Workers, including those using Durable Objects and Browser Rendering, 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.
AI Search now supports reranking for improved retrieval quality and allows you to set the system prompt directly in your API requests.
You can now enable 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 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"}});Previously, system prompts 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 // Dynamically set query and system prompt in AI Searchasync 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 usageconst query = "What is Cloudflare?";const tone = "friendly";const answer = await getAnswer(query, tone);console.log(answer);Learn more about Reranking and System Prompt in AI Search.
Developers can now programmatically retrieve a list of all file formats supported by the Markdown Conversion utility in Workers AI.
You can use the
env.AIbinding:TypeScript await env.AI.toMarkdown().supported()Or call the REST API:
Terminal window 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:
[{"extension": ".pdf","mimeType": "application/pdf",},{"extension": ".jpeg","mimeType": "image/jpeg",},...]Learn more about our Markdown Conversion utility.
AI Crawl Control now includes a Robots.txt tab that provides insights into how AI crawlers interact with your
robots.txtfiles.The Robots.txt tab allows you to:
- Monitor the health status of
robots.txtfiles across all your hostnames, including HTTP status codes, and identify hostnames that need arobots.txtfile. - Track the total number of requests to each
robots.txtfile, with breakdowns of successful versus unsuccessful requests. - Check whether your
robots.txtfiles contain Content Signals ↗ directives for AI training, search, and AI input. - Identify crawlers that request paths explicitly disallowed by your
robots.txtdirectives, including the crawler name, operator, violated path, specific directive, and violation count. - Filter
robots.txtrequest data by crawler, operator, category, and custom time ranges.
When you identify non-compliant crawlers, you can:
- Block the crawler in the Crawlers tab
- Create custom WAF rules for path-specific security
- Use Redirect Rules 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.
- Monitor the health status of
AI Crawl Control now provides enhanced metrics and CSV data exports to help you better understand AI crawler activity across your sites.
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)
Interactive chart showing crawler requests over time with filterable dimensions Identify traffic sources with referrer analytics:
- View top referrers driving traffic to your site
- Understand discovery patterns and content popularity from AI operators
Bar chart showing top referrers and their respective traffic volumes Download your filtered view as a CSV:
- Includes all applied filters and groupings
- Useful for custom reporting and deeper analysis
- Log in to the Cloudflare dashboard, and select your account and domain.
- Go to AI Crawl Control > Metrics.
- Use the grouping tabs to explore different views of your data.
- Apply filters to focus on specific crawlers, time ranges, or response codes.
- Select Download CSV to export your filtered data for further analysis.
Learn more about AI Crawl Control.
Deepgram's newest Flux model
@cf/deepgram/fluxis 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 ↗ for more details on the release.The Flux model can be used in conjunction with Deepgram's speech-to-text model
@cf/deepgram/nova-3and text-to-speech model@cf/deepgram/aura-1to 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.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 for pricing details in the future.
The new Flux model is WebSocket only as it requires live bi-directional streaming in order to recognize speech activity.
- Create a worker that establishes a websocket connection with
@cf/deepgram/flux
JavaScript 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>;- Deploy your worker
Terminal window npx wrangler deploy- Write a client script to connect to your worker and start sending random audio bytes to it
JavaScript 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 sourceconst 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 dataconsole.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;}- Create a worker that establishes a websocket connection with
We’re shipping three updates to Browser Rendering:
- Playwright support is now Generally Available and synced with Playwright v1.55 ↗, giving you a stable foundation for critical automation and AI-agent workflows.
- We’re also adding Stagehand support (Beta) so you can combine code with natural language instructions to build more resilient automations.
- Finally, we’ve tripled limits for paid plans across both the REST API and Workers Bindings to help you scale.
To get started with Stagehand, refer to the Stagehand example that uses Stagehand and Workers AI to search for a movie on this example movie directory ↗, 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 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 onconst 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 expectedawait 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();
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:
- Add provider keys to AI Gateway
- Go to AI > AI Gateway in the dashboard.
- Select or create an AI gateway.
- In Provider Keys, choose your provider, click Add, and enter the key.
- 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.
- 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-searchrequest:JavaScript export default {async fetch(request, env) {// Query your AI Search instance with a natural language question to an OpenAI modelconst 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 textreturn 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 and Discord ↗ for more updates!
- Add provider keys to AI Gateway
AutoRAG 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-searchandsearch, and see which files are retrieved most often.
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-searchandsearchendpoints. - Top file retrievals: Identify which files are most frequently retrieved in a given period.
Try it today in AutoRAG.
We've shipped a new release for the Agents SDK ↗ bringing full compatibility with AI SDK v5 ↗ 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.
Creates a new chat interface with enhanced v5 capabilities.
TypeScript // Basic chat setupconst { messages, sendMessage, addToolResult } = useAgentChat({agent,experimental_automaticToolResolution: true,tools,});// With custom tool confirmationconst chat = useAgentChat({agent,experimental_automaticToolResolution: true,toolsRequiringConfirmation: ["dangerousOperation"],});Tools are automatically categorized based on their configuration:
TypeScript 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>;Send messages using the new v5 format with parts array:
TypeScript // Text messagesendMessage({role: "user",parts: [{ type: "text", text: "Hello, assistant!" }],});// Multi-part message with filesendMessage({role: "user",parts: [{ type: "text", text: "Analyze this image:" },{ type: "image", image: imageData },],});Simplified logic for detecting pending tool confirmations:
TypeScript const pendingToolCallConfirmation = messages.some((m) =>m.parts?.some((part) => isToolUIPart(part) && part.state === "input-available",),);// Handle tool confirmationif (pendingToolCallConfirmation) {await addToolResult({toolCallId: part.toolCallId,tool: getToolName(part),output: "User approved the action",});}Seamlessly handle legacy message formats without code changes.
TypeScript // All these formats are automatically converted:// Legacy v4 string contentconst legacyMessage = {role: "user",content: "Hello world",};// Legacy v4 with tool callsconst 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"// }]// }Migrate tool definitions to use the new
inputSchemaproperty.TypeScript // 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);},},};Seamless integration with Cloudflare Workers AI models through the updated workers-ai-provider v2.0.0.
Use Cloudflare Workers AI models directly in your agent workflows:
TypeScript 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");Workers AI models now support v5 file handling with automatic conversion:
TypeScript // Send images and files to Workers AI modelssendMessage({role: "user",parts: [{ type: "text", text: "Analyze this image:" },{type: "file",data: imageBuffer,mediaType: "image/jpeg",},],});// Workers AI provider automatically converts to proper formatEnhanced streaming support with automatic warning detection:
TypeScript // Streaming with Workers AI modelsconst result = await streamText({model: createWorkersAI({ binding: env.AI })("@cf/meta/llama-3.2-3b-instruct"),messages,onChunk: (chunk) => {// Enhanced streaming with warning handlingconsole.log(chunk);},});Update your imports to use the new v5 types:
TypeScript // 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 compatibilityimport type { UIMessage as Message } from "ai";import { useChat } from "@ai-sdk/react";- Migration Guide ↗ - Comprehensive migration documentation
- AI SDK v5 Documentation ↗ - Official AI SDK migration guide
- An Example PR showing the migration from AI SDK v4 to v5 ↗
- GitHub Issues ↗ - Report bugs or request features
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?
We're excited to be a launch partner alongside Google ↗ 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-300mis 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:
- Go to Create in the AI Search dashboard ↗
- Follow the setup flow for your new RAG instance
- In the Generate Index step, open up More embedding models and select
@cf/google/embeddinggemma-300mas your embedding model - Complete the setup to create an AI Search
Try it out and let us know what you think!
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
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
Example 402 response:
HTTP 402 Payment RequiredDate: Mon, 24 Aug 2025 12:56:49 GMTContent-type: application/jsonServer: cloudflareCf-Ray: 967e8da599d0c3fa-EWRCf-Team: 2902f6db750000c3fa1e2ef400000001{"message": "Please contact the site owner for access."}
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 ↗ and Leonardo ↗, 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 ↗ for more details and check out some of the new models on our platform:
@cf/deepgram/aura-1is a text-to-speech model that allows you to input text and have it come to life in a customizable voice@cf/deepgram/nova-3is speech-to-text model that transcribes multilingual audio at a blazingly fast speed@cf/pipecat-ai/smart-turn-v2helps you detect when someone is done speaking@cf/leonardo/lucid-originis a text-to-image model that generates images with sharp graphic design, stunning full-HD renders, or highly specific creative direction@cf/leonardo/phoenix-1.0is a text-to-image model with exceptional prompt adherence and coherent text
You can filter out new partner models with the
Partnercapability on our Models page.As well, we're introducing WebSocket support for some of our audio models, which you can filter though the
Realtimecapability on our 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:
import jsonimport osimport asyncioimport websocketsuri = 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())
You can now list all vector identifiers in a Vectorize index using the new
list-vectorsoperation. 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 for detailed usage guidance.
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 ↗. 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 ↗ by navigating into your gateway -> Provider Keys -> Add.
You can also create your secret with the newly available ai_gateway scope via wrangler ↗, the Secrets Store dashboard ↗, or the API ↗.
Then, pass the key in the request header using its Secrets Store reference:
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:
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 ↗!
We’ve shipped a major release for the @cloudflare/sandbox ↗ SDK, turning it into a full-featured, container-based execution platform that runs securely on Cloudflare Workers.
This update adds live streaming of output, persistent Python and JavaScript code interpreters with rich output support (charts, tables, HTML, JSON), file system access, Git operations, full background process control, and the ability to expose running services via public URLs.
This makes it ideal for building AI agents, CI runners, cloud REPLs, data analysis pipelines, or full developer tools — all without managing infrastructure.
Create persistent code contexts with support for rich visual + structured outputs.
Creates a new code execution context with persistent state.
TypeScript // Create a Python contextconst pythonCtx = await sandbox.createCodeContext({ language: "python" });// Create a JavaScript contextconst jsCtx = await sandbox.createCodeContext({ language: "javascript" });Options:
- language: Programming language ('python' | 'javascript' | 'typescript')
- cwd: Working directory (default: /workspace)
- envVars: Environment variables for the context
Executes code with optional streaming callbacks.
TypeScript // Simple executionconst execution = await sandbox.runCode('print("Hello World")', {context: pythonCtx,});// With streaming callbacksawait sandbox.runCode(`for i in range(5):print(f"Step {i}")time.sleep(1)`,{context: pythonCtx,onStdout: (output) => console.log("Real-time:", output.text),onResult: (result) => console.log("Result:", result),},);Options:
- language: Programming language ('python' | 'javascript' | 'typescript')
- cwd: Working directory (default: /workspace)
- envVars: Environment variables for the context
Returns a streaming response for real-time processing.
TypeScript const stream = await sandbox.runCodeStream("import time; [print(i) for i in range(10)]",);// Process the stream as neededInterpreter outputs are auto-formatted and returned in multiple formats:
- text
- html (e.g., Pandas tables)
- png, svg (e.g., Matplotlib charts)
- json (structured data)
- chart (parsed visualizations)
TypeScript const result = await sandbox.runCode(`import seaborn as snsimport matplotlib.pyplot as pltdata = sns.load_dataset("flights")pivot = data.pivot("month", "year", "passengers")sns.heatmap(pivot, annot=True, fmt="d")plt.title("Flight Passengers")plt.show()pivot.to_dict()`,{ context: pythonCtx },);if (result.png) {console.log("Chart output:", result.png);}Start background processes and expose them with live URLs.
TypeScript await sandbox.startProcess("python -m http.server 8000");const preview = await sandbox.exposePort(8000);console.log("Live preview at:", preview.url);Start, inspect, and terminate long-running background processes.
TypeScript const process = await sandbox.startProcess("node server.js");console.log(`Started process ${process.id} with PID ${process.pid}`);// Monitor the processconst logStream = await sandbox.streamProcessLogs(process.id);for await (const log of parseSSEStream<LogEvent>(logStream)) {console.log(`Server: ${log.data}`);}- listProcesses() - List all running processes
- getProcess(id) - Get detailed process status
- killProcess(id, signal) - Terminate specific processes
- killAllProcesses() - Kill all processes
- streamProcessLogs(id, options) - Stream logs from running processes
- getProcessLogs(id) - Get accumulated process output
Clone Git repositories directly into the sandbox.
TypeScript await sandbox.gitCheckout("https://github.com/user/repo", {branch: "main",targetDir: "my-project",});Sandboxes are still experimental. We're using them to explore how isolated, container-like workloads might scale on Cloudflare — and to help define the developer experience around them.
The latest releases of @cloudflare/agents ↗ brings major improvements to MCP transport protocols support and agents connectivity. Key updates include:
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 // Request user confirmation via elicitationconst 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 ↗ to see elicitation in action.
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 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.
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
You can use
.queue()to enqueue background work — ideal for tasks like processing user messages, sending notifications etc.TypeScript 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 backgroundawait 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.
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 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 replyawait 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 export default {async email(email, env) {await routeAgentEmail(email, env, {resolver: createAddressBasedEmailResolver("EmailAgent"),});},};You can find a full example here ↗.
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 export class MyAgent extends Agent {async suggestReply(message) {// getCurrentAgent() now correctly works, even when called inside an RPC methodconst { agent } = getCurrentAgent()!;return generateText({prompt: `Suggest a reply to: "${message}" from "${agent.name}"`,tools: [replyWithEmoji],});}}Try it out and tell us what you build!
We're thrilled to be a Day 0 partner with OpenAI ↗ to bring their latest open models ↗ to Workers AI, including support for Responses API, Code Interpreter, and Web Search (coming soon).
Get started with the new models at
@cf/openai/gpt-oss-120band@cf/openai/gpt-oss-20b. Check out the blog ↗ for more details about the new models, and thegpt-oss-120bandgpt-oss-20bmodel pages for more information about pricing and context windows.If you call the model through:
- Workers Binding, it will accept/return Responses API –
env.AI.run(“@cf/openai/gpt-oss-120b”) - REST API on
/runendpoint, it will accept/return Responses API –https://api.cloudflare.com/client/v4/accounts/<account_id>/ai/run/@cf/openai/gpt-oss-120b - REST API on new
/responsesendpoint, it will accept/return Responses API –https://api.cloudflare.com/client/v4/accounts/<account_id>/ai/v1/responses - REST API for OpenAI Compatible endpoint, it will return Chat Completions (coming soon) –
https://api.cloudflare.com/client/v4/accounts/<account_id>/ai/v1/chat/completions
curl https://api.cloudflare.com/client/v4/accounts/<account_id>/ai/v1/responses \-H "Content-Type: application/json" \-H "Authorization: Bearer $CLOUDFLARE_API_KEY" \-d '{"model": "@cf/openai/gpt-oss-120b","reasoning": {"effort": "medium"},"input": [{"role": "user","content": "What are the benefits of open-source models?"}]}'The model is natively trained to support stateful code execution, and we've implemented support for this feature using our Sandbox SDK ↗ and Containers ↗. Cloudflare's Developer Platform is uniquely positioned to support this feature, so we're very excited to bring our products together to support this new use case.
We are working to implement Web Search for the model, where users can bring their own Exa API Key so the model can browse the Internet.
- Workers Binding, it will accept/return Responses API –
We’ve launched pricing for Browser Rendering, including a free tier and a pay-as-you-go model that scales with your needs. Starting August 20, 2025, Cloudflare will begin billing for Browser Rendering.
There are two ways to use Browser Rendering. Depending on the method you use, here’s how billing will work:
- REST API: Charged for Duration only ($/browser hour)
- Workers Bindings: Charged for both Duration and Concurrency ($/browser hour and # of concurrent browsers)
Included usage and pricing by plan
Plan Included duration Included concurrency Price (beyond included) Workers Free 10 minutes per day 3 concurrent browsers N/A Workers Paid 10 hours per month 10 concurrent browsers (averaged monthly) 1. REST API: $0.09 per additional browser hour
2. Workers Bindings: $0.09 per additional browser hour
$2.00 per additional concurrent browserWhat you need to know:
- Workers Free Plan: 10 minutes of browser usage per day with 3 concurrent browsers at no charge.
- Workers Paid Plan: 10 hours of browser usage per month with 10 concurrent browsers (averaged monthly) at no charge. Additional usage is charged as shown above.
You can monitor usage via the Cloudflare dashboard ↗. Go to Compute (Workers) > Browser Rendering.
If you've been using Browser Rendering and do not wish to incur charges, ensure your usage stays within your plan's included usage. To estimate costs, take a look at these example pricing scenarios.
You can now run your Browser Rendering locally using
npx wrangler dev, which spins up a browser directly on your machine before deploying to Cloudflare's global network. By running tests locally, you can quickly develop, debug, and test changes without needing to deploy or worry about usage costs.
Get started with this example guide that shows how to use Cloudflare's fork of Puppeteer (you can also use Playwright) to take screenshots of webpages and store the results in Workers KV.
You can now expect 3-5× faster indexing in AutoRAG, and with it, a brand new Jobs view to help you monitor indexing progress.
With each AutoRAG, indexing jobs are automatically triggered to sync your data source (i.e. R2 bucket) with your Vectorize index, ensuring new or updated files are reflected in your query results. You can also trigger jobs manually via the Sync API or by clicking “Sync index” in the dashboard.
With the new jobs observability, you can now:
- View the status, job ID, source, start time, duration and last sync time for each indexing job
- Inspect real-time logs of job events (e.g.
Starting indexing data source...) - See a history of past indexing jobs under the Jobs tab of your AutoRAG
This makes it easier to understand what’s happening behind the scenes.
Coming soon: We’re adding APIs to programmatically check indexing status, making it even easier to integrate AutoRAG into your workflows.
Try it out today on the Cloudflare dashboard ↗.
We are introducing a new feature of AI Crawl Control — Pay Per Crawl. Pay Per Crawl enables site owners to require payment from AI crawlers every time the crawlers access their content, thereby fostering a fairer Internet by enabling site owners to control and monetize how their content gets used by AI.
For Site Owners:
- Set pricing and select which crawlers to charge for content access
- Manage payments via Stripe
- Monitor analytics on successful content deliveries
For AI Crawler Owners:
- Use HTTP headers to request and accept pricing
- Receive clear confirmations on charges for accessed content
Learn more in the Pay Per Crawl documentation.
We redesigned the AI Crawl Control dashboard to provide more intuitive and granular control over AI crawlers.
- From the new AI Crawlers tab: block specific AI crawlers.
- From the new Metrics tab: view AI Crawl Control metrics.
To get started, explore:
AI is supercharging app development for everyone, but we need a safe way to run untrusted, LLM-written code. We’re introducing Sandboxes ↗, which let your Worker run actual processes in a secure, container-based environment.
TypeScript import { getSandbox } from "@cloudflare/sandbox";export { Sandbox } from "@cloudflare/sandbox";export default {async fetch(request: Request, env: Env) {const sandbox = getSandbox(env.Sandbox, "my-sandbox");return sandbox.exec("ls", ["-la"]);},};exec(command: string, args: string[], options?: { stream?: boolean }):Execute a command in the sandbox.gitCheckout(repoUrl: string, options: { branch?: string; targetDir?: string; stream?: boolean }): Checkout a git repository in the sandbox.mkdir(path: string, options: { recursive?: boolean; stream?: boolean }): Create a directory in the sandbox.writeFile(path: string, content: string, options: { encoding?: string; stream?: boolean }): Write content to a file in the sandbox.readFile(path: string, options: { encoding?: string; stream?: boolean }): Read content from a file in the sandbox.deleteFile(path: string, options?: { stream?: boolean }): Delete a file from the sandbox.renameFile(oldPath: string, newPath: string, options?: { stream?: boolean }): Rename a file in the sandbox.moveFile(sourcePath: string, destinationPath: string, options?: { stream?: boolean }): Move a file from one location to another in the sandbox.ping(): Ping the sandbox.
Sandboxes are still experimental. We're using them to explore how isolated, container-like workloads might scale on Cloudflare — and to help define the developer experience around them.
You can try it today from your Worker, with just a few lines of code. Let us know what you build.
In AutoRAG, you can now view your object's custom metadata in the response from
/searchand/ai-search, and optionally add acontextfield in the custom metadata of an object to provide additional guidance for AI-generated answers.You can add custom metadata to an object when uploading it to your R2 bucket.
When you run a search, AutoRAG now returns any custom metadata associated with the object. This metadata appears in the response inside
attributesthenfile, and can be used for downstream processing.For example, the
attributessection of your search response may look like:{"attributes": {"timestamp": 1750001460000,"folder": "docs/","filename": "launch-checklist.md","file": {"url": "https://wiki.company.com/docs/launch-checklist","context": "A checklist for internal launch readiness, including legal, engineering, and marketing steps."}}}When you include a custom metadata field named
context, AutoRAG attaches that value to each chunk of the file. When you run an/ai-searchquery, thiscontextis passed to the LLM and can be used as additional input when generating an answer.We recommend using the
contextfield to describe supplemental information you want the LLM to consider, such as a summary of the document or a source URL. If you have several different metadata attributes, you can join them together however you choose within thecontextstring.For example:
{"context": "summary: 'Checklist for internal product launch readiness, including legal, engineering, and marketing steps.'; url: 'https://wiki.company.com/docs/launch-checklist'"}This gives you more control over how your content is interpreted, without requiring you to modify the original contents of the file.
Learn more in AutoRAG's metadata filtering documentation.