Developer Platform Updates & Release Notes

The Markdown Conversion service now supports a new output conversion option that controls the format of the converted content.

Set output.format to text to receive plain text with Markdown syntax removed. The default value is markdown, so existing conversions are unchanged.

Use the env.AI binding:

await env.AI.toMarkdown(
	{ name: "page.html", blob: new Blob([html]) },
	{
		conversionOptions: {
			output: { format: "text" },
		},
	},
);

await env.AI.toMarkdown(
	{ name: "page.html", blob: new Blob([html]) },
	{
		conversionOptions: {
			output: { format: "text" },
		},
	},
);

Or call the REST API:

curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/tomarkdown \
  -H 'Authorization: Bearer {API_TOKEN}' \
  -F 'files=@index.html' \
  -F 'conversionOptions={"output": {"format": "text"}}'

When you request text output, the format field of each result is set to text. For more details, refer to Conversion Options.

With Workflows, you can configure built-in retry behavior for each step. Previously, you could configure step retries with fixed delay durations, such as seconds, minutes, or hours, and backoff strategies such as constant, linear, or exponential.

Step retries now support dynamic delay functions. Instead of choosing only a base delay and backoff strategy, pass a function to retries.delay and calculate the next delay from the failed attempt and thrown error.

This is useful when retries should depend on the failure. Your Workflow may need to wait longer after a rate-limit error, but retry sooner after a short network failure. The delay function can also accommodate provider guidance if, for example, a downstream API returns a Retry-After value in its error messaging.

await step.do(
	"sync customer",
	{
		retries: {
			limit: 5,
			delay: ({ ctx, error }) => {
				if (error.message.includes("rate limit")) {
					return `${ctx.attempt * 30} seconds`;
				}

				return "10 seconds";
			},
		},
	},
	async () => {
		await syncCustomer();
	},
);

await step.do(
	"sync customer",
	{
		retries: {
			limit: 5,
			delay: ({ ctx, error }) => {
				if (error.message.includes("rate limit")) {
					return `${ctx.attempt * 30} seconds`;
				}

				return "10 seconds";
			},
		},
	},
	async () => {
		await syncCustomer();
	},
);
``` …

If your account does not already have a key-value (KV) backed Durable Object namespace, you can no longer create new ones. New Durable Object namespaces must use the SQLite storage backend, which has been recommended for all new Durable Objects since it became generally available ↗︎ in 2024.

Create a new class with a new_sqlite_classes migration:

{
  "$schema": "./node_modules/wrangler/config-schema.json",
  "migrations": [
    {
      "tag": "v1",
      "new_sqlite_classes": [
        "MyDurableObject"
      ]
    }
  ]
}

[[migrations]]
tag = "v1"
new_sqlite_classes = ["MyDurableObject"]

SQLite-backed Durable Objects have feature parity with the key-value backend — including the key-value storage API — and additionally support relational SQL queries and point-in-time recovery to restore an object's storage to any point in the past 30 days. …

Wrangler now collects npm package dependency information from your project's package.json during wrangler deploy and wrangler versions upload, and includes it in the upload metadata sent to the Cloudflare API. This data, each dependency's name, declared version range, and exact installed version, enables dependency analytics and future supply chain security features such as vulnerability alerting.

To opt out, set dependencies_instrumentation.enabled to false in your Wrangler configuration file:

{
	"dependencies_instrumentation": {
		"enabled": false
	}
}

[dependencies_instrumentation]
enabled = false

For more details, refer to Wrangler configuration.

In AI Search, you can upload files to an instance, or connect a data source such as an R2 bucket, to make your content searchable with natural language. Each file becomes an item identified by an object key (its filename or path). The list items endpoint returns the items in an instance.

That endpoint now accepts a key query parameter, so you can look up a single item by its exact object key without paging through the full list. This complements the existing item_id filter for when you know the key but not the ID.

curl "https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/ai-search/instances/<INSTANCE_NAME>/items?key=docs/readme.md" \
  -H "Authorization: Bearer <API_TOKEN>"

Keys are unique per data source, so combine key with source (for example, source=builtin) to disambiguate when the same key exists across multiple sources.

For more information, refer to managing items.

Workers AI Markdown conversion (toMarkdown) now supports .gif and .bmp image files, in addition to the JPEG, PNG, WebP, and SVG formats already supported.

GIF and BMP files run through the same image pipeline as other formats. Each image is resized if needed (and for animated GIFs, only the first frame is used), then passed to an object-detection model to identify what it contains. Those detected objects prompt a vision model that writes a natural-language description of the image, which becomes searchable, machine-readable Markdown.

AI Search uses toMarkdown automatically to process the files it ingests, so any .gif and .bmp files are included the next time your index syncs, with no configuration changes required. This helps when your content mixes formats, for example a support knowledge base full of screenshots or an archive of BMP scans.

Learn more about Markdown conversion and the full list of AI Search's supported file types.

You can now query your R2 Data Catalog tables with R2 SQL directly from the Cloudflare dashboard, without installing a CLI or wiring up a client. This makes it easy to explore your Apache Iceberg ↗︎ data, validate queries, and inspect results in one place.

R2 SQL Query Editor

To get started, go to R2 Data Catalog ↗︎ in the Cloudflare dashboard and select Query data to launch the built-in SQL editor. From there you can:

• Write and run queries interactively — Iterate on R2 SQL directly in the browser with syntax highlighting and autocomplete, instead of re-running commands through Wrangler or the REST API.
• Explore your data — Explore your namespaces and tables alongside the editor so you can discover what's queryable without leaving the page or using other tools.
• Understand results and performance — View result sets with per-query statistics, export them, and get helpful EXPLAIN outputs to see exactly how a query runs.

Note

Your R2 SQL credential is generated and stored for you and can be rotated in the R2 Data Catalog settings page.

Partnering with Moondream ↗︎ to bring their latest model @cf/moondream/moondream3.1-9B-A2B to Workers AI. Moondream 3.1 is a fast vision language model built on a mixture-of-experts architecture with 9B total parameters and 2B active, delivering frontier-level visual reasoning while retaining fast, cost-efficient inference.

Moondream 3.1 is designed for real-world vision tasks, with a 32K token context window for handling complex queries and structured outputs.

Key capabilities

• Query — ask open-ended questions about an image, with an optional reasoning parameter
• Caption — generate short, normal, or long descriptions of an image
• Point — return coordinates for objects matching a target phrase
• Detect — return bounding boxes for objects matching a target phrase

Real-time vision at the edge

Vision workloads like live camera feeds, robotics, content moderation, and interactive agents need answers in milliseconds, not seconds. Moondream 3.1's small active footprint (2B active parameters) pairs well with Workers AI's serverless, globally distributed inference: requests run close to your users, and streaming responses start returning tokens almost immediately. …

Cloudflare Drop ↗︎ lets you deploy a static site to Cloudflare without requiring a Cloudflare account to get started.

Cloudflare Drag and Drop upload screen for browsing folders or ZIP files

Upload a folder or zip file of static assets (static HTML, CSS, JavaScript, images, and fonts) and get a temporary live preview that stays live for 1 hour. During that window, you can test the site, share the preview URL, or claim the deployment to keep it.

Cloudflare Drag and Drop temporary live preview screen with claim and copy claim link actions

When you are ready to make the deployment permanent, click Claim to sign in or create a Cloudflare account. You can claim the site into an existing Cloudflare account or create a new account for the deployment.

Note

If you are creating a new account, you will need to verify your email address before continuing. …

Workflows pricing now includes per-step billing. Requests and CPU time billing have been enabled since the initial public beta and is not changing.

Workflows adds step billing

A step is each unit of work executed by a Workflow, including step operations such as sleeping or waiting for events.

You can query Workflows analytics, including stepCount for a Workflow instance, with the GraphQL Analytics API.

Steps and storage billing to take effect August 10th, 2026

Starting no earlier than August 10th, 2026, Cloudflare will begin billing for step and storage usage on Workers Paid plans.

Storage pricing has been published since Workflows became generally available and is not changing. Storage is measured as persisted Workflow state in GB-months.

Dimension

Workers Free

Workers Paid

Steps

3,000 included per day

500,000 included per month, then $0.80 per additional 100,000 steps

Storage

1 GB-month included

1 GB-month included, then $0.20 per additional GB-month …

R2 Data Catalog is a managed Apache Iceberg ↗︎ catalog built directly into your R2 bucket. Iceberg tracks your data through a tree of metadata files, so every insert, update, and delete must go through a catalog transaction. Manually adding, modifying, or deleting objects outside the catalog can leave pointers referencing files that no longer exist, corrupting the table into an inconsistent state that is difficult to recover from.

To help prevent this, the R2 dashboard and Wrangler now warn you when you attempt a manual delete operation on a Data Catalog-enabled bucket.

Dashboard

When you try to delete objects from a bucket that has R2 Data Catalog enabled, the dashboard displays a warning explaining that the operation could leave the catalog in an invalid state, with a link to the documentation for deleting data correctly. You can cancel the operation or choose to proceed anyway.

R2 dashboard warning shown before deleting objects from a Data Catalog-enabled bucket

Wrangler

Wrangler now checks whether a bucket is Data Catalog-enabled before running a delete and warns you before continuing:

Data Catalog is enabled for this bucket. …

Browser Run now supports a standalone /accessibilityTree endpoint, giving agent and automation workflows direct access to the browser's accessibility tree for a rendered webpage.

An accessibility tree is the browser's structured view of a rendered page: roles, names, states, values, and hierarchy. It is useful for accessibility tooling, but also for AI agents and automation workflows that need page structure without the noise of raw HTML or the cost of screenshots.

For AI agents, this means less inference from pixels and less parsing HTML. You can provide the page structure directly, helping agents identify available elements and determine which actions they can take.

With the new /accessibilityTree endpoint, you can request the accessibility tree directly when you only need the semantic structure of a page. If you need multiple page formats in a single API call, you can use the /snapshot endpoint, which also returns Markdown, HTML, and screenshots.

curl -X POST 'https://api.cloudflare.com/client/v4/accounts/<accountId>/browser-run/accessibilityTree' \
  -H 'Authorization: Bearer <apiToken>' \
  -H 'Content-Type: application/json' \
  -d '{
    "url": "https://example.com/"
}'

{
	"success": true,
	"result": {
		"accessibilityTree": {
			"role": "RootWebArea",
			"name": "Example Domain",
			"children": [
				{ …

A new declarative exports field in your Wrangler configuration file replaces the imperative migrations array for managing Durable Object class lifecycle. Instead of writing an ordered list of migration steps with unique tags, you declare each Durable Object class your Worker exports and Cloudflare compares that against what's already deployed to determine what Durable Object state needs to be created, renamed, or deleted.

With legacy migrations, renaming ChatRoom to Room requires retaining both tagged steps:

Before — legacy migrationsjsonc

{
	"migrations": [
		{ "tag": "v1", "new_sqlite_classes": ["ChatRoom"] },
		{
			"tag": "v2",
			"renamed_classes": [{ "from": "ChatRoom", "to": "Room" }],
		},
	],
}

With exports, you instead declare Room as the current class and mark ChatRoom as renamed:

After — declarative exportsjsonc

{
	"exports": {
		"ChatRoom": {
			"type": "durable-object",
			"state": "renamed",
			"renamed_to": "Room",
		},
		"Room": { "type": "durable-object", "storage": "sqlite" },
	},
}

Each entry is keyed by class name. The state field carries the lifecycle (created by default — a live class — plus tombstone states deleted, renamed, and transferred, and the expecting-transfer receiving state for cross-Worker transfers). …

A new declarative exports field in your Wrangler configuration file replaces the imperative migrations array for managing Durable Object class lifecycle. Instead of writing an ordered list of migration steps with unique tags, you declare each Durable Object class your Worker exports and Cloudflare compares that against what's already deployed to determine what Durable Object state needs to be created, renamed, or deleted.

With legacy migrations, renaming ChatRoom to Room requires retaining both tagged steps:

Before — legacy migrationsjsonc

{
	"migrations": [
		{ "tag": "v1", "new_sqlite_classes": ["ChatRoom"] },
		{
			"tag": "v2",
			"renamed_classes": [{ "from": "ChatRoom", "to": "Room" }],
		},
	],
}

With exports, you instead declare Room as the current class and mark ChatRoom as renamed:

After — declarative exportsjsonc

{
	"exports": {
		"ChatRoom": {
			"type": "durable-object",
			"state": "renamed",
			"renamed_to": "Room",
		},
		"Room": { "type": "durable-object", "storage": "sqlite" },
	},
}

Each entry is keyed by class name. The state field carries the lifecycle (created by default — a live class — plus tombstone states deleted, renamed, and transferred, and the expecting-transfer receiving state for cross-Worker transfers). …

We have released version 5 of @cloudflare/workers-types ↗︎. This release simplifies the package to expose only the latest runtime types.

We still recommend that you generate types for your Worker using wrangler types, but if you want to use the package directly, you can install it with your package manager of choice:

npmyarnpnpmbun

npm i -D @cloudflare/workers-types@latest

yarn add -D @cloudflare/workers-types@latest

pnpm add -D @cloudflare/workers-types@latest

bun add -d @cloudflare/workers-types@latest

The package now exposes two entrypoints:

• @cloudflare/workers-types reflects the latest compatibility date, using the latest stable compatibility flags.
• @cloudflare/workers-types/experimental reflects APIs behind experimental compatibility flags.

The dated entrypoints, such as @cloudflare/workers-types/2022-11-30 and @cloudflare/workers-types/2023-03-01, are removed. With runtime type generation in Wrangler v4, you can generate these with the wrangler types command to create types locked to your Worker's compatibility date.

For more information, refer to TypeScript language support.

When you connect a data source to your AI Search instance, AI Search runs sync jobs to keep your index up to date with your content. You can now manage those jobs directly from Wrangler.

For example, you can trigger a sync job from your CI/CD or automated pipelines with the jobs create command so your index refreshes when you push a change:

wrangler ai-search jobs create my-instance

This creates an asynchronous sync job that checks for changes in your data source, and sends new, modified, or deleted files to be indexed. The following commands are available:

Command

Description

wrangler ai-search jobs create

Trigger a new sync job

wrangler ai-search jobs list

List sync jobs for an instance

wrangler ai-search jobs get

Get details for a job

wrangler ai-search jobs cancel

Cancel a running job

wrangler ai-search jobs logs

View log entries for a job

All commands accept --namespace/-n (defaults to default) and --json for structured output that automation and AI agents can parse directly. The list and logs commands also support --page and --per-page for pagination, and cancel prompts for confirmation unless you pass -y/--force. …

Wrangler CLI now supports auth profiles: named logins that you scope to specific Cloudflare accounts and switch between automatically, based on the directory you are working in.

A profile is a named OAuth login bound to a directory. Commands run in that directory, and its subdirectories, use the matching account — so you can move between accounts without re-running wrangler login.

Use profiles to keep a separate login for each client when working at an agency, or to separate staging and production into different accounts. Pair a profile with an account_id in your Wrangler configuration file so a command cannot reach the wrong account.

# Create a profile for each account, choosing which accounts it can reach
wrangler auth create client-a
wrangler auth activate client-a ~/clients/client-a

wrangler auth create client-b
wrangler auth activate client-b ~/clients/client-b

Use the --profile flag to run a single command with a specific profile:

wrangler deploy --profile personal

In CI and other automated environments, CLOUDFLARE_API_TOKEN still takes precedence over all profiles.

For setup, the resolution order, and the full command reference, refer to Authentication profiles.

Containers now support Google Artifact Registry ↗︎ images. After you configure credentials, you can use a fully qualified Google Artifact Registry image reference in your Wrangler configuration instead of first pushing the image to Cloudflare Registry.

Provide the service account email with --gar-email and pipe the service account JSON key through stdin:

cat <PATH_TO_KEY> | npx wrangler containers registries configure <REGION>-docker.pkg.dev --gar-email=<SERVICE_ACCOUNT_EMAIL> --secret-name=<SECRET_NAME>

{
  "$schema": "./node_modules/wrangler/config-schema.json",
  "containers": [
    {
      "image": "<REGION>-docker.pkg.dev/<PROJECT_ID>/<REPOSITORY>/<IMAGE>:<TAG>"
    }
  ]
}

# Example: us-central1-docker.pkg.dev/my-project/my-repo/my-image:latest
[[containers]]
image = "<REGION>-docker.pkg.dev/<PROJECT_ID>/<REPOSITORY>/<IMAGE>:<TAG>"

Only *-docker.pkg.dev hosts are supported. To configure credentials, refer to Use private Google Artifact Registry images.

For more information, refer to Image management.

The Images binding is now billed per unique transformation, matching the model already used for URL-based transformations. Repeat requests for the same combination of source image and parameters within the same calendar month are counted only once.

Previously, every call to the binding counted as a separate transformation regardless of whether the image or parameters were unique. With this change, you can call the binding on hot paths without paying for each individual request.

Calls to .info() are no longer billed.

For more information, refer to Images pricing and the Images binding documentation.

We have greatly improved the throughput of the Vectorize write-ahead log (WAL) ↗︎. As a result, we have significantly reduced the end-to-end latency for a vector change to become queryable: median latency has dropped from 2 minutes to under 30 seconds, and p99 latency from 5 minutes to under 2 minutes.

Vectorize p99 WAL batch end-to-end latency improved

This means inserts, upserts, and deletes are reflected in query results faster, improving the freshness of semantic search, recommendation, and retrieval-augmented generation (RAG) workloads. You do not need to change your code or configuration to benefit from this improvement.

For more information, refer to the Vectorize documentation.