---
title: AI Changelog
image: https://edgetunnel-b2h.pages.dev/cf-twitter-card.png
---

> Documentation Index  
> Fetch the complete documentation index at: https://edgetunnel-b2h.pages.dev/changelog/llms.txt  
> Use this file to discover all available pages before exploring further. 

[Skip to content](#%5Ftop) 

# Changelog

New updates and improvements at Cloudflare.

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AI

![hero image](https://edgetunnel-b2h.pages.dev/_astro/hero.CVYJHPAd_26AMqX.svg) 

Jan 15, 2026
1. ### [Launching FLUX.2 \[klein\] 4B on Workers AI](https://edgetunnel-b2h.pages.dev/changelog/post/2026-01-15-flux-2-klein-4b-workers-ai/)  
[ Workers AI ](https://edgetunnel-b2h.pages.dev/workers-ai/)  
We've partnered with Black Forest Labs (BFL) again to bring their optimized FLUX.2 \[klein\] 4B model to Workers AI! This distilled model offers faster generation and cost-effective pricing, while maintaining great output quality. With a fixed 4-step inference process, Klein 4B is ideal for rapid prototyping and real-time applications where speed matters.  
Read the [BFL blog ↗](https://bfl.ai/blog) to learn more about the model itself, or try it out yourself on our [multi modal playground ↗](https://multi-modal.ai.cloudflare.com/).  
Pricing documentation is available on the [model page](https://edgetunnel-b2h.pages.dev/workers-ai/models/flux-2-klein-4b/) or [pricing page](https://edgetunnel-b2h.pages.dev/workers-ai/platform/pricing/).  
#### Workers AI Platform specifics  
The model hosted on Workers AI is optimized for speed with a **fixed 4-step inference process** and supports up to 4 image inputs. Since this is a distilled model, the `steps` parameter is fixed at 4 and cannot be adjusted. Like FLUX.2 \[dev\], this image model uses multipart form data inputs, even if you just have a prompt.  
With the REST API, the multipart form data input looks like this:  
```bash  
curl --request POST \
  --url 'https://api.cloudflare.com/client/v4/accounts/{ACCOUNT}/ai/run/@cf/black-forest-labs/flux-2-klein-4b' \
  --header 'Authorization: Bearer {TOKEN}' \
  --header 'Content-Type: multipart/form-data' \
  --form 'prompt=a sunset at the alps' \
  --form width=1024 \
  --form height=1024  
```  
With the Workers AI binding, you can use it as such:

**JavaScript**  
```javascript  
const form = new FormData();  
form.append("prompt", "a sunset with a dog");  
form.append("width", "1024");  
form.append("height", "1024");  
// FormData doesn't expose its serialized body or boundary. Passing it to a  
// Request (or Response) constructor serializes it and generates the Content-Type  
// header with the boundary, which is required for the server to parse the multipart fields.  
const formResponse = new Response(form);  
const formStream = formResponse.body;  
const formContentType = formResponse.headers.get('content-type');  
const resp = await env.AI.run("@cf/black-forest-labs/flux-2-klein-4b", {  
  multipart: {  
    body: formStream,  
    contentType: formContentType,  
  },  
});  
```  
The parameters you can send to the model are detailed here:  
JSON Schema for Model **Required Parameters**
  * `prompt` (string) - Text description of the image to generate

**Optional Parameters**

  * `input_image_0` (string) - Binary image
  * `input_image_1` (string) - Binary image
  * `input_image_2` (string) - Binary image
  * `input_image_3` (string) - Binary image
  * `guidance` (float) - Guidance scale for generation. Higher values follow the prompt more closely
  * `width` (integer) - Width of the image, default `1024` Range: 256-1920
  * `height` (integer) - Height of the image, default `768` Range: 256-1920
  * `seed` (integer) - Seed for reproducibility

**Note:** Since this is a distilled model, the `steps` parameter is fixed at 4 and cannot be adjusted.  
```plaintext  
## Multi-Reference Images  
The FLUX.2 klein-4b model supports generating images based on reference images, just like FLUX.2 [dev]. You can use this feature to apply the style of one image to another, add a new character to an image, or iterate on past generated images. You would use it with the same multipart form data structure, with the input images in binary. The model supports up to 4 input images.  
For the prompt, you can reference the images based on the index, like `take the subject of image 1 and style it like image 0` or even use natural language like `place the dog beside the woman`.  
Note: you have to name the input parameter as `input_image_0`, `input_image_1`, `input_image_2`, `input_image_3` for it to work correctly. All input images must be smaller than 512x512.  
```bash  
curl --request POST \
  --url 'https://api.cloudflare.com/client/v4/accounts/{ACCOUNT}/ai/run/@cf/black-forest-labs/flux-2-klein-4b' \
  --header 'Authorization: Bearer {TOKEN}' \
  --header 'Content-Type: multipart/form-data' \
  --form 'prompt=take the subject of image 1 and style it like image 0' \
  --form input_image_0=@/Users/johndoe/Desktop/icedoutkeanu.png \
  --form input_image_1=@/Users/johndoe/Desktop/me.png \
  --form width=1024 \
  --form height=1024  
```  
Through Workers AI Binding:

**JavaScript**  
```javascript  
//helper function to convert ReadableStream to Blob  
async function streamToBlob(stream: ReadableStream, contentType: string): Promise<Blob> {  
  const reader = stream.getReader();  
  const chunks = [];  
  while (true) {  
    const { done, value } = await reader.read();  
    if (done) break;  
    chunks.push(value);  
  }  
  return new Blob(chunks, { type: contentType });  
}  
const image0 = await fetch("http://image-url");  
const image1 = await fetch("http://image-url");  
const form = new FormData();  
const image_blob0 = await streamToBlob(image0.body, "image/png");  
const image_blob1 = await streamToBlob(image1.body, "image/png");  
form.append('input_image_0', image_blob0)  
form.append('input_image_1', image_blob1)  
form.append('prompt', 'take the subject of image 1 and style it like image 0')  
// FormData doesn't expose its serialized body or boundary. Passing it to a  
// Request (or Response) constructor serializes it and generates the Content-Type  
// header with the boundary, which is required for the server to parse the multipart fields.  
const formResponse = new Response(form);  
const formStream = formResponse.body;  
const formContentType = formResponse.headers.get('content-type');  
const resp = await env.AI.run("@cf/black-forest-labs/flux-2-klein-4b", {  
    multipart: {  
        body: formStream,  
        contentType: formContentType  
    }  
})  
```

Jan 13, 2026
1. ### [AI Crawl Control Read Only role now available](https://edgetunnel-b2h.pages.dev/changelog/post/2026-01-13-ai-crawl-control-read-only-role/)  
[ AI Crawl Control ](https://edgetunnel-b2h.pages.dev/ai-crawl-control/)  
Account administrators can now assign the **AI Crawl Control Read Only** role to provide read-only access to AI Crawl Control at the domain level.  
Users with this role can view the **Overview**, **Crawlers**, **Metrics**, **Robots.txt**, and **Settings** tabs but cannot modify crawler actions or settings.  
This role is specific for AI Crawl Control. You still require correct permissions to access other areas / features of the dashboard.  
To assign, go to **Manage Account** \> **Members** and add a policy with the **AI Crawl Control Read Only** role scoped to the desired domain.

Dec 22, 2025
1. ### [Agents SDK v0.3.0, workers-ai-provider v3.0.0, and ai-gateway-provider v3.0.0 with AI SDK v6 support](https://edgetunnel-b2h.pages.dev/changelog/post/2025-12-22-agents-sdk-ai-sdk-v6/)  
[ Agents ](https://edgetunnel-b2h.pages.dev/agents/)[ Workers ](https://edgetunnel-b2h.pages.dev/workers/)  
We've shipped a new release for the [Agents SDK ↗](https://github.com/cloudflare/agents) v0.3.0 bringing full compatibility with [AI SDK v6 ↗](https://ai-sdk.dev/docs/introduction) and introducing the unified tool pattern, dynamic tool approval, and enhanced React hooks with improved tool handling.  
This release includes improved streaming and tool support, dynamic tool approval (for "human in the loop" systems), enhanced React hooks with `onToolCall` callback, improved error handling for streaming responses, and seamless migration from v5 patterns.  
This makes it ideal for building production AI chat interfaces with Cloudflare Workers AI models, agent workflows, human-in-the-loop systems, or any application requiring reliable tool execution and approval workflows.  
Additionally, we've updated **workers-ai-provider v3.0.0**, the official provider for Cloudflare Workers AI models, and **ai-gateway-provider v3.0.0**, the provider for Cloudflare AI Gateway, to be compatible with AI SDK v6.  
#### Agents SDK v0.3.0  
#### Unified Tool Pattern  
AI SDK v6 introduces a unified tool pattern where all tools are defined on the server using the `tool()` function. This replaces the previous client-side `AITool` pattern.  
#### Server-Side Tool Definition

**TypeScript**  
```ts  
import { tool } from "ai";  
import { z } from "zod";  
// Server: Define ALL tools on the server  
const tools = {  
  // Server-executed tool  
  getWeather: tool({  
    description: "Get weather for a city",  
    inputSchema: z.object({ city: z.string() }),  
    execute: async ({ city }) => fetchWeather(city)  
  }),  
  // Client-executed tool (no execute = client handles via onToolCall)  
  getLocation: tool({  
    description: "Get user location from browser",  
    inputSchema: z.object({})  
    // No execute function  
  }),  
  // Tool requiring approval (dynamic based on input)  
  processPayment: tool({  
    description: "Process a payment",  
    inputSchema: z.object({ amount: z.number() }),  
    needsApproval: async ({ amount }) => amount > 100,  
    execute: async ({ amount }) => charge(amount)  
  })  
};  
```  
#### Client-Side Tool Handling

**TypeScript**  
```ts  
// Client: Handle client-side tools via onToolCall callback  
import { useAgentChat } from "agents/ai-react";  
const { messages, sendMessage, addToolOutput } = useAgentChat({  
  agent,  
  onToolCall: async ({ toolCall, addToolOutput }) => {  
    if (toolCall.toolName === "getLocation") {  
      const position = await new Promise((resolve, reject) => {  
        navigator.geolocation.getCurrentPosition(resolve, reject);  
      });  
      addToolOutput({  
        toolCallId: toolCall.toolCallId,  
        output: {  
          lat: position.coords.latitude,  
          lng: position.coords.longitude  
        }  
      });  
    }  
  }  
});  
```

**Key benefits of the unified tool pattern:**

  * **Server-defined tools**: All tools are defined in one place on the server
  * **Dynamic approval**: Use `needsApproval` to conditionally require user confirmation
  * **Cleaner client code**: Use `onToolCall` callback instead of managing tool configs
  * **Type safety**: Full TypeScript support with proper tool typing  
#### useAgentChat(options)  
Creates a new chat interface with enhanced v6 capabilities.

**TypeScript**  
```ts  
// Basic chat setup with onToolCall  
const { messages, sendMessage, addToolOutput } = useAgentChat({  
  agent,  
  onToolCall: async ({ toolCall, addToolOutput }) => {  
    // Handle client-side tool execution  
    await addToolOutput({  
      toolCallId: toolCall.toolCallId,  
      output: { result: "success" }  
    });  
  }  
});  
```  
#### Dynamic Tool Approval  
Use `needsApproval` on server tools to conditionally require user confirmation:

**TypeScript**  
```ts  
const paymentTool = tool({  
  description: "Process a payment",  
  inputSchema: z.object({  
    amount: z.number(),  
    recipient: z.string()  
  }),  
  needsApproval: async ({ amount }) => amount > 1000,  
  execute: async ({ amount, recipient }) => {  
    return await processPayment(amount, recipient);  
  }  
});  
```  
#### Tool Confirmation Detection  
The `isToolUIPart` and `getToolName` functions now check both static and dynamic tool parts:

**TypeScript**  
```ts  
import { isToolUIPart, getToolName } from "ai";  
const pendingToolCallConfirmation = messages.some((m) =>  
  m.parts?.some(  
    (part) => isToolUIPart(part) && part.state === "input-available",  
  ),  
);  
// Handle tool confirmation  
if (pendingToolCallConfirmation) {  
  await addToolOutput({  
    toolCallId: part.toolCallId,  
    output: "User approved the action"  
  });  
}  
```  
If you need the v5 behavior (static-only checks), use the new functions:

**TypeScript**  
```ts  
import { isStaticToolUIPart, getStaticToolName } from "ai";  
```  
#### convertToModelMessages() is now async  
The `convertToModelMessages()` function is now asynchronous. Update all calls to await the result:

**TypeScript**  
```ts  
import { convertToModelMessages } from "ai";  
const result = streamText({  
  messages: await convertToModelMessages(this.messages),  
  model: openai("gpt-4o")  
});  
```  
#### ModelMessage type  
The `CoreMessage` type has been removed. Use `ModelMessage` instead:

**TypeScript**  
```ts  
import { convertToModelMessages, type ModelMessage } from "ai";  
const modelMessages: ModelMessage[] = await convertToModelMessages(messages);  
```  
#### generateObject mode option removed  
The `mode` option for `generateObject` has been removed:

**TypeScript**  
```ts  
// Before (v5)  
const result = await generateObject({  
  mode: "json",  
  model,  
  schema,  
  prompt  
});  
// After (v6)  
const result = await generateObject({  
  model,  
  schema,  
  prompt  
});  
```  
#### Structured Output with generateText  
While `generateObject` and `streamObject` are still functional, the recommended approach is to use `generateText`/`streamText` with the `Output.object()` helper:

**TypeScript**  
```ts  
import { generateText, Output, stepCountIs } from "ai";  
const { output } = await generateText({  
  model: openai("gpt-4"),  
  output: Output.object({  
    schema: z.object({ name: z.string() })  
  }),  
  stopWhen: stepCountIs(2),  
  prompt: "Generate a name"  
});  
```  
> **Note**: When using structured output with `generateText`, you must configure multiple steps with `stopWhen` because generating the structured output is itself a step.  
#### workers-ai-provider v3.0.0  
Seamless integration with Cloudflare Workers AI models through the updated workers-ai-provider v3.0.0 with AI SDK v6 support.  
#### Model Setup with Workers AI  
Use Cloudflare Workers AI models directly in your agent workflows:

**TypeScript**  
```ts  
import { createWorkersAI } from "workers-ai-provider";  
import { useAgentChat } from "agents/ai-react";  
// Create Workers AI model (v3.0.0 - enhanced v6 internals)  
const model = createWorkersAI({  
  binding: env.AI,  
})("@cf/meta/llama-3.2-3b-instruct");  
```  
#### Enhanced File and Image Support  
Workers AI models now support v6 file handling with automatic conversion:

**TypeScript**  
```ts  
// Send images and files to Workers AI models  
sendMessage({  
  role: "user",  
  parts: [  
    { type: "text", text: "Analyze this image:" },  
    {  
      type: "file",  
      data: imageBuffer,  
      mediaType: "image/jpeg",  
    },  
  ],  
});  
// Workers AI provider automatically converts to proper format  
```  
#### Streaming with Workers AI  
Enhanced streaming support with automatic warning detection:

**TypeScript**  
```ts  
// Streaming with Workers AI models  
const result = await streamText({  
  model: createWorkersAI({ binding: env.AI })("@cf/meta/llama-3.2-3b-instruct"),  
  messages: await convertToModelMessages(messages),  
  onChunk: (chunk) => {  
    // Enhanced streaming with warning handling  
    console.log(chunk);  
  },  
});  
```  
#### ai-gateway-provider v3.0.0  
The ai-gateway-provider v3.0.0 now supports AI SDK v6, enabling you to use Cloudflare AI Gateway with multiple AI providers including Anthropic, Azure, AWS Bedrock, Google Vertex, and Perplexity.  
#### AI Gateway Setup  
Use Cloudflare AI Gateway to add analytics, caching, and rate limiting to your AI applications:

**TypeScript**  
```ts  
import { createAIGateway } from "ai-gateway-provider";  
// Create AI Gateway provider (v3.0.0 - enhanced v6 internals)  
const model = createAIGateway({  
  gatewayUrl: "https://gateway.ai.cloudflare.com/v1/your-account-id/gateway",  
  headers: {  
    "Authorization": `Bearer ${env.AI_GATEWAY_TOKEN}`  
  }  
})({  
  provider: "openai",  
  model: "gpt-4o"  
});  
```  
#### Migration from v5  
#### Deprecated APIs  
The following APIs are deprecated in favor of the unified tool pattern:

| Deprecated                            | Replacement                                      |
| ------------------------------------- | ------------------------------------------------ |
| AITool type                           | Use AI SDK's tool() function on server           |
| extractClientToolSchemas()            | Define tools on server, no client schemas needed |
| createToolsFromClientSchemas()        | Define tools on server with tool()               |
| toolsRequiringConfirmation option     | Use needsApproval on server tools                |
| experimental\_automaticToolResolution | Use onToolCall callback                          |
| tools option in useAgentChat          | Use onToolCall for client-side execution         |
| addToolResult()                       | Use addToolOutput()                              |  
#### Breaking Changes Summary

  1. **Unified Tool Pattern**: All tools must be defined on the server using `tool()`
  2. **`convertToModelMessages()` is async**: Add `await` to all calls
  3. **`CoreMessage` removed**: Use `ModelMessage` instead
  4. **`generateObject` mode removed**: Remove `mode` option
  5. **`isToolUIPart` behavior changed**: Now checks both static and dynamic tool parts  
#### Installation  
Update your dependencies to use the latest versions:  
```bash  
npm install agents@^0.3.0 workers-ai-provider@^3.0.0 ai-gateway-provider@^3.0.0 ai@^6.0.0 @ai-sdk/react@^3.0.0 @ai-sdk/openai@^3.0.0  
```  
#### Resources

  * [Migration Guide ↗](https://github.com/cloudflare/agents/blob/main/docs/migration-to-ai-sdk-v6.md) \- Comprehensive migration documentation from v5 to v6
  * [AI SDK v6 Documentation ↗](https://ai-sdk.dev/docs/migration-guides/migration-guide-6-0) \- Official AI SDK migration guide
  * [AI SDK v6 Announcement ↗](https://vercel.com/blog/ai-sdk-6) \- Learn about new features in v6
  * [AI SDK Documentation ↗](https://sdk.vercel.ai/docs) \- Complete AI SDK reference
  * [GitHub Issues ↗](https://github.com/cloudflare/agents/issues) \- Report bugs or request features  
#### Feedback Welcome  
We'd love your feedback! We're particularly interested in feedback on:

  * **Migration experience** \- How smooth was the upgrade from v5 to v6?
  * **Unified tool pattern** \- How does the new server-defined tool pattern work for you?
  * **Dynamic tool approval** \- Does the `needsApproval` feature meet your needs?
  * **AI Gateway integration** \- How well does the new provider work with your setup?

Dec 18, 2025
1. ### [New AI Crawl Control Overview tab](https://edgetunnel-b2h.pages.dev/changelog/post/2025-12-18-overview-tab/)  
[ AI Crawl Control ](https://edgetunnel-b2h.pages.dev/ai-crawl-control/)  
The **Overview** tab is now the default view in AI Crawl Control. The previous default view with controls for individual AI crawlers is available in the **Crawlers** tab.  
#### What's new

  * **Executive summary** — Monitor total requests, volume change, most common status code, most popular path, and high-volume activity
  * **Operator grouping** — Track crawlers by their operating companies (OpenAI, Microsoft, Google, ByteDance, Anthropic, Meta)
  * **Customizable filters** — Filter your snapshot by date range, crawler, operator, hostname, or path  
![AI Crawl Control Overview tab showing executive summary, metrics, and crawler groups](https://edgetunnel-b2h.pages.dev/_astro/ai-crawl-control-overview-tab.Duwqq4bm_ZmNhCs.webp)  
#### Get started

  1. Log in to the Cloudflare dashboard and select your account and domain.
  2. Go to **AI Crawl Control**, where the **Overview** tab opens by default with your activity snapshot.
  3. Use filters to customize your view by date range, crawler, operator, hostname, or path.
  4. Navigate to the **Crawlers** tab to manage controls for individual crawlers.  
Learn more about [analyzing AI traffic](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/analyze-ai-traffic/) and [managing AI crawlers](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/manage-ai-crawlers/).

Dec 10, 2025
1. ### [Pay Per Crawl (Private beta) - Discovery API, custom pricing, and advanced configuration](https://edgetunnel-b2h.pages.dev/changelog/post/2025-12-10-pay-per-crawl-enhancements/)  
[ AI Crawl Control ](https://edgetunnel-b2h.pages.dev/ai-crawl-control/)  
Pay Per Crawl is introducing enhancements for both AI crawler operators and site owners, focusing on programmatic discovery, flexible pricing models, and granular configuration control.  
#### For AI crawler operators  
#### Discovery API  
A new authenticated API endpoint allows verified crawlers to programmatically discover domains participating in Pay Per Crawl. Crawlers can use this to build optimized crawl queues, cache domain lists, and identify new participating sites. This eliminates the need to discover payable content through trial requests.  
The API endpoint is `GET https://crawlers-api.ai-audit.cfdata.org/charged_zones` and requires Web Bot Auth authentication. Refer to [Discover payable content](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/pay-per-crawl/use-pay-per-crawl-as-ai-owner/discover-payable-content/) for authentication steps, request parameters, and response schema.  
#### Payment header signature requirement  
Payment headers (`crawler-exact-price` or `crawler-max-price`) must now be included in the Web Bot Auth `signature-input` header components. This security enhancement prevents payment header tampering, ensures authenticated payment intent, validates crawler identity with payment commitment, and protects against replay attacks with modified pricing. Crawlers must add their payment header to the list of signed components when [constructing the signature-input header](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/pay-per-crawl/use-pay-per-crawl-as-ai-owner/crawl-pages/#22-sign-your-request-with-web-bot-auth).  
#### New `crawler-error` header  
Pay Per Crawl error responses now include a new `crawler-error` header with 11 specific [error codes](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/pay-per-crawl/use-pay-per-crawl-as-ai-owner/error-codes/) for programmatic handling. Error response bodies remain unchanged for compatibility. These codes enable robust error handling, automated retry logic, and accurate spending tracking.  
#### For site owners  
#### Configure free pages  
Site owners can now offer free access to specific pages like homepages, navigation, or discovery pages while charging for other content. Create a [Configuration Rule](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/pay-per-crawl/use-pay-per-crawl-as-site-owner/advanced-configuration/#disable-pay-per-crawl-by-uri-pattern) in **Rules** \> **Configuration Rules**, set your URI pattern using wildcard, exact, or prefix matching on the **URI Full** field, and enable the **Disable Pay Per Crawl** setting. When disabled for a URI pattern, crawler requests pass through without blocking or charging.  
Some paths are always free to crawl. These paths are: `/robots.txt`, `/sitemap.xml`, `/security.txt`, `/.well-known/security.txt`, `/crawlers.json`.  
#### Get started

**AI crawler operators**: [Discover payable content](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/pay-per-crawl/use-pay-per-crawl-as-ai-owner/discover-payable-content/) | [Crawl pages](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/pay-per-crawl/use-pay-per-crawl-as-ai-owner/crawl-pages/)

**Site owners**: [Advanced configuration](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/pay-per-crawl/use-pay-per-crawl-as-site-owner/advanced-configuration/)

Nov 26, 2025
1. ### [Agents SDK v0.2.24 with resumable streaming, MCP improvements, and schedule fixes](https://edgetunnel-b2h.pages.dev/changelog/post/2025-11-26-agents-resumable-streaming/)  
[ Agents ](https://edgetunnel-b2h.pages.dev/agents/)[ Workers ](https://edgetunnel-b2h.pages.dev/workers/)  
The latest release of [@cloudflare/agents ↗](https://github.com/cloudflare/agents) brings resumable streaming, significant MCP client improvements, and critical fixes for schedules and Durable Object lifecycle management.  
#### Resumable streaming  
`AIChatAgent` now supports resumable streaming, allowing clients to reconnect and continue receiving streamed responses without losing data. This is useful for:

  * Long-running AI responses
  * Users on unreliable networks
  * Users switching between devices mid-conversation
  * Background tasks where users navigate away and return
  * Real-time collaboration where multiple clients need to stay in sync  
Streams are maintained across page refreshes, broken connections, and syncing across open tabs and devices.  
#### Other improvements

  * Default JSON schema validator added to MCP client
  * [Schedules ↗](https://edgetunnel-b2h.pages.dev/agents/runtime/execution/schedule-tasks/) can now safely destroy the agent  
#### MCP client API improvements  
The `MCPClientManager` API has been redesigned for better clarity and control:

  * **New `registerServer()` method**: Register MCP servers without immediately connecting
  * **New `connectToServer()` method**: Establish connections to registered servers
  * **Improved reconnect logic**: `restoreConnectionsFromStorage()` now properly handles failed connections

**TypeScript**  
```ts  
// Register a server to Agent  
const { id } = await this.mcp.registerServer({  
  name: "my-server",  
  url: "https://my-mcp-server.example.com",  
});  
// Connect when ready  
await this.mcp.connectToServer(id);  
// Discover tools, prompts and resources  
await this.mcp.discoverIfConnected(id);  
```  
The SDK now includes a formalized `MCPConnectionState` enum with states: `idle`, `connecting`, `authenticating`, `connected`, `discovering`, and `ready`.  
#### Enhanced MCP discovery  
MCP discovery fetches the available tools, prompts, and resources from an MCP server so your agent knows what capabilities are available. The `MCPClientConnection` class now includes a dedicated `discover()` method with improved reliability:

  * Supports cancellation via AbortController
  * Configurable timeout (default 15s)
  * Discovery failures now throw errors immediately instead of silently continuing  
#### Bug fixes

  * Fixed a bug where [schedules ↗](https://edgetunnel-b2h.pages.dev/agents/runtime/execution/schedule-tasks/) meant to fire immediately with this.schedule(0, ...) or `this.schedule(new Date(), ...)` would not fire
  * Fixed an issue where schedules that took longer than 30 seconds would occasionally time out
  * Fixed SSE transport now properly forwards session IDs and request headers
  * Fixed AI SDK stream events conversion to UIMessageStreamPart  
#### Upgrade  
To update to the latest version:  
```sh  
npm i agents@latest  
```

Nov 25, 2025
1. ### [Launching FLUX.2 \[dev\] on Workers AI](https://edgetunnel-b2h.pages.dev/changelog/post/2025-11-25-flux-2-dev-workers-ai/)  
[ Workers AI ](https://edgetunnel-b2h.pages.dev/workers-ai/)  
We've partnered with Black Forest Labs (BFL) to bring their latest FLUX.2 \[dev\] model to Workers AI! This model excels in generating high-fidelity images with physical world grounding, multi-language support, and digital asset creation. You can also create specific super images with granular controls like JSON prompting.  
Read the [BFL blog ↗](https://bfl.ai/flux2) to learn more about the model itself. Read our [Cloudflare blog ↗](https://blog.cloudflare.com/flux-2-workers-ai) to see the model in action, or try it out yourself on our [multi modal playground ↗](https://multi-modal.ai.cloudflare.com/).  
Pricing documentation is available on the [model page](https://edgetunnel-b2h.pages.dev/workers-ai/models/flux-2-dev/) or [pricing page](https://edgetunnel-b2h.pages.dev/workers-ai/platform/pricing/). Note, we expect to drop pricing in the next few days after iterating on the model performance.  
#### Workers AI Platform specifics  
The model hosted on Workers AI is able to support up to 4 image inputs (512x512 per input image). Note, this image model is one of the most powerful in the catalog and is expected to be slower than the other image models we currently support. One catch to look out for is that this model takes multipart form data inputs, even if you just have a prompt.  
With the REST API, the multipart form data input looks like this:  
```bash  
curl --request POST \
  --url 'https://api.cloudflare.com/client/v4/accounts/{ACCOUNT}/ai/run/@cf/black-forest-labs/flux-2-dev' \
  --header 'Authorization: Bearer {TOKEN}' \
  --header 'Content-Type: multipart/form-data' \
  --form 'prompt=a sunset at the alps' \
  --form steps=25
  --form width=1024
  --form height=1024  
```  
With the Workers AI binding, you can use it as such:

**JavaScript**  
```javascript  
const form = new FormData();  
form.append('prompt', 'a sunset with a dog');  
form.append('width', '1024');  
form.append('height', '1024');  
//this dummy request is temporary hack  
//we're pushing a change to address this soon  
const formRequest = new Request('http://dummy', {  
  method: 'POST',  
  body: form  
});  
const formStream = formRequest.body;  
const formContentType = formRequest.headers.get('content-type') || 'multipart/form-data';  
const resp = await env.AI.run("@cf/black-forest-labs/flux-2-dev", {  
  multipart: {  
    body: formStream,  
    contentType: formContentType  
  }  
});  
```  
The parameters you can send to the model are detailed here:  
JSON Schema for Model **Required Parameters**
  * `prompt` (string) - Text description of the image to generate

**Optional Parameters**

  * `input_image_0` (string) - Binary image
  * `input_image_1` (string) - Binary image
  * `input_image_2` (string) - Binary image
  * `input_image_3` (string) - Binary image
  * `steps` (integer) - Number of inference steps. Higher values may improve quality but increase generation time
  * `guidance` (float) - Guidance scale for generation. Higher values follow the prompt more closely
  * `width` (integer) - Width of the image, default `1024` Range: 256-1920
  * `height` (integer) - Height of the image, default `768` Range: 256-1920
  * `seed` (integer) - Seed for reproducibility  
```plaintext  
## Multi-Reference Images  
The FLUX.2 model is great at generating images based on reference images. You can use this feature to apply the style of one image to another, add a new character to an image, or iterate on past generate images. You would use it with the same multipart form data structure, with the input images in binary.  
For the prompt, you can reference the images based on the index, like `take the subject of image 1 and style it like image 0` or even use natural language like `place the dog beside the woman`.  
Note: you have to name the input parameter as `input_image_0`, `input_image_1`, `input_image_2` for it to work correctly. All input images must be smaller than 512x512.  
```bash  
curl --request POST \
  --url 'https://api.cloudflare.com/client/v4/accounts/{ACCOUNT}/ai/run/@cf/black-forest-labs/flux-2-dev' \
  --header 'Authorization: Bearer {TOKEN}' \
  --header 'Content-Type: multipart/form-data' \
  --form 'prompt=take the subject of image 1 and style it like image 0' \
  --form input_image_0=@/Users/johndoe/Desktop/icedoutkeanu.png \
  --form input_image_1=@/Users/johndoe/Desktop/me.png \
  --form steps=25
  --form width=1024
  --form height=1024  
```  
Through Workers AI Binding:

**JavaScript**  
```javascript  
//helper function to convert ReadableStream to Blob  
async function streamToBlob(stream: ReadableStream, contentType: string): Promise<Blob> {  
  const reader = stream.getReader();  
  const chunks = [];  
  while (true) {  
    const { done, value } = await reader.read();  
    if (done) break;  
    chunks.push(value);  
  }  
  return new Blob(chunks, { type: contentType });  
}  
const image0 = await fetch("http://image-url");  
const image1 = await fetch("http://image-url");  
const form = new FormData();  
const image_blob0 = await streamToBlob(image0.body, "image/png");  
const image_blob1 = await streamToBlob(image1.body, "image/png");  
form.append('input_image_0', image_blob0)  
form.append('input_image_1', image_blob1)  
form.append('prompt', 'take the subject of image 1and style it like image 0')  
//this dummy request is temporary hack  
//we're pushing a change to address this soon  
const formRequest = new Request('http://dummy', {  
  method: 'POST',  
  body: form  
});  
const formStream = formRequest.body;  
const formContentType = formRequest.headers.get('content-type') || 'multipart/form-data';  
const resp = await env.AI.run("@cf/black-forest-labs/flux-2-dev", {  
    multipart: {  
        body: form,  
        contentType: "multipart/form-data"  
    }  
})  
```  
#### JSON Prompting  
The model supports prompting in JSON to get more granular control over images. You would pass the JSON as the value of the 'prompt' field in the multipart form data. See the JSON schema below on the base parameters you can pass to the model.  
JSON Prompting Schema  
```json  
{  
  "type": "object",  
  "properties": {  
    "scene": {  
      "type": "string",  
      "description": "Overall scene setting or location"  
    },  
    "subjects": {  
      "type": "array",  
      "items": {  
        "type": "object",  
        "properties": {  
          "type": {  
            "type": "string",  
            "description": "Type of subject (e.g., desert nomad, blacksmith, DJ, falcon)"  
          },  
          "description": {  
            "type": "string",  
            "description": "Physical attributes, clothing, accessories"  
          },  
          "pose": {  
            "type": "string",  
            "description": "Action or stance"  
          },  
          "position": {  
            "type": "string",  
            "enum": ["foreground", "midground", "background"],  
            "description": "Depth placement in scene"  
          }  
        },  
        "required": ["type", "description", "pose", "position"]  
      }  
    },  
    "style": {  
      "type": "string",  
      "description": "Artistic rendering style (e.g., digital painting, photorealistic, pixel art, noir sci-fi, lifestyle photo, wabi-sabi photo)"  
    },  
    "color_palette": {  
      "type": "array",  
      "items": { "type": "string" },  
      "minItems": 3,  
      "maxItems": 3,  
      "description": "Exactly 3 main colors for the scene (e.g., ['navy', 'neon yellow', 'magenta'])"  
    },  
    "lighting": {  
      "type": "string",  
      "description": "Lighting condition and direction (e.g., fog-filtered sun, moonlight with star glints, dappled sunlight)"  
    },  
    "mood": {  
      "type": "string",  
      "description": "Emotional atmosphere (e.g., harsh and determined, playful and modern, peaceful and dreamy)"  
    },  
    "background": {  
      "type": "string",  
      "description": "Background environment details"  
    },  
    "composition": {  
      "type": "string",  
      "enum": [  
        "rule of thirds",  
        "circular arrangement",  
        "framed by foreground",  
        "minimalist negative space",  
        "S-curve",  
        "vanishing point center",  
        "dynamic off-center",  
        "leading leads",  
        "golden spiral",  
        "diagonal energy",  
        "strong verticals",  
        "triangular arrangement"  
      ],  
      "description": "Compositional technique"  
    },  
    "camera": {  
      "type": "object",  
      "properties": {  
        "angle": {  
          "type": "string",  
          "enum": ["eye level", "low angle", "slightly low", "bird's-eye", "worm's-eye", "over-the-shoulder", "isometric"],  
          "description": "Camera perspective"  
        },  
        "distance": {  
          "type": "string",  
          "enum": ["close-up", "medium close-up", "medium shot", "medium wide", "wide shot", "extreme wide"],  
          "description": "Framing distance"  
        },  
        "focus": {  
          "type": "string",  
          "enum": ["deep focus", "macro focus", "selective focus", "sharp on subject", "soft background"],  
          "description": "Focus type"  
        },  
        "lens": {  
          "type": "string",  
          "enum": ["14mm", "24mm", "35mm", "50mm", "70mm", "85mm"],  
          "description": "Focal length (wide to telephoto)"  
        },  
        "f-number": {  
          "type": "string",  
          "description": "Aperture (e.g., f/2.8, the smaller the number the more blurry the background)"  
        },  
        "ISO": {  
          "type": "number",  
          "description": "Light sensitivity value (comfortable range between 100 & 6400, lower = less sensitivity)"  
        }  
      }  
    },  
    "effects": {  
      "type": "array",  
      "items": { "type": "string" },  
      "description": "Post-processing effects (e.g., 'lens flare small', 'subtle film grain', 'soft bloom', 'god rays', 'chromatic aberration mild')"  
    }  
  },  
  "required": ["scene", "subjects"]  
}  
```  
#### Other features to try

  * The model also supports the most common latin and non-latin character languages
  * You can prompt the model with specific hex codes like `#2ECC71`
  * Try creating digital assets like landing pages, comic strips, infographics too!

Nov 19, 2025
1. ### [AI Search support for crawling login protected website content](https://edgetunnel-b2h.pages.dev/changelog/post/2025-11-19-add-extra-headers-for-website-crawling/)  
[ AI Search ](https://edgetunnel-b2h.pages.dev/ai-search/)  
[AI Search](https://edgetunnel-b2h.pages.dev/ai-search/) now supports [custom HTTP headers](https://edgetunnel-b2h.pages.dev/ai-search/configuration/data-source/website/#extra-headers-for-access-protected-content) for website crawling, solving a common problem where valuable content behind authentication or access controls could not be indexed.  
Previously, AI Search could only crawl publicly accessible pages, leaving knowledge bases, documentation, and other protected content out of your search results. With custom headers support, you can now include authentication credentials that allow the crawler to access this protected content.  
This is particularly useful for indexing content like:

  * **Internal documentation** behind corporate login systems
  * **Premium content** that requires users to provide access to unlock
  * **Sites protected by Cloudflare Access** using service tokens  
To add custom headers when creating an AI Search instance, select **Parse options**. In the **Extra headers** section, you can add up to five custom headers per Website data source.  
![Custom headers configuration in AI Search](https://edgetunnel-b2h.pages.dev/_astro/ai-search-extra-headers.B7A2spby_lEmuv.webp)  
For example, to crawl a site protected by [Cloudflare Access](https://edgetunnel-b2h.pages.dev/cloudflare-one/access-controls/), you can add service token credentials as custom headers:  
```plaintext  
CF-Access-Client-Id: your-token-id.access  
CF-Access-Client-Secret: your-token-secret  
```  
The crawler will automatically include these headers in all requests, allowing it to access protected pages that would otherwise be blocked.  
Learn more about [configuring custom headers for website crawling](https://edgetunnel-b2h.pages.dev/ai-search/configuration/data-source/website/#extra-headers-for-access-protected-content) in AI Search.

Nov 10, 2025
1. ### [Crawler drilldowns with extended actions menu](https://edgetunnel-b2h.pages.dev/changelog/post/2025-11-10-ai-crawl-control-crawler-info/)  
[ AI Crawl Control ](https://edgetunnel-b2h.pages.dev/ai-crawl-control/)  
AI Crawl Control now supports per-crawler drilldowns with an extended actions menu and status code analytics. Drill down into Metrics, Cloudflare Radar, and Security Analytics, or export crawler data for use in [WAF custom rules](https://edgetunnel-b2h.pages.dev/waf/custom-rules/), [Redirect Rules](https://edgetunnel-b2h.pages.dev/rules/url-forwarding/), and robots.txt files.  
#### What's new  
#### Status code distribution chart  
The **Metrics** tab includes a status code distribution chart showing HTTP response codes (2xx, 3xx, 4xx, 5xx) over time. Filter by individual crawler, category, operator, or time range to analyze how specific crawlers interact with your site.  
![AI Crawl Control status code distribution chart](https://edgetunnel-b2h.pages.dev/_astro/ai-crawl-control-status-codes.DESJcAiK_TSrPM.webp)  
#### Extended actions menu  
Each crawler row includes a three-dot menu with per-crawler actions:

  * **View Metrics** — Filter the AI Crawl Control Metrics page to the selected crawler.
  * **View on Cloudflare Radar** — Access verified crawler details on Cloudflare Radar.
  * **Copy User Agent** — Copy user agent strings for use in WAF custom rules, Redirect Rules, or robots.txt files.
  * **View in Security Analytics** — Filter Security Analytics by detection IDs (Bot Management customers).
  * **Copy Detection ID** — Copy detection IDs for use in WAF custom rules (Bot Management customers).  
![AI Crawl Control crawler actions menu](https://edgetunnel-b2h.pages.dev/_astro/ai-crawl-control-crawler-info.Dwc39LqI_182so6.webp)  
#### Get started

  1. Log in to the Cloudflare dashboard, and select your account and domain.
  2. Go to **AI Crawl Control** \> **Metrics** to access the status code distribution chart.
  3. Go to **AI Crawl Control** \> **Crawlers** and select the three-dot menu for any crawler to access per-crawler actions.
  4. Select multiple crawlers to use bulk copy buttons for user agents or detection IDs.  
Learn more about [AI Crawl Control](https://edgetunnel-b2h.pages.dev/ai-crawl-control/).

Oct 31, 2025
1. ### [Workers WebSocket message size limit increased from 1 MiB to 32 MiB](https://edgetunnel-b2h.pages.dev/changelog/post/2025-10-31-increased-websocket-message-size-limit/)  
[ Workers ](https://edgetunnel-b2h.pages.dev/workers/)[ Durable Objects ](https://edgetunnel-b2h.pages.dev/durable-objects/)[ Browser Run ](https://edgetunnel-b2h.pages.dev/browser-run/)  
Workers, including those using [Durable Objects](https://edgetunnel-b2h.pages.dev/durable-objects/) and [Browser Rendering](https://edgetunnel-b2h.pages.dev/browser-run/), may now process WebSocket messages up to 32 MiB in size. Previously, this limit was 1 MiB.  
This change allows Workers to handle use cases requiring large message sizes, such as processing Chrome Devtools Protocol messages.  
For more information, please see the [Durable Objects startup limits](https://edgetunnel-b2h.pages.dev/durable-objects/platform/limits/#sqlite-backed-durable-objects-general-limits).

Oct 28, 2025
1. ### [Reranking and API-based system prompt configuration in AI Search](https://edgetunnel-b2h.pages.dev/changelog/post/2025-10-27-ai-search-reranking-system-prompt/)  
[ AI Search ](https://edgetunnel-b2h.pages.dev/ai-search/)  
[AI Search](https://edgetunnel-b2h.pages.dev/ai-search/) now supports reranking for improved retrieval quality and allows you to set the system prompt directly in your API requests.  
#### Rerank for more relevant results  
You can now enable [reranking](https://edgetunnel-b2h.pages.dev/ai-search/configuration/retrieval/reranking/) to reorder retrieved documents based on their semantic relevance to the user’s query. Reranking helps improve accuracy, especially for large or noisy datasets where vector similarity alone may not produce the optimal ordering.  
You can enable and configure reranking in the dashboard or directly in your API requests:

**JavaScript**  
```javascript  
const answer = await env.AI.autorag("my-autorag").aiSearch({  
  query: "How do I train a llama to deliver coffee?",  
  model: "@cf/meta/llama-3.3-70b-instruct-fp8-fast",  
  reranking: {  
    enabled: true,  
    model: "@cf/baai/bge-reranker-base",  
  },  
});  
```  
#### Set system prompts in API  
Previously, [system prompts](https://edgetunnel-b2h.pages.dev/ai-search/configuration/retrieval/system-prompt/) could only be configured in the dashboard. You can now define them directly in your API requests, giving you per-query control over behavior. For example:

**JavaScript**  
```javascript  
// Dynamically set query and system prompt in AI Search  
async function getAnswer(query, tone) {  
  const systemPrompt = `You are a ${tone} assistant.`;  
  const response = await env.AI.autorag("my-autorag").aiSearch({  
    query: query,  
    system_prompt: systemPrompt,  
  });  
  return response;  
}  
// Example usage  
const query = "What is Cloudflare?";  
const tone = "friendly";  
const answer = await getAnswer(query, tone);  
console.log(answer);  
```  
Learn more about [Reranking](https://edgetunnel-b2h.pages.dev/ai-search/configuration/retrieval/reranking/) and [System Prompt](https://edgetunnel-b2h.pages.dev/ai-search/configuration/retrieval/system-prompt/) in AI Search.

Oct 23, 2025
1. ### [Workers AI Markdown Conversion: New endpoint to list supported formats](https://edgetunnel-b2h.pages.dev/changelog/post/2025-10-23-new-markdown-conversion-endpoint/)  
[ Workers AI ](https://edgetunnel-b2h.pages.dev/workers-ai/)  
Developers can now programmatically retrieve a list of all file formats supported by the [Markdown Conversion utility](https://edgetunnel-b2h.pages.dev/workers-ai/features/markdown-conversion/) in Workers AI.  
You can use the [env.AI](https://edgetunnel-b2h.pages.dev/workers-ai/configuration/bindings/) binding:

**TypeScript**  
```typescript  
await env.AI.toMarkdown().supported()  
```  
Or call the REST API:  
```bash  
curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/tomarkdown/supported \
  -H 'Authorization: Bearer {API_TOKEN}'  
```  
Both return a list of file formats that users can convert into Markdown:  
```json  
[  
  {  
    "extension": ".pdf",  
    "mimeType": "application/pdf",  
  },  
  {  
    "extension": ".jpeg",  
    "mimeType": "image/jpeg",  
  },  
  ...  
]  
```  
Learn more about our [Markdown Conversion utility](https://edgetunnel-b2h.pages.dev/workers-ai/features/markdown-conversion/).

Oct 21, 2025
1. ### [New Robots.txt tab for tracking crawler compliance](https://edgetunnel-b2h.pages.dev/changelog/post/2025-10-21-track-robots-txt/)  
[ AI Crawl Control ](https://edgetunnel-b2h.pages.dev/ai-crawl-control/)  
AI Crawl Control now includes a **Robots.txt** tab that provides insights into how AI crawlers interact with your `robots.txt` files.  
#### What's new  
The Robots.txt tab allows you to:

  * Monitor the health status of `robots.txt` files across all your hostnames, including HTTP status codes, and identify hostnames that need a `robots.txt` file.
  * Track the total number of requests to each `robots.txt` file, with breakdowns of successful versus unsuccessful requests.
  * Check whether your `robots.txt` files contain [Content Signals ↗](https://contentsignals.org/) directives for AI training, search, and AI input.
  * Identify crawlers that request paths explicitly disallowed by your `robots.txt` directives, including the crawler name, operator, violated path, specific directive, and violation count.
  * Filter `robots.txt` request data by crawler, operator, category, and custom time ranges.  
#### Take action  
When you identify non-compliant crawlers, you can:

  * Block the crawler in the [Crawlers tab](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/manage-ai-crawlers/)
  * Create custom [WAF rules](https://edgetunnel-b2h.pages.dev/waf/) for path-specific security
  * Use [Redirect Rules](https://edgetunnel-b2h.pages.dev/rules/url-forwarding/) to guide crawlers to appropriate areas of your site  
To get started, go to **AI Crawl Control** \> **Robots.txt** in the Cloudflare dashboard. Learn more in the [Track robots.txt documentation](https://edgetunnel-b2h.pages.dev/ai-crawl-control/features/track-robots-txt/).

Oct 14, 2025
1. ### [Enhanced AI Crawl Control metrics with new drilldowns and filters](https://edgetunnel-b2h.pages.dev/changelog/post/2025-10-14-enhanced-metrics-drilldowns/)  
[ AI Crawl Control ](https://edgetunnel-b2h.pages.dev/ai-crawl-control/)  
AI Crawl Control now provides enhanced metrics and CSV data exports to help you better understand AI crawler activity across your sites.  
#### What's new  
#### Track crawler requests over time  
Visualize crawler activity patterns over time, and group data by different dimensions:

  * **By Crawler** — Track activity from individual AI crawlers (GPTBot, ClaudeBot, Bytespider)
  * **By Category** — Analyze crawler purpose or type
  * **By Operator** — Discover which companies (OpenAI, Anthropic, ByteDance) are crawling your site
  * **By Host** — Break down activity across multiple subdomains
  * **By Status Code** — Monitor HTTP response codes to crawlers (200s, 300s, 400s, 500s)  
![AI Crawl Control requests over time chart with grouping tabs](https://edgetunnel-b2h.pages.dev/_astro/ai-crawl-control-requests-over-time.BtRyz0OT_ZpotRm.webp "Interactive chart showing crawler requests over time with filterable dimensions")  
Interactive chart showing crawler requests over time with filterable dimensions  
#### Analyze referrer data (Paid plans)  
Identify traffic sources with referrer analytics:

  * View top referrers driving traffic to your site
  * Understand discovery patterns and content popularity from AI operators  
![AI Crawl Control top referrers breakdown](https://edgetunnel-b2h.pages.dev/_astro/ai-crawl-control-top-referrers.CEUAwpd8_YrhT4.webp "Bar chart showing top referrers and their respective traffic volumes")  
Bar chart showing top referrers and their respective traffic volumes  
#### Export data  
Download your filtered view as a CSV:

  * Includes all applied filters and groupings
  * Useful for custom reporting and deeper analysis  
#### Get started

  1. Log in to the Cloudflare dashboard, and select your account and domain.
  2. Go to **AI Crawl Control** \> **Metrics**.
  3. Use the grouping tabs to explore different views of your data.
  4. Apply filters to focus on specific crawlers, time ranges, or response codes.
  5. Select **Download CSV** to export your filtered data for further analysis.  
Learn more about [AI Crawl Control](https://edgetunnel-b2h.pages.dev/ai-crawl-control).

Oct 02, 2025
1. ### [New Deepgram Flux model available on Workers AI](https://edgetunnel-b2h.pages.dev/changelog/post/2025-10-02-deepgram-flux/)  
[ Workers AI ](https://edgetunnel-b2h.pages.dev/workers-ai/)  
Deepgram's newest Flux model [@cf/deepgram/flux](https://edgetunnel-b2h.pages.dev/workers-ai/models/flux/) is now available on Workers AI, hosted directly on Cloudflare's infrastructure. We're excited to be a launch partner with Deepgram and offer their new Speech Recognition model built specifically for enabling voice agents. Check out [Deepgram's blog ↗](https://deepgram.com/flux) for more details on the release.  
The Flux model can be used in conjunction with Deepgram's speech-to-text model [@cf/deepgram/nova-3](https://edgetunnel-b2h.pages.dev/workers-ai/models/nova-3/) and text-to-speech model [@cf/deepgram/aura-1](https://edgetunnel-b2h.pages.dev/workers-ai/models/aura-1/) to build end-to-end voice agents. Having Deepgram on Workers AI takes advantage of our edge GPU infrastructure, for ultra low latency voice AI applications.  
#### Promotional Pricing  
For the month of October 2025, Deepgram's Flux model will be free to use on Workers AI. Official pricing will be announced soon and charged after the promotional pricing period ends on October 31, 2025\. Check out the [model page](https://edgetunnel-b2h.pages.dev/workers-ai/models/flux/) for pricing details in the future.  
#### Example Usage  
The new Flux model is WebSocket only as it requires live bi-directional streaming in order to recognize speech activity.

  1. Create a worker that establishes a websocket connection with `@cf/deepgram/flux`

**JavaScript**  
```js  
export default {  
  async fetch(request, env, ctx): Promise<Response> {  
    const resp = await env.AI.run("@cf/deepgram/flux", {  
      encoding: "linear16",  
      sample_rate: "16000"  
    }, {  
      websocket: true  
    });  
    return resp;  
  },  
} satisfies ExportedHandler<Env>;  
```

  1. Deploy your worker  
```bash  
npx wrangler deploy  
```

  1. Write a client script to connect to your worker and start sending random audio bytes to it

**JavaScript**  
```js  
const ws = new WebSocket('wss://<your-worker-url.com>');  
ws.onopen = () => {  
  console.log('Connected to WebSocket');  
  // Generate and send random audio bytes  
  // You can replace this part with a function  
  // that reads from your mic or other audio source  
  const audioData = generateRandomAudio();  
  ws.send(audioData);  
  console.log('Audio data sent');  
};  
ws.onmessage = (event) => {  
  // Transcription will be received here  
  // Add your custom logic to parse the data  
  console.log('Received:', event.data);  
};  
ws.onerror = (error) => {  
  console.error('WebSocket error:', error);  
};  
ws.onclose = () => {  
  console.log('WebSocket closed');  
};  
// Generate random audio data (1 second of noise at 44.1kHz, mono)  
function generateRandomAudio() {  
  const sampleRate = 44100;  
  const duration = 1;  
  const numSamples = sampleRate * duration;  
  const buffer = new ArrayBuffer(numSamples * 2);  
  const view = new Int16Array(buffer);  
  for (let i = 0; i < numSamples; i++) {  
    view[i] = Math.floor(Math.random() * 65536 - 32768);  
  }  
  return buffer;  
}  
```

Sep 25, 2025
1. ### [Browser Rendering Playwright GA, Stagehand support (Beta), and higher limits](https://edgetunnel-b2h.pages.dev/changelog/post/2025-09-25-br-playwright-ga-stagehand-limits/)  
[ Browser Run ](https://edgetunnel-b2h.pages.dev/browser-run/)  
We’re shipping three updates to Browser Rendering:

  * Playwright support is now Generally Available and synced with [Playwright v1.55 ↗](https://playwright.dev/docs/release-notes#version-155), giving you a stable foundation for critical automation and AI-agent workflows.
  * We’re also adding [Stagehand support (Beta)](https://edgetunnel-b2h.pages.dev/browser-run/stagehand/) so you can combine code with natural language instructions to build more resilient automations.
  * Finally, we’ve tripled [limits](https://edgetunnel-b2h.pages.dev/browser-run/limits/#workers-paid) for paid plans across both the [REST API](https://edgetunnel-b2h.pages.dev/browser-run/quick-actions/) and [Browser Sessions](https://edgetunnel-b2h.pages.dev/browser-run/#integration-methods) to help you scale.  
To get started with Stagehand, refer to the [Stagehand](https://edgetunnel-b2h.pages.dev/browser-run/stagehand/) example that uses Stagehand and [Workers AI](https://edgetunnel-b2h.pages.dev/workers-ai/) to search for a movie on this [example movie directory ↗](https://demo.playwright.dev/movies), extract its details using natural language (title, year, rating, duration, and genre), and return the information along with a screenshot of the webpage.

**Stagehand example**  
```ts  
const stagehand = new Stagehand({  
  env: "LOCAL",  
  localBrowserLaunchOptions: { cdpUrl: endpointURLString(env.BROWSER) },  
  llmClient: new WorkersAIClient(env.AI),  
  verbose: 1,  
});  
await stagehand.init();  
const page = stagehand.page;  
await page.goto("https://demo.playwright.dev/movies");  
// if search is a multi-step action, stagehand will return an array of actions it needs to act on  
const actions = await page.observe('Search for "Furiosa"');  
for (const action of actions) await page.act(action);  
await page.act("Click the search result");  
// normal playwright functions work as expected  
await page.waitForSelector(".info-wrapper .cast");  
let movieInfo = await page.extract({  
  instruction: "Extract movie information",  
  schema: z.object({  
    title: z.string(),  
    year: z.number(),  
    rating: z.number(),  
    genres: z.array(z.string()),  
    duration: z.number().describe("Duration in minutes"),  
  }),  
});  
await stagehand.close();  
```  
![Stagehand video](https://edgetunnel-b2h.pages.dev/images/browser-run/speedystagehand.gif)

Sep 25, 2025
1. ### [AI Search (formerly AutoRAG) now with More Models To Choose From](https://edgetunnel-b2h.pages.dev/changelog/post/2025-09-25-ai-search-more-models/)  
[ AI Search ](https://edgetunnel-b2h.pages.dev/ai-search/)  
AutoRAG is now AI Search! The new name marks a new and bigger mission: to make world-class search infrastructure available to every developer and business.  
With AI Search you can now use models from different providers like OpenAI and Anthropic. By attaching your provider keys to the AI Gateway linked to your AI Search instance, you can use many more models for both embedding and inference.  
To use AI Search with other [model providers](https://edgetunnel-b2h.pages.dev/ai-search/configuration/models/):

  1. **Add provider keys to AI Gateway**  
    1. Go to AI > AI Gateway in the dashboard.
    2. Select or create an AI gateway.
    3. In Provider Keys, choose your provider, click Add, and enter the key.
  2. **Connect a gateway to AI Search**: When creating a new AI Search, select the AI Gateway with your provider keys. For an existing AI Search, go to Settings and switch to a gateway that has your keys under Resources.
  3. **Select models**: Embedding models are only available to be changed when creating a new AI Search. Generation model can be selected when creating a new AI Search and can be changed at any time in Settings.  
Once configured, your AI Search instance will be able to reference models available through your AI Gateway when making a `/ai-search` request:

**JavaScript**  
```javascript  
export default {  
  async fetch(request, env) {  
    // Query your AI Search instance with a natural language question to an OpenAI model  
    const result = await env.AI.autorag("my-ai-search").aiSearch({  
      query: "What's new for Cloudflare Birthday Week?",  
      model: "openai/gpt-5"  
    });  
    // Return only the generated answer as plain text  
    return new Response(result.response, {  
      headers: { "Content-Type": "text/plain" },  
    });  
  },  
};  
```  
In the coming weeks we will also roll out updates to align the APIs with the new name. The existing APIs will continue to be supported for the time being. Stay tuned to the [AI Search Changelog](https://edgetunnel-b2h.pages.dev/changelog/product/ai-search/) and [Discord ↗](https://discord.cloudflare.com/) for more updates!

Sep 19, 2025
1. ### [New Metrics View in AutoRAG](https://edgetunnel-b2h.pages.dev/changelog/post/2025-09-19-autorag-metrics/)  
[ AI Search ](https://edgetunnel-b2h.pages.dev/ai-search/)  
[AutoRAG](https://edgetunnel-b2h.pages.dev/ai-search/) now includes a **Metrics** tab that shows how your data is indexed and searched. Get a clear view of the health of your indexing pipeline, compare usage between `ai-search` and `search`, and see which files are retrieved most often.  
![Metrics](https://edgetunnel-b2h.pages.dev/_astro/metrics.BBUwKIos_zR8bd.webp)  
You can find these metrics within each AutoRAG instance:

  * Indexing: Track how files are ingested and see status changes over time.
  * Search breakdown: Compare usage between `ai-search` and `search` endpoints.
  * Top file retrievals: Identify which files are most frequently retrieved in a given period.  
Try it today in [AutoRAG](https://edgetunnel-b2h.pages.dev/ai-search/get-started/).

Sep 10, 2025
1. ### [Agents SDK v0.1.0 and workers-ai-provider v2.0.0 with AI SDK v5 support](https://edgetunnel-b2h.pages.dev/changelog/post/2025-09-03-agents-sdk-beta-v5/)  
[ Agents ](https://edgetunnel-b2h.pages.dev/agents/)[ Workers ](https://edgetunnel-b2h.pages.dev/workers/)  
We've shipped a new release for the [Agents SDK ↗](https://github.com/cloudflare/agents) bringing full compatibility with [AI SDK v5 ↗](https://ai-sdk.dev/docs/introduction) and introducing automatic message migration that handles all legacy formats transparently.  
This release includes improved streaming and tool support, tool confirmation detection (for "human in the loop" systems), enhanced React hooks with automatic tool resolution, improved error handling for streaming responses, and seamless migration utilities that work behind the scenes.  
This makes it ideal for building production AI chat interfaces with Cloudflare Workers AI models, agent workflows, human-in-the-loop systems, or any application requiring reliable message handling across SDK versions — all while maintaining backward compatibility.  
Additionally, we've updated workers-ai-provider v2.0.0, the official provider for Cloudflare Workers AI models, to be compatible with AI SDK v5.  
#### useAgentChat(options)  
Creates a new chat interface with enhanced v5 capabilities.

**TypeScript**  
```ts  
// Basic chat setup  
const { messages, sendMessage, addToolResult } = useAgentChat({  
  agent,  
  experimental_automaticToolResolution: true,  
  tools,  
});  
// With custom tool confirmation  
const chat = useAgentChat({  
  agent,  
  experimental_automaticToolResolution: true,  
  toolsRequiringConfirmation: ["dangerousOperation"],  
});  
```  
#### Automatic Tool Resolution  
Tools are automatically categorized based on their configuration:

**TypeScript**  
```ts  
const tools = {  
  // Auto-executes (has execute function)  
  getLocalTime: {  
    description: "Get current local time",  
    inputSchema: z.object({}),  
    execute: async () => new Date().toLocaleString(),  
  },  
  // Requires confirmation (no execute function)  
  deleteFile: {  
    description: "Delete a file from the system",  
    inputSchema: z.object({  
      filename: z.string(),  
    }),  
  },  
  // Server-executed (no client confirmation)  
  analyzeData: {  
    description: "Analyze dataset on server",  
    inputSchema: z.object({ data: z.array(z.number()) }),  
    serverExecuted: true,  
  },  
} satisfies Record<string, AITool>;  
```  
#### Message Handling  
Send messages using the new v5 format with parts array:

**TypeScript**  
```ts  
// Text message  
sendMessage({  
  role: "user",  
  parts: [{ type: "text", text: "Hello, assistant!" }],  
});  
// Multi-part message with file  
sendMessage({  
  role: "user",  
  parts: [  
    { type: "text", text: "Analyze this image:" },  
    { type: "image", image: imageData },  
  ],  
});  
```  
#### Tool Confirmation Detection  
Simplified logic for detecting pending tool confirmations:

**TypeScript**  
```ts  
const pendingToolCallConfirmation = messages.some((m) =>  
  m.parts?.some(  
    (part) => isToolUIPart(part) && part.state === "input-available",  
  ),  
);  
// Handle tool confirmation  
if (pendingToolCallConfirmation) {  
  await addToolResult({  
    toolCallId: part.toolCallId,  
    tool: getToolName(part),  
    output: "User approved the action",  
  });  
}  
```  
#### Automatic Message Migration  
Seamlessly handle legacy message formats without code changes.

**TypeScript**  
```ts  
// All these formats are automatically converted:  
// Legacy v4 string content  
const legacyMessage = {  
  role: "user",  
  content: "Hello world",  
};  
// Legacy v4 with tool calls  
const legacyWithTools = {  
  role: "assistant",  
  content: "",  
  toolInvocations: [  
    {  
      toolCallId: "123",  
      toolName: "weather",  
      args: { city: "SF" },  
      state: "result",  
      result: "Sunny, 72°F",  
    },  
  ],  
};  
// Automatically becomes v5 format:  
// {  
//   role: "assistant",  
//   parts: [{  
//     type: "tool-call",  
//     toolCallId: "123",  
//     toolName: "weather",  
//     args: { city: "SF" },  
//     state: "result",  
//     result: "Sunny, 72°F"  
//   }]  
// }  
```  
#### Tool Definition Updates  
Migrate tool definitions to use the new `inputSchema` property.

**TypeScript**  
```ts  
// Before (AI SDK v4)  
const tools = {  
  weather: {  
    description: "Get weather information",  
    parameters: z.object({  
      city: z.string(),  
    }),  
    execute: async (args) => {  
      return await getWeather(args.city);  
    },  
  },  
};  
// After (AI SDK v5)  
const tools = {  
  weather: {  
    description: "Get weather information",  
    inputSchema: z.object({  
      city: z.string(),  
    }),  
    execute: async (args) => {  
      return await getWeather(args.city);  
    },  
  },  
};  
```  
#### Cloudflare Workers AI Integration  
Seamless integration with Cloudflare Workers AI models through the updated workers-ai-provider v2.0.0.  
#### Model Setup with Workers AI  
Use Cloudflare Workers AI models directly in your agent workflows:

**TypeScript**  
```ts  
import { createWorkersAI } from "workers-ai-provider";  
import { useAgentChat } from "agents/ai-react";  
// Create Workers AI model (v2.0.0 - same API, enhanced v5 internals)  
const model = createWorkersAI({  
  binding: env.AI,  
})("@cf/meta/llama-3.2-3b-instruct");  
```  
#### Enhanced File and Image Support  
Workers AI models now support v5 file handling with automatic conversion:

**TypeScript**  
```ts  
// Send images and files to Workers AI models  
sendMessage({  
  role: "user",  
  parts: [  
    { type: "text", text: "Analyze this image:" },  
    {  
      type: "file",  
      data: imageBuffer,  
      mediaType: "image/jpeg",  
    },  
  ],  
});  
// Workers AI provider automatically converts to proper format  
```  
#### Streaming with Workers AI  
Enhanced streaming support with automatic warning detection:

**TypeScript**  
```ts  
// Streaming with Workers AI models  
const result = await streamText({  
  model: createWorkersAI({ binding: env.AI })("@cf/meta/llama-3.2-3b-instruct"),  
  messages,  
  onChunk: (chunk) => {  
    // Enhanced streaming with warning handling  
    console.log(chunk);  
  },  
});  
```  
#### Import Updates  
Update your imports to use the new v5 types:

**TypeScript**  
```ts  
// Before (AI SDK v4)  
import type { Message } from "ai";  
import { useChat } from "ai/react";  
// After (AI SDK v5)  
import type { UIMessage } from "ai";  
// or alias for compatibility  
import type { UIMessage as Message } from "ai";  
import { useChat } from "@ai-sdk/react";  
```  
#### Resources

  * [Migration Guide ↗](https://github.com/cloudflare/agents/blob/main/docs/migration-to-ai-sdk-v5.md) \- Comprehensive migration documentation
  * [AI SDK v5 Documentation ↗](https://ai-sdk.dev/docs/migration-guides/migration-guide-5-0) \- Official AI SDK migration guide
  * [An Example PR showing the migration from AI SDK v4 to v5 ↗](https://github.com/cloudflare/agents-starter/pull/105)
  * [GitHub Issues ↗](https://github.com/cloudflare/agents/issues) \- Report bugs or request features  
#### Feedback Welcome  
We'd love your feedback! We're particularly interested in feedback on:

  * **Migration experience** \- How smooth was the upgrade process?
  * **Tool confirmation workflow** \- Does the new automatic detection work as expected?
  * **Message format handling** \- Any edge cases with legacy message conversion?

Sep 05, 2025
1. ### [Introducing EmbeddingGemma from Google on Workers AI](https://edgetunnel-b2h.pages.dev/changelog/post/2025-09-05-embeddinggemma/)  
[ Workers AI ](https://edgetunnel-b2h.pages.dev/workers-ai/)  
We're excited to be a launch partner alongside [Google ↗](https://developers.googleblog.com/en/introducing-embeddinggemma/) to bring their newest embedding model, **EmbeddingGemma**, to Workers AI that delivers best-in-class performance for its size, enabling RAG and semantic search use cases.  
[@cf/google/embeddinggemma-300m](https://edgetunnel-b2h.pages.dev/workers-ai/models/embeddinggemma-300m/) is a 300M parameter embedding model from Google, built from Gemma 3 and the same research used to create Gemini models. This multilingual model supports 100+ languages, making it ideal for RAG systems, semantic search, content classification, and clustering tasks.

**Using EmbeddingGemma in AI Search:**Now you can leverage EmbeddingGemma directly through AI Search for your RAG pipelines. EmbeddingGemma's multilingual capabilities make it perfect for global applications that need to understand and retrieve content across different languages with exceptional accuracy.  
To use EmbeddingGemma for your AI Search projects:

  1. Go to **Create** in the [AI Search dashboard ↗](https://dash.cloudflare.com/?to=/:account/ai/ai-search)
  2. Follow the setup flow for your new RAG instance
  3. In the **Generate Index** step, open up **More embedding models** and select `@cf/google/embeddinggemma-300m` as your embedding model
  4. Complete the setup to create an AI Search  
Try it out and let us know what you think!

Aug 27, 2025
1. ### [Enhanced crawler insights and custom 402 responses](https://edgetunnel-b2h.pages.dev/changelog/post/2025-08-27-ai-crawl-control-launch/)  
[ AI Crawl Control ](https://edgetunnel-b2h.pages.dev/ai-crawl-control/)  
We improved AI crawler management with detailed analytics and introduced custom HTTP 402 responses for blocked crawlers. AI Audit has been renamed to AI Crawl Control and is now generally available.

**Enhanced Crawlers tab:**

  * View total allowed and blocked requests for each AI crawler
  * Trend charts show crawler activity over your selected time range per crawler  
![Updated AI Crawl Control table showing request counts and trend charts](https://edgetunnel-b2h.pages.dev/_astro/ai-crawl-control-table.BDr0Qd-5_ZKex0W.webp)  

**Custom block responses (paid plans):**You can now return HTTP 402 "Payment Required" responses when blocking AI crawlers, enabling direct communication with crawler operators about licensing terms.  
For users on paid plans, when blocking AI crawlers you can configure:

  * **Response code:** Choose between 403 Forbidden or 402 Payment Required
  * **Response body:** Add a custom message with your licensing contact information  
![AI Crawl Control block response configuration interface](https://edgetunnel-b2h.pages.dev/_astro/ai-crawl-control-block-response.L4duQj7-_Z2mHb4X.webp)  
Example 402 response:  
```http  
HTTP 402 Payment Required  
Date: Mon, 24 Aug 2025 12:56:49 GMT  
Content-type: application/json  
Server: cloudflare  
Cf-Ray: 967e8da599d0c3fa-EWR  
Cf-Team: 2902f6db750000c3fa1e2ef400000001  
{  
  "message": "Please contact the site owner for access."  
}  
```

Aug 27, 2025
1. ### [Deepgram and Leonardo partner models now available on Workers AI](https://edgetunnel-b2h.pages.dev/changelog/post/2025-08-27-partner-models/)  
[ Workers AI ](https://edgetunnel-b2h.pages.dev/workers-ai/)  
New state-of-the-art models have landed on Workers AI! This time, we're introducing new **partner models** trained by our friends at [Deepgram ↗](https://deepgram.com) and [Leonardo ↗](https://leonardo.ai), hosted on Workers AI infrastructure.  
As well, we're introuding a new turn detection model that enables you to detect when someone is done speaking — useful for building voice agents!  
Read the [blog ↗](https://blog.cloudflare.com/workers-ai-partner-models) for more details and check out some of the new models on our platform:

  * [@cf/deepgram/aura-1](https://edgetunnel-b2h.pages.dev/workers-ai/models/aura-1) is a text-to-speech model that allows you to input text and have it come to life in a customizable voice
  * [@cf/deepgram/nova-3](https://edgetunnel-b2h.pages.dev/workers-ai/models/nova-3) is speech-to-text model that transcribes multilingual audio at a blazingly fast speed
  * [@cf/pipecat-ai/smart-turn-v2](https://edgetunnel-b2h.pages.dev/workers-ai/models/smart-turn-v2) helps you detect when someone is done speaking
  * [@cf/leonardo/lucid-origin](https://edgetunnel-b2h.pages.dev/workers-ai/models/lucid-origin) is a text-to-image model that generates images with sharp graphic design, stunning full-HD renders, or highly specific creative direction
  * [@cf/leonardo/phoenix-1.0](https://edgetunnel-b2h.pages.dev/workers-ai/models/phoenix-1.0) is a text-to-image model with exceptional prompt adherence and coherent text  
You can filter out new partner models with the `Partner` capability on our [Models](https://edgetunnel-b2h.pages.dev/workers-ai/models) page.  
As well, we're introducing WebSocket support for some of our audio models, which you can filter though the `Realtime` capability on our [Models](https://edgetunnel-b2h.pages.dev/workers-ai/models) page. WebSockets allows you to create a bi-directional connection to our inference server with low latency — perfect for those that are building voice agents.  
An example python snippet on how to use WebSockets with our new Aura model:  
```plaintext  
import json  
import os  
import asyncio  
import websockets  
uri = f"wss://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/deepgram/aura-1"  
input = [  
    "Line one, out of three lines that will be provided to the aura model.",  
    "Line two, out of three lines that will be provided to the aura model.",  
    "Line three, out of three lines that will be provided to the aura model. This is a last line.",  
]  
async def text_to_speech():  
    async with websockets.connect(uri, additional_headers={"Authorization": os.getenv("CF_TOKEN")}) as websocket:  
        print("connection established")  
        for line in input:  
            print(f"sending `{line}`")  
            await websocket.send(json.dumps({"type": "Speak", "text": line}))  
            print("line was sent, flushing")  
            await websocket.send(json.dumps({"type": "Flush"}))  
            print("flushed, recving")  
            resp = await websocket.recv()  
            print(f"response received {resp}")  
if __name__ == "__main__":  
    asyncio.run(text_to_speech())  
```

Aug 26, 2025
1. ### [List all vectors in a Vectorize index with the new list-vectors operation](https://edgetunnel-b2h.pages.dev/changelog/post/2025-08-26-vectorize-list-vectors/)  
[ Vectorize ](https://edgetunnel-b2h.pages.dev/vectorize/)  
You can now list all vector identifiers in a Vectorize index using the new `list-vectors` operation. This enables bulk operations, auditing, and data migration workflows through paginated requests that maintain snapshot consistency.  
The operation is available via Wrangler CLI and REST API. Refer to the [list-vectors best practices guide](https://edgetunnel-b2h.pages.dev/vectorize/best-practices/list-vectors/) for detailed usage guidance.

Aug 25, 2025
1. ### [Manage and deploy your AI provider keys through Bring Your Own Key (BYOK) with AI Gateway, now powered by Cloudflare Secrets Store](https://edgetunnel-b2h.pages.dev/changelog/post/2025-08-25-secrets-store-ai-gateway/)  
[ Secrets Store ](https://edgetunnel-b2h.pages.dev/secrets-store/)[ AI Gateway ](https://edgetunnel-b2h.pages.dev/ai-gateway/)[ SSL/TLS ](https://edgetunnel-b2h.pages.dev/ssl/)  
Cloudflare Secrets Store is now integrated with AI Gateway, allowing you to store, manage, and deploy your AI provider keys in a secure and seamless configuration through [Bring Your Own Key ↗](https://edgetunnel-b2h.pages.dev/ai-gateway/configuration/bring-your-own-keys/). Instead of passing your AI provider keys directly in every request header, you can centrally manage each key with Secrets Store and deploy in your gateway configuration using only a reference, rather than passing the value in plain text.  
You can now create a secret directly from your AI Gateway [in the dashboard ↗](http://dash.cloudflare.com/?to=/:account/ai-gateway) by navigating into your gateway -> **Provider Keys** \-> **Add**.  
![Import repo or choose template](https://edgetunnel-b2h.pages.dev/_astro/add-secret-ai-gateway.B-SIPr6s_jJjDD.webp)  
You can also create your secret with the newly available **ai\_gateway** scope via [wrangler ↗](https://edgetunnel-b2h.pages.dev/workers/wrangler/commands/), the [Secrets Store dashboard ↗](http://dash.cloudflare.com/?to=/:account/secrets-store), or the [API ↗](https://edgetunnel-b2h.pages.dev/api/resources/secrets%5Fstore/).  
Then, pass the key in the request header using its Secrets Store reference:  
```bash  
curl -X POST https://gateway.ai.cloudflare.com/v1/<ACCOUNT_ID>/my-gateway/anthropic/v1/messages \
 --header 'cf-aig-authorization: ANTHROPIC_KEY_1 \
 --header 'anthropic-version: 2023-06-01' \
 --header 'Content-Type: application/json' \
 --data  '{"model": "claude-3-opus-20240229", "messages": [{"role": "user", "content": "What is Cloudflare?"}]}'  
```  
Or, using Javascript:  
```plaintext  
import Anthropic from '@anthropic-ai/sdk';  
const anthropic = new Anthropic({  
 apiKey: "ANTHROPIC_KEY_1",  
 baseURL: "https://gateway.ai.cloudflare.com/v1/<ACCOUNT_ID>/my-gateway/anthropic",  
});  
const message = await anthropic.messages.create({  
 model: 'claude-3-opus-20240229',  
 messages: [{role: "user", content: "What is Cloudflare?"}],  
 max_tokens: 1024  
});  
```  
For more information, check out the [blog ↗](https://blog.cloudflare.com/ai-gateway-aug-2025-refresh)!

Aug 05, 2025
1. ### [Agents SDK adds MCP Elicitation support, http-streamable support, task queues, email integration and more](https://edgetunnel-b2h.pages.dev/changelog/post/2025-08-05-agents-mcp-update/)  
[ Agents ](https://edgetunnel-b2h.pages.dev/agents/)[ Workers ](https://edgetunnel-b2h.pages.dev/workers/)  
The latest releases of [@cloudflare/agents ↗](https://github.com/cloudflare/agents) brings major improvements to MCP transport protocols support and agents connectivity. Key updates include:  
#### MCP elicitation support  
MCP servers can now request user input during tool execution, enabling interactive workflows like confirmations, forms, and multi-step processes. This feature uses durable storage to preserve elicitation state even during agent hibernation, ensuring seamless user interactions across agent lifecycle events.

**TypeScript**  
```ts  
// Request user confirmation via elicitation  
const confirmation = await this.elicitInput({  
  message: `Are you sure you want to increment the counter by ${amount}?`,  
  requestedSchema: {  
    type: "object",  
    properties: {  
      confirmed: {  
        type: "boolean",  
        title: "Confirm increment",  
        description: "Check to confirm the increment",  
      },  
    },  
    required: ["confirmed"],  
  },  
});  
```  
Check out our [demo ↗](https://github.com/whoiskatrin/agents/tree/main/examples/mcp-elicitation-demo) to see elicitation in action.  
#### HTTP streamable transport for MCP  
MCP now supports HTTP streamable transport which is recommended over SSE. This transport type offers:

  * **Better performance**: More efficient data streaming and reduced overhead
  * **Improved reliability**: Enhanced connection stability and error recover- **Automatic fallback**: If streamable transport is not available, it gracefully falls back to SSE

**TypeScript**  
```ts  
export default MyMCP.serve("/mcp", {  
  binding: "MyMCP",  
});  
```  
The SDK automatically selects the best available transport method, gracefully falling back from streamable-http to SSE when needed.  
#### Enhanced MCP connectivity  
Significant improvements to MCP server connections and transport reliability:

  * **Auto transport selection**: Automatically determines the best transport method, falling back from streamable-http to SSE as needed
  * **Improved error handling**: Better connection state management and error reporting for MCP servers
  * **Reliable prop updates**: Centralized agent property updates ensure consistency across different contexts  
#### Lightweight .queue for fast task deferral  
You can use `.queue()` to enqueue background work — ideal for tasks like processing user messages, sending notifications etc.

**TypeScript**  
```ts  
class MyAgent extends Agent {  
  doSomethingExpensive(payload) {  
    // a long running process that you want to run in the background  
  }  
  queueSomething() {  
    await this.queue("doSomethingExpensive", somePayload); // this will NOT block further execution, and runs in the background  
    await this.queue("doSomethingExpensive", someOtherPayload); // the callback will NOT run until the previous callback is complete  
    // ... call as many times as you want  
  }  
}  
```  
Want to try it yourself? Just define a method like processMessage in your agent, and you’re ready to scale.  
#### New email adapter  
Want to build an AI agent that can receive and respond to emails automatically? With the new email adapter and onEmail lifecycle method, now you can.

**TypeScript**  
```ts  
export class EmailAgent extends Agent {  
  async onEmail(email: AgentEmail) {  
    const raw = await email.getRaw();  
    const parsed = await PostalMime.parse(raw);  
    // create a response based on the email contents  
    // and then send a reply  
    await this.replyToEmail(email, {  
      fromName: "Email Agent",  
      body: `Thanks for your email! You've sent us "${parsed.subject}". We'll process it shortly.`,  
    });  
  }  
}  
```  
You route incoming mail like this:

**TypeScript**  
```ts  
export default {  
  async email(email, env) {  
    await routeAgentEmail(email, env, {  
      resolver: createAddressBasedEmailResolver("EmailAgent"),  
    });  
  },  
};  
```  
You can find a full example [here ↗](https://github.com/cloudflare/agents/tree/main/examples/email-agent).  
#### Automatic context wrapping for custom methods  
Custom methods are now automatically wrapped with the agent's context, so calling `getCurrentAgent()` should work regardless of where in an agent's lifecycle it's called. Previously this would not work on RPC calls, but now just works out of the box.

**TypeScript**  
```ts  
export class MyAgent extends Agent {  
  async suggestReply(message) {  
    // getCurrentAgent() now correctly works, even when called inside an RPC method  
    const { agent } = getCurrentAgent()!;  
    return generateText({  
      prompt: `Suggest a reply to: "${message}" from "${agent.name}"`,  
      tools: [replyWithEmoji],  
    });  
  }  
}  
```  
Try it out and tell us what you build!

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```