Add an AI Gateway

You’ve now wired Durable Objects, hibernatable WebSockets, a container, and a Workflow into your Worker. The last piece in the Cloudflare track is an AI Gateway — a stable account-scoped endpoint that fronts every model provider (Workers AI, OpenAI, Anthropic, Bedrock, …) and gives you caching, rate limiting, retries, DLP, and a single dashboard of every request, token, and cost.

In Alchemy the gateway is a single resource. Once you’ve declared and bound it, .model({...}) returns an Effect LanguageModel.LanguageModel Layer — and from there you’re using the same generateText / streamText APIs you’d use against any other provider.

Declare the gateway

Create src/AiGateway.ts with a single resource definition. The two flags below enable response caching (60-second TTL) and request logging — every prompt, completion, latency, and token count will show up in the AI Gateway dashboard.

src/AiGateway.ts

import * as Cloudflare from "alchemy/Cloudflare";

export const Gateway = Cloudflare.AiGateway("Gateway", {
  cacheTtl: 60,
  collectLogs: true,
});

Every prop is optional, but explicit defaults make the intent visible. We’ll tune more knobs at the end of the tutorial.

Add it to the stack

alchemy.run.ts

import { Gateway } from "./src/AiGateway.ts";
 import Api from "./src/Api.ts";

 export default Alchemy.Stack(
   "CloudflareWorkerExample",
   { providers: Cloudflare.providers(), state: Cloudflare.state() },
   Effect.gen(function* () {
     const api = yield* Api;
    const gateway = yield* Gateway;

     return {
       url: api.url.as<string>(),
      gatewayId: gateway.gatewayId,
     };
   }),
 );

yield* Gateway registers the resource so it gets created/updated on the next deploy. gateway.gatewayId is exposed as a stack output so you can find it in the dashboard.

Bind the gateway into the Worker

Cloudflare.AiGateway.bind(Gateway) returns a typed client whose methods are wrapped in Effect — run for raw inference, getUrl for the gateway endpoint, getLog/patchLog for the request log, and model({...}) for building a LanguageModel layer.

src/Api.ts

 import * as Cloudflare from "alchemy/Cloudflare";
 import * as Effect from "effect/Effect";
import { Gateway } from "./AiGateway.ts";

 export default class Api extends Cloudflare.Worker<Api>()(
   "Api",
   { main: import.meta.path },
   Effect.gen(function* () {
    const aiGateway = yield* Cloudflare.AiGateway.bind(Gateway);

     return {
       fetch: Effect.gen(function* () {
         // …existing routes
       }),
     };
  }),
  }).pipe(Effect.provide(Cloudflare.AiGatewayBindingLive)),
 ) {}

Cloudflare.AiGatewayBindingLive is the runtime side of the binding. Provide it once at the bottom of the Init layer chain and every bind(...) further up will resolve.

Build a LanguageModel layer

Call aiGateway.model({...}) with a Workers AI model id and parameters. The result is a Layer<LanguageModel.LanguageModel, …> that satisfies Effect’s standard AI service.

Effect.gen(function* () {
  const aiGateway = yield* Cloudflare.AiGateway.bind(Gateway);

  const languageModel = aiGateway.model({
    client: aiGateway,
    model: "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
    parameters: { temperature: 0.7, maxTokens: 1024 },
  });

  return {
    fetch: Effect.gen(function* () {
      // …
    }),
  };
})

parameters is the per-call default; any individual call can still override it. The Init phase is the right place to build this layer — construction is pure and the binding factory only exists here.

Generate text on /generate

LanguageModel.generateText returns a typed response with text, finishReason, structured token usage, and any toolCalls. Provide the languageModel layer to the handler and call it like any other Effect.

import { LanguageModel } from "effect/unstable/ai";
import { HttpServerRequest } from "effect/unstable/http/HttpServerRequest";
import * as HttpServerResponse from "effect/unstable/http/HttpServerResponse";

  return {
    fetch: Effect.gen(function* () {
      const request = yield* HttpServerRequest;
      const url = new URL(request.url, "http://api");

      if (url.pathname === "/generate" && request.method === "POST") {
        const body = (yield* request.json) as { prompt?: string };
        const prompt = body.prompt?.trim() ?? "Say hello in one sentence.";

        const response = yield* LanguageModel.generateText({ prompt }).pipe(
          Effect.orDie,
        );

        return yield* HttpServerResponse.json({
          text: response.text,
          usage: {
            inputTokens: response.usage.inputTokens.total,
            outputTokens: response.usage.outputTokens.total,
          },
        });
      }

      return HttpServerResponse.text("Not Found", { status: 404 });
    }),
    }).pipe(Effect.provide(languageModel)),
  };

Effect.provide(languageModel) makes LanguageModel.LanguageModel available to every handler in fetch. Effect.orDie collapses AiError to a defect so a model failure surfaces as a 500 — if you need typed handling instead, Effect.catchTag("AiError", …) works.

Try it

bun alchemy deploy
curl -X POST "$(bun alchemy stack output url)/generate" \
  -H "content-type: application/json" \
  -d '{"prompt":"Write a haiku about Effect"}'

The first call takes ~1–2 seconds. Send the exact same prompt again and it returns in milliseconds — that’s the cacheTtl: 60 config doing its job. Open the Cloudflare dashboard → AI → AI Gateway → your gateway and you’ll see both requests, with the second flagged as a cache hit, plus latency and token usage on every entry.

Stream tokens on /stream

For chat-style UIs you want tokens to arrive as the model produces them, not in one big response. LanguageModel.streamText returns an Effect.Stream of typed response parts — text-delta, tool-call, finish, and so on. Pipe it through Sse.encode and HttpServerResponse.stream to get a server-sent-event stream that flushes through the Worker → edge → client without buffering.

import * as Stream from "effect/Stream";
import * as Sse from "effect/unstable/encoding/Sse";

      if (url.pathname === "/stream" && request.method === "POST") {
        const body = (yield* request.json) as { prompt?: string };
        const prompt = body.prompt?.trim() ?? "Tell me a haiku about Effect.";

        const stream = LanguageModel.streamText({ prompt }).pipe(
          Stream.provide(languageModel),
          Sse.encode,
        );

        return HttpServerResponse.stream(stream, {
          headers: {
            "content-type": "text/event-stream",
            "cache-control": "no-cache",
            "x-accel-buffering": "no",
          },
        });
      }

Stream.provide(languageModel) is the stream-aware equivalent of Effect.provide — the LanguageModel needs to be available for the entire lifetime of the stream, not just the initial setup.

curl -N -X POST "$(bun alchemy stack output url)/stream" \
  -H "content-type: application/json" \
  -d '{"prompt":"Write a haiku about Effect, slowly."}'

-N disables curl’s response buffering so you see each SSE event as soon as the Worker flushes it.

Tune caching, rate limits, and DLP

Every prop on Cloudflare.AiGateway maps to an update API call — no replacement, no downtime. A production-grade config might look like:

export const Gateway = Cloudflare.AiGateway("Gateway", {
  id: "prod-gateway",
  cacheTtl: 300,
  cacheInvalidateOnUpdate: true,
  rateLimitingInterval: 60,
  rateLimitingLimit: 100,
  rateLimitingTechnique: "sliding",
  collectLogs: true,
  logManagement: 100_000,
  logManagementStrategy: "DELETE_OLDEST",
  authentication: true,
});

Bumping cacheTtl, rateLimitingLimit, or toggling authentication is a single bun alchemy deploy away — the diff updates the gateway in place.

What you have now

• A Cloudflare.AiGateway resource with caching, logging, and (when you want them) rate limiting and auth.
• An Effect LanguageModel.LanguageModel Layer that proxies Workers AI through that gateway.
• /generate and /stream routes that use the standard LanguageModel.generateText and streamText APIs — provider- agnostic, so swapping Workers AI for OpenAI or Anthropic later is a layer-level change, not a code-level one.

The natural next step is to give the model memory. The Add a Chat Agent tutorial wraps the same LanguageModel with Chat.Service and stores the conversation in a Durable Object’s state.storage, so every session is resumable across requests, restarts, and hibernation.

For the wider API surface — generateObject, Toolkit, structured outputs, and how the same LanguageModel composes inside an HTTP API or RPC handler — see the Effect AI guide.