adaline-prompts

name	adaline-prompts
description	Create and manage prompts in Adaline via the v2 API or SDK clients. Use when programmatically creating prompts, updating prompt drafts, listing prompts, or reading prompt/playground data.

Adaline Prompts

Concepts

Prompts are the central artifact in Adaline's Prompt surface. A prompt has metadata, a draft, optional playground data, and deployable snapshots.

Key terms:

• Prompt — a named artifact inside a project
• Draft — editable prompt config, messages, and tools
• Config — provider/model selection plus flexible settings
• Messages — ordered role messages with structured content arrays
• Tools — function definitions with optional HTTP request configuration
• Variables — derived from {{variable}} placeholders and returned on prompt/draft resources

Configuration

Set these environment variables when credentials are available:

• ADALINE_API_KEY — workspace API key from Admin > API Keys
• ADALINE_PROJECT_ID — project to create/list prompts in

Base URL: https://api.adaline.ai/v2

Quick Triage

Symptom	First Fix
List returns empty	Include the correct projectId query parameter
Pagination skips records	Use pagination.nextCursor, not legacy page-number pagination
Prompt creation fails	Ensure draft.messages[].content is an array of content objects
Tool schema fails	Use { "type": "function", "definition": { ... } }, not legacy top-level name/parameters
Draft changes not in production	Deploy a prompt snapshot in the Platform UI, then fetch with the deployments skill

API Endpoints

# List prompts
curl "https://api.adaline.ai/v2/prompts?projectId=$ADALINE_PROJECT_ID&limit=20" \
  -H "Authorization: Bearer $ADALINE_API_KEY"

# Create prompt
curl -X POST "https://api.adaline.ai/v2/prompts" \
  -H "Authorization: Bearer $ADALINE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "projectId": "project_abc123",
    "title": "Support triage",
    "icon": { "type": "emoji", "value": "🎧" },
    "draft": {
      "config": {
        "provider": "openai",
        "model": "gpt-4o",
        "settings": { "temperature": 0.3 }
      },
      "messages": [
        {
          "role": "system",
          "content": [
            { "modality": "text", "value": "You are a helpful support assistant." }
          ]
        },
        {
          "role": "user",
          "content": [
            { "modality": "text", "value": "Answer {{question}}" }
          ]
        }
      ],
      "tools": []
    }
  }'

Draft Structure

{
  "config": {
    "provider": "openai",
    "model": "gpt-4o",
    "settings": {
      "temperature": 0.3,
      "maxTokens": 1024
    }
  },
  "messages": [
    {
      "role": "system",
      "content": [
        { "modality": "text", "value": "You are helpful." }
      ]
    }
  ],
  "tools": [
    {
      "type": "function",
      "definition" 
         
         
         
           
           
                
          
           
        
      
    
  

Message Content

Prompt message content is structured. Supported modality values include text, image, pdf, tool-call, tool-response, reasoning, search-result, and error.

{
  "role": "user",
  "content": [
    { "modality": "text", "value": "Describe this image." },
    {
      "modality": "image",
      "detail": "auto",
      "value": { "type": "url", "url": "https://example.com/image.png" }
    }
  ]
}

SDK Usage

The current TypeScript and Python SDKs expose prompt namespace clients:

await adaline.prompts.list({ projectId, limit: 20 });
await adaline.prompts.create({ prompt });
await adaline.prompts.get({ promptId, expand: 'playground' });
await adaline.prompts.update({ promptId, prompt: { title: 'New title' } });
await adaline.prompts.draft.get({ promptId });

await adaline.prompts.list(project_id=project_id, limit=20)
await adaline.prompts.create(prompt=prompt)
await adaline.prompts.get(prompt_id=prompt_id, expand="playground")
await adaline.prompts.update(prompt_id=prompt_id, prompt=patch)
await adaline.prompts.draft.get(prompt_id=prompt_id)

Best Practices

1. Use cursor pagination (limit + cursor) for list endpoints.
2. Put model parameters inside config.settings.
3. Let Adaline derive variables from message placeholders; do not rely on legacy create-body variables arrays.
4. Keep prompt creation separate from deployment. Prompt edits change the draft; deployments produce immutable runtime snapshots.

References

See references/api.md for the full REST contract and examples.