Registry indexed
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.
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.
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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.
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 |
Model access keys follow the standard credential hierarchy:
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.
# .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
# 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
| 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.
| 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 |
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
---
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](../../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](reference/serverless-inference.md) |
| Build agents with knowledge bases | ADK | [agent-development-kit.md](reference/agent-development-kit.md) |
| Content filtering / guardrails | ADK | [agent-development-kit.md](reference/agent-development-kit.md) |
| Multi-agent workflows | ADK | [agent-development-kit.md](reference/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`
```yaml
# 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
```yaml
# .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
# 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](reference/serverless-inference.md)
---
## Quick Start: Agent Development Kit
```bash
# 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
```
```python
# 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](reference/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 |
```bash
# List all available models
doctl genai list-models
```
Check [Gradient AI Models](https://docs.digitalocean.com/products/gradient-ai-platform/details/models/) for current availability.
---
## Reference Files
- **[serverless-inference.md](reference/serverless-inference.md)** — SDK setup, API parameters, examples
- **[agent-development-kit.md](reference/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](https://docs.digitalocean.com/products/gradient-ai-platform/)
- [Available Models](https://docs.digitalocean.com/products/gradient-ai-platform/details/models/)
- [Serverless Inference](https://docs.digitalocean.com/products/gradient-ai-platform/how-to/serverless-inference/)
- [Agent Development Kit](https://docs.digitalocean.com/products/gradient-ai-platform/how-to/adk/)
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
62/100
Promising
Trust
55/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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}Listing source
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72/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.