Adaline Deployments

Concepts

Adaline deployments are immutable prompt snapshots that your application fetches at runtime. The public v2 API currently exposes deployment read operations: create and promote deployments in the Adaline Platform UI, then fetch the approved snapshot from code.

Key terms:

• Deployment — a prompt snapshot deployed to an environment
• Deployment environment — an isolation boundary such as development, staging, or production
• Latest deployment — the current snapshot for a prompt/environment pair, fetched with deploymentId=latest
• Pinned deployment — a concrete deployment ID fetched directly for reproducibility

Configuration

Set these environment variables when credentials are available:

• ADALINE_API_KEY — workspace API key from Admin > API Keys
• ADALINE_PROMPT_ID — prompt to fetch
• ADALINE_DEPLOYMENT_ENVIRONMENT_ID — environment for latest lookup

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

How It Works

1. Build and test a prompt in the Prompt area of Adaline.
2. Deploy the prompt snapshot to an environment in the Platform UI.
3. Fetch the current snapshot with GET /deployments?promptId=...&deploymentId=latest&deploymentEnvironmentId=....
4. Cache the returned Deployment in your app and refresh it on a timer, restart, or product-specific webhook signal.

Each deployment includes prompt.config, prompt.messages, prompt.tools, and prompt.variables. Config values use providerName, providerId, model, and flexible settings.

Quick Triage

Approach 1: REST API

# Latest deployment for an environment
curl "https://api.adaline.ai/v2/deployments?promptId=$ADALINE_PROMPT_ID&deploymentId=latest&deploymentEnvironmentId=$ADALINE_DEPLOYMENT_ENVIRONMENT_ID" \
  -H "Authorization: Bearer $ADALINE_API_KEY"

# Specific deployment by ID
curl "https://api.adaline.ai/v2/deployments?promptId=$ADALINE_PROMPT_ID&deploymentId=deploy_abc123" \
  -H "Authorization: Bearer $ADALINE_API_KEY"

Approach 2: TypeScript SDK

import { Adaline } from '@adaline/client';

const adaline = new Adaline(); // reads ADALINE_API_KEY

const deployment = await adaline.getLatestDeployment({
  promptId: process.env.ADALINE_PROMPT_ID!,
  deploymentEnvironmentId: process.env.ADALINE_DEPLOYMENT_ENVIRONMENT_ID!,
});

const pinned = await adaline.getDeployment({
  promptId: process.env.ADALINE_PROMPT_ID!,
  deploymentId: 'deploy_abc123',
});

Install: npm install @adaline/client @adaline/api

See references/typescript-sdk.md for a complete inference example.

Approach 3: Python SDK

import asyncio
from adaline import Adaline

async def main():
    adaline = Adaline()  # reads ADALINE_API_KEY

    deployment = await adaline.get_latest_deployment(
        prompt_id="prompt_abc123",
        deployment_environment_id="environment_abc123",
    )

    pinned = await adaline.get_deployment(
        prompt_id="prompt_abc123",
        deployment_id="deploy_abc123",
    )

asyncio.run(main())

Install: pip install adaline-client adaline-api

See references/python-sdk.md for a complete inference example.

Best Practices

1. Use latest lookup for normal runtime traffic and pinned deployment IDs for reproducible tests.
2. Cache deployments in memory; do not fetch on every user request.
3. Store IDs in environment variables or configuration, not source code.
4. Substitute variables only in text content. Preserve image, PDF, tool-call, tool-response, and reasoning content as structured objects.
5. Pass deployment.prompt.config.settings through to the provider after adapting provider-specific casing where needed.

References

See references/api.md for the REST contract. See references/typescript-sdk.md for TypeScript SDK usage. See references/python-sdk.md for Python SDK usage.