AI Core

AI Core

Foundation layer that configures Vercel AI Gateway as the unified provider for 200+ models and exports a single getModel() function consumed by all downstream AI skills.

Prerequisites

• Next.js app with src/ directory and App Router
• Environment configuration (env-config skill) with src/env.ts

Installation

bun add ai @ai-sdk/gateway

Environment Variables

Add to .env.local:

# AI Gateway
AI_GATEWAY_API_KEY=your-gateway-api-key-here
AI_GATEWAY_MODEL=google/gemini-2.5-flash-lite

Load from 1Password

If AI_GATEWAY_API_KEY is empty or a placeholder in .env.local, load it from 1Password:

# Check if already set
grep -q "AI_GATEWAY_API_KEY=your-" .env.local 2>/dev/null && echo "Key is placeholder — load from 1Password"

# Load from 1Password (uses env-from-1password skill)
op item get "Dev Environment" --account=my.1password.com --fields label=notesPlain | tr -d '"' > /tmp/op-env.txt
grep "AI_GATEWAY_API_KEY" /tmp/op-env.txt >> .env.local && rm /tmp/op-env.txt

Or use the /env-from-1password skill to load all keys at once — it writes AI_GATEWAY_API_KEY (and other service keys) to .env.local.

Update src/env.ts

Add the AI Gateway variables to your environment schema:

Modify src/env.ts

Add to the server object:

  server: {
    // ... existing variables
    AI_GATEWAY_API_KEY: z.string().optional(),
    AI_GATEWAY_MODEL: z.string().default("google/gemini-2.5-flash-lite"),
  },

Add to the runtimeEnv object:

  runtimeEnv: {
    // ... existing variables
    AI_GATEWAY_API_KEY: process.env.AI_GATEWAY_API_KEY,
    AI_GATEWAY_MODEL: process.env.AI_GATEWAY_MODEL,
  },

What Gets Created

src/
└── lib/
    └── ai.ts          # gateway() provider, getModel()

Setup Steps

Step 1: Create src/lib/ai.ts

import { gateway } from "@ai-sdk/gateway";

/**
 * Returns an AI model via AI Gateway.
 *
 * Uses the `AI_GATEWAY_MODEL` env var as the default model.
 * Downstream skills can override by passing a specific modelId
 * (e.g., `getModel("anthropic/claude-sonnet-4")` for reasoning).
 *
 * @param modelId - Optional model identifier in "provider/model" format.
 *                  Defaults to `AI_GATEWAY_MODEL` env var, then `google/gemini-2.5-flash-lite`.
 * @returns A model object compatible with AI SDK's `streamText`, `generateText`, etc.
 */
export function getModel(modelId?: string) {
  return gateway(
    modelId ?? process.env.AI_GATEWAY_MODEL ?? "google/gemini-2.5-flash-lite"
  );
}

/**
 * Returns an embedding model via AI Gateway.
 *
 * @param modelId - Optional embedding model identifier.
 *                  Defaults to `openai/text-embedding-3-small`.
 * @returns An embedding model compatible with AI SDK's `embed()` and `embedMany()`.
 */
export function getEmbeddingModel(modelId?: string) {
  return gateway.textEmbeddingModel(
    modelId ?? "openai/text-embedding-3-small"
  );
}

Usage

import { getModel } from "@/lib/ai";
import { streamText, generateText } from "ai";

// Use default model (google/gemini-2.5-flash-lite)
const result = await streamText({
  model: getModel(),
  prompt: "Hello, world!",
});

// Override with a specific model
const reasoning = await generateText({
  model: getModel("anthropic/claude-sonnet-4"),
  prompt: "Explain quantum entanglement step by step.",
});

// Generate embeddings
import { getEmbeddingModel } from "@/lib/ai";
import { embed } from "ai";

const { embedding } = await embed({
  model: getEmbeddingModel(),
  value: "Some text to embed",
});

Available Models via AI Gateway

Ollama (Local)

To run models locally via Ollama instead of AI Gateway, swap the provider in src/lib/ai.ts:

bun add @ai-sdk/ollama
ollama pull qwen3:8b

import { ollama } from "@ai-sdk/ollama";

export function getModel() {
  return ollama("qwen3:8b");
}

qwen3:8b is the Ollama model — best-in-class tool calling at 8B, strong markdown output, and the standard choice for local agentic apps in 2026. No env vars or API keys needed.

For local dev without an AI Gateway account, set OLLAMA_BASE_URL in .env.local and conditionally switch providers:

import { gateway } from "@ai-sdk/gateway";
import { ollama } from "@ai-sdk/ollama";

export function getModel(modelId?: string) {
  if (process.env.OLLAMA_BASE_URL) {
    return ollama("qwen3:8b");
  }
  return gateway(
    modelId ?? process.env.AI_GATEWAY_MODEL ?? "google/gemini-2.5-flash-lite"
  );
}

Add to .env.local to enable Ollama:

OLLAMA_BASE_URL=http://localhost:11434

Acceptance Criteria

• tsc passes with no errors
• getModel() returns a valid model object usable with streamText() / generateText()
• getModel("anthropic/claude-sonnet-4") returns a different model than the default
• getEmbeddingModel() returns a valid embedding model usable with embed()
• bun run build succeeds