// 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.
| 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. |
Prompts are the central artifact in Adaline's Prompt surface. A prompt has metadata, a draft, optional playground data, and deployable snapshots.
Key terms:
settingscontent arrays{{variable}} placeholders and returned on prompt/draft resourcesSet these environment variables when credentials are available:
ADALINE_API_KEY — workspace API key from Admin > API KeysADALINE_PROJECT_ID — project to create/list prompts inBase URL: https://api.adaline.ai/v2
| 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 |
# 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": []
}
}'
{
"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": {
"name": "lookup_order",
"description": "Look up an order by ID",
"parameters": {
"type": "object",
"properties": {
"order_id": { "type": "string" }
},
"required": ["order_id"]
}
}
}
]
}
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" }
}
]
}
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)
limit + cursor) for list endpoints.config.settings.variables arrays.See references/api.md for the full REST contract and examples.
Create and manage evaluation datasets in Adaline. Use when building test cases, adding dataset columns/rows, importing data, or triggering dynamic columns.
Fetch deployed prompt snapshots from Adaline at runtime. Use when integrating prompt deployments, environment-based latest lookups, prompt caching, or pinned deployment IDs.
Run and manage evaluations in Adaline to test prompt quality at scale. Use when creating evaluation runs, polling status, analyzing results, or cancelling runs.
Create and manage evaluators in Adaline to score prompt outputs. Use when setting up LLM-as-a-judge, JavaScript, text-matcher, cost, latency, or response-length evaluators.
High-level guide for integrating your AI application with Adaline. Use when starting a new Adaline integration, choosing between API/SDK approaches, or planning which Adaline features to adopt.
Send traces and spans to Adaline for AI agent observability. Use when instrumenting LLM calls, tools, retrieval, embeddings, guardrails, or custom operations.