Changelog

AI Search now supports CSS content selectors for website data sources. You can now define which parts of a crawled page are extracted and indexed by specifying CSS selectors paired with URL glob patterns.

Content selectors solve the problem of indexing only relevant content while ignoring navigation, sidebars, footers, and other boilerplate. When a page URL matches a glob pattern, only elements matching the corresponding CSS selector are extracted and converted to Markdown for indexing.

Configure content selectors via the dashboard or API:

curl "https://api.cloudflare.com/client/v4/accounts/{account_id}/ai-search/instances" \
  -H "Authorization: Bearer {api_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "id": "my-ai-search",
    "source": "https://example.com",
    "type": "web-crawler",
    "source_params": {
      "web_crawler": {
        "parse_options": {
          "content_selector": [
            {
              "path": "**/blog/**",
              "selector": "article .post-body"
            }
          ]
        }
      }
    }
  }'

Selectors are evaluated in order, and the first matching pattern wins. You can define up to 10 content selector entries per instance.

For configuration details and examples, refer to the content selectors documentation.

AI Search now supports four additional Workers AI models across text generation and embedding.

GLM-4.7-Flash is a lightweight model from Zhipu AI with a 131,072 token context window, suitable for long-document summarization and retrieval tasks. Qwen3-30B-A3B is a mixture-of-experts model from Alibaba that activates only 3 billion parameters per forward pass, keeping inference fast while maintaining strong response quality.

Qwen3-Embedding-0.6B supports up to 4,096 input tokens, making it a good fit for indexing longer text chunks. EmbeddingGemma-300M from Google produces 768-dimension vectors and is optimized for low-latency embedding workloads.

All four models are available without additional provider keys since they run on Workers AI. Select them when creating or updating an AI Search instance in the dashboard or through the API.

For the full list of supported models, refer to Supported models.

The Workers runtime now automatically sends a reciprocal Close frame when it receives a Close frame from the peer. The readyState transitions to CLOSED before the close event fires. This matches the WebSocket specification ↗ and standard browser behavior.

This change is enabled by default for Workers using compatibility dates on or after 2026-04-07 (via the web_socket_auto_reply_to_close compatibility flag). Existing code that manually calls close() inside the close event handler will continue to work — the call is silently ignored when the WebSocket is already closed.

const [client, server] = Object.values(new WebSocketPair());
server.accept();

server.addEventListener("close", (event) => {
	// readyState is already CLOSED — no need to call server.close().
	console.log(server.readyState); // WebSocket.CLOSED
	console.log(event.code); // 1000
	console.log(event.wasClean); // true
});

The automatic close behavior can interfere with WebSocket proxying, where a Worker sits between a client and a backend and needs to coordinate the close on both sides independently. To support this use case, pass { allowHalfOpen: true } to accept():

const [client, server] = Object.values(new WebSocketPair());

server.accept({ allowHalfOpen: true });

server.addEventListener("close", (event) => {
	// readyState is still CLOSING here, giving you time
	// to coordinate the close on the other side.
	console.log(server.readyState); // WebSocket.CLOSING

	// Manually close when ready.
	server.close(event.code, "done");
});

For more information, refer to WebSockets Close behavior.

You can now specify placement constraints to control where your Containers run.

Use regions to limit placement to specific geographic areas. Use jurisdiction to restrict containers to compliance boundaries — eu maps to European regions (EEUR, WEUR) and fedramp maps to North American regions (ENAM, WNAM).

Refer to Containers placement for more details.

We are partnering with Google to bring @cf/google/gemma-4-26b-a4b-it to Workers AI. Gemma 4 26B A4B is a Mixture-of-Experts (MoE) model built from Gemini 3 research, with 26B total parameters and only 4B active per forward pass. By activating a small subset of parameters during inference, the model runs almost as fast as a 4B-parameter model while delivering the quality of a much larger one.

Gemma 4 is Google's most capable family of open models, designed to maximize intelligence-per-parameter.

• Mixture-of-Experts architecture with 8 active experts out of 128 total (plus 1 shared expert), delivering frontier-level performance at a fraction of the compute cost of dense models
• 256,000 token context window for retaining full conversation history, tool definitions, and long documents across extended sessions
• Built-in thinking mode that lets the model reason step-by-step before answering, improving accuracy on complex tasks
• Vision understanding for object detection, document and PDF parsing, screen and UI understanding, chart comprehension, OCR (including multilingual), and handwriting recognition, with support for variable aspect ratios and resolutions
• Function calling with native support for structured tool use, enabling agentic workflows and multi-step planning
• Multilingual with out-of-the-box support for 35+ languages, pre-trained on 140+ languages
• Coding for code generation, completion, and correction

Use Gemma 4 26B A4B through the Workers AI binding (env.AI.run()), the REST API at /run or /v1/chat/completions, or the OpenAI-compatible endpoint.

For more information, refer to the Gemma 4 26B A4B model page.

AI Gateway now supports automatic retries at the gateway level. When an upstream provider returns an error, your gateway retries the request based on the retry policy you configure, without requiring any client-side changes.

You can configure the retry count (up to 5 attempts), the delay between retries (from 100ms to 5 seconds), and the backoff strategy (Constant, Linear, or Exponential). These defaults apply to all requests through the gateway, and per-request headers can override them.

This is particularly useful when you do not control the client making the request and cannot implement retry logic on the caller side. For more complex failover scenarios — such as failing across different providers — use Dynamic Routing.

For more information, refer to Manage gateways.

All wrangler workflows commands now accept a --local flag to target a Workflow running in a local wrangler dev session instead of the production API.

You can now manage the full Workflow lifecycle locally, including triggering Workflows, listing instances, pausing, resuming, restarting, terminating, and sending events:

npx wrangler workflows list --local
npx wrangler workflows trigger my-workflow --local
npx wrangler workflows instances list my-workflow --local
npx wrangler workflows instances pause my-workflow <INSTANCE_ID> --local
npx wrangler workflows instances send-event my-workflow <INSTANCE_ID> --type my-event --local

All commands also accept --port to target a specific wrangler dev session (defaults to 8787).

For more information, refer to Workflows local development.

AI Search supports a wrangler ai-search command namespace. Use it to manage instances from the command line.

The following commands are available:

The create command guides you through setup, choosing a name, source type (r2 or web), and data source. You can also pass all options as flags for non-interactive use:

wrangler ai-search create my-instance --type r2 --source my-bucket

Use wrangler ai-search search to query an instance directly from the CLI:

wrangler ai-search search my-instance --query "how do I configure caching?"

All commands support --json for structured output that scripts and AI agents can parse directly.

For full usage details, refer to the Wrangler commands documentation.

Workers Builds now supports Deploy Hooks — trigger builds from your headless CMS, a Cron Trigger, a Slack bot, or any system that can send an HTTP request.

Each Deploy Hook is a unique URL tied to a specific branch. Send it a POST and your Worker builds and deploys.

curl -X POST "https://api.cloudflare.com/client/v4/workers/builds/deploy_hooks/<DEPLOY_HOOK_ID>"

To create one, go to Workers & Pages > your Worker > Settings > Builds > Deploy Hooks.

Since a Deploy Hook is a URL, you can also call it from another Worker. For example, a Worker with a Cron Trigger can rebuild your project on a schedule:

export default {
	async scheduled(event, env, ctx) {
		ctx.waitUntil(fetch(env.DEPLOY_HOOK_URL, { method: "POST" }));
	},
};

export default {
  async scheduled(event: ScheduledEvent, env: Env, ctx: ExecutionContext): Promise<void> {
    ctx.waitUntil(fetch(env.DEPLOY_HOOK_URL, { method: "POST" }));
  },
} satisfies ExportedHandler<Env>;

You can also use Deploy Hooks to rebuild when your CMS publishes new content or deploy from a Slack slash command.

• Automatic deduplication: If a Deploy Hook fires multiple times before the first build starts running, redundant builds are automatically skipped. This keeps your build queue clean when webhooks retry or CMS events arrive in bursts.
• Last triggered: The dashboard shows when each hook was last triggered.
• Build source: Your Worker's build history shows which Deploy Hook started each build by name.

Deploy Hooks are rate limited to 10 builds per minute per Worker and 100 builds per minute per account. For all limits, see Limits & pricing.

To get started, read the Deploy Hooks documentation.

Three new properties are now available on request.cf in Workers that expose Layer 4 transport telemetry from the client connection. These properties let your Worker make decisions based on real-time connection quality signals — such as round-trip time and data delivery rate — without requiring any client-side changes.

Previously, this telemetry was only available via the Server-Timing: cfL4 response header. These new properties surface the same data directly in the Workers runtime, so you can use it for routing, logging, or response customization.

export default {
  async fetch(request) {
    const cf = request.cf;

    const rtt = cf.clientTcpRtt ?? cf.clientQuicRtt ?? 0;
    const deliveryRate = cf.edgeL4?.deliveryRate ?? 0;
    const transport = cf.clientTcpRtt ? "TCP" : "QUIC";

    console.log(`Transport: ${transport}, RTT: ${rtt}ms, Delivery rate: ${deliveryRate} B/s`);

    const headers = new Headers(request.headers);
    headers.set("X-Client-RTT", String(rtt));
    headers.set("X-Delivery-Rate", String(deliveryRate));

    return fetch(new Request(request, { headers }));
  },
};

For more information, refer to Workers Runtime APIs: Request.

Four new fields are now available on request.cf.tlsClientAuth in Workers for requests that include a mutual TLS (mTLS) client certificate. These fields encode the client certificate and its intermediate chain in RFC 9440 ↗ format — the same standard format used by the Client-Cert and Client-Cert-Chain HTTP headers — so your Worker can forward them directly to your origin without any custom parsing or encoding logic.

export default {
  async fetch(request) {
    const tls = request.cf.tlsClientAuth;

    // Only forward if cert was verified and chain is complete
    if (!tls || !tls.certVerified || tls.certRevoked || tls.certChainRFC9440TooLarge) {
      return new Response("Unauthorized", { status: 401 });
    }

    const headers = new Headers(request.headers);
    headers.set("Client-Cert", tls.certRFC9440);
    headers.set("Client-Cert-Chain", tls.certChainRFC9440);

    return fetch(new Request(request, { headers }));
  },
};

For more information, refer to Client certificate variables and Mutual TLS authentication.

Containers and Sandboxes now support connecting directly to Workers over HTTP. This allows you to call Workers functions and bindings, like KV or R2, from within the container at specific hostnames.

Define an outbound handler to capture any HTTP request or use outboundByHost to capture requests to individual hostnames and IPs.

export class MyApp extends Sandbox {}

MyApp.outbound = async (request, env, ctx) => {
	// you can run arbitrary functions defined in your Worker on any HTTP request
	return await someWorkersFunction(request.body);
};

MyApp.outboundByHost = {
	"my.worker": async (request, env, ctx) => {
		return await anotherFunction(request.body);
	},
};

In this example, requests from the container to http://my.worker will run the function defined within outboundByHost, and any other HTTP requests will run the outbound handler. These handlers run entirely inside the Workers runtime, outside of the container sandbox.

Each handler has access to env, so it can call any binding set in Wrangler config. Code inside the container makes a standard HTTP request to that hostname and the outbound Worker translates it into a binding call.

export class MyApp extends Sandbox {}

MyApp.outboundByHost = {
	"my.kv": async (request, env, ctx) => {
		const key = new URL(request.url).pathname.slice(1);
		const value = await env.KV.get(key);
		return new Response(value ?? "", { status: value ? 200 : 404 });
	},
	"my.r2": async (request, env, ctx) => {
		const key = new URL(request.url).pathname.slice(1);
		const object = await env.BUCKET.get(key);
		return new Response(object?.body ?? "", { status: object ? 200 : 404 });
	},
};

Now, from inside the container sandbox, curl http://my.kv/some-key will access Workers KV and curl http://my.r2/some-object will access R2.

Use ctx.containerId to reference the container's automatically provisioned Durable Object.

export class MyContainer extends Container {}

MyContainer.outboundByHost = {
	"get-state.do": async (request, env, ctx) => {
		const id = env.MY_CONTAINER.idFromString(ctx.containerId);
		const stub = env.MY_CONTAINER.get(id);
		return stub.getStateForKey(request.body);
	},
};

This provides an easy way to associate state with any container instance, and includes a built-in SQLite database.

Upgrade to @cloudflare/containers version 0.2.0 or later, or @cloudflare/sandbox version 0.8.0 or later to use outbound Workers.

Refer to Containers outbound traffic and Sandboxes outbound traffic for more details and examples.

ctx.id.jurisdiction inside a Durable Object now reports the jurisdiction the object was created in — for example "eu" when accessed through env.MY_DURABLE_OBJECT.jurisdiction("eu") — so you can make region-aware decisions without passing the jurisdiction through method arguments or persisting it in storage. For the full list of ID-construction paths that preserve jurisdiction, refer to the Durable Object ID documentation.

export class RegionalRoom extends DurableObject {
	async fetch(request) {
		// "eu" when accessed through env.MY_DURABLE_OBJECT.jurisdiction("eu")
		const region = this.ctx.id.jurisdiction;
		return new Response(`Hello from ${region ?? "the default region"}!`);
	}
}

// Worker
export default {
	async fetch(request, env) {
		const stub = env.MY_DURABLE_OBJECT.jurisdiction("eu").getByName("general");
		return stub.fetch(request);
	},
};

ctx.id.jurisdiction is undefined for Durable Objects that were not created in a jurisdiction-restricted namespace. Alarms scheduled before 2026-03-15 also do not have jurisdiction stored; to backfill the value, reschedule the alarm from a fetch() or RPC handler.

The new secrets configuration property lets you declare the secret names your Worker requires in your Wrangler configuration file. Required secrets are validated during local development and deploy, and used as the source of truth for type generation.

{
	"secrets": {
		"required": ["API_KEY", "DB_PASSWORD"],
	},
}

[secrets]
required = [ "API_KEY", "DB_PASSWORD" ]

When secrets is defined, wrangler dev and vite dev load only the keys listed in secrets.required from .dev.vars or .env/process.env. Additional keys in those files are excluded. If any required secrets are missing, a warning is logged listing the missing names.

wrangler types generates typed bindings from secrets.required instead of inferring names from .dev.vars or .env. This lets you run type generation in CI or other environments where those files are not present. Per-environment secrets are supported — the aggregated Env type marks secrets that only appear in some environments as optional.

wrangler deploy and wrangler versions upload validate that all secrets in secrets.required are configured on the Worker before the operation succeeds. If any required secrets are missing, the command fails with an error listing which secrets need to be set.

For more information, refer to the secrets configuration property reference.

Containers now support Docker Hub ↗ images. You can use a fully qualified Docker Hub image reference in your Wrangler configuration ↗ instead of first pushing the image to Cloudflare Registry.

{
	"containers": [
		{
			// Example: docker.io/cloudflare/sandbox:0.7.18
			"image": "docker.io/<NAMESPACE>/<REPOSITORY>:<TAG>",
		},
	],
}

[[containers]]
image = "docker.io/<NAMESPACE>/<REPOSITORY>:<TAG>"

Containers also support private Docker Hub images. To configure credentials, refer to Use private Docker Hub images.

For more information, refer to Image management.

Dynamic Workers are now in open beta ↗ for all paid Workers users. You can now have a Worker spin up other Workers, called Dynamic Workers, at runtime to execute code on-demand in a secure, sandboxed environment. Dynamic Workers start in milliseconds, making them well suited for fast, secure code execution at scale.

• Code Mode: LLMs are trained to write code. Run tool-calling logic written in code instead of stepping through many tool calls, which can save up to 80% in inference tokens and cost.
• AI agents executing code: Run code for tasks like data analysis, file transformation, API calls, and chained actions.
• Running AI-generated code: Run generated code for prototypes, projects, and automations in a secure, isolated sandboxed environment.
• Fast development and previews: Load prototypes, previews, and playgrounds in milliseconds.
• Custom automations: Create custom tools on the fly that execute a task, call an integration, or automate a workflow.

Dynamic Workers support two loading modes:

• load(code) — for one-time code execution (equivalent to calling get() with a null ID).
• get(id, callback) — caches a Dynamic Worker by ID so it can stay warm across requests. Use this when the same code will receive subsequent requests.

export default {
	async fetch(request, env) {
		const worker = env.LOADER.load({
			compatibilityDate: "2026-01-01",
			mainModule: "src/index.js",
			modules: {
				"src/index.js": `
					export default {
						fetch() {
							return new Response("Hello from a dynamic Worker");
						},
					};
				`,
			},
			// Block all outbound network access from the Dynamic Worker.
			globalOutbound: null,
		});

		return worker.getEntrypoint().fetch(request);
	},
};

export default {
	async fetch(request: Request, env: Env): Promise<Response> {
		const worker = env.LOADER.load({
			compatibilityDate: "2026-01-01",
			mainModule: "src/index.js",
			modules: {
				"src/index.js": `
					export default {
						fetch() {
							return new Response("Hello from a dynamic Worker");
						},
					};
				`,
			},
			// Block all outbound network access from the Dynamic Worker.
			globalOutbound: null,
		});

		return worker.getEntrypoint().fetch(request);
	},
};

Here are 3 new libraries to help you build with Dynamic Workers:

• @cloudflare/codemode ↗: Replace individual tool calls with a single code() tool, so LLMs write and execute TypeScript that orchestrates multiple API calls in one pass.

• @cloudflare/worker-bundler ↗: Resolve npm dependencies and bundle source files into ready-to-load modules for Dynamic Workers, all at runtime.

• @cloudflare/shell ↗: Give your agent a virtual filesystem inside a Dynamic Worker with persistent storage backed by SQLite and R2.

Dynamic Workers Starter

Use this starter ↗ to deploy a Worker that can load and execute Dynamic Workers.

Dynamic Workers Playground

Deploy the Dynamic Workers Playground ↗ to write or import code, bundle it at runtime with @cloudflare/worker-bundler, execute it through a Dynamic Worker, and see real-time responses and execution logs.

For the full API reference and configuration options, refer to the Dynamic Workers documentation.

Dynamic Workers pricing is based on three dimensions: Dynamic Workers created daily, requests, and CPU time.

¹ Uses Workers Standard rates and will appear as part of your existing Workers bill, not as separate Dynamic Workers charges.

Note: Dynamic Workers requests and CPU time are already billed as part of your Workers plan and will count toward your Workers requests and CPU usage. The Dynamic Workers created daily charge is not yet active — you will not be billed for the number of Dynamic Workers created at this time. Pricing information is shared in advance so you can estimate future costs.

Workflow instance methods pause(), resume(), restart(), and terminate() are now available in local development when using wrangler dev.

You can now test the full Workflow instance lifecycle locally:

const instance = await env.MY_WORKFLOW.create({
	id: "my-instance-id",
});

await instance.pause(); // pauses a running workflow instance
await instance.resume(); // resumes a paused instance
await instance.restart(); // restarts the instance from the beginning
await instance.terminate(); // terminates the instance immediately

The latest release of the Agents SDK ↗ exposes agent state as a readable property, prevents duplicate schedule rows across Durable Object restarts, brings full TypeScript inference to AgentClient, and migrates to Zod 4.

Both useAgent (React) and AgentClient (vanilla JS) now expose a state property that reflects the current agent state. Previously, reading state required manually tracking it through the onStateUpdate callback.

React (useAgent)

const agent = useAgent({
	agent: "game-agent",
	name: "room-123",
});

// Read state directly — no separate useState + onStateUpdate needed
return <div>Score: {agent.state?.score}</div>;

// Spread for partial updates
agent.setState({ ...agent.state, score: (agent.state?.score ?? 0) + 10 });

const agent = useAgent<GameAgent, GameState>({
	agent: "game-agent",
	name: "room-123",
});

// Read state directly — no separate useState + onStateUpdate needed
return <div>Score: {agent.state?.score}</div>;

// Spread for partial updates
agent.setState({ ...agent.state, score: (agent.state?.score ?? 0) + 10 });

agent.state is reactive — the component re-renders when state changes from either the server or a client-side setState() call.

Vanilla JS (AgentClient)

const client = new AgentClient({
	agent: "game-agent",
	name: "room-123",
	host: "your-worker.workers.dev",
});

client.setState({ score: 100 });
console.log(client.state); // { score: 100 }

const client = new AgentClient<GameAgent>({
	agent: "game-agent",
	name: "room-123",
	host: "your-worker.workers.dev",
});

client.setState({ score: 100 });
console.log(client.state); // { score: 100 }

State starts as undefined and is populated when the server sends the initial state on connect (from initialState) or when setState() is called. Use optional chaining (agent.state?.field) for safe access. The onStateUpdate callback continues to work as before — the new state property is additive.

schedule() now supports an idempotent option that deduplicates by (type, callback, payload), preventing duplicate rows from accumulating when called in places that run on every Durable Object restart such as onStart().

Cron schedules are idempotent by default. Calling schedule("0 * * * *", "tick") multiple times with the same callback, expression, and payload returns the existing schedule row instead of creating a new one. Pass { idempotent: false } to override.

Delayed and date-scheduled types support opt-in idempotency:

import { Agent } from "agents";

class MyAgent extends Agent {
	async onStart() {
		// Safe across restarts — only one row is created
		await this.schedule(60, "maintenance", undefined, { idempotent: true });
	}
}

import { Agent } from "agents";

class MyAgent extends Agent {
	async onStart() {
		// Safe across restarts — only one row is created
		await this.schedule(60, "maintenance", undefined, { idempotent: true });
	}
}

Two new warnings help catch common foot-guns:

• Calling schedule() inside onStart() without { idempotent: true } emits a console.warn with actionable guidance (once per callback; skipped for cron and when idempotent is set explicitly).
• If an alarm cycle processes 10 or more stale one-shot rows for the same callback, the SDK emits a console.warn and a schedule:duplicate_warning diagnostics channel event.

AgentClient now accepts an optional agent type parameter for full type inference on RPC calls, matching the typed experience already available with useAgent.

const client = new AgentClient({
	agent: "my-agent",
	host: window.location.host,
});

// Typed call — method name autocompletes, args and return type inferred
const value = await client.call("getValue");

// Typed stub — direct RPC-style proxy
await client.stub.getValue();
await client.stub.add(1, 2);

const client = new AgentClient<MyAgent>({
	agent: "my-agent",
	host: window.location.host,
});

// Typed call — method name autocompletes, args and return type inferred
const value = await client.call("getValue");

// Typed stub — direct RPC-style proxy
await client.stub.getValue();
await client.stub.add(1, 2);

State is automatically inferred from the agent type, so onStateUpdate is also typed:

const client = new AgentClient({
	agent: "my-agent",
	host: window.location.host,
	onStateUpdate: (state) => {
		// state is typed as MyAgent's state type
	},
});

const client = new AgentClient<MyAgent>({
	agent: "my-agent",
	host: window.location.host,
	onStateUpdate: (state) => {
		// state is typed as MyAgent's state type
	},
});

Existing untyped usage continues to work without changes. The RPC type utilities (AgentMethods, AgentStub, RPCMethods) are now exported from agents/client for advanced typing scenarios. agents, @cloudflare/ai-chat, and @cloudflare/codemode now require zod ^4.0.0. Zod v3 is no longer supported.

• Turn serialization — onChatMessage() and _reply() work is now queued so user requests, tool continuations, and saveMessages() never stream concurrently.
• Duplicate messages on stop — Clicking stop during an active stream no longer splits the assistant message into two entries.
• Duplicate messages after tool calls — Orphaned client IDs no longer leak into persistent storage.

keepAlive() now uses a lightweight in-memory ref count instead of schedule rows. Multiple concurrent callers share a single alarm cycle. The @experimental tag has been removed from both keepAlive() and keepAliveWhile().

A new entry point @cloudflare/codemode/tanstack-ai adds support for TanStack AI's ↗ chat() as an alternative to the Vercel AI SDK's streamText():

import {
	createCodeTool,
	tanstackTools,
} from "@cloudflare/codemode/tanstack-ai";
import { chat } from "@tanstack/ai";

const codeTool = createCodeTool({
	tools: [tanstackTools(myServerTools)],
	executor,
});

const stream = chat({ adapter, tools: [codeTool], messages });

import { createCodeTool, tanstackTools } from "@cloudflare/codemode/tanstack-ai";
import { chat } from "@tanstack/ai";

const codeTool = createCodeTool({
	tools: [tanstackTools(myServerTools)],
	executor,
});

const stream = chat({ adapter, tools: [codeTool], messages });

To update to the latest version:

npm i agents@latest @cloudflare/ai-chat@latest

AI Search now offers new REST API endpoints for search and chat that use an OpenAI compatible format. This means you can use the familiar messages array structure that works with existing OpenAI SDKs and tools. The messages array also lets you pass previous messages within a session, so the model can maintain context across multiple turns.

Here is an example request to the Chat Completions endpoint using the new messages array format:

curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai-search/instances/{NAME}/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {API_TOKEN}" \
  -d '{
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful documentation assistant."
      },
      {
        "role": "user",
        "content": "How do I get started?"
      }
    ]
  }'

For more details, refer to the AI Search REST API guide.

If you are using the previous AutoRAG API endpoints (/autorag/rags/), we recommend migrating to the new endpoints. The previous AutoRAG API endpoints will continue to be fully supported.

Refer to the migration guide for step-by-step instructions.

AI Search now supports public endpoints, UI snippets, and MCP, making it easy to add search to your website or connect AI agents.

Public endpoints allow you to expose AI Search capabilities without requiring API authentication. To enable public endpoints:

1. Go to AI Search in the Cloudflare dashboard. Go to AI Search ↗
2. Select your instance, and turn on Public Endpoint in Settings. For more details, refer to Public endpoint configuration.

UI snippets are pre-built search and chat components you can embed in your website. Visit search.ai.cloudflare.com ↗ to configure and preview components for your AI Search instance.

To add a search modal to your page:

<script
	type="module"
	src="https://<PUBLIC_ENDPOINT_ID>.search.ai.cloudflare.com/assets/v0.0.25/search-snippet.es.js"
></script>

<search-modal-snippet
	api-url="https://<PUBLIC_ENDPOINT_ID>.search.ai.cloudflare.com/"
	placeholder="Search..."
>
</search-modal-snippet>

For more details, refer to the UI snippets documentation.

The MCP endpoint allows AI agents to search your content via the Model Context Protocol. Connect your MCP client to:

https://<PUBLIC_ENDPOINT_ID>.search.ai.cloudflare.com/mcp

For more details, refer to the MCP documentation.

AI Search now supports custom metadata filtering, allowing you to define your own metadata fields and filter search results based on attributes like category, version, or any custom field you define.

You can define up to 5 custom metadata fields per AI Search instance. Each field has a name and data type (text, number, or boolean):

curl -X POST https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai-search/instances \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {API_TOKEN}" \
  -d '{
    "id": "my-instance",
    "type": "r2",
    "source": "my-bucket",
    "custom_metadata": [
      { "field_name": "category", "data_type": "text" },
      { "field_name": "version", "data_type": "number" },
      { "field_name": "is_public", "data_type": "boolean" }
    ]
  }'

How you attach metadata depends on your data source:

• R2 bucket: Set metadata using S3-compatible custom headers (x-amz-meta-*) when uploading objects. Refer to R2 custom metadata for examples.
• Website: Add <meta> tags to your HTML pages. Refer to Website custom metadata for details.

Use custom metadata fields in your search queries alongside built-in attributes like folder and timestamp:

curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai-search/instances/{NAME}/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {API_TOKEN}" \
  -d '{
    "messages": [
      {
        "content": "How do I configure authentication?",
        "role": "user"
      }
    ],
    "ai_search_options": {
      "retrieval": {
        "filters": {
          "category": "documentation",
          "version": { "$gte": 2.0 }
        }
      }
    }
  }'

Learn more in the metadata filtering documentation.

R2 SQL now supports an expanded SQL grammar so you can write richer analytical queries without exporting data. This release adds CASE expressions, column aliases, arithmetic in clauses, 163 scalar functions, 33 aggregate functions, EXPLAIN, Common Table Expressions (CTEs),and full struct/array/map access. R2 SQL is Cloudflare's serverless, distributed, analytics query engine for querying Apache Iceberg ↗ tables stored in R2 Data Catalog. This page documents the supported SQL syntax.

• Column aliases — SELECT col AS alias now works in all clauses
• CASE expressions — conditional logic directly in SQL (searched and simple forms)
• Scalar functions — 163 new functions across math, string, datetime, regex, crypto, encoding, and type inspection categories
• Aggregate functions — statistical (variance, stddev, correlation, regression), bitwise, boolean, and positional aggregates join the existing basic and approximate functions
• Complex types — query struct fields with bracket notation, use 46 array functions, and extract map keys/values
• Common table expressions (CTEs) — use WITH ... AS to define named temporary result sets. Chained CTEs are supported. All CTEs must reference the same single table.
• Full expression support — arithmetic, type casting (CAST, TRY_CAST, :: shorthand), and EXTRACT in SELECT, WHERE, GROUP BY, HAVING, and ORDER BY

SELECT source,
    CASE
        WHEN AVG(price) > 30 THEN 'premium'
        WHEN AVG(price) > 10 THEN 'mid-tier'
        ELSE 'budget'
    END AS tier,
    round(stddev(price), 2) AS price_volatility,
    approx_percentile_cont(price, 0.95) AS p95_price
FROM my_namespace.sales_data
GROUP BY source

SELECT product_name,
    pricing['price'] AS price,
    array_to_string(tags, ', ') AS tag_list
FROM my_namespace.products
WHERE array_has(tags, 'Action')
ORDER BY pricing['price'] DESC
LIMIT 10

WITH monthly AS (
    SELECT date_trunc('month', sale_timestamp) AS month,
        department,
        COUNT(*) AS transactions,
        round(AVG(total_amount), 2) AS avg_amount
    FROM my_namespace.sales_data
    WHERE sale_timestamp BETWEEN '2025-01-01T00:00:00Z' AND '2025-12-31T23:59:59Z'
    GROUP BY date_trunc('month', sale_timestamp), department
),
ranked AS (
    SELECT month, department, transactions, avg_amount,
        CASE
            WHEN avg_amount > 1000 THEN 'high-value'
            WHEN avg_amount > 500 THEN 'mid-value'
            ELSE 'standard'
        END AS tier
    FROM monthly
    WHERE transactions > 100
)
SELECT * FROM ranked
ORDER BY month, avg_amount DESC

For the full function reference and syntax details, refer to the SQL reference. For limitations and best practices, refer to Limitations and best practices.

In the Cloudflare One dashboard, the overview page for a specific Cloudflare Tunnel now shows all replicas of that tunnel and supports streaming logs from multiple replicas at once.

Previously, you could only stream logs from one replica at a time. With this update:

• Replicas on the tunnel overview — All active replicas for the selected tunnel now appear on that tunnel's overview page under Connectors. Select any replica to stream its logs.
• Multi-connector log streaming — Stream logs from multiple replicas simultaneously, making it easier to correlate events across your infrastructure during debugging or incident response. To try it out, log in to Cloudflare One ↗ and go to Networks > Connectors > Cloudflare Tunnels. Select View logs next to the tunnel you want to monitor.

For more information, refer to Tunnel log streams and Deploy replicas.

Each VPC Service now has a Metrics tab so you can monitor connection health and debug failures without leaving the dashboard.

• Connections — See successful and failed connections over time, broken down by what is responsible: your origin (Bad Upstream), your configuration (Client), or Cloudflare (Internal).
• Latency — Track connection and DNS resolution latency trends.
• Errors — Drill into specific error codes grouped by category, with filters to isolate upstream, client, or internal failures.

You can also view and edit your VPC Service configuration, host details, and port assignments from the Settings tab.

For a full list of error codes and what they mean, refer to Troubleshooting.

Hyperdrive now supports custom TLS/SSL certificates for MySQL databases, bringing the same certificate options previously available for PostgreSQL to MySQL connections.

You can now configure:

• Server certificate verification with VERIFY_CA or VERIFY_IDENTITY SSL modes to verify that your MySQL database server's certificate is signed by the expected certificate authority (CA).
• Client certificates (mTLS) for Hyperdrive to authenticate itself to your MySQL database with credentials beyond username and password.

Create a Hyperdrive configuration with custom certificates for MySQL:

# Upload a CA certificate
npx wrangler cert upload certificate-authority --ca-cert your-ca-cert.pem --name your-custom-ca-name

# Create a Hyperdrive with VERIFY_IDENTITY mode
npx wrangler hyperdrive create your-hyperdrive-config \
  --connection-string="mysql://user:password@hostname:port/database" \
  --ca-certificate-id <CA_CERT_ID> \
  --sslmode VERIFY_IDENTITY

For more information, refer to SSL/TLS certificates for Hyperdrive and MySQL TLS/SSL modes.