Developer Platform Updates & Release Notes

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.

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.

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 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.

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

Pass a headers option to .response() to set headers without rebuilding the Response.

Content-Type is always taken from the optimized image and can't be overridden by a specified header.

Set Cache-Control with Workers Cache to cache your optimized image at the edge.

For more information, refer to Optimize with Workers, Draw overlays and watermarks, and Manage hosted images with Workers.

AI Gateway monthly usage invoices

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.

For more information on D1 errors and how to handle them, refer to the D1 error list.

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.

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

Use GLM-5.3 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 model page and pricing.

@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.

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.

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.js
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.ts
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().