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  1. Agents SDK v0.6.0: RPC transport for MCP, optional OAuth, hardened schema conversion, and @cloudflare/ai-chat fixes

    Agents Workers

    The latest release of the Agents SDK ↗ lets you define an Agent and an McpAgent in the same Worker and connect them over RPC — no HTTP, no network overhead. It also makes OAuth opt-in for simple MCP connections, hardens the schema converter for production workloads, and ships a batch of @cloudflare/ai-chat reliability fixes.

    RPC transport for MCP

    You can now connect an Agent to an McpAgent in the same Worker using a Durable Object binding instead of an HTTP URL. The connection stays entirely within the Cloudflare runtime — no network round-trips, no serialization overhead.

    Pass the Durable Object namespace directly to addMcpServer:

    JavaScript
    import { Agent } from "agents";
    

    export class MyAgent extends Agent {
      async onStart() {
        // Connect via DO binding — no HTTP, no network overhead
        await this.addMcpServer("counter", env.MY_MCP);
    

        // With props for per-user context
        await this.addMcpServer("counter", env.MY_MCP, {
          props: { userId: "user-123", role: "admin" },
        });
      }
    }

    The addMcpServer method now accepts string | DurableObjectNamespace as the second parameter with full TypeScript overloads, so HTTP and RPC paths are type-safe and cannot be mixed.

    Key capabilities:

    • Hibernation support — RPC connections survive Durable Object hibernation automatically. The binding name and props are persisted to storage and restored on wake-up, matching the behavior of HTTP MCP connections.
    • Deduplication — Calling addMcpServer with the same server name returns the existing connection instead of creating duplicates. Connection IDs are stable across hibernation restore.
    • Smaller surface area — The RPC transport internals have been rewritten and reduced from 609 lines to 245 lines. RPCServerTransport now uses JSONRPCMessageSchema from the MCP SDK for validation instead of hand-written checks.

    Optional OAuth for MCP connections

    addMcpServer() no longer eagerly creates an OAuth provider for every connection. For servers that do not require authentication, a simple call is all you need:

    JavaScript
    // No callbackHost, no OAuth config — just works
    await this.addMcpServer("my-server", "https://mcp.example.com");

    If the server responds with a 401, the SDK throws a clear error: "This MCP server requires OAuth authentication. Provide callbackHost in addMcpServer options to enable the OAuth flow." The restore-from-storage flow also handles missing callback URLs gracefully, skipping auth provider creation for non-OAuth servers.

    Hardened JSON Schema to TypeScript converter

    The schema converter used by generateTypes() and getAITools() now handles edge cases that previously caused crashes in production:

    • Depth and circular reference guards — Prevents stack overflows on recursive or deeply nested schemas
    • $ref resolution — Supports internal JSON Pointers (#/definitions/..., #/$defs/..., #)
    • Tuple support — prefixItems (JSON Schema 2020-12) and array items (draft-07)
    • OpenAPI 3.0 nullable: true — Supported across all schema branches
    • Per-tool error isolation — One malformed schema cannot crash the full pipeline in generateTypes() or getAITools()
    • Missing inputSchema fallback — getAITools() falls back to { type: "object" } instead of throwing

    @cloudflare/ai-chat fixes

    • Tool denial flow — Denied tool approvals (approved: false) now transition to output-denied with a tool_result, fixing Anthropic provider compatibility. Custom denial messages are supported via state: "output-error" and errorText.
    • Abort/cancel support — Streaming responses now properly cancel the reader loop when the abort signal fires and send a done signal to the client.
    • Duplicate message persistence — persistMessages() now reconciles assistant messages by content and order, preventing duplicate rows when clients resend full history.
    • requestId in OnChatMessageOptions — Handlers can now send properly-tagged error responses for pre-stream failures.
    • redacted_thinking preservation — The message sanitizer no longer strips Anthropic redacted_thinking blocks.
    • /get-messages reliability — Endpoint handling moved from a prototype onRequest() override to a constructor wrapper, so it works even when users override onRequest without calling super.onRequest().
    • Client tool APIs undeprecated — createToolsFromClientSchemas, clientTools, AITool, extractClientToolSchemas, and the tools option on useAgentChat are restored for SDK use cases where tools are defined dynamically at runtime.
    • jsonSchema initialization — Fixed jsonSchema not initialized error when calling getAITools() in onChatMessage.

    Upgrade

    To update to the latest version:

    Terminal window
    npm i agents@latest @cloudflare/ai-chat@latest

  1. Backup and restore API for Sandbox SDK

    Agents R2 Containers

    Sandboxes now support createBackup() and restoreBackup() methods for creating and restoring point-in-time snapshots of directories.

    This allows you to restore environments quickly. For instance, in order to develop in a sandbox, you may need to include a user's codebase and run a build step. Unfortunately git clone and npm install can take minutes, and you don't want to run these steps every time the user starts their sandbox.

    Now, after the initial setup, you can just call createBackup(), then restoreBackup() the next time this environment is needed. This makes it practical to pick up exactly where a user left off, even after days of inactivity, without repeating expensive setup steps.

    TypeScript
    const sandbox = getSandbox(env.Sandbox, "my-sandbox");
    

    // Make non-trivial changes to the file system
    await sandbox.gitCheckout(endUserRepo, { targetDir: "/workspace" });
    await sandbox.exec("npm install", { cwd: "/workspace" });
    

    // Create a point-in-time backup of the directory
    const backup = await sandbox.createBackup({ dir: "/workspace" });
    

    // Store the handle for later use
    await env.KV.put(`backup:${userId}`, JSON.stringify(backup));
    

    // ... in a future session...
    

    // Restore instead of re-cloning and reinstalling
    await sandbox.restoreBackup(backup);

    Backups are stored in R2 and can take advantage of R2 object lifecycle rules to ensure they do not persist forever.

    Key capabilities:

    • Persist and reuse across sandbox sessions — Easily store backup handles in KV, D1, or Durable Object storage for use in subsequent sessions
    • Usable across multiple instances — Fork a backup across many sandboxes for parallel work
    • Named backups — Provide optional human-readable labels for easier management
    • TTLs — Set time-to-live durations so backups are automatically removed from storage once they are no longer neeeded

    To get started, refer to the backup and restore guide for setup instructions and usage patterns, or the Backups API reference for full method documentation.

  1. @cloudflare/codemode v0.1.0: a new runtime agnostic modular architecture

    Agents Workers

    The @cloudflare/codemode ↗ package has been rewritten into a modular, runtime-agnostic SDK.

    Code Mode ↗ enables LLMs to write and execute code that orchestrates your tools, instead of calling them one at a time. This can (and does) yield significant token savings, reduces context window pressure and improves overall model performance on a task.

    The new Executor interface is runtime agnostic and comes with a prebuilt DynamicWorkerExecutor to run generated code in a Dynamic Worker Loader.

    Breaking changes

    • Removed experimental_codemode() and CodeModeProxy — the package no longer owns an LLM call or model choice
    • New import path: createCodeTool() is now exported from @cloudflare/codemode/ai

    New features

    • createCodeTool() — Returns a standard AI SDK Tool to use in your AI agents.
    • Executor interface — Minimal execute(code, fns) contract. Implement for any code sandboxing primitive or runtime.

    DynamicWorkerExecutor

    Runs code in a Dynamic Worker. It comes with the following features:

    • Network isolation — fetch() and connect() blocked by default (globalOutbound: null) when using DynamicWorkerExecutor
    • Console capture — console.log/warn/error captured and returned in ExecuteResult.logs
    • Execution timeout — Configurable via timeout option (default 30s)

    Usage

    JavaScript
    import { createCodeTool } from "@cloudflare/codemode/ai";
    import { DynamicWorkerExecutor } from "@cloudflare/codemode";
    import { streamText } from "ai";
    

    const executor = new DynamicWorkerExecutor({ loader: env.LOADER });
    const codemode = createCodeTool({ tools: myTools, executor });
    

    const result = streamText({
      model,
      tools: { codemode },
      messages,
    });

    Wrangler configuration

    {
      "worker_loaders": [{ "binding": "LOADER" }],
    }

    See the Code Mode documentation for full API reference and examples.

    Upgrade

    Terminal window
    npm i @cloudflare/codemode@latest

  1. AI dashboard experience improvements

    AI Gateway Workers AI

    Workers AI and AI Gateway have received a series of dashboard improvements to help you get started faster and manage your AI workloads more easily.

    Navigation and discoverability

    AI now has its own top-level section in the Cloudflare dashboard sidebar, so you can find AI features without digging through menus.

    AI sidebar navigation in the Cloudflare dashboard

    Onboarding and getting started

    Getting started with AI Gateway is now simpler. When you create your first gateway, we now show your gateway's OpenAI-compatible endpoint and step-by-step guidance to help you configure it. The Playground also includes helpful prompts, and usage pages have clear next steps if you have not made any requests yet.

    AI Gateway onboarding flow

    We've also combined the previously separate code example sections into one view with dropdown selectors for API type, provider, SDK, and authentication method so you can now customize the exact code snippet you need from one place.

    Dynamic Routing

    • The route builder is now more performant and responsive.
    • You can now copy route names to your clipboard with a single click.
    • Code examples use the Universal Endpoint format, making it easier to integrate routes into your application.

    Observability and analytics

    • Small monetary values now display correctly in cost analytics charts, so you can accurately track spending at any scale.

    Accessibility

    • Improvements to keyboard navigation within the AI Gateway, specifically when exploring usage by provider.
    • Improvements to sorting and filtering components on the Workers AI models page.

    For more information, refer to the AI Gateway documentation.

  1. Agents SDK v0.5.0: Protocol message control, retry utilities, data parts, and @cloudflare/ai-chat v0.1.0

    Agents Workers

    The latest release of the Agents SDK ↗ adds built-in retry utilities, per-connection protocol message control, and a fully rewritten @cloudflare/ai-chat with data parts, tool approval persistence, and zero breaking changes.

    Retry utilities

    A new this.retry() method lets you retry any async operation with exponential backoff and jitter. You can pass an optional shouldRetry predicate to bail early on non-retryable errors.

    JavaScript
    class MyAgent extends Agent {
      async onRequest(request) {
        const data = await this.retry(() => callUnreliableService(), {
          maxAttempts: 4,
          shouldRetry: (err) => !(err instanceof PermanentError),
        });
        return Response.json(data);
      }
    }

    Retry options are also available per-task on queue(), schedule(), scheduleEvery(), and addMcpServer():

    JavaScript
    // Per-task retry configuration, persisted in SQLite alongside the task
    await this.schedule(
      Date.now() + 60_000,
      "sendReport",
      { userId: "abc" },
      {
        retry: { maxAttempts: 5 },
      },
    );
    

    // Class-level retry defaults
    class MyAgent extends Agent {
      static options = {
        retry: { maxAttempts: 3 },
      };
    }

    Retry options are validated eagerly at enqueue/schedule time, and invalid values throw immediately. Internal retries have also been added for workflow operations (terminateWorkflow, pauseWorkflow, and others) with Durable Object-aware error detection.

    Per-connection protocol message control

    Agents automatically send JSON text frames (identity, state, MCP server lists) to every WebSocket connection. You can now suppress these per-connection for clients that cannot handle them — binary-only devices, MQTT clients, or lightweight embedded systems.

    JavaScript
    class MyAgent extends Agent {
      shouldSendProtocolMessages(connection, ctx) {
        // Suppress protocol messages for MQTT clients
        const subprotocol = ctx.request.headers.get("Sec-WebSocket-Protocol");
        return subprotocol !== "mqtt";
      }
    }

    Connections with protocol messages disabled still fully participate in RPC and regular messaging. Use isConnectionProtocolEnabled(connection) to check a connection's status at any time. The flag persists across Durable Object hibernation.

    See Protocol messages for full documentation.

    @cloudflare/ai-chat v0.1.0

    The first stable release of @cloudflare/ai-chat ships alongside this release with a major refactor of AIChatAgent internals — new ResumableStream class, WebSocket ChatTransport, and simplified SSE parsing — with zero breaking changes. Existing code using AIChatAgent and useAgentChat works as-is.

    Key new features:

    • Data parts — Attach typed JSON blobs (data-*) to messages alongside text. Supports reconciliation (type+id updates in-place), append, and transient parts (ephemeral via onData callback). See Data parts.
    • Tool approval persistence — The needsApproval approval UI now survives page refresh and DO hibernation. The streaming message is persisted to SQLite when a tool enters approval-requested state.
    • maxPersistedMessages — Cap SQLite message storage with automatic oldest-message deletion.
    • body option on useAgentChat — Send custom data with every request (static or dynamic).
    • Incremental persistence — Hash-based cache to skip redundant SQL writes.
    • Row size guard — Automatic two-pass compaction when messages approach the SQLite 2 MB limit.
    • autoContinueAfterToolResult defaults to true — Client-side tool results and tool approvals now automatically trigger a server continuation, matching server-executed tool behavior. Set autoContinueAfterToolResult: false in useAgentChat to restore the previous behavior.

    Notable bug fixes:

    • Resolved stream resumption race conditions
    • Resolved an issue where setMessages functional updater sent empty arrays
    • Resolved an issue where client tool schemas were lost after DO hibernation
    • Resolved InvalidPromptError after tool approval (approval.id was dropped)
    • Resolved an issue where message metadata was not propagated on broadcast/resume paths
    • Resolved an issue where clearAll() did not clear in-memory chunk buffers
    • Resolved an issue where reasoning-delta silently dropped data when reasoning-start was missed during stream resumption

    Synchronous queue and schedule getters

    getQueue(), getQueues(), getSchedule(), dequeue(), dequeueAll(), and dequeueAllByCallback() were unnecessarily async despite only performing synchronous SQL operations. They now return values directly instead of wrapping them in Promises. This is backward compatible — existing code using await on these methods will continue to work.

    Other improvements

    • Fix TypeScript "excessively deep" error — A depth counter on CanSerialize and IsSerializableParam types bails out to true after 10 levels of recursion, preventing the "Type instantiation is excessively deep" error with deeply nested types like AI SDK CoreMessage[].
    • POST SSE keepalive — The POST SSE handler now sends event: ping every 30 seconds to keep the connection alive, matching the existing GET SSE handler behavior. This prevents POST response streams from being silently dropped by proxies during long-running tool calls.
    • Widened peer dependency ranges — Peer dependency ranges across packages have been widened to prevent cascading major bumps during 0.x minor releases. @cloudflare/ai-chat and @cloudflare/codemode are now marked as optional peer dependencies.

    Upgrade

    To update to the latest version:

    Terminal window
    npm i agents@latest @cloudflare/ai-chat@latest

  1. Introducing GLM-4.7-Flash on Workers AI, @cloudflare/tanstack-ai, and workers-ai-provider v3.1.1

    Workers Agents Workers AI

    We're excited to announce GLM-4.7-Flash on Workers AI, a fast and efficient text generation model optimized for multilingual dialogue and instruction-following tasks, along with the brand-new @cloudflare/tanstack-ai ↗ package and workers-ai-provider v3.1.1 ↗.

    You can now run AI agents entirely on Cloudflare. With GLM-4.7-Flash's multi-turn tool calling support, plus full compatibility with TanStack AI and the Vercel AI SDK, you have everything you need to build agentic applications that run completely at the edge.

    GLM-4.7-Flash — Multilingual Text Generation Model

    @cf/zai-org/glm-4.7-flash is a multilingual model with a 131,072 token context window, making it ideal for long-form content generation, complex reasoning tasks, and multilingual applications.

    Key Features and Use Cases:

    • Multi-turn Tool Calling for Agents: Build AI agents that can call functions and tools across multiple conversation turns
    • Multilingual Support: Built to handle content generation in multiple languages effectively
    • Large Context Window: 131,072 tokens for long-form writing, complex reasoning, and processing long documents
    • Fast Inference: Optimized for low-latency responses in chatbots and virtual assistants
    • Instruction Following: Excellent at following complex instructions for code generation and structured tasks

    Use GLM-4.7-Flash through the Workers AI binding (env.AI.run()), the REST API at /run or /v1/chat/completions, AI Gateway, or via workers-ai-provider for the Vercel AI SDK.

    Pricing is available on the model page or pricing page.

    @cloudflare/tanstack-ai v0.1.1 — TanStack AI adapters for Workers AI and AI Gateway

    We've released @cloudflare/tanstack-ai, a new package that brings Workers AI and AI Gateway support to TanStack AI ↗. This provides a framework-agnostic alternative for developers who prefer TanStack's approach to building AI applications.

    Workers AI adapters support four configuration modes — plain binding (env.AI), plain REST, AI Gateway binding (env.AI.gateway(id)), and AI Gateway REST — across all capabilities:

    • Chat (createWorkersAiChat) — Streaming chat completions with tool calling, structured output, and reasoning text streaming.
    • Image generation (createWorkersAiImage) — Text-to-image models.
    • Transcription (createWorkersAiTranscription) — Speech-to-text.
    • Text-to-speech (createWorkersAiTts) — Audio generation.
    • Summarization (createWorkersAiSummarize) — Text summarization.

    AI Gateway adapters route requests from third-party providers — OpenAI, Anthropic, Gemini, Grok, and OpenRouter — through Cloudflare AI Gateway for caching, rate limiting, and unified billing.

    To get started:

    Terminal window
    npm install @cloudflare/tanstack-ai @tanstack/ai

    workers-ai-provider v3.1.1 — transcription, speech, reranking, and reliability

    The Workers AI provider for the Vercel AI SDK ↗ now supports three new capabilities beyond chat and image generation:

    • Transcription (provider.transcription(model)) — Speech-to-text with automatic handling of model-specific input formats across binding and REST paths.
    • Text-to-speech (provider.speech(model)) — Audio generation with support for voice and speed options.
    • Reranking (provider.reranking(model)) — Document reranking for RAG pipelines and search result ordering.
    TypeScript
    import { createWorkersAI } from "workers-ai-provider";
    import {
      experimental_transcribe,
      experimental_generateSpeech,
      rerank,
    } from "ai";
    

    const workersai = createWorkersAI({ binding: env.AI });
    

    const transcript = await experimental_transcribe({
      model: workersai.transcription("@cf/openai/whisper-large-v3-turbo"),
      audio: audioData,
      mediaType: "audio/wav",
    });
    

    const speech = await experimental_generateSpeech({
      model: workersai.speech("@cf/deepgram/aura-1"),
      text: "Hello world",
      voice: "asteria",
    });
    

    const ranked = await rerank({
      model: workersai.reranking("@cf/baai/bge-reranker-base"),
      query: "What is machine learning?",
      documents: ["ML is a branch of AI.", "The weather is sunny."],
    });

    This release also includes a comprehensive reliability overhaul (v3.0.5):

    • Fixed streaming — Responses now stream token-by-token instead of buffering all chunks, using a proper TransformStream pipeline with backpressure.
    • Fixed tool calling — Resolved issues with tool call ID sanitization, conversation history preservation, and a heuristic that silently fell back to non-streaming mode when tools were defined.
    • Premature stream termination detection — Streams that end unexpectedly now report finishReason: "error" instead of silently reporting "stop".
    • AI Search support — Added createAISearch as the canonical export (renamed from AutoRAG). createAutoRAG still works with a deprecation warning.

    To upgrade:

    Terminal window
    npm install workers-ai-provider@latest ai

    Resources

  1. Analytics enhancements

    AI Crawl Control

    AI Crawl Control metrics have been enhanced with new views, improved filtering, and better data visualization.

    AI Crawl Control path patterns

    Path pattern grouping

    • In the Metrics tab > Most popular paths table, use the new Patterns tab that groups requests by URI pattern (/blog/*, /api/v1/*, /docs/*) to identify which site areas crawlers target most. Refer to the screenshot above.

    Enhanced referral analytics

    • Destination patterns show which site areas receive AI-driven referral traffic.
    • In the Metrics tab, a new Referrals over time chart shows trends by operator or source.

    Data transfer metrics

    • In the Metrics tab > Allowed requests over time chart, toggle Bytes to show bandwidth consumption.
    • In the Crawlers tab, a new Bytes Transferred column shows bandwidth per crawler.

    Image exports

    • Export charts and tables as images for reports and presentations.

    Learn more about analyzing AI traffic.

  1. Agents SDK v0.4.0: Readonly connections, MCP security improvements, x402 v2 migration, and custom MCP OAuth providers

    Agents Workers

    The latest release of the Agents SDK ↗ brings readonly connections, MCP protocol and security improvements, x402 payment protocol v2 migration, and the ability to customize OAuth for MCP server connections.

    Readonly connections

    Agents can now restrict WebSocket clients to read-only access, preventing them from modifying agent state. This is useful for dashboards, spectator views, or any scenario where clients should observe but not mutate.

    New hooks: shouldConnectionBeReadonly, setConnectionReadonly, isConnectionReadonly. Readonly connections block both client-side setState() and mutating @callable() methods, and the readonly flag survives hibernation.

    JavaScript
    class MyAgent extends Agent {
      shouldConnectionBeReadonly(connection) {
        // Make spectators readonly
        return connection.url.includes("spectator");
      }
    }

    Custom MCP OAuth providers

    The new createMcpOAuthProvider method on the Agent class allows subclasses to override the default OAuth provider used when connecting to MCP servers. This enables custom authentication strategies such as pre-registered client credentials or mTLS, beyond the built-in dynamic client registration.

    JavaScript
    class MyAgent extends Agent {
      createMcpOAuthProvider(callbackUrl) {
        return new MyCustomOAuthProvider(this.ctx.storage, this.name, callbackUrl);
      }
    }

    MCP SDK upgrade to 1.26.0

    Upgraded the MCP SDK to 1.26.0 to prevent cross-client response leakage. Stateless MCP Servers should now create a new McpServer instance per request instead of sharing a single instance. A guard is added in this version of the MCP SDK which will prevent connection to a Server instance that has already been connected to a transport. Developers will need to modify their code if they declare their McpServer instance as a global variable.

    MCP OAuth callback URL security fix

    Added callbackPath option to addMcpServer to prevent instance name leakage in MCP OAuth callback URLs. When sendIdentityOnConnect is false, callbackPath is now required — the default callback URL would expose the instance name, undermining the security intent. Also fixes callback request detection to match via the state parameter instead of a loose /callback URL substring check, enabling custom callback paths.

    Deprecate onStateUpdate in favor of onStateChanged

    onStateChanged is a drop-in rename of onStateUpdate (same signature, same behavior). onStateUpdate still works but emits a one-time console warning per class. validateStateChange rejections now propagate a CF_AGENT_STATE_ERROR message back to the client.

    x402 v2 migration

    Migrated the x402 MCP payment integration from the legacy x402 package to @x402/core and @x402/evm v2.

    Breaking changes for x402 users:

    • Peer dependencies changed: replace x402 with @x402/core and @x402/evm
    • PaymentRequirements type now uses v2 fields (e.g. amount instead of maxAmountRequired)
    • X402ClientConfig.account type changed from viem.Account to ClientEvmSigner (structurally compatible with privateKeyToAccount())
    Terminal window
    npm uninstall x402
    npm install @x402/core @x402/evm

    Network identifiers now accept both legacy names and CAIP-2 format:

    TypeScript
    // Legacy name (auto-converted)
    {
      network: "base-sepolia",
    }
    

    // CAIP-2 format (preferred)
    {
      network: "eip155:84532",
    }

    Other x402 changes:

    • X402ClientConfig.network is now optional — the client auto-selects from available payment requirements
    • Server-side lazy initialization: facilitator connection is deferred until the first paid tool invocation
    • Payment tokens support both v2 (PAYMENT-SIGNATURE) and v1 (X-PAYMENT) HTTP headers
    • Added normalizeNetwork export for converting legacy network names to CAIP-2 format
    • Re-exports PaymentRequirements, PaymentRequired, Network, FacilitatorConfig, and ClientEvmSigner from agents/x402

    Other improvements

    • Fix useAgent and AgentClient crashing when using basePath routing
    • CORS handling delegated to partyserver's native support (simpler, more reliable)
    • Client-side onStateUpdateError callback for handling rejected state updates

    Upgrade

    To update to the latest version:

    Terminal window
    npm i agents@latest

  1. Interactive browser terminals in Sandboxes

    Agents

    The Sandbox SDK ↗ now supports PTY (pseudo-terminal) passthrough, enabling browser-based terminal UIs to connect to sandbox shells via WebSocket.

    sandbox.terminal(request)

    The new terminal() method proxies a WebSocket upgrade to the container's PTY endpoint, with output buffering for replay on reconnect.

    JavaScript
    // Worker: proxy WebSocket to container terminal
    return sandbox.terminal(request, { cols: 80, rows: 24 });

    Multiple terminals per sandbox

    Each session can have its own terminal with an isolated working directory and environment, so users can run separate shells side-by-side in the same container.

    JavaScript
    // Multiple isolated terminals in the same sandbox
    const dev = await sandbox.getSession("dev");
    return dev.terminal(request);

    xterm.js addon

    The new @cloudflare/sandbox/xterm export provides a SandboxAddon for xterm.js ↗ with automatic reconnection (exponential backoff + jitter), buffered output replay, and resize forwarding.

    JavaScript
    import { SandboxAddon } from "@cloudflare/sandbox/xterm";
    

    const addon = new SandboxAddon({
      getWebSocketUrl: ({ sandboxId, origin }) =>
        `${origin}/ws/terminal?id=${sandboxId}`,
      onStateChange: (state, error) => updateUI(state),
    });
    terminal.loadAddon(addon);
    addon.connect({ sandboxId: "my-sandbox" });

    Upgrade

    To update to the latest version:

    Terminal window
    npm i @cloudflare/sandbox@latest

  1. AI Search now with more granular controls over indexing

    AI Search

    Get your content updates into AI Search faster and avoid a full rescan when you do not need it.

    Reindex individual files without a full sync

    Updated a file or need to retry one that errored? When you know exactly which file changed, you can now reindex it directly instead of rescanning your entire data source.

    Go to Overview > Indexed Items and select the sync icon next to any file to reindex it immediately.

    Sync individual files from Indexed Items

    Crawl only the sitemap you need

    By default, AI Search crawls all sitemaps listed in your robots.txt, up to the maximum files per index limit. If your site has multiple sitemaps but you only want to index a specific set, you can now specify a single sitemap URL to limit what the crawler visits.

    For example, if your robots.txt lists both blog-sitemap.xml and docs-sitemap.xml, you can specify just https://example.com/docs-sitemap.xml to index only your documentation.

    Configure your selection anytime in Settings > Parsing options > Specific sitemaps, then trigger a sync to apply the changes.

    Specify a sitemap in Parsinh options

    Learn more about indexing controls and website crawling configuration.

  1. New reference documentation

    AI Crawl Control

    New reference documentation is now available for AI Crawl Control:

    • GraphQL API reference — Query examples for crawler requests, top paths, referral traffic, and data transfer. Includes key filters for detection IDs, user agents, and referrer domains.
    • Bot reference — Detection IDs and user agents for major AI crawlers from OpenAI, Anthropic, Google, Meta, and others.
    • Worker templates — Deploy the x402 Payment-Gated Proxy to monetize crawler access or charge bots while letting humans through free.

  1. Agents SDK v0.3.7: Workflows integration, synchronous state, and scheduleEvery()

    Agents Workflows

    The latest release of the Agents SDK ↗ brings first-class support for Cloudflare Workflows, synchronous state management, and new scheduling capabilities.

    Cloudflare Workflows integration

    Agents excel at real-time communication and state management. Workflows excel at durable execution. Together, they enable powerful patterns where Agents handle WebSocket connections while Workflows handle long-running tasks, retries, and human-in-the-loop flows.

    Use the new AgentWorkflow class to define workflows with typed access to your Agent:

    JavaScript
    import { AgentWorkflow } from "agents/workflows";
    export class ProcessingWorkflow extends AgentWorkflow {
      async run(event, step) {
        // Call Agent methods via RPC
        await this.agent.updateStatus(event.payload.taskId, "processing");
    

        // Non-durable: progress reporting to clients
        await this.reportProgress({ step: "process", percent: 0.5 });
        this.broadcastToClients({ type: "update", taskId: event.payload.taskId });
    

        // Durable via step: idempotent, won't repeat on retry
        await step.mergeAgentState({ taskProgress: 0.5 });
    

        const result = await step.do("process", async () => {
          return processData(event.payload.data);
        });
    

        await step.reportComplete(result);
        return result;
      }
    }

    Start workflows from your Agent with runWorkflow() and handle lifecycle events:

    JavaScript
    export class MyAgent extends Agent {
      async startTask(taskId, data) {
        const instanceId = await this.runWorkflow("PROCESSING_WORKFLOW", {
          taskId,
          data,
        });
        return { instanceId };
      }
    

      async onWorkflowProgress(workflowName, instanceId, progress) {
        this.broadcast(JSON.stringify({ type: "progress", progress }));
      }
    

      async onWorkflowComplete(workflowName, instanceId, result) {
        console.log(`Workflow ${instanceId} completed`);
      }
    

      async onWorkflowError(workflowName, instanceId, error) {
        console.error(`Workflow ${instanceId} failed:`, error);
      }
    }

    Key workflow methods on your Agent:

    • runWorkflow(workflowName, params, options?) — Start a workflow with optional metadata
    • getWorkflow(workflowId) / getWorkflows(criteria?) — Query workflows with cursor-based pagination
    • approveWorkflow(workflowId) / rejectWorkflow(workflowId) — Human-in-the-loop approval flows
    • pauseWorkflow(), resumeWorkflow(), terminateWorkflow() — Workflow control

    Synchronous setState()

    State updates are now synchronous with a new validateStateChange() validation hook:

    JavaScript
    export class MyAgent extends Agent {
      validateStateChange(oldState, newState) {
        // Return false to reject the change
        if (newState.count < 0) return false;
        // Return modified state to transform
        return { ...newState, lastUpdated: Date.now() };
      }
    }

    scheduleEvery() for recurring tasks

    The new scheduleEvery() method enables fixed-interval recurring tasks with built-in overlap prevention:

    JavaScript
    // Run every 5 minutes
    await this.scheduleEvery("syncData", 5 * 60 * 1000, { source: "api" });

    Callable system improvements

    • Client-side RPC timeout — Set timeouts on callable method invocations
    • StreamingResponse.error(message) — Graceful stream error signaling
    • getCallableMethods() — Introspection API for discovering callable methods
    • Connection close handling — Pending calls are automatically rejected on disconnect
    JavaScript
    await agent.call("method", [args], {
      timeout: 5000,
      stream: { onChunk, onDone, onError },
    });

    Email and routing enhancements

    Secure email reply routing — Email replies are now secured with HMAC-SHA256 signed headers, preventing unauthorized routing of emails to agent instances.

    Routing improvements:

    • basePath option to bypass default URL construction for custom routing
    • Server-sent identity — Agents send name and agent type on connect
    • New onIdentity and onIdentityChange callbacks on the client
    JavaScript
    const agent = useAgent({
      basePath: "user",
      onIdentity: (name, agentType) => console.log(`Connected to ${name}`),
    });

    Upgrade

    To update to the latest version:

    Terminal window
    npm i agents@latest

    For the complete Workflows API reference and patterns, see Run Workflows.

  1. Launching FLUX.2 [klein] 9B on Workers AI

    Workers AI

    We have partnered with Black Forest Labs (BFL) again to bring their optimized FLUX.2 [klein] 9B model to Workers AI. This distilled model offers enhanced quality compared to the 4B variant, while maintaining cost-effective pricing. With a fixed 4-step inference process, Klein 9B is ideal for rapid prototyping and real-time applications where both speed and quality matter.

    Read the BFL blog ↗ to learn more about the model itself, or try it out yourself on our multi modal playground ↗.

    Pricing documentation is available on the model page or pricing page.

    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] and FLUX.2 [klein] 4B, 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:

    Terminal window
    curl --request POST \
      --url 'https://api.cloudflare.com/client/v4/accounts/{ACCOUNT}/ai/run/@cf/black-forest-labs/flux-2-klein-9b' \
      --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
    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-9b", {
      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.

    Multi-reference images

    The FLUX.2 klein-9b model supports generating images based on reference images, just like FLUX.2 [dev] and FLUX.2 [klein] 4B. 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.

    You must 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.

    Terminal window
    curl --request POST \
      --url 'https://api.cloudflare.com/client/v4/accounts/{ACCOUNT}/ai/run/@cf/black-forest-labs/flux-2-klein-9b' \
      --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
    //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-9b", {
        multipart: {
            body: formStream,
            contentType: formContentType
        }
    })

  1. Vectorize indexes now support up to 10 million vectors

    Vectorize

    You can now store up to 10 million vectors in a single Vectorize index, doubling the previous limit of 5 million vectors. This enables larger-scale semantic search, recommendation systems, and retrieval-augmented generation (RAG) applications without splitting data across multiple indexes.

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

  1. AI Search path filtering for website and R2 data sources

    AI Search

    AI Search now includes path filtering for both website and R2 data sources. You can now control which content gets indexed by defining include and exclude rules for paths.

    By controlling what gets indexed, you can improve the relevance and quality of your search results. You can also use path filtering to split a single data source across multiple AI Search instances for specialized search experiences.

    Path filtering configuration in AI Search

    Path filtering uses micromatch ↗ patterns, so you can use * to match within a directory and ** to match across directories.

    Use caseIncludeExclude
    Index docs but skip drafts**/docs/****/docs/drafts/**
    Keep admin pages out of results—**/admin/**
    Index only English content**/en/**—

    Configure path filters when creating a new instance or update them anytime from Settings. Check out path filtering to learn more.

  1. Create AI Search instances programmatically via REST API

    AI Search

    You can now create AI Search instances programmatically using the API. For example, use the API to create instances for each customer in a multi-tenant application or manage AI Search alongside your other infrastructure.

    If you have created an AI Search instance via the dashboard before, you already have a service API token registered and can start creating instances programmatically right away. If not, follow the API guide to set up your first instance.

    For example, you can now create separate search instances for each language on your website:

    Terminal window
    for lang in en fr es de; do
      curl -X POST "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/ai-search/instances" \
        -H "Authorization: Bearer $API_TOKEN" \
        -H "Content-Type: application/json" \
        --data '{
          "id": "docs-'"$lang"'",
          "type": "web-crawler",
          "source": "example.com",
          "source_params": {
            "path_include": ["**/'"$lang"'/**"]
          }
        }'
    done

    Refer to the REST API reference for additional configuration options.

  1. Launching FLUX.2 [klein] 4B on Workers AI

    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 ↗ to learn more about the model itself, or try it out yourself on our multi modal playground ↗.

    Pricing documentation is available on the model page or pricing page.

    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:

    Terminal window
    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
    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.

    ## 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
    //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
        }
    })

  1. AI Crawl Control Read Only role now available

    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.

  1. Agents SDK v0.3.0, workers-ai-provider v3.0.0, and ai-gateway-provider v3.0.0 with AI SDK v6 support

    Agents Workers

    We've shipped a new release for the Agents SDK ↗ v0.3.0 bringing full compatibility with AI SDK v6 ↗ 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
    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
    // 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
    // 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
    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
    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
    import { isStaticToolUIPart, getStaticToolName } from "ai";

    convertToModelMessages() is now async

    The convertToModelMessages() function is now asynchronous. Update all calls to await the result:

    TypeScript
    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
    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
    // 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
    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
    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
    // 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
    // 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
    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:

    DeprecatedReplacement
    AITool typeUse AI SDK's tool() function on server
    extractClientToolSchemas()Define tools on server, no client schemas needed
    createToolsFromClientSchemas()Define tools on server with tool()
    toolsRequiringConfirmation optionUse needsApproval on server tools
    experimental_automaticToolResolutionUse onToolCall callback
    tools option in useAgentChatUse 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:

    Terminal window
    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

    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?

  1. New AI Crawl Control Overview tab

    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

    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 and managing AI crawlers.

  1. Pay Per Crawl (Private beta) - Discovery API, custom pricing, and advanced configuration

    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 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.

    New crawler-error header

    Pay Per Crawl error responses now include a new crawler-error header with 11 specific 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 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 | Crawl pages

    Site owners: Advanced configuration

  1. Agents SDK v0.2.24 with resumable streaming, MCP improvements, and schedule fixes

    Agents Workers

    The latest release of @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 ↗ 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
    // 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 ↗ 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 convertion to UIMessageStreamPart

    Upgrade

    To update to the latest version:

    Terminal window
    npm i agents@latest

  1. Launching FLUX.2 [dev] on Workers AI

    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 ↗ to learn more about the model itself. Read our Cloudflare blog ↗ to see the model in action, or try it out yourself on our multi modal playground ↗.

    Pricing documentation is available on the model page or pricing page. 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:

    Terminal window
    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
    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
    ## 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
    //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
    {
      "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!

  1. AI Search support for crawling login protected website content

    AI Search

    AI Search now supports custom HTTP headers 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

    For example, to crawl a site protected by Cloudflare Access, you can add service token credentials as custom headers:

    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 in AI Search.

  1. Crawler drilldowns with extended actions menu

    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, Redirect Rules, 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

    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

    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.

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