Changelog

Workers AI and AI Gateway now provide a unified path for accessing models and managing inference traffic. Use the same AI binding and REST API to call models hosted on Workers AI or by supported third-party providers, with AI Gateway providing observability, logging, caching, security, and billing controls.

The AI binding supports both Workers AI and third-party models through env.AI.run(). The REST API provides shared /ai/ endpoints with Cloudflare authentication across providers.

Route a Workers AI request through AI Gateway by specifying a gateway ID. Use default to automatically create a gateway on the first authenticated request, or specify an existing gateway to separate applications and workloads:

const response = await env.AI.run(
	"@cf/zai-org/glm-5.2",
	{
		messages: [{ role: "user", content: "What is the capital of France?" }],
	},
	{
		gateway: { id: "default" },
	},
);

const response = await env.AI.run(
	"@cf/zai-org/glm-5.2",
	{
		messages: [{ role: "user", content: "What is the capital of France?" }],
	},
	{
		gateway: { id: "default" },
	},
);

Requests routed through AI Gateway can be logged and included in analytics for request volume, errors, latency, token usage, and costs. You can also configure controls such as caching, rate limiting, and request retries on the gateway.

You can now use prepaid AI Gateway credits to pay for Workers AI inference. This provides one credit balance for Workers AI and supported third-party model providers. To use credits for Workers AI, set the gateway's Workers AI billing setting to Unified billing. Workers AI requests routed through that gateway deduct from your credit balance in real time.

Prepaid credits also provide access to the following Workers AI frontier models without requiring the Workers Paid plan. Each frontier Workers AI model has a rate limit of 50 requests per minute per account, per model when billed with AI Gateway credits, compared to 20 requests per minute through standard Workers AI billing:

• @cf/moonshotai/kimi-k2.6
• @cf/moonshotai/kimi-k2.7-code
• @cf/zai-org/glm-5.2

These limits are designed for typical agentic and coding workloads, where requests to frontier models can take longer to complete.

For details, refer to Workers AI limits, Workers AI pricing, Unified Billing, and the AI Gateway model catalog.

Cloudflare Mesh nodes can now run as Docker containers. The cloudflare/mesh ↗ image is available on Docker Hub for Docker Compose, Kubernetes, and any OCI-compatible runtime — no host-level package installation required.

The image supports amd64 and arm64 architectures and includes built-in source NAT so return traffic routes correctly without VPC route table changes.

• Docker Compose — add a cloudflare-mesh service to your compose.yaml and connect your entire stack to a private network.
• Kubernetes StatefulSet — deploy a standalone Mesh node with persistent registration state.
• Kubernetes sidecar — add the Mesh image as a sidecar container in a Pod to connect an application to Cloudflare without application changes.
• CI/CD — pull the image in a pipeline step, join the Mesh, run integration tests against private infrastructure, and tear down. The node disappears when the container exits.

For high availability, run multiple replicas with the same Mesh node token. Cloudflare operates replicas in active-passive mode with automatic failover.

For setup steps, runtime configuration, and deployment examples, refer to Run Mesh in Docker / Kubernetes.

Radar expands its Routing section ↗ with two widgets on AS pages, such as AS13335 ↗, that describe how a network reaches the rest of the Internet: the paths it takes toward the Tier-1 ↗ networks, and the mix of direct upstreams carrying its routes. Both are derived from RouteViews ↗ RIB snapshots, unioned across selected collectors.

The AS-level connectivity graph aggregates the BGP paths an AS uses to reach the Tier-1 networks, unioned across all the prefixes it announces, as observed by selected RouteViews collectors. It reads from left to right, starting at the queried AS and ending at the Tier-1 networks, and each node is labeled with its AS number, country, and organization name. Tier-1 nodes are marked so they stand apart from the intermediate networks that lead to them.

By default, the graph shows the network's direct connections to Tier-1 networks plus the indirect paths, which keeps the view readable. A Show full paths toggle expands it to every observed path, including transit through Tier-1 networks the AS already connects to. An IP version selector switches between IPv4 and IPv6, because the paths reaching Tier-1 networks may differ between the two address families.

This is the AS-level counterpart to the Real-time connectivity graph on prefix pages, such as the one for 1.1.1.0/24 ↗. Instead of covering a single prefix, it covers the union of paths for all prefixes an AS announces, which makes it a fast way to read a network's transit hierarchy: which providers it depends on, how many hops separate it from the core, and whether its paths to the core are diverse or concentrated. For more information on the prefix-level graph, refer to BGP real-time routes.

The Upstream providers widget tracks the share of an AS's observed paths carried by each of its direct upstream networks over time, drawn as a stacked area chart. Up to 10 upstreams appear as their own series and the remaining ones are grouped into Other. Transit changes such as adding a provider, dropping one, or moving traffic between them appear as movement between bands rather than as a single aggregate number. As with the connectivity graph, an IP version selector switches between IPv4 and IPv6.

The data behind both widgets is also available through two new endpoints on the BGP API:

• /bgp/routes/paths/{asn} — Returns the ordered AS path segments an AS uses to reach the Tier-1 networks, each with its observed path count, peer count, and contributing collectors, alongside the name and country of every ASN in the response. Pass collector to scope the result to a single RouteViews collector.
• /bgp/routes/upstreams/{asn}/timeseries — Returns the share of an AS's observed paths carried by each direct upstream over time. Use limit to control how many upstreams come back as separate series before the rest are grouped into an OTHER series, and ipVersion to select the address family.

Visit the AS13335 routing page ↗ to explore both widgets, or swap in any other AS number.

Cloudflare Radar now includes Radar Researcher ↗, a beta AI-powered assistant for exploring Internet trends and traffic data in plain language. Open Researcher from the header on any Radar page to ask questions by voice or text, receive explanations, and view interactive charts based on Radar API data.

To ask about a specific chart, select Explain with AI to start a conversation with its underlying data and context.

You can explore further with suggested follow-up questions, find earlier conversations through searchable history, and share conversations through shareable links.

Alongside the user-facing Researcher experience, Radar now supports WebMCP, allowing browser-based AI agents to navigate Radar, search data, and use tools such as URL scanning and domain lookup.

To get started, visit Cloudflare Radar ↗.

AI Search gets you from a data source to a working search endpoint quickly. This release adds what you need to put that endpoint in front of real users: your own domain, authentication, and one endpoint across several instances. It also adds crawling for sites without a complete sitemap, so your index covers everything you want it to find.

Each of the following is a new option. The previous behavior is still the default, so nothing changes until you change it.

A public endpoint is a URL that a site or app can query directly, with no authentication in front of it. By default that URL is a generated hostname on search.ai.cloudflare.com. You can now serve the same endpoint from a custom domain, a hostname in a zone that you own:

https://search.example.com/search

Once your endpoint is on your own domain, you can put Cloudflare Access in front of it. For example, you usually want to give /mcp to specific agents rather than to anyone who finds the URL. Agents authenticate with an Access service token, and people who open the endpoint in a browser sign in through your identity provider.

A namespace can expose its own public endpoint with /search, /chat/completions, and /mcp paths that fan out across the instances you choose:

curl https://ns-<NAMESPACE_ENDPOINT_ID>.search.ai.cloudflare.com/search \
  --header "Content-Type: application/json" \
  --data '{
    "messages": [{ "content": "How do I configure AI Search?", "role": "user" }],
    "ai_search_options": { "instance_ids": ["docs", "support"] }
  }'

Website data sources support a new discover parse type. It starts at the source URL and collects pages from both your sitemaps and the links it finds while crawling:

curl -X POST "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",
    "type": "web-crawler",
    "source": "example.com",
    "source_params": {
      "web_crawler": {
        "parse_type": "discover",
        "discover_options": { "source": "links", "limit": 5000, "depth": 3 }
      }
    }
  }'

To learn more, refer to the AI Search documentation.

Kitesurf is Cloudflare's new stateless, highly scalable browser that runs entirely on top of Workers and is designed for AI agents. It is available for free while in beta.

Compared to Chromium, Kitesurf uses 3–7× less CPU and memory for common agentic tasks like screenshots and HTML extraction, so you can run more sessions and scale better for bursty, AI-driven workloads.

Your existing clients already work. To opt in, add the browser=kitesurf parameter to any Browser Run CDP or Quick Action endpoint:

curl -X POST 'https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/browser-run/screenshot?browser=kitesurf' \
  -H 'Authorization: Bearer <API_TOKEN>' \
  -H 'Content-Type: application/json' \
  -d '{
    "url": "https://example.com"
  }' \
  --output "screenshot.png"

You can also explore Kitesurf without writing any code in the public playground ↗.

For more information, refer to the Kitesurf documentation and the blog announcement ↗.

AI Gateway now includes User Insights, a dashboard that gives you two things at once: clear visibility into how much your organization spends on AI, and a security signal that surfaces users whose usage suddenly looks abnormal. It works on the traffic already flowing through your gateway, so there is no additional setup.

On the spend side, User Insights shows organization-wide totals for cost, requests, tokens, and adoption, and lets you drill into an individual user to see their spend, top models and providers, cache hit rate, and more. To attribute usage to individual users, add a user identifier with custom metadata or put your gateway behind Cloudflare Access.

On the security side, User Insights baselines each user's normal usage from their 95th percentile (p95) session cost over the last 30 days, then flags sessions that exceed both that baseline and an organization-level threshold. A sudden jump above a user's own pattern is often the first sign of a compromised credential or a misbehaving agent, so you can investigate before it shows up on your bill.

User Insights is available to all AI Gateway customers at no additional cost.

AI Gateway now integrates with Cloudflare Access, giving you two new capabilities:

• Protect your gateway endpoint. Put your AI Gateway behind Access so you can set policies that control who is allowed to call a specific gateway's endpoint.
• Identity-aware controls. When traffic reaches AI Gateway through an Access-protected custom domain, AI Gateway can use the authenticated user's Access identity in logs, analytics, routing, and spend controls.

With identity-aware controls, you can set spend limits by authenticated user, control which gateways different users can access, filter logs by user, and build policies without passing user IDs from the client application. AI Gateway adds the verified Access user ID to request metadata as cf.user_id.

For setup instructions, refer to Cloudflare Access.

OAuth consent screens now display a shield icon with explanatory text beneath the consent screen title. Each shield icon indicates who owns the application and whether its domain ownership is verified.

• Green filled shield: Cloudflare owns and manages the application.
• Blue outlined shield: A third-party application with verified ownership of its domain.
• Amber filled shield: A third-party application without verified ownership of a domain.

Domain verification only confirms that the application owner controls the displayed domain.

For more information, refer to Authorizing an application.

Agent tracing is now available for applications built with the Agents SDK. Traces show each agent turn alongside model calls, tool runs, approvals, token usage, and Workers runtime operations.

Turn on Workers tracing in your Wrangler configuration:

{
  "$schema": "./node_modules/wrangler/config-schema.json",
  "observability": {
    "traces": {
      "enabled": true
    }
  }
}

[observability.traces]
enabled = true

Think and Flue applications emit agent traces automatically. For direct AI SDK calls, wrap the AI SDK namespace once. wrapAISDK() supports AI SDK v6 and v7. This AI SDK v7 example also supplies the agent identity:

import * as ai from "ai";
import { wrapAISDK } from "agents/observability/ai";

const tracedAI = wrapAISDK(ai);

await tracedAI.generateText({
	model,
	prompt: "Find an available appointment",
	runtimeContext: {
		agentId: "booking-agent-production",
		conversationId: "conversation-123",
	},
	telemetry: {
		functionId: "booking-agent",
		includeRuntimeContext: {
			agentId: true,
			conversationId: true,
		},
	},
});

import * as ai from "ai";
import { wrapAISDK } from "agents/observability/ai";

const tracedAI = wrapAISDK(ai);

await tracedAI.generateText({
	model,
	prompt: "Find an available appointment",
	runtimeContext: {
		agentId: "booking-agent-production",
		conversationId: "conversation-123",
	},
	telemetry: {
		functionId: "booking-agent",
		includeRuntimeContext: {
			agentId: true,
			conversationId: true,
		},
	},
});

Message and tool payload recording is off by default. Turn it on only when the payloads are safe to store:

const tracedAI = wrapAISDK(ai, {
	storeMessages: true,
	storeTools: true,
});

const tracedAI = wrapAISDK(ai, {
	storeMessages: true,
	storeTools: true,
});

Open the Agents tab ↗ in the Cloudflare dashboard to inspect sessions, replay conversations, and view trace waterfalls. For advanced setup, privacy controls, and trace structure, refer to Agent tracing.

You can now run your CI/CD pipeline on your Artifacts repo by defining a CI Workflow with the CI SDK ↗, automatically triggered on Artifacts push events.

This allows you to:

• Automatically build and deploy application code stored in Artifacts.
• Run linting, type checking, tests, and other checks on every push.
• Reuse dependencies when the lockfile (i.e. pnpm-lock.yaml) has not changed.
• Stop deployment when a check or build fails.
• Restrict API token access to the deployment step.
• Deploy the output to a Worker or a Workers for Platforms User Worker.

Define your CI steps with @cloudflare/ci. Each ci.runner() spins up an isolated sandbox, and the cache option reuses installed dependencies across each sandboxed step in your CI job.

Point cache.inputs at your lockfile (i.e. pnpm-lock.yaml, bun.lock), and the install step only runs again when that lockfile changes:

const deps = await ci.runner({
	name: "install",
	command: "bun install --frozen-lockfile",
	cache: { inputs: ["package.json", "bun.lock"] },
});

await Promise.all([
	deps.runner({ name: "lint", command: "bun run lint" }),
	deps.runner({ name: "test", command: "bun run test" }),
	deps.runner({ name: "typecheck", command: "bun run typecheck" }),
	deps.runner({ name: "build", command: "bun run build" }),
]);

await deps.runner({ name: "deploy", command: "bun wrangler deploy" });

const deps = await ci.runner({
	name: "install",
	command: "bun install --frozen-lockfile",
	cache: { inputs: ["package.json", "bun.lock"] },
});

await Promise.all([
	deps.runner({ name: "lint", command: "bun run lint" }),
	deps.runner({ name: "test", command: "bun run test" }),
	deps.runner({ name: "typecheck", command: "bun run typecheck" }),
	deps.runner({ name: "build", command: "bun run build" }),
]);

await deps.runner({ name: "deploy", command: "bun wrangler deploy" });

To start the Workflow automatically after each push, add a cf.artifacts.repo.pushed trigger to your Wrangler configuration:

{
	"triggers": {
		"events": [
			{
				"type": "cf.artifacts.repo.pushed",
				"filter": {
					"namespace": "CI",
					"repoName": "my-repo",
				},
				"target": {
					"scriptName": "my-ci-worker",
					"workflowName": "ci-workflow",
				},
			},
		],
	},
}

[[triggers.events]]
type = "cf.artifacts.repo.pushed"

  [triggers.events.filter]
  namespace = "CI"
  repoName = "my-repo"

  [triggers.events.target]
  scriptName = "my-ci-worker"
  workflowName = "ci-workflow"

To learn more, refer to Build and deploy Artifacts repos.

You can now create standalone Free accounts directly from the Cloudflare dashboard using the new Create Account button. This feature is currently available to all users.

When creating a Free account:

• You can create up to 5 Free accounts.
• Your user account must have at least 7 days of tenure to be eligible.
• The account is created immediately and ready to use.

To create a Free account, go to the Cloudflare dashboard ↗ and select Create Account from either the account switcher in the top left (where your account name appears) or from the Accounts page.

• This feature can only be used to create a Cloudflare Free account. To create an Enterprise Account under your existing contract, please contact Cloudflare Support.
• All users can create a Cloudflare Free account, however, Enterprises wish to restrict this action to only Super Administrators. We will deliver this improvement in a future release.

After creating your Free account, you can:

• Add a payment method to enable additional Cloudflare products and services.
• Update billing information to manage payment methods, billing address, or tax IDs.
• Review how Cloudflare billing works to understand the billing lifecycle and charge types.
• Assign accounts to an Enterprise Organization to centrally manage multiple accounts from a single dashboard.

You can now store up to 20 million vectors in a single Vectorize index, doubling the previous limit of 10 million vectors. This enables larger-scale semantic search, recommendation systems, and retrieval-augmented generation (RAG) applications without splitting data across multiple indexes.

Vectorize continues to support indexes with up to 1,536 dimensions per vector at 32-bit precision. Refer to the Vectorize limits documentation for complete details.

This release introduces new rules and updates Microsoft SharePoint RCE alongside enhanced SSRF cloud protection rule actions.

Key Findings

• CVE-2026-50522: An insecure deserialization vulnerability in Microsoft SharePoint Server. This may allow an unauthenticated attacker to execute arbitrary code using crafted requests.
• CVE-2026-66066: An improper input processing vulnerability in Ruby on Rails Active Storage image variant transformations. This may allow an unauthenticated attacker to perform arbitrary file reads and achieve Remote Code Execution (RCE) using maliciously crafted payload requests.
• Generic Cloud Protections: Added improved detection logic targeting Server-Side Request Forgery (SSRF) in cloud-hosted applications.

wrangler dev and vite dev automatically capture structured OpenTelemetry traces and correlated console logs during local Worker invocations.

When the tooling detects an AI agent session, it prints a terminal hint pointing to the Local Explorer API at /cdn-cgi/explorer/api. The API serves an OpenAPI schema and exposes a read-only observability query endpoint for discovering telemetry, querying traces and logs, and inspecting binding state.

The agent can identify the exact failing operation, fix the code, rerun the request, and verify the result. This debug loop requires no deployment or temporary logs.

Humans can inspect the same traces and correlated console logs in the Local Explorer browser UI. Each trace shows spans, timing, attributes, and errors.

Automatic spans cover handler calls, outbound fetch() calls, and binding calls. Custom spans appear alongside these automatic spans.

For more details, refer to the Local Explorer documentation.

Cloudflare Access administrators can now control whether a self-hosted application preemptively sets authorization cookies across its public hostnames.

Previously, Access automatically used eager redirects for applications with five or fewer hostnames. Applications with more than five hostnames received cookies as users visited each hostname. Administrators can now choose either behavior, regardless of the number of hostnames.

The new Eager redirect cookie setting is turned on by default for new applications. After a user signs in, Access redirects the browser through each hostname and sets a CF_Authorization cookie. This supports applications that need to make requests across hostnames before the user visits each one.

For applications with many hostnames, the redirect chain can cause sign-in loops in some browsers. Turn off the setting to issue the cookie only when a user visits each hostname.

To configure the setting, refer to Authorization cookie.

We're releasing an early preview of @cloudflare/computer ↗, an open-source agent runtime that gives every agent its own computer. The runtime dynamically orchestrates between fast, efficient isolates and full Linux containers, so the agent always runs on the right compute primitive for the task at hand.

@cloudflare/computer provides a virtual filesystem backed by SQLite, which you can populate from cloud storage, source control, or any files you choose. Agents can read, write, and edit files, run shell commands, and interact with Git repositories. All operations are gated, audited, and observed.

Install the package via npm:

npm install @cloudflare/computer

Instantiate a Workspace inside any Durable Object to give your agent a filesystem and execution runtime:

import { Workspace } from "@cloudflare/computer";

export class Agent {
	workspace = new Workspace({
		storage: this.ctx.storage,
	});
}

Several execution backends are included or you can write your own:

• Isolate runtime — fast, horizontally scalable execution via just-bash and Dynamic Workers, ideal for file manipulation and data processing.
• Container runtime — full Linux environment via Cloudflare Containers, mounted through FUSE, for tasks that need native binaries, package managers, or a complete userland.

The AI SDK-compatible toolkit provides common agent tools (read, write, edit, ls, exec) and guides the model to choose the appropriate backend for each task.

For more examples, including a step-by-step tutorial, visit the @cloudflare/computer repository ↗.

Read the announcement blog post for more details: Your agent needs a computer, not a container ↗.

Billing is now enabled for Cloudflare Pipelines on non-enterprise accounts. Pipelines usage beyond the included free tier will appear on your next invoice.

Pipelines charges based on two usage dimensions. Ingress into a Pipeline stream remains free regardless of volume:

• SQL transforms: $0.04 / GB for stateless transforms (filter, reshape, unnest, cast, compute).
• Sinks (egress): $0.03 / GB for JSON output, $0.06 / GB for Parquet or Iceberg output.

Workers Paid plans include 50 GB / month for both SQL transforms and sinks. Standard R2 storage and operations charges apply for data written to R2 buckets, and R2 Data Catalog charges apply when writing to Iceberg tables.

For example, a pipeline that ingests 500 GB of event data per month, uses a SQL transform to filter and reshape it, and writes 300 GB to an R2 Data Catalog Iceberg table would be billed as follows:

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

Billing is now enabled for R2 Data Catalog on non-enterprise accounts. R2 Data Catalog usage beyond the included free tier will appear on your next invoice.

R2 Data Catalog charges based on two dimensions, in addition to standard R2 storage and operations:

• 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.

Each dimension includes a monthly free tier: 1 million catalog operations, 10 GB of compaction data processed, and 1 million compaction objects processed.

For example, a single Iceberg table with 50 GB of data, 500,000 catalog operations per month, and compaction turned on that processes 20 GB across 200,000 files would be billed as follows:

Standard R2 storage charges ($0.015 / GB-month) apply separately for the 50 GB of data stored.

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

Billing is now enabled for R2 SQL on non-enterprise accounts. R2 SQL usage beyond the included free tier will appear on your next invoice.

R2 SQL charges based on a single dimension:

• Data scanned: $0.0025 / GB ($2.50 / TB) of compressed data read from R2 to execute your query.

All plans include 10 GB of data scanned per month. Each query is billed for a minimum of 10 MB of data scanned. R2 SQL pricing is additive to standard R2 storage and operations and R2 Data Catalog charges. R2 does not charge for egress, so there is no additional data transfer cost.

For example, a user who stores 500 GB of Parquet data in R2 Data Catalog and runs queries that scan a total of 50 GB of compressed data during the month would be billed as follows:

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

You can now call methods between Python and JavaScript Workers using Workers RPC. This works through Service bindings without extra dependencies, schema definitions, or serialization code.

Cross-language RPC calls behave like ordinary function calls. Exceptions propagate to the call site. You can pass structured cloneable types ↗ as parameters or return values, and Pyodide Foreign Function Interface (FFI) automatically converts types between languages.

Define a method in a TypeScript Worker:

import { WorkerEntrypoint } from "cloudflare:workers";

export class RpcService extends WorkerEntrypoint {
	async add(a, b) {
		return a + b;
	}
}

import { WorkerEntrypoint } from "cloudflare:workers";

export class RpcService extends WorkerEntrypoint {
	async add(a: number, b: number): Promise<number> {
		return a + b;
	}
}

Call it from a Python Worker through a Service binding:

from workers import Response, WorkerEntrypoint

class Default(WorkerEntrypoint):
	async def fetch(self, request):
		rpc = self.env.RPC
		result = await rpc.add(42, 144)
		return Response.json({"result": result})

Configure the Service binding in the Python Worker's Wrangler configuration:

{
	"services": [
		{
			"binding": "RPC",
			"service": "ts-rpc-server",
			"entrypoint": "RpcService"
		}
	]
}

[[services]]
binding = "RPC"
service = "ts-rpc-server"
entrypoint = "RpcService"

Define a method in a Python Worker:

from workers import WorkerEntrypoint

class Default(WorkerEntrypoint):
	async def highlight_code(self, code: str, language: str) -> dict:
		from pygments.formatters import HtmlFormatter
		from pygments import highlight
		from pygments.lexers import get_lexer_by_name

		lexer = get_lexer_by_name(language, stripall=True)
		formatter = HtmlFormatter(linenos=True, cssclass="highlight", style="monokai")
		highlighted_html = highlight(code, lexer, formatter)
		css = formatter.get_style_defs(".highlight")

		return {
			"html": highlighted_html,
			"css": css
		}

Call it from a JavaScript Worker through a Service binding:

export default {
	async fetch(request, env) {
		const rpc = env.PYTHON_RPC;
		const result = await rpc.highlight_code("print(42)", "python");
		return Response.json(result);
	},
};

export default {
	async fetch(request, env) {
		const rpc = env.PYTHON_RPC;
		const result = await rpc.highlight_code("print(42)", "python");
		return Response.json(result);
	},
};

Configure the Service binding in the JavaScript Worker's Wrangler configuration:

{
	"services": [
		{
			"binding": "PYTHON_RPC",
			"service": "py-rpc-server"
		}
	]
}

[[services]]
binding = "PYTHON_RPC"
service = "py-rpc-server"

For more details on the announcement, read the blog post ↗.

For more information, refer to the Workers RPC documentation and the Python Workers overview.

A new Beta release for the Windows Cloudflare One Client is now available on the beta releases downloads page.

This beta release includes the following changes and improvements:

• Improved connection reliability: the client now swaps protocol order after repeated connectivity-check failures, which helps when HTTP/3 is blocked after the QUIC handshake.
• Fixed issue where a certificate error could be incorrectly displayed right after the connection is established.
• A DNS search domain parsing failure no longer prevents connection.
• Fixed a MASQUE issue where the tunnel could stall while uploading at a high rate.
• Fixed being unable to switch organizations when the client was stuck in the "Device not in organization" state.
• Fixed the Home Screen dropdown popup not anchoring correctly.
• Fixed a crash during dialog dismissal.
• Increased tolerance for configurations with a large number of local domain fallback resolver IPs, so DNS resolution behaves correctly even when more fallback resolvers are configured than recommended.
• Fixed a networking issue where IPv6 multicast routes were being assigned to the WARP tunnel interface.
• Fixed fatal errors on UI load on Windows 10.
• Fixed a crash during Windows notification initialization.
• Made the Windows domain-joined posture check more reliable.
• Fixed orphaned credentials left behind on multi-user uninstall.
• A successful re-authentication will cause the device profile to be re-evaluated.
• Improved dashboard-managed client updates by running the updater only when needed.

A new Beta release for the macOS Cloudflare One Client is now available on the beta releases downloads page.

This beta release includes the following changes and improvements:

• Improved connection reliability: the client now swaps protocol order after repeated connectivity-check failures, which helps when HTTP/3 is blocked after the QUIC handshake.
• Fixed issue where a certificate error could be incorrectly displayed right after the connection is established.
• A DNS search domain parsing failure no longer prevents connection.
• Fixed a MASQUE issue where the tunnel could stall while uploading at a high rate.
• Fixed being unable to switch organizations when the client was stuck in the "Device not in organization" state.
• Fixed the Home Screen dropdown popup not anchoring correctly.
• Fixed a crash during dialog dismissal.
• Increased tolerance for configurations with a large number of local domain fallback resolver IPs, so DNS resolution behaves correctly even when more fallback resolvers are configured than recommended.
• Fixed the WARP client stealing window focus (for example, during reauth).
• Fixed a client crash when connecting to a captive portal over Wi-Fi.
• Fixed the system tray icon showing "disconnected" while the UI showed "connected".
• A successful re-authentication will cause the device profile to be re-evaluated.
• Improved dashboard-managed client updates by running the updater only when needed.

MCP server portals can now connect to upstream MCP servers that require a pre-registered OAuth client. This supports OAuth providers that do not offer Dynamic Client Registration or have disabled it. This unlocks portal connections to major SaaS providers such as Slack and GitHub, whose MCP servers do not yet support DCR.

When adding an MCP server, administrators can enter the client ID and client secret from an OAuth application registered with the upstream provider. The configuration also supports custom OAuth endpoints, scopes, and the client_secret_post and client_secret_basic token endpoint authentication methods.

Cloudflare stores the client secret encrypted. Users still authenticate to the upstream server with their own accounts when they connect through a portal.

For setup instructions, refer to Configure manual OAuth credentials.