Changelog

Workers using a VPC Network binding with network_id: "cf1:network" now egress to public Internet destinations through Cloudflare Gateway. This means your existing Zero Trust traffic policies — DNS, HTTP, Network, and egress — extend to traffic that originates from your Workers, the same way they do for WARP users today.

What you get by default:

• Visibility. Worker egress shows up in Gateway DNS, HTTP, and Network logs alongside your other traffic, so you can audit what your Workers are calling and when.
• Enforcement. Any existing Gateway policy whose selectors match a Worker request will apply — including allow / block lists, DNS category filtering, and HTTP destination rules. If you have already blocked a category for your workforce, your Workers inherit that block.

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

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

// Egress to a public destination — subject to your Gateway policies and logged
const response = await env.EGRESS.fetch("https://api.example.com/data");

// Egress to a public destination — subject to your Gateway policies and logged
const response = await env.EGRESS.fetch("https://api.example.com/data");

For configuration options, refer to VPC Networks. For policy authoring, refer to Cloudflare Gateway traffic policies.

Pay-as-you-go customers can now view billable usage and create budget alerts directly from the product overview pages for Workers & Pages, D1, R2, Workers KV, Queues, Vectorize, Durable Objects, and Containers. A new sidebar widget shows current-period spend and the billing cycle date range, alongside a button to create a budget alert.

The widget pulls from the same data as the Billable Usage dashboard and aligns to your billing cycle (or the current day on Free plans), so the numbers match your invoice. Enterprise contract accounts are not yet supported.

Selecting Create budget alert opens the budget alert flow inline so you can set a dollar threshold in the same place you are reviewing usage. Budget alerts apply to your total account-level spend across all products, not just the product page you create them from.

For more information, refer to the Usage-based billing documentation.

The pipeline field inside the pipelines binding configuration in your Wrangler configuration file has been renamed to stream. The old field is deprecated but still accepted.

Update your configuration to use stream to avoid the deprecation warning.

Before (deprecated):

{
  "$schema": "./node_modules/wrangler/config-schema.json",
  "pipelines": [
    {
      "binding": "MY_PIPELINE",
      "pipeline": "<STREAM_ID>"
    }
  ]
}

[[pipelines]]
binding = "MY_PIPELINE"
pipeline = "<STREAM_ID>"

After:

{
  "$schema": "./node_modules/wrangler/config-schema.json",
  "pipelines": [
    {
      "binding": "MY_PIPELINE",
      "stream": "<STREAM_ID>"
    }
  ]
}

[[pipelines]]
binding = "MY_PIPELINE"
stream = "<STREAM_ID>"

No other changes are required. The binding name, TypeScript types, and runtime API (env.MY_PIPELINE.send(...)) remain the same.

For more information on configuring pipeline bindings, refer to Writing to streams.

You can now create, update, or delete multiple secrets for your Worker in a single request using the bulk secrets endpoint.

• Include a secret with a value to create or update.
• Set a secret to null to delete.
• Secrets not included in the request are left unchanged.

The following example creates API_KEY, updates the already existing DB_PASSWORD, and deletes OLD_SECRET:

{
  "secrets": {
    "API_KEY": { "type": "secret_text", "name": "API_KEY", "text": "my-api-key" },
    "DB_PASSWORD": { "type": "secret_text", "name": "DB_PASSWORD", "text": "my-db-password" },
    "OLD_SECRET": null
  }
}

You can do the same from the command line using wrangler secret bulk:

npx wrangler secret bulk < secrets.json

To delete a key, set its value to null in the JSON file. Deletion is not supported with .env files.

Each request supports up to 100 total operations (creates, updates, and deletes combined).

Wrangler can now store the OAuth credentials returned by wrangler login in an AES-256-GCM ↗-encrypted file, with the encryption key held in your operating system keychain. The default behavior is unchanged — credentials still live in a plaintext TOML file unless you opt in.

To opt in, run:

npx wrangler login --use-keyring

The choice is persisted across Wrangler invocations. Opt back out with npx wrangler login --no-use-keyring, or override the preference for a single command with the CLOUDFLARE_AUTH_USE_KEYRING environment variable.

wrangler whoami now reports where credentials are stored:

🔐 Credentials are stored in: Encrypted file (~/.config/.wrangler/config/default.enc) with key in macOS Keychain (service=wrangler, account=default)

Per-platform backends:

• macOS uses the built-in Keychain via /usr/bin/security.
• Linux uses libsecret ↗ via the secret-tool CLI from the libsecret-tools package.
• Windows uses Credential Manager via @napi-rs/keyring ↗, installed on-demand the first time you opt in.

Refer to Storing OAuth credentials in the OS keychain for the full details, including the migration behavior on opt-in/opt-out and the CLOUDFLARE_AUTH_USE_KEYRING environment variable.

You can now attach cron schedules directly to a Workflow binding in wrangler.jsonc. Each scheduled run creates a new Workflow instance automatically, so you do not need to define a separate Worker with a scheduled handler just to trigger your Workflow on an interval.

For example, you can configure hourly, every-15-minute, or weekday schedules on the same Workflow:

{
	"workflows": [
		{
			"name": "my-scheduled-workflow",
			"binding": "MY_WORKFLOW",
			"class_name": "MyScheduledWorkflow",
			"schedules": ["0 * * * *", "*/15 * * * *", "0 9 * * MON-FRI"],
		},
	],
}

Cron workloads get all the same benefits of Workflows with built-in retries, multi-step durable execution, and configurable timeouts of Workflows.

import {
	WorkflowEntrypoint,
	WorkflowEvent,
	WorkflowStep,
} from "cloudflare:workers";

// Runs automatically on each cron schedule defined for the MY_WORKFLOW binding in wrangler.jsonc.
export class MyScheduledWorkflow extends WorkflowEntrypoint<Env> {
	async run(event: WorkflowEvent, step: WorkflowStep) {
		const data = await step.do("fetch source data", async () => {
			return await fetchSourceData();
		});

		// If this step fails, only this step is retried with the custom logic below
		await step.do(
			"process and store results",
			{
				retries: { limit: 5, delay: "30 seconds", backoff: "exponential" },
				timeout: "10 minutes",
			},
			async () => {
				await processAndStore(data);
			},
		);
	}
}

This makes it easier to build recurring, scheduled jobs such as database backups, invoice generation, report aggregation, and cleanup tasks without wiring up a separate Cron Trigger entrypoint.

For more information, refer to Trigger Workflows.

The latest release of the Agents SDK ↗ adds four new ways to build with @cloudflare/think: on-demand Agent Skills, chat messengers (starting with Telegram), declarative scheduled tasks, and durable reasoning steps inside Workflows. This release also significantly hardens durable chat recovery, so turns reliably ride through deploys, evictions, and stalled model streams in production.

Give an agent a catalog of on-demand instructions, resources, and scripts. A skill source adds a catalog to the system prompt, and the model activates a skill only when a task matches — so a large library of capabilities does not bloat every prompt.

import { Think, skills } from "@cloudflare/think";
import bundledSkills from "agents:skills";

export class SkillsAgent extends Think {
	getSkills() {
		return [
			bundledSkills,
			skills.r2(this.env.SKILLS_BUCKET, { prefix: "skills/" }),
		];
	}
}

import { Think, skills } from "@cloudflare/think";
import bundledSkills from "agents:skills";

export class SkillsAgent extends Think<Env> {
	getSkills() {
		return [
			bundledSkills,
			skills.r2(this.env.SKILLS_BUCKET, { prefix: "skills/" }),
		];
	}
}

The agents:skills import bundles a local ./skills directory through the Agents Vite plugin (one directory per skill, each with a SKILL.md). Skills can also load from R2 or a manifest. When skills are available, Think exposes activate_skill, read_skill_resource, and an optional run_skill_script tool. Skill loading is resilient: a duplicate or failing source is skipped with a warning instead of breaking the agent.

Agent Skills are experimental, and script execution in particular is early. The API may change in a future release. We would love your feedback — tell us what you are building and what is missing in the Agents repository ↗.

Connect a Think agent directly to a chat platform. Think owns the webhook route, conversation routing, durable reply fiber, and streamed delivery back to the provider. Telegram ships as the first provider.

import { Think } from "@cloudflare/think";
import {
	defineMessengers,
	ThinkMessengerStateAgent,
} from "@cloudflare/think/messengers";
import telegramMessenger from "@cloudflare/think/messengers/telegram";

export { ThinkMessengerStateAgent };

export class SupportAgent extends Think {
	getMessengers() {
		return defineMessengers({
			telegram: telegramMessenger({
				token: this.env.TELEGRAM_BOT_TOKEN,
				userName: "support_bot",
				secretToken: this.env.TELEGRAM_WEBHOOK_SECRET_TOKEN,
			}),
		});
	}
}

import { Think } from "@cloudflare/think";
import {
	defineMessengers,
	ThinkMessengerStateAgent,
} from "@cloudflare/think/messengers";
import telegramMessenger from "@cloudflare/think/messengers/telegram";

export { ThinkMessengerStateAgent };

export class SupportAgent extends Think<Env> {
	getMessengers() {
		return defineMessengers({
			telegram: telegramMessenger({
				token: this.env.TELEGRAM_BOT_TOKEN,
				userName: "support_bot",
				secretToken: this.env.TELEGRAM_WEBHOOK_SECRET_TOKEN,
			}),
		});
	}
}

Each Chat SDK thread maps to its own Think sub-agent by default, so group chats and direct messages do not share memory. Multiple bots, custom conversation routing, and custom providers are all supported.

Declare recurring, timezone-aware prompts and handlers with a typed domain-specific language (DSL). Think reconciles the declarations on startup and re-arms the next occurrence after each run, backed by durable idempotent submissions.

import { Think, defineScheduledTasks } from "@cloudflare/think";

export class DigestAgent extends Think {
	getScheduledTasks() {
		return defineScheduledTasks({
			weeklyCommitReport: {
				schedule: "every week on monday at 09:00",
				prompt:
					"Compile my GitHub commits for the last week and summarize them.",
			},
			workout: {
				schedule: "every day at 08:00 in Europe/London",
				prompt: "Start my workout.",
			},
		});
	}
}

import { Think, defineScheduledTasks } from "@cloudflare/think";

export class DigestAgent extends Think<Env> {
	getScheduledTasks() {
		return defineScheduledTasks({
			weeklyCommitReport: {
				schedule: "every week on monday at 09:00",
				prompt:
					"Compile my GitHub commits for the last week and summarize them.",
			},
			workout: {
				schedule: "every day at 08:00 in Europe/London",
				prompt: "Start my workout.",
			},
		});
	}
}

Run a model-driven reasoning step inside a Cloudflare Workflow with ThinkWorkflow and step.prompt(), with durable typed structured output, long waits, and approval gates.

import { z } from "zod";
import { ThinkWorkflow } from "@cloudflare/think/workflows";

const draftSchema = z.object({
	title: z.string(),
	summary: z.string(),
	labels: z.array(z.string()),
});

export class TriageWorkflow extends ThinkWorkflow {
	async run(event, step) {
		const draft = await step.prompt("triage-issue", {
			prompt: `Triage issue #${event.payload.issueNumber}`,
			output: draftSchema,
			timeout: "3 days",
		});

		await step.do("apply-labels", async () => {
			await this.agent.applyLabels(draft.labels);
		});
	}
}

import { z } from "zod";
import { ThinkWorkflow } from "@cloudflare/think/workflows";
import type { ThinkWorkflowStep } from "@cloudflare/think/workflows";
import type { AgentWorkflowEvent } from "agents/workflows";

const draftSchema = z.object({
	title: z.string(),
	summary: z.string(),
	labels: z.array(z.string()),
});

export class TriageWorkflow extends ThinkWorkflow<TriageAgent, Params> {
	async run(event: AgentWorkflowEvent<Params>, step: ThinkWorkflowStep) {
		const draft = await step.prompt("triage-issue", {
			prompt: `Triage issue #${event.payload.issueNumber}`,
			output: draftSchema,
			timeout: "3 days",
		});

		await step.do("apply-labels", async () => {
			await this.agent.applyLabels(draft.labels);
		});
	}
}

Durable chat turns have always been designed to survive a mid-turn deploy or Durable Object eviction. This release is a major hardening pass on that machinery for production.

• Better recovery during deploys. Turns now ride through continuous deploys and evictions without losing completed work or re-running tools that already ran.
• A live "recovering…" signal. useAgentChat exposes a new isRecovering flag, so a recovering turn shows progress instead of looking frozen. Most UIs render isStreaming || isRecovering as "busy".
• Stalled streams recover. Set chatStreamStallTimeoutMs to route a hung provider stream into the same recovery path instead of leaving an infinite spinner.
• Sub-agents re-attach. On parent recovery, an in-flight agentTool() child is re-attached to its result rather than abandoned and re-run, so long-running children no longer lose work under deploys.

• Resumable streams — In-flight tool calls over Server-Sent Events (SSE) survive a dropped connection. Clients reconnect with Last-Event-ID and replay anything they missed.
• Readable server IDs — addMcpServer accepts an optional id, so tools surface as readable keys (for example tool_github_create_pull_request) instead of opaque connection IDs.
• Better handling of concurrent requests — Overlapping JSON-RPC requests are now correctly correlated to their responses across the HTTP and RPC transports.

• Compaction — A Session's tokenCounter now also drives the compaction boundary decision ("what to compress"), not just the fire/no-fire trigger.
• @cloudflare/worker-bundler — Adds a virtualModules option to createWorker to provide in-memory module source during bundling.
• Client-tool continuations — Parallel tool results now coalesce into a single continuation, immediate resume requests attach to the pending continuation, and server-side needsApproval continuations resume reliably after approval.

To update to the latest version:

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

yarn add agents@latest @cloudflare/think@latest @cloudflare/ai-chat@latest

pnpm add agents@latest @cloudflare/think@latest @cloudflare/ai-chat@latest

bun add agents@latest @cloudflare/think@latest @cloudflare/ai-chat@latest

Refer to the Agents API reference and Chat agents documentation for more information.

Sandboxes can expose a service running inside the container on a public preview URL through the sandbox.tunnels namespace. The SDK uses cloudflared inside the sandbox so you can share a running service without configuring exposePort() or a custom domain.

By default, sandbox.tunnels.get(port) creates a quick tunnel ↗ on a zero-config *.trycloudflare.com URL — no Cloudflare account, DNS record, or custom domain required. This is perfect for quick development and for .workers.dev deployments.

import { getSandbox } from "@cloudflare/sandbox";

const sandbox = getSandbox(env.Sandbox, "my-sandbox");
await sandbox.startProcess("python -m http.server 8080");

const tunnel = await sandbox.tunnels.get(8080);
console.log(tunnel.url); // → https://random-words-here.trycloudflare.com

import { getSandbox } from "@cloudflare/sandbox";

const sandbox = getSandbox(env.Sandbox, "my-sandbox");
await sandbox.startProcess("python -m http.server 8080");

const tunnel = await sandbox.tunnels.get(8080);
console.log(tunnel.url); // → https://random-words-here.trycloudflare.com

For more control you can create a named tunnel through sandbox.tunnels.get(port, { name }). A named tunnel binds a hostname (<name>.<your-zone>) backed by a Cloudflare Tunnel and a CNAME record on your zone resulting in something like https://my-app-preview.example.com ↗.

Unlike quick tunnels, which generate a new random URL each time, a named tunnel produces a persistent URL that survives container restarts. This makes named tunnels suitable for production use cases where you want control over the tunnel and it's origin.

const tunnel = await sandbox.tunnels.get(8080, { name: "my-app-preview" });
console.log(tunnel.url); // → https://my-app-preview.example.com

const tunnel = await sandbox.tunnels.get(8080, { name: "my-app-preview" });
console.log(tunnel.url); // → https://my-app-preview.example.com

Calling sandbox.destroy() tears down the Cloudflare Tunnel and the associated DNS record alongside the container, so you do not leave dangling tunnels or records behind.

To update to the latest version:

npm i @cloudflare/sandbox@latest

yarn add @cloudflare/sandbox@latest

pnpm add @cloudflare/sandbox@latest

bun add @cloudflare/sandbox@latest

For full API details, refer to the Sandbox tunnels reference.

You can now point wrangler d1 migrations apply at a nested migrations layout — such as the one produced by Drizzle ↗ (migrations/0001_init/migration.sql) — using the new migrations_pattern D1 binding config:

{
	"d1_databases": [
		{
			"binding": "DB",
			"database_name": "my-database",
			"database_id": "<UUID>",
			"migrations_dir": "migrations",
			"migrations_pattern": "migrations/*/migration.sql",
		},
	],
}

migrations_pattern is a glob (relative to your Wrangler config file) used to discover migration files. It defaults to ${migrations_dir}/*.sql, so existing projects keep working unchanged. Each migration's name is recorded in the migrations table as a path relative to migrations_dir.

To learn more, visit D1's migrations documentation.

Cloudflare Realtime SFU is a WebRTC Selective Forwarding Unit that runs on Cloudflare's global network, so you can route live audio, video, and data between WebRTC clients around the world without managing SFU infrastructure or regions.

When you use the WebSocket adapter to stream WebRTC media to a WebSocket endpoint, the adapter now auto-reconnects and buffers audio and video after brief endpoint disconnects or restarts.

Many teams also use Realtime SFU as the media layer for backend applications, such as transcription, recording, note-taking, and agentic media-processing services. These systems often need to consume live WebRTC audio or video from the SFU in backend infrastructure, including Durable Objects, Workers, Containers, or external services, without running a WebRTC client themselves.

The WebSocket adapter bridges that gap by streaming WebRTC media from the SFU to a standard WebSocket endpoint as application-consumable payloads: PCM audio frames and JPEG video frames.

When you use the WebSocket adapter in Stream mode (egress) to send live audio or video from the SFU to your own WebSocket endpoint, the SFU now automatically reconnects after brief endpoint disconnects or restarts. This is especially helpful for long-running media pipelines where the WebSocket endpoint may briefly restart while a recording, transcription, or live analysis job is still in progress.

Previously, a brief disconnect from your WebSocket endpoint could close the adapter and require your application to recreate it before media could resume. Now, the SFU retries the same endpoint for up to 5 seconds with no API change required. If the endpoint comes back within that window, audio and video delivery resumes automatically.

The reconnect behavior also includes live-first media buffering, so brief interruptions reduce media loss without replaying stale video.

During reconnect:

• Audio uses a short bounded backlog to reduce audible loss. If the interruption lasts longer than the backlog can cover, older audio may be dropped.
• Video resumes from the latest available JPEG frame instead of replaying stale frames.
• Recovery is best effort and does not guarantee gapless or exactly-once delivery.

If the endpoint remains unavailable after the 5-second reconnect window, the adapter closes and must be recreated.

• WebSocket adapter
• Automatic reconnection for streaming
• Get started with Realtime SFU
• Realtime SFU example architecture
• Realtime vs Regular SFUs
• Global SFU Network Visualization ↗

You can now call Browser Run Quick Actions directly from a Cloudflare Worker using the quickAction() method on the browser binding. This simplifies how Workers interact with Browser Run by removing the need for API tokens or external HTTP requests. Your Worker communicates with Browser Run directly over Cloudflare's network, resulting in simpler code and lower latency.

With the quickAction() method you can:

• Capture screenshots from URLs or HTML
• Generate PDFs with custom styling, headers, and footers
• Extract HTML content from fully rendered pages
• Convert pages to Markdown
• Extract structured JSON using AI
• Scrape elements with CSS selectors
• Get all links from a page
• Capture snapshots (HTML + screenshot in one request)

To get started, add a browser binding to your Wrangler configuration:

{
  "compatibility_date": "2026-03-24",
  "browser": {
    "binding": "BROWSER"
  }
}

compatibility_date = "2026-03-24"

[browser]
binding = "BROWSER"

Then call any Quick Action directly from your Worker. For example, to capture a screenshot:

const screenshot = await env.BROWSER.quickAction("screenshot", {
	url: "https://www.cloudflare.com/",
});

const screenshot = await env.BROWSER.quickAction("screenshot", {
  url: "https://www.cloudflare.com/",
});

The quickAction() method requires a compatibility date of 2026-03-24 or later.

For setup instructions and the full list of available actions, refer to Browser Run Quick Actions.

Wrangler supports using wrangler containers ssh as an OpenSSH ProxyCommand for Containers. This lets your local SSH client connect to a running Container through Wrangler.

ssh -o ProxyCommand="wrangler containers ssh %h" cloudchamber@<INSTANCE_ID>

When standard input and output are piped, Wrangler forwards data to the SSH server in the Container. You can also pass --stdio to force this mode.

For more information, refer to the SSH documentation.

You can now send emails with display names on recipient addresses in addition to the existing from support. Pass an object with email and an optional name field for to, cc, bcc, replyTo, or from:

export default {
	async fetch(request, env) {
		const response = await env.EMAIL.send({
			from: { email: "support@example.com", name: "Support Team" },
			to: { email: "jane@example.com", name: "Jane Doe" },
			cc: [
				"manager@company.com",
				{ email: "team@company.com", name: "Engineering Team" },
			],
			subject: "Welcome!",
			html: "<h1>Thanks for joining!</h1>",
			text: "Thanks for joining!",
		});

		return Response.json({ messageId: response.messageId });
	},
};

export default {
	async fetch(request, env): Promise<Response> {
		const response = await env.EMAIL.send({
			from: { email: "support@example.com", name: "Support Team" },
			to: { email: "jane@example.com", name: "Jane Doe" },
			cc: [
				"manager@company.com",
				{ email: "team@company.com", name: "Engineering Team" },
			],
			subject: "Welcome!",
			html: "<h1>Thanks for joining!</h1>",
			text: "Thanks for joining!",
		});

		return Response.json({ messageId: response.messageId });
	},
} satisfies ExportedHandler<Env>;

Plain strings remain fully supported for backward compatibility, and you can mix strings and named objects in the same array.

Refer to the Workers API and REST API documentation for full request examples.

Cloudflare Pipelines is a streaming data platform that ingests events, transforms them with SQL, and writes to R2 as JSON, Parquet, or Apache Iceberg ↗ tables. Pipelines now has published pricing based on two usage dimensions: the volume of data processed by SQL transforms and the volume of data delivered to sinks. Ingress into a Pipeline stream is free.

Billing is not yet enabled. We will provide at least 30 days notice before we start charging for Pipelines usage.

Pipelines pricing model is designed to charge per GB based on what you use:

• Streams (ingress): Free, regardless of volume.
• SQL transforms: $0.04 / GB for stateless transforms (filter, reshape, unnest, cast, compute).
• Sinks: $0.03 / GB for JSON, $0.06 / GB for Parquet or Iceberg output.

Workers Free plans include 1 GB / month for each dimension. Workers Paid plans include 50 GB / month.

For full pricing details and billing examples, refer to Pipelines pricing.

R2 Data Catalog is a managed Apache Iceberg ↗ data catalog built directly into R2 buckets, queryable by any Iceberg-compatible engine such as Spark, Snowflake, and DuckDB. R2 Data Catalog now has published pricing for catalog operations and table compaction, in addition to standard R2 storage and operations.

Billing is not yet enabled. We will provide at least 30 days notice before we start charging for R2 Data Catalog usage.

Pricing is based on two dimensions:

• Catalog operations: $9.00 / million operations for metadata requests such as creating tables, reading table metadata, and updating table properties.
• Compaction: $0.005 / GB processed and $2.00 / million objects processed. These charges only apply when automatic compaction is turned on for a table.

Both dimensions include a monthly free tier: 1 million catalog operations, 10 GB of compaction data processed, and 1 million compaction objects processed.

For full pricing details and billing examples, refer to R2 Data Catalog pricing.

R2 Data Catalog is a managed Apache Iceberg ↗ data catalog built directly into your R2 bucket. It exposes a standard Iceberg REST catalog interface so you can connect query engines like Spark, Snowflake, DuckDB, and R2 SQL to your data in R2.

R2 Data Catalog now has a dedicated section in the Cloudflare dashboard, replacing the previous settings panel embedded in R2 bucket configuration. The new experience includes:

• Catalog overview — View all your catalogs in one place with catalog request counts, bucket sizes, and table maintenance status at a glance.
• Guided setup wizard — Create a catalog in three steps: choose or create an R2 bucket, configure table maintenance (compaction and snapshot expiration), and review. The wizard creates the bucket and generates a service credential automatically.
• Settings management — A dedicated settings page for each catalog with sections for general configuration, table maintenance, service credentials, and disabling the catalog. You can now enable and configure snapshot expiration directly from the dashboard.
• Built-in metrics — Five charts on each catalog's metrics tab: bytes compacted, files compacted, catalog requests, storage size, and snapshots expired.

To get started, go to R2 Data Catalog in the Cloudflare dashboard or refer to the getting started guide and manage catalogs documentation.

R2 SQL is a serverless, distributed query engine that runs SQL against Apache Iceberg ↗ tables stored in R2 Data Catalog. R2 SQL now has published pricing based on a single dimension: the volume of compressed data scanned to execute your queries. At $2.50 / TB ($0.0025 / GB), R2 SQL is priced at half the cost of AWS Athena and less than half of Google BigQuery on-demand.

Billing is not yet enabled. We will provide at least 30 days notice before we start charging for R2 SQL usage.

Data scanned is measured on compressed bytes read from R2 object storage. This matches what you see in your R2 bucket — if a Parquet file is 100 MB on disk, scanning that file bills for 100 MB. Each query has a minimum billing increment of 10 MB.

All plans include 10 GB of data scanned per month. Standard R2 storage and operations and R2 Data Catalog charges apply separately.

For full pricing details and billing examples, refer to R2 SQL pricing.

You can now record specific participant audio tracks in RealtimeKit with track recording. Track recording creates separate WebM files for each participant instead of a single composite recording, which is useful for post-processing, transcription, and regulated or content-sensitive workflows.

To record specific participants, pass user_ids when starting a track recording:

curl --request POST \
  --url https://api.cloudflare.com/client/v4/accounts/<account_id>/realtime/kit/<app_id>/recordings/track \
  --header 'Authorization: Bearer <api_token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "meeting_id": "97440c6a-140b-40a9-9499-b23fd7a3868a",
  "user_ids": ["user-123", "user-456"]
}'

To pass user_ids for selective track recording, use the following minimum SDK versions:

• Web Core: @cloudflare/realtimekit version 1.4.0 or later
• Web UI Kit: @cloudflare/realtimekit-ui, @cloudflare/realtimekit-react-ui, or @cloudflare/realtimekit-angular-ui version 1.1.2 or later
• Android Core or iOS Core: version 2.0.0 or later
• Android UI Kit or iOS UI Kit: version 1.1.0 or later

RealtimeKit provides SDKs and UI components so that you can build your own meeting experience on Cloudflare's global WebRTC infrastructure. Teams today build products ranging from telehealth to education on RealtimeKit for global audiences. You can get started today with our Quickstart or take a look at our Cloudflare Meet repo ↗ as a reference.

Flows are automated rules that pair conditions (such as file extension, URL path, or query parameter) with parameters. Set up a flow to automatically apply image optimization to matching requests on your zone without writing code or changing URLs.

There are two modes for transformation flows:

• Provider flows — Migrate from another image optimization service. Your existing URLs continue to work while Cloudflare rewrites provider-specific parameters to their Cloudflare equivalents. Currently, Cloudflare supports provider flows for Fastly Image Optimizer.
• Custom flows — Define your own conditions and actions for use cases like automatic format conversion, responsive sizing with width=auto, or directory-based optimization.

To get started, go to Images > Transformations > Automation in the Cloudflare dashboard ↗.

Learn more about transformation flows.

Starting with cloudflared version 2026.5.2 ↗, Cloudflare Tunnel automates the entire connectivity pre-checks workflow directly inside the binary. Previously, customers had to install dig and netcat and run those commands by hand to verify their environment. Now cloudflared does it natively at startup — and surfaces actionable remediation when something is blocked.

On every cloudflared tunnel run (and cloudflared tunnel diag), the binary now natively checks:

• DNS resolution — region1.v2.argotunnel.com and region2.v2.argotunnel.com resolve to valid Cloudflare IPs.
• Transport connectivity — outbound UDP (QUIC) and TCP (HTTP/2) on port 7844.
• Management API — outbound TCP/443 to api.cloudflare.com for software updates.

Results are printed in a scannable CLI table with three states:

• ✅ Pass — the check succeeded.
• ⚠️ Warn — a non-blocking issue, for example the Management API is unreachable so automatic updates will not work, but the tunnel will still come up.
• ❌ Fail — a blocking issue, with a specific remediation hint (for example, Allow outbound UDP on port 7844).

If DNS is unresolvable, or both UDP and TCP fail on port 7844, cloudflared exits early with the failure rather than looping on opaque failed to dial errors.

Pre-checks now run automatically on every start, which also catches regressions like overnight firewall policy changes — no need to remember to rerun the troubleshooting guide.

To get the new behavior, upgrade cloudflared to version 2026.5.2 or later. For more details, refer to the Connectivity pre-checks documentation.

Flagship is now in public beta. Evaluate feature flags directly from Cloudflare Workers with no outbound HTTP calls, using globally distributed flag configuration backed by Workers KV and Durable Objects. Flagship supports typed flag values, targeting rules, percentage rollouts, audit history, and OpenFeature-compatible SDKs.

Evaluate a flag from a Worker in a few lines of code:

export default {
	async fetch(request, env) {
		const showNewCheckout = await env.FLAGS.getBooleanValue(
			"new-checkout",
			false,
		);

		return new Response(showNewCheckout ? "New checkout" : "Standard checkout");
	},
};

export default {
	async fetch(request: Request, env: Env): Promise<Response> {
		const showNewCheckout = await env.FLAGS.getBooleanValue("new-checkout", false);

		return new Response(
			showNewCheckout ? "New checkout" : "Standard checkout",
		);
	},
} satisfies ExportedHandler<Env>;

Start creating flags from the Cloudflare dashboard today. Refer to the Flagship documentation to get started.

AI Gateway now uses the AI REST API on api.cloudflare.com. You can call any model — whether from OpenAI, Anthropic, Google, or hosted on Workers AI — through one unified API, using the same endpoints and authentication regardless of provider. Four endpoints are available:

• POST /ai/run — universal endpoint for all models and modalities
• POST /ai/v1/chat/completions — OpenAI SDK compatible
• POST /ai/v1/responses — OpenAI Responses API compatible
• POST /ai/v1/messages — Anthropic SDK compatible

curl -X POST "https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/v1/chat/completions" \
  --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "openai/gpt-5.5",
    "messages": [{"role": "user", "content": "What is Cloudflare?"}]
  }'

All AI Gateway features — logging, caching, rate limiting, and guardrails — are applied automatically. Third-party models are billed through Unified Billing, so you do not need to manage separate provider API keys.

Third-party model requests are routed through your account's default gateway, which is created automatically on first use. To route requests through a specific gateway, add the cf-aig-gateway-id header.

If you are already calling Workers AI models through the existing REST API, that path (/ai/run/@cf/{model}) continues to work. To call Workers AI models through AI Gateway, use the @cf/ model prefix (for example, @cf/moonshotai/kimi-k2.6) and include the cf-aig-gateway-id header to specify which gateway to route through.

For more details and examples, refer to the REST API documentation.

You can now scope Cloudflare permissions to individual Cloudflare Tunnel instances and Cloudflare Mesh nodes. Administrators can delegate access to specific Tunnels or Mesh nodes without granting account-wide control over private networking.

When you add a member or create a permission policy, the resource picker now lists Cloudflare Tunnel instances and Cloudflare Mesh nodes as scopable resource types. You can:

• Grant a read-only role on a single Cloudflare Tunnel instance to a support operator for log streaming and diagnostics — without exposing other Tunnels or destructive actions.
• Grant a write role on a specific Cloudflare Mesh node to an application team — without giving them access to the rest of your private network.
• Scope a single policy to one or many Tunnels and Mesh nodes at once.

Granular permissions are a parallel layer to existing account-level roles — they do not replace them.

• Existing account-level roles continue to work. A member with Cloudflare Access or Cloudflare Zero Trust retains write access to every Tunnel and Mesh node in the account. This ensures backward compatibility for existing automation and tokens.
• Granular permissions are additive. For any API request on a specific Tunnel or Mesh node, access is granted if the principal has either the account-level role or a granular permission for that resource.
• Resource enumeration is authorization-aware. Listing endpoints (GET /accounts/{id}/cfd_tunnel, GET /accounts/{id}/warp_connector) return only the resources the principal has at least read access to.

• Configure granular permissions for Cloudflare Tunnel.
• Configure granular permissions for Cloudflare Tunnel and Cloudflare Mesh in Cloudflare One.
• Review the resource-scoped roles on the Cloudflare role reference.

You can now use VPC Network bindings with network_id: "cf1:network" to reach your full private network from Workers, including:

• Cloudflare Mesh nodes and client devices
• Subnet routes and hostname routes announced through Cloudflare Tunnel or Cloudflare Mesh
• Destinations connected through Cloudflare WAN on-ramps — GRE, IPsec, and CNI

This means a single VPC Network binding can route Worker requests to private services regardless of how those services are connected to Cloudflare: through a Cloudflare Tunnel from a cloud VPC, a Mesh node on a private subnet, or a Cloudflare WAN on-ramp from your data center or branch site.

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

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

At runtime, the URL you pass to fetch() determines the destination:

// Reach a service behind a Cloudflare WAN IPsec on-ramp
const response = await env.PRIVATE_NETWORK.fetch("http://10.50.0.100:8080/api");

For configuration options, refer to VPC Networks.

You can now receive event notifications for Artifacts repository changes and consume them from a Worker to build commit-driven automation.

This allows you to:

• Run custom workflows when a repository is created or imported
• Kick off a build and deploy a change when an agent pushes to a repo
• Trigger a review agent on every push

Available events include:

• Account-level events (artifacts source) — repo.created, repo.deleted, repo.forked, repo.imported
• Repository-level events (artifacts.repo source) — pushed, cloned, fetched

To learn more, refer to Artifacts documentation.