Developer Platform Updates & Release Notes

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/local/explorer on your local dev server.

Supported resources

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.

OpenAPI-powered REST API

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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. …

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.

Before: New connections are blocked until an earlier connection fully completes

A 7th fetch is queued until an earlier connection fully completes, including reading its entire response body

After: New connections can start as soon as response headers arrive

A 7th fetch starts as soon as any earlier connection receives its response headers …

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. …

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

Text generation

Model

Context window (tokens)

@cf/zai-org/glm-4.7-flash

131,072

@cf/qwen/qwen3-30b-a3b-fp8

32,000

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.

Embedding

Model

Vector dims

Input tokens

Metric

@cf/qwen/qwen3-embedding-0.6b

1,024

4,096

cosine

@cf/google/embeddinggemma-300m

768

512

cosine

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
});

Half-open mode for WebSocket proxying

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():

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

Constraint

Values

Use case

regions

ENAM, WNAM, EEUR, WEUR

Geographic placement

jurisdiction

eu, fedramp

Compliance boundaries

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.

Key capabilities

• 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 …

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.

Retry Requests settings in the AI Gateway dashboard

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:

Command

Description

wrangler ai-search create

Create a new instance with an interactive wizard

wrangler ai-search list

List all instances in your account

wrangler ai-search get

Get details of a specific instance

wrangler ai-search update

Update the configuration of an instance

wrangler ai-search delete

Delete an instance

wrangler ai-search search

Run a search query against an instance

wrangler ai-search stats

Get usage statistics for an instance

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.

Built-in optimizations

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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.

New properties

Property

Type

Description

clientTcpRtt

number | undefined

The smoothed TCP round-trip time (RTT) between Cloudflare and the client in milliseconds. Only present for TCP connections (HTTP/1, HTTP/2). For example, 22.

clientQuicRtt

number | undefined

The smoothed QUIC round-trip time (RTT) between Cloudflare and the client in milliseconds. Only present for QUIC connections (HTTP/3). For example, 42.

edgeL4

Object | undefined

Layer 4 transport statistics. Contains deliveryRate (number) — the most recent data delivery rate estimate for the connection, in bytes per second. For example, 123456.

Example: Log connection quality metrics

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

    const rtt = cf.clientTcpRtt ?? cf.clientQuicRtt ?? 0; …

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.

New fields

Field

Type

Description

certRFC9440

String

The client leaf certificate in RFC 9440 format (:base64-DER:). Empty if no client certificate was presented.

certRFC9440TooLarge

Boolean

true if the leaf certificate exceeded 10 KB and was omitted from certRFC9440.

certChainRFC9440

String

The intermediate certificate chain in RFC 9440 format as a comma-separated list. Empty if no intermediates were sent or if the chain exceeded 16 KB.

certChainRFC9440TooLarge

Boolean

true if the intermediate chain exceeded 16 KB and was omitted from certChainRFC9440.

Example: forwarding client certificate headers to your origin

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) { …

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.

Run Worker code

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.

Access Workers bindings

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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.

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" ]

Local development

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.

Type generation

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.

Deploy

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. …

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.

Use Dynamic Workers for

• 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.

Executing Dynamic Workers

Dynamic Workers support two loading modes:

• load(code) — for one-time code execution (equivalent to calling get() with a null ID). …