Developer Platform Updates & Release Notes

R2 Data Access Logs are now generally available. Turn on logging for a bucket to record object read, write, list, multipart upload, and delete operations with response status codes below 400.

Data Access Logs cover requests made through the S3-compatible API, Cloudflare API and dashboard, Workers bindings, and public buckets through r2.dev or custom domains. Events are available in Workers Observability, where you can filter by bucket, operation, interface, actor, and other request fields.

Log delivery is asynchronous and best effort. Events may be delayed or omitted, so do not rely on Data Access Logs as a complete record of bucket activity.

Data Access Logs are available for non-jurisdictional buckets. For setup instructions, supported operations, and the event field reference, refer to R2 Data Access Logs.

You can now deploy Workers with larger dependencies, heavier frameworks, and more code without hitting size limits.

When you deploy a Worker, Wrangler bundles your code and compresses it before uploading. Previously, Cloudflare checked that compressed size and rejected deploys over 3 MB (Free) or 10 MB (Paid). That limit has been removed. Cloudflare now only checks the uncompressed size of your bundle, which is 64 MiB across all plans.

To check your Worker's bundle size before deploying:

wrangler deploy --outdir bundled/ --dry-run

Total Upload: 259.61 KiB / gzip: 47.23 KiB

The Total Upload value is your uncompressed bundle size. This is what counts against the 64 MiB limit. The gzip value is shown for reference but is no longer a limit.

For more information, refer to the Worker size limits documentation.

We've added more ways to manage and manipulate images with the Images binding. Here's what's new:

Render text into an image. Output a string of text into its own image or draw it over another image.

• Use the .text() method to rasterize text with the Images binding.
• Style content using the font, size, and color options.
• The draw array in cf.image now accepts a text key.

Manage hosted images without an API token.

• Metadata filtering: Pass filter.metadata to .list() to return images by custom metadata. Match a bounded range by setting two operators in one condition, for example, priority: { gte: 2, lte: 5 }.
• Server-side signing: Get a signed URL for a private image with .signedUrl().
• User uploads: Create a Direct Creator Upload link with .createDirectUpload() so that a client can upload an image to your storage.

Set headers in a single call. …

Cursor self-hosted machines ↗︎ let you run Cursor Cloud Agents on Cloudflare. Each assigned session runs in its own isolated environment backed by Cloudflare Containers.

Cursor Cloud Agents environment selector showing the cloudflare-pool self-hosted machine pool

Cursor hosts the agent loop, inference, and planning. Cloudflare runs commands, file edits, repository operations, and other tools inside infrastructure that you control. The open-source Cursor Cloudflare Workers template ↗︎ deploys the Worker, Durable Object namespace, container application, R2 bucket binding, and cron trigger used by the integration.

To get started, refer to Run Cursor Cloud Agents on Cloudflare via self-hosted machines.

Python web frameworks following the Web Server Gateway Interface (WSGI) ↗︎ or Asynchronous Server Gateway Interface (ASGI) ↗︎ specification can now be used in Python Workers.

Using web frameworks with Python Workers

Based on the web framework you are using, you can use either wsgi or asgi from the workers module.

WSGI frameworks

For WSGI frameworks like Django or Flask:

from workers import wsgi

from django.core.wsgi import get_wsgi_application

app = get_wsgi_application()
Default = wsgi.entrypoint(app)

The wsgi.entrypoint is equivalent to creating a WorkerEntrypoint class and using the wsgi.fetch method. If you want more control over the WorkerEntrypoint class, you can do so:

from workers import wsgi, WorkerEntrypoint

class Default(WorkerEntrypoint):
    async def fetch(self, request):
        return await wsgi.fetch(app, request, self.env)

ASGI frameworks

For ASGI frameworks like FastAPI or Starlette:

from workers import asgi

from fastapi import FastAPI

app = FastAPI()
Default = asgi.entrypoint(app)

For more information about using individual web frameworks, refer to the packages documentation in Python Workers.

AI Gateway monthly usage invoices, issued at the beginning of each month for the previous month's usage, now show a single total cost for each model. These invoices no longer break out input and output token quantities and unit prices into separate line items. This change does not apply to invoices for AI Gateway credit purchases.

For example, an invoice that previously included these separate line items:

• anthropic claude-haiku-4-5-20251001 Input Tokens: 40,000 tokens at $0.000001 ($0.04)
• anthropic claude-haiku-4-5-20251001 Output Tokens: 24,000 tokens at $0.000005 ($0.12)

The updated invoice includes one line item: anthropic/claude-haiku-4.5: $0.16.

AI Gateway has also standardized model names across invoices and logs. Model variants that previously appeared with provider-specific version suffixes now use a consistent provider/model identifier.

For more information, refer to the Unified Billing documentation and AI Gateway logging documentation.

Beginning September 1, 2026, D1 queries on the Workers Free plan will fail when an account exceeds the daily row read or row write limits. Queries via the Workers Binding API and the REST API will return errors until the limit resets at midnight UTC. Stored data is not affected.

You will receive email alerts when the daily limit is reached. The following errors indicate that a limit has been exceeded:

Error

Description

Your account has exceeded D1's free tier daily row read limit. Upgrade to a paid plan or wait until tomorrow (midnight UTC) to continue.

The account has reached its daily row read limit.

Your account has exceeded D1's free tier daily row write limit. Upgrade to a paid plan or wait until tomorrow (midnight UTC) to continue.

The account has reached its daily row write limit.

Inspect database query activity before the enforcement date to identify queries that may exceed these limits. To reduce row reads, add indexes to tables and review queries that perform full table scans. If usage requires higher limits after optimization, upgrade to a Workers Paid plan. …

The /crawl endpoint now respects the use directive of the Content Signals ↗︎ standard, letting site owners express the maximum level at which their content may be used.

You can declare your intended level with the new contentUse parameter. Allowed values, from least to most permissive, are reference and full, and the default is full. If a target site's robots.txt sets a use level that is more restrictive than your declared contentUse, the crawl request is rejected with a 400 error.

curl -X POST 'https://api.cloudflare.com/client/v4/accounts/{account_id}/browser-rendering/crawl' \
  -H 'Authorization: Bearer <apiToken>' \
  -H 'Content-Type: application/json' \
  -d '{
    "url": "https://example.com",
    "contentUse": "reference",
    "formats": ["markdown"]
  }'

For more information, refer to Content Signals in the /crawl endpoint documentation.

The /crawl endpoint now respects the use directive of the Content Signals ↗︎ standard, letting site owners express the maximum level at which their content may be used.

You can declare your intended level with the new contentUse parameter. Allowed values, from least to most permissive, are reference and full, and the default is full. If a target site's robots.txt sets a use level that is more restrictive than your declared contentUse, the crawl request is rejected with a 400 error.

curl -X POST 'https://api.cloudflare.com/client/v4/accounts/{account_id}/browser-rendering/crawl' \
  -H 'Authorization: Bearer <apiToken>' \
  -H 'Content-Type: application/json' \
  -d '{
    "url": "https://example.com",
    "contentUse": "reference",
    "formats": ["markdown"]
  }'

For more information, refer to Content Signals in the /crawl endpoint documentation.

@cf/zai-org/glm-5.3 is now available on Workers AI. It is Z.ai's flagship agentic coding model, built for long-running, tool-driven development workflows rather than single-turn chat.

GLM-5.3 uses the same base model as GLM-5.2, with every gain coming from post-training. The results are substantial on coding and agentic benchmarks: Z.ai reports ↗︎ a 50% improvement over GLM-5.2 on its in-house Z.ai Code Bench, and calls GLM-5.3 the most capable open-weights model for coding. On public benchmarks, it scores 88.2 on Terminal Bench 2.1 (up from 81.0), 28.3 on Terminal Bench 3.0 — open-source state of the art, up from 4.6 — 66.9 on DeepSWE (up from 46.2), 78.1 on FrontierSWE (up from 67.5), and 42.5 on SWE-Marathon (up from 19.4). It is also the top-scoring model in Z.ai's comparisons on CyberGym for vulnerability discovery (84.5) and on long-horizon automation tasks like AutomationBench (48.2).

The price-to-performance ratio is the compelling part. On Workers AI, GLM-5.3 costs the same as GLM-5.2 — $1.40 per M input tokens, $0.26 per M cached input tokens, and $4.40 per M output tokens — while roughly doubling GLM-5.2's scores on long-horizon benchmarks like SWE-Marathon, and improving them by more than 6x on Terminal Bench 3.0. …

Durable Objects can have up to ten distinct Dynamic Workers with in-flight requests, increased from four. This limit applies across all concurrent requests to the same Durable Object because they share an input/output (I/O) context. Other Workers can have up to four distinct Dynamic Workers with in-flight requests per request.

Multiple in-flight requests to the same Dynamic Worker count as one toward this limit.

For more information, refer to Dynamic Workers limits.

AI Search now supports six additional Workers AI models for text generation:

Model

Context window (tokens)

@cf/deepseek-ai/deepseek-v4-flash-0731

1,048,576

@cf/deepseek-ai/deepseek-v4-pro-0813

1,048,576

@cf/openai/gpt-oss-120b

128,000

@cf/openai/gpt-oss-20b

128,000

@cf/qwen/qwen3.8-27b

262,144

@cf/moonshotai/kimi-k2.7-code

262,144

These models run on Workers AI, so they do not require an additional provider key. Select a model 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.

You can now create app-scoped API tokens for Flagship. These tokens grant access only to the Flagship apps you select, instead of every app in the account.

When you create a custom token, open the resource dropdown (it defaults to Entire Account) and select Specified Flagship apps. Then choose the app and a Flagship App permission: Evaluate, Read, or Write. Account-wide Flagship Evaluate, Read, and Write permissions still exist when you need access to every app.

Use app-scoped tokens in trusted server-side environments, such as Wrangler, CI, or a backend service that should only touch one app.

To create a token, refer to API tokens or open the app-scoped token form ↗︎ in the dashboard.

@cf/zai-org/glm-5.3-flash is now available on Workers AI. It is the first natively multimodal model in the GLM-5 series, built on a Mixture-of-Experts architecture with 320B total parameters and 18B active per token.

GLM-5.3 Flash is the first GLM-family model on Workers AI to support multimodal inputs. It outperforms GLM-5.2 across benchmarks and real-world workloads at a lower price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.

GLM-5.3 Flash requires the Workers Paid plan or prepaid AI Gateway credits.

Use GLM-5.3 Flash through the Workers AI binding (env.AI.run()), the REST API, the OpenAI-compatible endpoint, or AI Gateway.

For more information, refer to the GLM-5.3 Flash model page and pricing.

By default, an alarm interrupted by ctx.abort() retries after the Durable Object resets. Pass { retryAlarm: false } when the alarm should stop instead:

src/index.jsjs

import { DurableObject } from "cloudflare:workers";

export class CleanupTask extends DurableObject {
	async alarm() {
		await this.ctx.storage.deleteAll();

		this.ctx.abort("Cleanup complete", { retryAlarm: false });
	}
}

src/index.tsts

import { DurableObject } from "cloudflare:workers";

export class CleanupTask extends DurableObject {
	async alarm(): Promise<void> {
		await this.ctx.storage.deleteAll();

		this.ctx.abort("Cleanup complete", { retryAlarm: false });
	}
}

For example, an alarm that deletes its storage can use this option to avoid repeating the cleanup or re-running the Durable Object constructor.

Alarms can run concurrently with other requests to the same Durable Object. If another request calls ctx.abort() while an alarm is running, the retryAlarm option on that call also controls whether the alarm retries.

The default retry prevents an unrelated request from permanently canceling the alarm. Set retryAlarm: false on every abort path that should stop an in-progress alarm, not only on calls from the alarm handler. Existing calls to ctx.abort() keep retrying alarms.

For local development, retryAlarm requires Wrangler 4.126.0 or later.

For more information, refer to ctx.abort().

By default, an alarm interrupted by ctx.abort() retries after the Durable Object resets. Pass { retryAlarm: false } when the alarm should stop instead:

src/index.jsjs

import { DurableObject } from "cloudflare:workers";

export class CleanupTask extends DurableObject {
	async alarm() {
		await this.ctx.storage.deleteAll();

		this.ctx.abort("Cleanup complete", { retryAlarm: false });
	}
}

src/index.tsts

import { DurableObject } from "cloudflare:workers";

export class CleanupTask extends DurableObject {
	async alarm(): Promise<void> {
		await this.ctx.storage.deleteAll();

		this.ctx.abort("Cleanup complete", { retryAlarm: false });
	}
}

For example, an alarm that deletes its storage can use this option to avoid repeating the cleanup or re-running the Durable Object constructor.

Alarms can run concurrently with other requests to the same Durable Object. If another request calls ctx.abort() while an alarm is running, the retryAlarm option on that call also controls whether the alarm retries.

The default retry prevents an unrelated request from permanently canceling the alarm. Set retryAlarm: false on every abort path that should stop an in-progress alarm, not only on calls from the alarm handler. Existing calls to ctx.abort() keep retrying alarms.

For local development, retryAlarm requires Wrangler 4.126.0 or later.

For more information, refer to ctx.abort().

Console methods now preserve exception details in your Worker's logs. When your Worker logs an exception, the corresponding log entry includes the exception name, message, and stack.

For example, your Worker can catch and log an exception:

try {
	throw new Error("Deliberately created exception");
} catch (error) {
	console.error("caught exception:", error);
}

try {
	throw new Error("Deliberately created exception");
} catch (error) {
	console.error("caught exception:", error);
}

If you use Workers Observability, your log is automatically enriched with structured error information. The following example shows how the enriched log appears in the Cloudflare dashboard:

Workers Observability log entry showing a caught exception and its stack trace

The exception's stack trace appears directly in the log message.

If you send telemetry to a Tail Worker, the Tail Worker now receives a log entry with an errorInfo array:

{
	"message": ["Request failed:", "RangeError: Value out of range"],
	"errorInfo": [
		null,
		{
			"name": "RangeError",
			"message": "Value out of range",
			"stack": "RangeError: Value out of range\n    at ..."
		}
	],
	"level": "error", …

Wrangler and the Cloudflare API MCP server now use optional OAuth scopes. During authorization, you can choose which optional scopes to grant instead of approving every scope requested by each client.

The consent dialog now includes the option to edit the permissions you grant to Wrangler or the Cloudflare API MCP server:

OAuth consent dialog with an Edit Permissions button

You can then choose which specific permissions to grant:

OAuth permission editor with controls for individual scopes

Required scopes remain selected. Choosing fewer optional scopes limits each tool's access to the permissions needed for your workflow.

If a command or tool call needs a scope that you declined, reauthorize the client and grant that scope.

For more information, refer to wrangler login and Edit optional permissions.