We are refreshing the Workers AI model catalog to make room for newer releases. Please update your apps to remove references to the models listed below before the deprecation date.
Recommended replacements
@cf/zai-org/glm-4.7-flash — fast multilingual model with multi-turn tool calling and coding capabilities.
We originally stated Kimi K2.5 would be deprecated on May 10, 2026, however we have extended the deprecation date to May 30, 2026. Requests will be automatically aliased to Kimi K2.6 on May 30, 2026, which has a higher price. Please review the @cf/moonshotai/kimi-k2.6 pricing and model capabilities prior to May 30, 2026 to ensure that the model suits your needs.
The -fast and -lora variants of models will remain active, including:
@cf/meta/llama-3.3-70b-instruct-fp8-fast
@cf/meta/llama-3.1-8b-instruct-fast
@cf/google/gemma-7b-it-lora
@cf/google/gemma-2b-it-lora
@cf/mistral/mistral-7b-instruct-v0.2-lora
@cf/meta-llama/llama-2-7b-chat-hf-lora
LoRA models may be deprecated in the future. We will be adding more LoRA capabilities to the catalog, and will communicate when new LoRA models come online to give users time to train new LoRAs before we deprecate old ones.
Multiple security vulnerabilities were disclosed by the React team and Vercel affecting React Server Components and Next.js. These include denial of service, middleware and proxy bypass, server-side request forgery, cross-site scripting, and cache poisoning issues across a range of severity levels.
We strongly recommend updating your application and its dependencies immediately. Patched versions are available for React (react-server-dom-webpack, react-server-dom-parcel, and react-server-dom-turbopack19.0.6, 19.1.7, and 19.2.6) and Next.js (15.5.16 and 16.2.5).
WAF protections
Cloudflare WAF rules deployed in response to prior React Server Component CVEs (CVE-2025-55184 ↗︎ and CVE-2026-23864 ↗︎) already provide coverage for the newly disclosed denial-of-service vulnerabilities. These rules are enabled by default with a Block action for all customers using the Cloudflare Managed Ruleset, including Free plan customers using the Free Managed Ruleset.
The existing rules detect the underlying attack patterns generically. As a result, they apply to the new CVE-2026-23870 ↗︎ denial-of-service vulnerability in Server Components and the corresponding Next.js advisory GHSA-8h8q-6873-q5fj ↗︎.
Cloudflare is investigating whether WAF rules can be safely and effectively deployed for three of the high-severity advisories: CVE-2026-23870 ↗︎ / GHSA-8h8q-6873-q5fj ↗︎, GHSA-267c-6grr-h53f ↗︎, and GHSA-mg66-mrh9-m8jx ↗︎. If it is possible to create a managed WAF rule that mitigates these CVEs and does not potentially break application behavior, Cloudflare will add additional managed WAF rules. These rules will be announced through the WAF changelog. Because these vulnerabilities were shared with Cloudflare with minimal advance notice, we are still investigating what WAF mitigations are possible.
Several of the disclosed vulnerabilities are not possible to block in WAF. We strongly recommend updating your applications so they are not purely reliant on WAF mitigations.
Vinext:Vinext ↗︎ is a Vite plugin that reimplements the Next.js API surface. Vinext's latest release is not vulnerable to any of the disclosed CVEs. Vinext's architecture differs from stock Next.js in ways that sidestep the affected code paths. For example, it does not implement the PPR resume protocol, does not expose Pages Router data-route endpoints, and strips internal headers such as x-nextjs-data at request boundaries. As an extra layer of defense, we added a React 19.2.6 or later requirement when running vinext init (PR #1118 ↗︎, PR #1112 ↗︎) to prevent accidentally running a vulnerable version of React with Vinext.
OpenNext on Cloudflare: OpenNext is an adapter that lets you deploy Next.js apps to the Cloudflare Workers platform. OpenNext itself is not directly vulnerable to the React denial-of-service CVE, but users must update the Next.js version in their application. The OpenNext team has updated the adapter to further harden against these vectors and released a new version of the Cloudflare adapter. Test fixtures and examples have been updated to use patched versions (PR #1255 ↗︎).
You can now interact with your Stream video library using new bindings for Workers! This allows customers to upload content to Stream, provision direct uploads, manage videos, and generate signed URLs from a Worker without making authenticated API calls. We're excited to bring Stream and Workers closer together to empower more programmatic pipelines, tighter integrations, and support generative AI and inference workloads.
Use the Stream binding when you want to:
Upload videos from URLs or create basic direct upload links for end users
Generate signed playback tokens without managing signing keys
Manage video metadata, captions, downloads, and watermarks
Build video pipelines entirely within Workers
To get started, add the Stream binding to your Wrangler configuration:
Generate a video with AI and upload directly to Stream or send a URL of a file you already have:
const aiResponse = await env.AI.run( "google/veo-3.1", { prompt: "A dog walking next to a river", duration: "10s", aspect_ratio: "16:9", resolution: "1080p", generate_audio: true, }, { gateway: { id: "experiments" }, },);// Veo will return a URL of the generated asset.const videoUrl = aiResponse.result.video;// Alternative option: a video of the Austin Office mobile// const videoUrl = 'https://pub-d9fcbc1abcd244c1821f38b99017347f.r2.dev/aus-mobile.mp4';// Upload to Stream by providing a URLconst streamVideo = await env.STREAM.upload(videoUrl);// The streamVideo response will include the video ID, playback and manifest// URLs, and other information, just like the REST API.
const aiResponse = await env.AI.run( 'google/veo-3.1', { prompt: 'A dog walking next to a river', duration: '10s', aspect_ratio: '16:9', resolution: '1080p', generate_audio: true, }, { gateway: { id: 'experiments' }, },);// Veo will return a URL of the generated asset.const videoUrl = aiResponse.result.video;// Alternative option: a video of the Austin Office mobile// const videoUrl = 'https://pub-d9fcbc1abcd244c1821f38b99017347f.r2.dev/aus-mobile.mp4';// Upload to Stream by providing a URLconst streamVideo = await env.STREAM.upload(videoUrl);// The streamVideo response will include the video ID, playback and manifest// URLs, and other information, just like the REST API.
Generate a signed URL without using a signing key or an API call:
const video_id = "ce800be43a9772f4bb02f35b860fb516";const token = await env.STREAM.video(video_id).generateToken();// Use the "token" in an iframe embed code, manifest URL, or thumbnail:const embedUrl = `https://customer-igynxd2rwhmuoxw8.cloudflarestream.com/${token}/iframe`;
const video_id = 'ce800be43a9772f4bb02f35b860fb516';const token = await env.STREAM.video(video_id).generateToken();// Use the "token" in an iframe embed code, manifest URL, or thumbnail:const embedUrl = `https://customer-igynxd2rwhmuoxw8.cloudflarestream.com/${token}/iframe`;
For setup instructions and the full API reference, refer to Bind to Workers API.
Get started with your Agent
Add a binding for Cloudflare Stream (env.STREAM). On the watch page, use the
Stream binding to get info based on the ID, and leverage video.meta.name as
the page title.
You can now get a single unified trace across Worker-to-Worker subrequests, with trace context propagating automatically. Previously, automatic tracing produced disconnected traces when a Worker called another Worker through a service binding or Durable Object.
This means you can:
Follow a request through your entire Worker architecture in one trace view
See service binding and Durable Object calls as nested child spans instead of separate traces
Debug cross-Worker request flows in the Cloudflare dashboard or in an external observability platform via OpenTelemetry
Up next, we are working on external trace context propagation using W3C Trace Context standards ↗︎, which will allow traces from your Workers to link with traces from services outside of Cloudflare.
Cloudflare Pipelines ingests streaming data via Workers or HTTP endpoints, transforms it with SQL, and writes it to R2 as Apache Iceberg tables. R2 Data Catalog manages those Iceberg tables, compaction, and compatibility with query engines like R2 SQL, Spark, and DuckDB.
This adds four new resources that let you define your entire data pipeline as infrastructure-as-code: a data catalog, a stream for ingestion, a sink that writes to R2 Data Catalog or R2, and a pipeline that connects them with SQL.
Here is a minimal example that creates a stream, an R2 Data Catalog sink, and a pipeline:
resource "cloudflare_pipeline_stream" "my_stream" { account_id = var.cloudflare_account_id name = "my_stream" format = { type = "json" } schema = { fields = [{ name = "value" type = "json" required = true }] } http = { enabled = true, authentication = false, cors = {} } worker_binding = { enabled = false }}resource "cloudflare_pipeline_sink" "my_sink" { account_id = var.cloudflare_account_id name = "my_sink" type = "r2_data_catalog" format = { type = "parquet" } schema = { fields = [] } config = { account_id = var.cloudflare_account_id bucket = "my-pipeline-bucket" table_name = "my_table" token = var.catalog_token }}resource "cloudflare_pipeline" "my_pipeline" { account_id = var.cloudflare_account_id name = "my_pipeline" sql = "INSERT INTO ${cloudflare_pipeline_sink.my_sink.name} SELECT * FROM ${cloudflare_pipeline_stream.my_stream.name}"}
For a full end-to-end example that includes R2 bucket creation, data catalog setup, and scoped API token provisioning, refer to the Pipelines Terraform documentation.
The Worker Loader loads Dynamic Workers on demand, which previously made durability challenging. Even within a Dynamic Worker, a Workflow might sleep for hours or days between steps, and by the time it resumes, the original Dynamic Worker code would no longer be in memory.
The library solves this by tagging each Workflow instance with metadata that identifies which Dynamic Worker to load — for example, a tenant ID — then reloading the matching Dynamic Worker through the Worker Loader whenever a Workflow awakens.
Because Dynamic Workers are created on-demand, you do not have to register each Workflow up front or manage them individually. Load the Workflow code in the Dynamic Worker when it is needed, and the Workflows engine handles persistence and retries behind the scenes. Your Workflow code itself is unaffected by the routing and behaves as normal.
This unlocks patterns where the Workflow code itself is dynamic. For example, this is useful with:
SaaS platforms where each tenant defines their own automation, such as onboarding sequences, approval chains, or billing retry logic.
AI agent frameworks where agents generate and execute multi-step plans at runtime, surviving restarts and waiting for human approval between tool calls.
Multi-tenant job systems where each customer submits their own processing logic and every step persists progress and retries on failure.
import { createDynamicWorkflowEntrypoint, DynamicWorkflowBinding, wrapWorkflowBinding, type WorkflowRunner,} from "@cloudflare/dynamic-workflows";export { DynamicWorkflowBinding };interface Env { WORKFLOWS: Workflow; LOADER: WorkerLoader;}function loadTenant(env: Env, tenantId: string) { return env.LOADER.get(tenantId, async () => ({ compatibilityDate: "2026-01-01", mainModule: "index.js", modules: { "index.js": await fetchTenantCode(tenantId) }, // The Dynamic Worker uses this exactly like a real Workflow binding; // every create() is tagged with { tenantId } automatically. env: { WORKFLOWS: wrapWorkflowBinding({ tenantId }) }, }));}// The entrypoint name must match `class_name` in the workflows binding of your Wrangler config file.export const DynamicWorkflow = createDynamicWorkflowEntrypoint<Env>( async ({ env, metadata }) => { const stub = loadTenant(env, metadata.tenantId as string); return stub.getEntrypoint("TenantWorkflow") as unknown as WorkflowRunner; },);export default { fetch(request: Request, env: Env) { const tenantId = request.headers.get("x-tenant-id")!; return loadTenant(env, tenantId).getEntrypoint().fetch(request); },};
This is a major version release that includes breaking changes to three packages: ai_search, email_security, and workers. These changes reflect upstream API specification updates that improve type correctness and consistency.
Please ensure you read through the list of changes below before moving to this version - this will help you understand any down or upstream issues it may cause to your environments.
Breaking Changes
See the v7.0.0 Migration Guide ↗︎ for before/after code examples and actions needed for each change.
AI Search - SearchForAgents Metadata Removed
The SearchForAgents nested type has been removed from all instance metadata structs. This field is no longer part of the API specification.
The Investigate.Get, Investigate.Move.New, and Investigate.Reclassify.New methods now use investigateID instead of postfixID as the path parameter name.
The observability telemetry filter parameter types have been restructured to support nested filter groups. New discriminated union types replace the previous flat filter arrays:
ObservabilityTelemetryKeysParams.Filters now accepts FiltersObjectFilterUnion (was []interface\{\})
ObservabilityTelemetryQueryParams.Parameters.Filters now accepts FiltersObjectFilterUnion
ObservabilityTelemetryValuesParams.Filters now accepts FiltersObjectFilterUnion
New types include FiltersObjectFiltersObject (for group filters with FilterCombination) and FiltersWorkersObservabilityFilterLeaf (for leaf filters with typed Operation, Type, and Value fields).
You can now empty an entire R2 bucket or delete folders directly from the dashboard. Emptying a bucket is required before you can delete it. Previously, this required scripting or configuring lifecycle rules. Now, the dashboard can handle it in a single action.
Empty a bucket
Go to your bucket's Settings tab and select Empty under the Empty Bucket section. This deletes all objects in the bucket while preserving the bucket and its configuration. For large buckets, the operation runs in the background and the dashboard displays progress.
Emptying a bucket is also a prerequisite for deleting it. The dashboard now guides you through both steps in one place.
Delete folders
R2 uses a flat object structure. The dashboard groups objects that share a common prefix into folders when the View prefixes as directories checkbox is selected. Deleting a folder removes every object under that prefix.
From the Objects tab, you can select one or more folders and delete them alongside individual objects.
This is a major release of the Cloudflare Python SDK. It drops support for Python 3.8, adds 11 new API services, introduces optional aiohttp backend support for improved async concurrency, and includes hundreds of type and method updates across the entire API surface.
Please review the breaking changes below before upgrading. A migration guide is available at v5.0.0 Migration Guide ↗︎.
Breaking Changes
Python 3.8 is no longer supported. The minimum required version is now Python 3.9.
typing-extensions minimum version bumped from >=4.10 to >=4.14.
The following resources have breaking changes. See the v5.0.0 Migration Guide ↗︎ for detailed migration instructions.
abusereports
acm.totaltls
apigateway.configurations
cloudforceone.threatevents
d1.database
intel.indicatorfeeds
logpush.edge
origintlsclientauth.hostnames
queues.consumers
radar.bgp
rulesets.rules
schemavalidation.schemas
snippets
zerotrust.dlp
zerotrust.networks
Features
aiohttp Backend Support
The async client now supports an optional aiohttp HTTP backend for improved concurrency performance. Install with pip install cloudflare[aiohttp] and use DefaultAioHttpClient() as the http_client parameter.
Python 3.13 and 3.14 Support
Python 3.13 and 3.14 are now tested and supported.
New Services
The following top-level resources are new in this release:
Resource
Client Path
Description
AI Search
aisearch
AI-powered search capabilities
Connectivity
connectivity
Connectivity testing and diagnostics
Email Sending
email_sending
Email send and send_raw endpoints
Fraud
fraud
Fraud detection and prevention
Google Tag Gateway
google_tag_gateway
Google Tag Gateway management
Organizations
organizations
Organization audit logs and management
R2 Data Catalog
r2_data_catalog
R2 Data Catalog operations
Realtime Kit
realtime_kit
Realtime communication (Calls/TURN)
Resource Tagging
resource_tagging
Resource tagging and labeling
Token Validation
token_validation
Token validation configuration and rules
Vulnerability Scanner
vulnerability_scanner
Vulnerability scanning, credential sets, and target environments
New Endpoints on Existing Services
api_gateway: Labels endpoints
billing: Billable usage PayGo endpoint
brand_protection: v2 endpoints
browser_rendering: DevTools methods
cache: Origin cloud regions resource
custom_origin_trust_store: Custom origin trust store
dns: dns_records/usage endpoints
email_security: Phishguard reports endpoint
iam: User groups and user group members resources
radar: Botnet Threat Feed and Post-Quantum endpoints
workers: Observability Destinations resources
zero_trust: Access Users, DEX rules, Device IP Profile, Device Subnet, WARP Connector connections and failover, WARP Subnet, Gateway PAC files
zones: Zone environments endpoints
Bug Fixes
Fixed polymorphic_serialization parameter in model_dump overrides
Added BaseModel base to response SchemaFieldStruct/SchemaFieldList stubs in Pipelines
Added missing model_rebuild/update_forward_refs for SharedEntryCustomEntry classes in DLP
Made RunQueryParametersNeedleValue a BaseModel with arbitrary_types_allowed in Workers
Removed duplicate notification_url field in webhook response types for Stream
Resolved pre-existing codegen type errors
Fixed type: ignore[call-arg] placement for mypy compatibility in Radar
Deprecations
Resources with @deprecated annotations on some methods include: accounts, addressing, ai-gateway, aisearch, api-gateway, billing, cloudforce-one, dns, email-routing, email-security, filters, firewall, images, intel, kv, logpush, origin-tls-client-auth, pages, pipelines, radar, rate-limits, registrar, rulesets, ssl, user, workers, workers-for-platforms, zero-trust, zones
This is a major version release of the Cloudflare TypeScript SDK. It includes 11 entirely new top-level API resources, new sub-resources and methods across 50+ existing resources, SDK infrastructure improvements, and breaking changes to the generated API surface from the v5.x line.
Please ensure you read through the list of changes below before moving to this version - this will help you understand any down or upstream issues it may cause to your environments.
Breaking Changes
SDK Infrastructure
Retry-After handling changed: The SDK now respects any server-specified Retry-After value for rate-limited requests. Previously, values over 60 seconds were ignored and a default backoff was used instead.
Empty response handling: Responses with content-length: 0 now return undefined instead of attempting to parse the body.
Environment variable reading: Empty string env vars (for example, CLOUDFLARE_API_TOKEN="") are now treated as unset.
Path query parameter merging: URL search params embedded in endpoint paths are now extracted and merged into the query object.
Removed Endpoints (17)
17 HTTP endpoints were removed from the SDK, affecting abuse-reports, cloudforce-one, dlp/profiles/predefined, email-security/investigate, email-security/settings, and intel/ip-list.
Method Signature Changes
client.ai.toMarkdown.transform(file, \{ ...params \}) -> client.ai.toMarkdown.transform(\{ ...params \}) -- file moved from positional arg into params body
client.radar.ai.toMarkdown.create(body, \{ ...params \}) -> client.radar.ai.toMarkdown.create(\{ ...params \}) -- body moved from positional arg into params
Cloudflare Web Analytics now supports Navigation Type reporting and filtering.
This update allows developers and performance analysts to see how users are navigating between pages — whether through a link click or form submission, a page reload, or using the browser's back/forward buttons — and whether a browser cache hit occurred for these behaviors.
Understanding navigation types is critical for optimizing user experience. For example, if a high volume of your traffic consists of "Back-forward" navigations versus "Back-forward Cache", those visitors are not benefiting from the Back/Forward Cache (bfcache) and therefore are experiencing higher load times due to potentially unnecessary network requests.
The same applies for regular "Navigate" entries — where "Navigate Cache", "Navigate Prefetch Cache" and "Prerender" would provide instant document retrieval — and "Reload", where "Reload cache" would be more optimal.
A high volume of "Reload" entries can also indicate a potential stability problem with your website.
By identifying these patterns, you can tune your browser caching strategies to ensure HTML documents are served instantaneously from local caches rather than requiring a roundtrip to the network.
Monitor Cache Effectiveness: See how often your site is served from the HTTP cache or bfcache.
Identify Performance Bottlenecks: Filter by the different types to understand performance opportunity of improving browser cache hit ratio.
Analyze navigation types in the Cloudflare dashboard
You can now find the Navigation Type dimension in the Web Analytics dashboard. You can filter to include/exclude one or more specific types using "equals", "does not equal", "in", or "not in" matchers.
To check the list of popular navigation types, select Page views on the Web Analytics sidebar and scroll down to the bottom:
You can now connect Hyperdrive to a private database through a Workers VPC service. This is the recommended way to connect Hyperdrive to a private database that is not exposed to the public Internet.
When creating a Hyperdrive configuration in the Cloudflare dashboard, choose Connect to private database and then Workers VPC. From there, you can select an existing VPC service or create a new one inline by picking a Cloudflare Tunnel and entering your origin host and TCP port.
You can also create a Hyperdrive configuration backed by a Workers VPC service from the command line:
Workers VPC services are reusable across Hyperdrive configurations and can also be bound directly to Workers, so you can share the same private connection across multiple products.
Queues, Cloudflare's managed message queue, now exposes realtime backlog metrics via the dashboard, REST API, and JavaScript API. Three new fields are available:
backlog_count — the number of unacknowledged messages in the queue
backlog_bytes — the total size of those messages in bytes
oldest_message_timestamp_ms — the timestamp of the oldest unacknowledged message
The following endpoints also now include a metadata.metrics object on the result field after successful message consumption:
Terraform Provider v5.19.0 introduces 14 new resources spanning AI Gateway, Pipelines, R2 Data Catalog, User Groups, Vulnerability Scanner, Workers Observability, and Zero Trust capabilities. This release significantly improves the v4 to v5 migration experience with automatic state upgraders for 26 resources, working seamlessly with the new tf-migrate CLI tool ↗︎ to automate resource renames, attribute updates, and moved block generation. Together, these enhancements reduce manual migration effort and minimize risk when upgrading from v4 to v5.
Note:cmd/migrate is deprecated in favor of tf-migrate and will be removed in a future release (#7062 ↗︎)
New Resources
cloudflare_ai_gateway: Manage AI Gateway instances
We're excited to announce tf-migrate, a purpose-built CLI tool that simplifies migrating from Cloudflare Terraform Provider v4 to v5.
v5 is stable and ready for production
Terraform Provider v5 is stable and actively receiving updates. We encourage all users to migrate to v5 to take advantage of ongoing enhancements and new capabilities.
Cloudflare uses tf-migrate to migrate our own infrastructure — the same tool we're providing to the community — ensuring the best possible migration experience.
What tf-migrate does
tf-migrate automates the tedious and error-prone parts of the v4 to v5 migration process:
Resource type renames – Automatically updates cloudflare_record → cloudflare_dns_record, cloudflare_access_application → cloudflare_zero_trust_access_application, and 40+ other renamed resources
Attribute transformations – Updates field names (e.g., value → content for DNS records) and restructures nested blocks
Moved block generation – Creates Terraform 1.8+ moved blocks to prevent resource replacements and ensure zero-downtime migrations
Cross-file reference updates – Automatically finds and updates all references to renamed resources across your entire configuration
Dry-run mode – Preview all changes before applying them to ensure safety
Combined with the automatic state upgraders introduced in v5.19+, tf-migrate eliminates the manual work and risk that previously made v5 migrations challenging. Tf-migrate operates directly on the config, and the built-in state upgraders handle the rest.
Supported resources
Tf-migrate currently supports the most common Terraform resources our customers use. We are actively working to expand coverage, with the most commonly used resources prioritized first.
For the complete list of supported resources and their migration status, refer to the v5 Stabilization Tracker ↗︎. This list is updated regularly as additional resources are stabilized and migration support is added.
Resources not yet supported by tf-migrate will need to be migrated manually using the version 5 upgrade guide ↗︎. The upgrade guide provides step-by-step instructions for handling resource renames, attribute changes, and state migrations.
We have been releasing Betas over the past month and a half while testing this tool. See the full changelog of those Betas here: tf-migrate releases ↗︎.
In this release, you'll see a number of breaking changes. This is primarily due to changes in OpenAPI definitions, which our libraries are based off of, and codegen updates that we rely on to read those OpenAPI definitions and produce our SDK libraries.
Please ensure you read through the list of changes below before moving to this version - this will help you understand any down or upstream issues it may cause to your environments.
InstanceSearchParams.Messages field removed along with InstanceSearchParamsMessage and InstanceSearchParamsMessagesRole types
AI Search - InstanceItem Service Removed
The InstanceItemService type has been removed. The items sub-resource at client.AISearch.Instances.Items no longer exists in the non-namespace path. Use client.AISearch.Namespaces.Instances.Items instead.
AI Search - Token Types Removed
The following types have been removed from the ai_search package:
Email Security - Investigate Move Return Type Change
The Investigate.Move.New() method now returns a raw slice instead of a paginated wrapper:
New() returns *[]InvestigateMoveNewResponse instead of *pagination.SinglePage[InvestigateMoveNewResponse]
NewAutoPaging() method removed
Hyperdrive - Config Params Restructured
The ConfigEditParams type lost its MTLS and Name fields. The HyperdriveMTLSParam type lost MTLS and Host fields. The Host field on origin config changed from param.Field[string] to a plain string.
IAM - UserGroupMember Params and Return Types Changed
The UserGroupMemberNewParams struct has been restructured and the New() method now returns a paginated response:
UserGroupMemberNewParams.Body renamed to UserGroupMemberNewParams.Members
UserGroupMemberNewParamsBody renamed to UserGroupMemberNewParamsMember
UserGroupMemberUpdateParams.Body renamed to UserGroupMemberUpdateParams.Members
UserGroupMemberUpdateParamsBody renamed to UserGroupMemberUpdateParamsMember
UserGroups.Members.New() returns *pagination.SinglePage[UserGroupMemberNewResponse] instead of *UserGroupMemberNewResponse
IAM - UserGroup List Direction Type Changed
The UserGroupListParams.Direction field changed from param.Field[string] to param.Field[UserGroupListParamsDirection] (typed enum with asc/desc values).
Pipelines - Delete Methods Now Return Typed Responses
Several delete methods across Pipelines now return typed responses instead of bare error:
Pipelines.DeleteV1() returns (*PipelineDeleteV1Response, error) instead of error
Pipelines.Sinks.Delete() returns (*SinkDeleteResponse, error) instead of error
Pipelines.Streams.Delete() returns (*StreamDeleteResponse, error) instead of error
Queues - Message Response Types Removed
The following response envelope types have been removed:
MessageBulkPushResponseSuccess
MessagePushResponseSuccess
MessageAckResponse fields RetryCount and Warnings removed
Secrets Store - Pagination Wrapper Removal and Type Changes
Methods now return direct types instead of SinglePage wrappers, and several internal types have been removed. Associated AutoPaging methods have also been removed:
Stores.New() returns *StoreNewResponse instead of *pagination.SinglePage[StoreNewResponse]
Stores.NewAutoPaging() method removed
Stores.Secrets.BulkDelete() returns *StoreSecretBulkDeleteResponse instead of *pagination.SinglePage[StoreSecretBulkDeleteResponse]
R2 Data Catalog, a managed Apache Iceberg ↗︎ catalog built into R2, now removes unreferenced data files during automatic snapshot expiration. This improvement reduces storage costs and eliminates the need to run manual maintenance jobs to reclaim space from deleted data.
Previously, snapshot expiration only cleaned up Iceberg metadata files such as manifests and manifest lists. Data files that were no longer referenced by active snapshots remained in R2 storage until you manually ran remove_orphan_files or expire_snapshots through an engine like Spark. This required extra operational overhead and left stale data files consuming storage.
Snapshot expiration now handles both metadata and data file cleanup automatically. When a snapshot is expired, any data files that are no longer referenced by retained snapshots are removed from R2 storage.
The Container logs page now displays related Worker and Durable Object logs alongside container logs. This co-locates all relevant log events for a container application in one place, making it easier to trace requests and debug issues.
You can filter to a single source when you need to isolate Container, Worker, or Durable Object output.
Pay-as-you-go customers can now monitor usage-based costs and configure spend alerts through two new features: the Billable Usage dashboard and Budget alerts.
Billable Usage dashboard
The Billable Usage dashboard provides daily visibility into usage-based costs across your Cloudflare account. The data comes from the same system that generates your monthly invoice, so the figures match your bill.
The dashboard displays:
A bar chart showing daily usage charges for your billing period
A sortable table breaking down usage by product, including total usage, billable usage, and cumulative costs
Ability to view previous billing periods
Usage data aligns to your billing cycle, not the calendar month. The total usage cost shown at the end of a completed billing period matches the usage overage charges on your corresponding invoice.
To access the dashboard, go to Manage Account > Billing > Billable Usage.
Budget alerts
Budget alerts allow you to set dollar-based thresholds for your account-level usage spend. You receive an email notification when your projected monthly spend reaches your configured threshold, giving you proactive visibility into your bill before month-end.
To configure a budget alert:
Go to Manage Account > Billing > Billable Usage.
Select Set Budget Alert.
Enter a budget threshold amount greater than $0.
Select Create.
Alternatively, configure alerts via Notifications > Add > Budget Alert.
You can create multiple budget alerts at different dollar amounts. The notifications system automatically deduplicates alerts if multiple thresholds trigger at the same time. Budget alerts are calculated daily based on your usage trends and fire once per billing cycle when your projected spend first crosses your threshold.
Both features are available to Pay-as-you-go accounts with usage-based products (Workers, R2, Images, etc.). Enterprise contract accounts are not supported.
Binary frames received on a WebSocket are now delivered to the message event as Blob ↗︎ objects by default. This matches the WebSocket specification ↗︎ and standard browser behavior. Previously, binary frames were always delivered as ArrayBuffer ↗︎. The binaryType property on WebSocket controls the delivery type on a per-WebSocket basis.
This change has been active for Workers with compatibility dates on or after 2026-03-17, via the websocket_standard_binary_type compatibility flag. We should have documented this change when it shipped but didn't. We're sorry for the trouble that caused. If your Worker handles binary WebSocket messages and assumes event.data is an ArrayBuffer, the frames will arrive as Blob instead, and a naive instanceof ArrayBuffer check will silently drop every frame.
To opt back into ArrayBuffer delivery, assign binaryType before calling accept(). This works regardless of the compatibility flag:
const resp = await fetch("https://example.com", { headers: { Upgrade: "websocket" },});const ws = resp.webSocket;// Opt back into ArrayBuffer delivery for this WebSocket.ws.binaryType = "arraybuffer";ws.accept();ws.addEventListener("message", (event) => { if (typeof event.data === "string") { // Text frame. } else { // event.data is an ArrayBuffer because we set binaryType above. }});
If you are not ready to migrate and want to keep ArrayBuffer as the default for all WebSockets in your Worker, add the no_websocket_standard_binary_type flag to your Wrangler configuration file.
This change has no effect on the Durable Object hibernatable WebSocket webSocketMessage handler, which continues to receive binary data as ArrayBuffer.
R2 SQL now supports functions for querying JSON data stored in Apache Iceberg tables, an easier way to parse query plans with EXPLAIN FORMAT JSON, and querying tables without partition keys stored in R2 Data Catalog.
JSON functions extract and manipulate JSON values directly in SQL without client-side processing:
SELECT json_get_str(doc, 'name') AS name, json_get_int(doc, 'user', 'profile', 'level') AS level, json_get_bool(doc, 'active') AS is_activeFROM my_namespace.sales_dataWHERE json_contains(doc, 'email')
For a full list of available functions, refer to JSON functions.
EXPLAIN FORMAT JSON returns query execution plans as structured JSON for programmatic analysis and observability integrations:
Unpartitioned Iceberg tables can now be queried directly, which is useful for smaller datasets or data without natural time dimensions. For tables with more than 1000 files, partitioning is still recommended for better performance.
Logpush has traditionally been great at delivering Cloudflare logs to a variety of destinations in JSON format. While JSON is flexible and easily readable, it can be inefficient to store and query at scale.
With this release, you can now send your logs directly to Pipelines to ingest, transform, and store your logs in R2 as Parquet files or Apache Iceberg tables managed by R2 Data Catalog. This makes the data footprint more compact and more efficient at querying your logs instantly with R2 SQL or any other query engine that supports Apache Iceberg or Parquet.
Transform logs before storage
Pipelines SQL runs on each log record in-flight, so you can reshape your data before it is written. For example, you can drop noisy fields, redact sensitive values, or derive new columns:
INSERT INTO http_logs_sinkSELECT ClientIP, EdgeResponseStatus, to_timestamp_micros(EdgeStartTimestamp) AS event_time, upper(ClientRequestMethod) AS method, sha256(ClientIP) AS hashed_ipFROM http_logs_streamWHERE EdgeResponseStatus >= 400;
Pipelines SQL supports string functions, regex, hashing, JSON extraction, timestamp conversion, conditional expressions, and more. For the full list, refer to the Pipelines SQL reference.
@cf/moonshotai/kimi-k2.6 is now available on Workers AI, in partnership with Moonshot AI for Day 0 support. Kimi K2.6 is a native multimodal agentic model from Moonshot AI that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.
Built on a Mixture-of-Experts architecture with 1T total parameters and 32B active per token, Kimi K2.6 delivers frontier-scale intelligence with efficient inference. It scores competitively against GPT-5.4 and Claude Opus 4.6 on agentic and coding benchmarks, including BrowseComp (83.2), SWE-Bench Verified (80.2), and Terminal-Bench 2.0 (66.7).
Key capabilities
262.1k token context window for retaining full conversation history, tool definitions, and codebases across long-running agent sessions
Long-horizon coding with significant improvements on complex, end-to-end coding tasks across languages including Rust, Go, and Python
Coding-driven design that transforms simple prompts and visual inputs into production-ready interfaces and full-stack workflows
Agent swarm orchestration scaling horizontally to 300 sub-agents executing 4,000 coordinated steps for complex autonomous tasks
Vision inputs for processing images alongside text
Thinking mode with configurable reasoning depth
Multi-turn tool calling for building agents that invoke tools across multiple conversation turns
Differences from Kimi K2.5
If you are migrating from Kimi K2.5, note the following API changes:
K2.6 uses chat_template_kwargs.thinking to control reasoning, replacing chat_template_kwargs.enable_thinking
K2.6 returns reasoning content in the reasoning field, replacing reasoning_content
Get started
Use Kimi K2.6 through the Workers AI binding (env.AI.run()), the REST API at /ai/run, or the OpenAI-compatible endpoint at /v1/chat/completions. You can also use AI Gateway with any of these endpoints.
New AI Search instances created after today will work differently. New instances come with built-in storage and a vector index, so you can upload a file, have it indexed immediately, and search it right away.
Additionally new Workers Bindings are now available to use with AI Search. The new namespace binding lets you create and manage instances at runtime, and cross-instance search API lets you query across multiple instances in one call.
Built-in storage and vector index
All new instances now comes with built-in storage which allows you to upload files directly to it using the Items API or the dashboard. No R2 buckets to set up, no external data sources to connect first.
const instance = env.AI_SEARCH.get("my-instance");// upload and wait for indexing to completeconst item = await instance.items.uploadAndPoll("faq.md", content);// search immediately after indexingconst results = await instance.search({ messages: [{ role: "user", content: "onboarding guide" }],});
Namespace binding
The new ai_search_namespaces binding replaces the previous env.AI.autorag() API provided through the AI binding. It gives your Worker access to all instances within a namespace and lets you create, update, and delete instances at runtime without redeploying.
Within the new AI Search binding, you now have access to a Search and Chat API on the namespace level. Pass an array of instance IDs and get one ranked list of results back.