Changelog

Privacy Proxy metrics are now queryable through Cloudflare's GraphQL Analytics API, the new default method for accessing Privacy Proxy observability data. All metrics are available through a single endpoint:

curl https://api.cloudflare.com/client/v4/graphql \
  --header "Authorization: Bearer <API_TOKEN>" \
  --header "Content-Type: application/json" \
  --data '{
    "query": "{ viewer { accounts(filter: { accountTag: $accountTag }) { privacyProxyRequestMetricsAdaptiveGroups(filter: { date_geq: $startDate, date_leq: $endDate }, limit: 10000, orderBy: [date_ASC]) { count dimensions { date } } } } }",
    "variables": {
      "accountTag": "<YOUR_ACCOUNT_TAG>",
      "startDate": "2026-04-04",
      "endDate": "2026-04-06"
    }
  }'

Four GraphQL nodes are now live, providing aggregate metrics across all key dimensions of your Privacy Proxy deployment:

• privacyProxyRequestMetricsAdaptiveGroups — Request volume, error rates, status codes, and proxy status breakdowns.
• privacyProxyIngressConnMetricsAdaptiveGroups — Client-to-proxy connection counts, bytes transferred, and latency percentiles.
• privacyProxyEgressConnMetricsAdaptiveGroups — Proxy-to-origin connection counts, bytes transferred, and latency percentiles.
• privacyProxyAuthMetricsAdaptiveGroups — Authentication attempt counts by method and result.

All nodes support filtering by time, data center (coloCode), and endpoint, with additional node-specific dimensions such as transport protocol and authentication method.

OpenTelemetry-based metrics export remains available. The GraphQL Analytics API is now the recommended default method — a plug-and-play method that requires no collector infrastructure, saving engineering overhead.

• GraphQL Analytics API for Privacy Proxy
• GraphQL Analytics API — getting started

Browser Rendering now supports wrangler browser commands, letting you create, manage, and view browser sessions directly from your terminal, streamlining your workflow. Since Wrangler handles authentication, you do not need to pass API tokens in your commands.

The following commands are available:

The create command spins up a browser instance on Cloudflare's network and returns a session URL. Once created, you can connect to the session using any CDP-compatible client like Puppeteer, Playwright, or MCP clients to automate browsing, scrape content, or debug remotely.

wrangler browser create

Use --keepAlive to set the session keep-alive duration (60-600 seconds):

wrangler browser create --keepAlive 300

The view command auto-selects when only one session exists, or prompts for selection when multiple sessions are available.

All commands support --json for structured output, and because these are CLI commands, you can incorporate them into scripts to automate session management.

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

VPC Network bindings now give your Workers access to any service in your private network without pre-registering individual hosts or ports. This complements existing VPC Service bindings, which scope each binding to a specific host and port.

You can bind to a Cloudflare Tunnel by tunnel_id to reach any service on the network where that tunnel is running, or bind to your Cloudflare Mesh network using cf1:network to reach any Mesh node, client device, or subnet route in your account:

{
  "vpc_networks": [
    {
      "binding": "MESH",
      "network_id": "cf1:network",
      "remote": true
    }
  ]
}

[[vpc_networks]]
binding = "MESH"
network_id = "cf1:network"
remote = true

At runtime, fetch() routes through the network to reach the service at the IP and port you specify:

const response = await env.MESH.fetch("http://10.0.1.50:8080/api/data");

For configuration options and examples, refer to VPC Networks and Connect Workers to Cloudflare Mesh.

Cloudflare Containers and Sandboxes are now generally available.

Containers let you run more workloads on the Workers platform, including resource-intensive applications, different languages, and CLI tools that need full Linux environments.

Since the initial launch of Containers, there have been significant improvements to Containers' performance, stability, and feature set. Some highlights include:

• Higher limits allow you to run thousands of containers concurrently.
• Active-CPU pricing means that you only pay for used CPU cycles.
• Easy connections to Workers and other bindings via hostnames help you extend your Containers with additional functionality.
• Docker Hub support makes it easy to use your existing images and registries.
• SSH support helps you access and debug issues in live containers.

The Sandbox SDK provides isolated environments for running untrusted code securely, with a simple TypeScript API for executing commands, managing files, and exposing services. This makes it easier to secure and manage your agents at scale. Some additions since launch include:

• Live preview URLs so agents can run long-lived services and verify in-flight changes.
• Persistent code interpreters for Python, JavaScript, and TypeScript, with rich structured outputs.
• Interactive PTY terminals for real browser-based terminal access with multiple isolated shells per sandbox.
• Backup and restore APIs to snapshot a workspace and quickly restore an agent's coding session without repeating expensive setup steps.
• Real-time filesystem watching so apps and agents can react immediately to file changes inside a sandbox.

For more information, refer to Containers and Sandbox SDK documentation.

Outbound Workers for Sandboxes and Containers now support zero-trust credential injection, TLS interception, allow/deny lists, and dynamic per-instance egress policies. These features give platforms running agentic workloads full control over what leaves the sandbox, without exposing secrets to untrusted workloads, like user-generated code or coding agents.

Because outbound handlers run in the Workers runtime, outside the sandbox, they can hold secrets the sandbox never sees. A sandboxed workload can make a plain request, and credentials are transparently attached before a request is forwarded upstream.

For instance, you could run an agent in a sandbox and ensure that any requests it makes to Github are authenticated. But it will never be able to access the credentials:

export class MySandbox extends Sandbox {}

MySandbox.outboundByHost = {
	"github.com": (request: Request, env: Env, ctx: OutboundHandlerContext) => {
		const requestWithAuth = new Request(request);
		requestWithAuth.headers.set("x-auth-token", env.SECRET);
		return fetch(requestWithAuth);
	},
};

You can easily inject unique credentials for different instances by using ctx.containerId:

MySandbox.outboundByHost = {
	"my-internal-vcs.dev": async (
		request: Request,
		env: Env,
		ctx: OutboundHandlerContext,
	) => {
		const authKey = await env.KEYS.get(ctx.containerId);

		const requestWithAuth = new Request(request);
		requestWithAuth.headers.set("x-auth-token", authKey);
		return fetch(requestWithAuth);
	},
};

No token is ever passed into the sandbox. You can rotate secrets in the Worker environment and every request will pick them up immediately.

Outbound Workers now intercept HTTPS traffic. A unique ephemeral certificate authority (CA) and private key are created for each sandbox instance. The CA is placed into the sandbox and trusted by default. The ephemeral private key never leaves the container runtime sidecar process and is never shared across instances.

With TLS interception active, outbound Workers can act as a transparent proxy for both HTTP and HTTPS traffic.

Easily filter outbound traffic with allowedHosts and deniedHosts. When allowedHosts is set, it becomes a deny-by-default allowlist. Both properties support glob patterns.

export class MySandbox extends Sandbox {
	allowedHosts = ["github.com", "npmjs.org"];
}

Define named outbound handlers then apply or remove them at runtime using setOutboundHandler() or setOutboundByHost(). This lets you change egress policy for a running sandbox without restarting it.

export class MySandbox extends Sandbox {}

MySandbox.outboundHandlers = {
	allowHosts: async (req: Request, env: Env, ctx: OutboundHandlerContext ) => {
		const url = new URL(req.url);
		if (ctx.params.allowedHostnames.includes(url.hostname)) {
			return fetch(req);
		}
		return new Response(null, { status: 403 });
	},

	noHttp: async () => {
		return new Response(null, { status: 403 });
	},
};

Apply handlers programmatically from your Worker:

const sandbox = getSandbox(env.Sandbox, userId);

// Open network for setup
await sandbox.setOutboundHandler("allowHosts", {
	allowedHostnames: ["github.com", "npmjs.org"],
});
await sandbox.exec("npm install");

// Lock down after setup
await sandbox.setOutboundHandler("noHttp");

Handlers accept params, so you can customize behavior per instance without defining separate handler functions.

Upgrade to @cloudflare/containers@0.3.0 or @cloudflare/sandbox@0.8.9 to use these features.

For more details, refer to Sandbox outbound traffic and Container outbound traffic.

Local Explorer is a browser-based interface and REST API for viewing and editing local resource data during development. It removes the need to write throwaway scripts or dig through .wrangler/state to understand what data your Worker has stored locally.

Local Explorer is available in Wrangler 4.82.1+ and the Cloudflare Vite plugin 1.32.0+. Start a local development session and press e in your terminal, or navigate to /cdn-cgi/explorer on your local dev server.

Local Explorer supports five resource types and works across multiple workers running locally:

• KV — Browse keys, view values and metadata, create, update, and delete key-value pairs.
• R2 — List objects, view metadata, upload files, and delete objects. Supports directory views and multi-select.
• D1 — Browse tables and rows, run arbitrary SQL queries, and edit schemas in a full data studio.
• Durable Objects (SQLite storage) — Browse individual object SQLite tables, run SQL queries, and edit schemas.
• Workflows — List instances, view status and step history, trigger new runs, and pause, resume, restart, or terminate instances.

Local Explorer exposes a REST API at /cdn-cgi/explorer/api that provides programmatic access to the same operations available in the browser. The root endpoint returns an OpenAPI specification ↗ describing all available endpoints, parameters, and response formats.

curl http://localhost:8787/cdn-cgi/explorer/api

Point an AI coding agent at /cdn-cgi/explorer/api and it can discover and interact with your local resources without manual setup. This enables iterative development loops where an agent can populate test data in KV or D1, inspect Durable Object state, trigger Workflow runs, or upload files to R2.

For more details, refer to the Local Explorer documentation.

Browser Rendering now exposes the Chrome DevTools Protocol (CDP), the low-level protocol that powers browser automation. The growing ecosystem of CDP-based agent tools, along with existing CDP automation scripts, can now use Browser Rendering directly.

Any CDP-compatible client, including Puppeteer and Playwright, can connect from any environment, whether that is Cloudflare Workers, your local machine, or a cloud environment. All you need is your Cloudflare API key.

For any existing CDP script, switching to Browser Rendering is a one-line change:

const puppeteer = require("puppeteer-core");

const browser = await puppeteer.connect({
	browserWSEndpoint: `wss://api.cloudflare.com/client/v4/accounts/${ACCOUNT_ID}/browser-rendering/devtools/browser?keep_alive=600000`,
	headers: { Authorization: `Bearer ${API_TOKEN}` },
});

const page = await browser.newPage();
await page.goto("https://example.com");
console.log(await page.title());
await browser.close();

Additionally, MCP clients like Claude Desktop, Claude Code, Cursor, and OpenCode can now use Browser Rendering as their remote browser via the chrome-devtools-mcp ↗ package.

Here is an example of how to configure Browser Rendering for Claude Desktop:

{
	"mcpServers": {
		"browser-rendering": {
			"command": "npx",
			"args": [
				"-y",
				"chrome-devtools-mcp@latest",
				"--wsEndpoint=wss://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/browser-rendering/devtools/browser?keep_alive=600000",
				"--wsHeaders={\"Authorization\":\"Bearer <API_TOKEN>\"}"
			]
		}
	}
}

To get started, refer to the CDP documentation.

The simultaneous open connections limit has been relaxed. Previously, each Worker invocation was limited to six open connections at a time for the entire lifetime of each connection, including while reading the response body. Now, a connection is freed as soon as response headers arrive, so the six-connection limit only constrains how many connections can be in the initial "waiting for headers" phase simultaneously.

This means Workers can now have many more connections open at the same time without queueing, as long as no more than six are waiting for their initial response. This eliminates the Response closed due to connection limit exception that could previously occur when the runtime canceled stalled connections to prevent deadlocks.

Previously, the runtime used a deadlock avoidance algorithm that watched each open connection for I/O activity. If all six connections appeared idle — even momentarily — the runtime would cancel the least-recently-used connection to make room for new requests. In practice, this heuristic was fragile. For example, when a response used Content-Encoding: gzip, the runtime's internal decompression created brief gaps between read and write operations. During these gaps, the connection appeared stalled despite being actively read by the Worker. If multiple connections hit these gaps at the same time, the runtime could spuriously cancel a connection that was working correctly. By only counting connections during the waiting-for-headers phase — where the runtime is fully in control and there is no ambiguity about whether the connection is active — this class of bug is eliminated entirely.

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