ai-services

name	ai-services
version	1.0.0
min_doctl_version	1.82.0
description	Configure DigitalOcean Gradient AI serverless inference and Agent Development Kit. Use when adding LLM inference, model access keys, serverless AI endpoints, or building AI agents with ADK on App Platform.
related_skills	["designer","deployment"]
deprecated	false

AI Services Skill

Configure DigitalOcean Gradient AI Platform for App Platform applications.

Tip: This is one specialized skill in the App Platform library. For complex multi-step projects, consider using the planner skill to generate a staged approach. For an overview of all available skills, see the root SKILL.md.

---

Quick Decision

What do you need?
├── Simple LLM API calls → Serverless Inference
│   OpenAI-compatible API, no agent management
│
└── Full AI agents → Agent Development Kit (ADK)
    Knowledge bases, RAG, guardrails, multi-agent routing

Need	Solution	Reference
Call LLM models directly	Serverless Inference	serverless-inference.md
Build agents with knowledge bases	ADK	agent-development-kit.md
Content filtering / guardrails	ADK	agent-development-kit.md
Multi-agent workflows	ADK	agent-development-kit.md

---

Credential Handling

Model access keys follow the standard credential hierarchy:

1. GitHub Secrets (recommended): User creates key → adds to GitHub Secrets → app spec references
2. App Platform Secrets: Set via doctl apps update with type: SECRET

# App Spec pattern
envs:
  - key: MODEL_ACCESS_KEY
    scope: RUN_TIME
    type: SECRET
    value: ${MODEL_ACCESS_KEY}   # From GitHub Secrets

Key creation: Control Panel → Serverless Inference → Model Access Keys

Keys shown only once after creation—store securely.

---

Quick Start: Serverless Inference

# .do/app.yaml
services:
  - name: api
    envs:
      - key: MODEL_ACCESS_KEY
        scope: RUN_TIME
        type: SECRET
        value: ${MODEL_ACCESS_KEY}
      - key: INFERENCE_ENDPOINT
        value: https://inference.do-ai.run

# Python SDK (OpenAI-compatible)
from openai import OpenAI
import os

client = OpenAI(
    base_url=os.environ["INFERENCE_ENDPOINT"] + "/v1",
    api_key=os.environ["MODEL_ACCESS_KEY"],
)

response = client.chat.completions.create(
    model="llama3.3-70b-instruct",
    messages=[{"role": "user", "content": "Hello!"}],
)

Full guide: See serverless-inference.md

---

Quick Start: Agent Development Kit

# Install and configure
pip install gradient-adk
gradient agent configure

# Run locally
gradient agent run
# → http://localhost:8080/run

# Deploy to DigitalOcean
gradient agent deploy

# Agent entrypoint
from gradient_adk import entrypoint

@entrypoint
def entry(payload, context):
    query = payload["prompt"]
    return {"response": "Hello from agent!"}

Full guide: See agent-development-kit.md

---

Available Models

Model	Use Case
llama3.3-70b-instruct	General purpose, high quality
llama3-8b	Faster, lower cost
mistral-7b	Efficient, multilingual

# List all available models
doctl genai list-models

Check Gradient AI Models for current availability.

---

Reference Files

• serverless-inference.md — SDK setup, API parameters, examples
• agent-development-kit.md — ADK workflow, knowledge bases, guardrails

---

Quick Troubleshooting

Error	Cause	Fix
401 Unauthorized	Invalid model access key	Verify key in GitHub Secrets
Model not found	Invalid model ID	Run doctl genai list-models
Rate limit exceeded	Too many requests	Implement exponential backoff
ADK deploy fails	Missing token scopes	Ensure genai CRUD + project read scopes

---

Integration with Other Skills

• → designer: Add AI service environment variables to app spec
• → deployment: Model access key stored in GitHub Secrets
• → devcontainers: Test AI integrations locally before deployment
• → planner: Plan AI-enabled app deployments

---

Documentation Links

• Gradient AI Platform
• Available Models
• Serverless Inference
• Agent Development Kit