This Actor	PromptLayer	LangSmith
Price	Free	$49-299/mo	$39-299/mo
Version Control	✅	✅	✅
A/B Testing	✅	✅	✅
Analytics	✅	✅	✅
Export	✅	❌	❌
Self-hosted	✅	❌	❌
No vendor lock-in	✅	❌	❌
Semantic versioning	✅	❌	❌
Full data ownership	✅	❌	❌

🔧 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 = new ApifyClient({ 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!

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Built for the Apify $1M Challenge 🚀

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