Cloudflare AI Updates & Release Notes

When you connect a data source to your AI Search instance, AI Search runs sync jobs to keep your index up to date with your content. You can now manage those jobs directly from Wrangler.

For example, you can trigger a sync job from your CI/CD or automated pipelines with the jobs create command so your index refreshes when you push a change:

Terminal window
wrangler ai-search jobs create my-instance

This creates an asynchronous sync job that checks for changes in your data source, and sends new, modified, or deleted files to be indexed. The following commands are available:

Command Description wrangler ai-search jobs create Trigger a new sync job wrangler ai-search jobs list List sync jobs for an instance wrangler ai-search jobs get Get details for a job wrangler ai-search jobs cancel Cancel a running job wrangler ai-search jobs logs View log entries for a job

All commands accept --namespace / -n (defaults to default) and --json for structured output that automation and AI agents can parse directly. The list and logs commands also support --page and --per-page for pagination, and cancel prompts for confirmation unless you pass -y / --force.

For full usage details, refer to the AI Search Wrangler commands documentation.

Original source

We have greatly improved the throughput of the Vectorize write-ahead log (WAL). As a result, we have significantly reduced the end-to-end latency for a vector change to become queryable: median latency has dropped from 2 minutes to under 30 seconds, and p99 latency from 5 minutes to under 2 minutes.

This means inserts, upserts, and deletes are reflected in query results faster, improving the freshness of semantic search, recommendation, and retrieval-augmented generation (RAG) workloads. You do not need to change your code or configuration to benefit from this improvement.

For more information, refer to the Vectorize documentation.

Original source

Background sub-agents with progress and milestones

The latest release of the Agents SDK makes it easier to run long work in the background, drive turns through one entry point, and keep chat agents working through deploys, evictions, and reconnects.

This release adds first-class detached (background) sub-agent runs with live progress and durable milestones, a single runTurn turn-admission entry point, and a large round of recovery and reliability fixes that continue converging @cloudflare/think and @cloudflare/ai-chat onto one model.

runAgentTool can now dispatch a sub-agent without blocking the calling turn. A detached run returns a handle immediately and is owned by a durable, eviction-surviving backbone instead of being abandoned when the dispatching turn ends.

Highlights

• Durable, exactly-once-on-the-happy-path completion via a warm fast path plus a self-scheduling reconcile backbone that survives eviction and deploys.
• Bounded. An absolute maxBudgetMs ceiling (default 24h) and cancelAgentTool(runId) keep abandoned runs from holding a concurrency slot forever.
• detached: { notify: true } lets a finished background run inject a message back into the chat so the model reacts to the result — no hand-wired onFinish needed.

Sub-agents can also report mid-run progress that rides their own turn stream back to the parent's connected clients:

Progress surfaces on AgentToolRunState.progress via useAgentToolEvents, so a background-runs tray can render a live bar without drilling in, and the latest snapshot is persisted for inspection after eviction. Naming a milestone promotes a signal to a durable, replayable row, and detached: { onMilestones } can surface a milestone as a synthetic chat message ("narrate" for a cheap status line, or "react" to drive a model turn).

One entry point for turns: runTurn

@cloudflare/think adds a public runTurn(options) facade that unifies turn admission behind a single mode:

stream mode accepts array and function inputs to match wait mode, and all entry points now route through a shared internal admission path that throws a clear error on nested blocking admissions that previously could deadlock.

Recovery and reliability

A large part of this release continues hardening recovery and converging @cloudflare/think and @cloudflare/ai-chat onto one model:

• Stream stall watchdog. AIChatAgent can detect and recover from a hung model/transport stream via the opt-in chatStreamStallTimeoutMs watchdog. With chatRecovery enabled the stall routes into the same bounded-recovery machinery a deploy or eviction uses; otherwise it surfaces as a terminal stream error so the spinner clears.
• Interrupted tool-call repair. AIChatAgent now repairs a transcript with a dead server-tool call before re-entering inference (parity with @cloudflare/think), so a recovered turn no longer fails with AI_MissingToolResultsError. An overridable repairInterruptedToolPart(part) hook lets apps customize the repaired shape.
• Stuck status after reconnect. Fixed AI SDK status getting stuck when a reconnect races a turn that has been accepted but has not started streaming yet, so the UI now renders the in-flight turn instead of settling on ready.
• Live "recovering…" on connect. AIChatAgent now replays the recovering status to a client that connects mid-recovery, so useAgentChat's isRecovering reflects in-progress recovery immediately instead of appearing frozen.
• Terminal connection failures. The client stops reconnecting on terminal WebSocket close events and exposes them via connectionError / onConnectionError on AgentClient, useAgent, and useAgentChat.
• Agent-tool child recovery. A healthy long-running sub-agent run is no longer abandoned as interrupted after a deploy (both @cloudflare/think and AIChatAgent).
• Workflows from sub-agent facets. Agent Workflows can now start from sub-agent facets, with callbacks and Workflow RPC routed back to the originating facet.
• Plus forward-progress crediting convergence, broadcast-first give-up ordering, an event-driven auto-continuation barrier, and structured row-size compaction in AIChatAgent.

Other improvements

• Shared chat React core. A new agents/chat/react entry exposes useAgentChat, transport helpers, and shared wire types, with syncMessagesToServer for server-authoritative transcript storage. @cloudflare/think/react and @cloudflare/ai-chat/react are now thin wrappers over it.
• Optional ai peer. The root agents and @cloudflare/codemode runtimes no longer reference AI SDK types, so they bundle without ai / zod installed; AI-specific entry points still require the peer when imported. just-bash likewise moves to an optional peer used only by the skills bash runner.
• Code Mode. The default DynamicWorkerExecutor timeout increases from 30s to 60s, executions now dispose the dynamically-loaded Worker and its RPC stub after each run (fixing a flaky isolate-shutdown assertion), connector imports are cleaned up, and the outer MCP tool-call context is passed to openApiMcpServer request callbacks.
• Voice. Voice turns now support AI SDK fullStream responses (and warn when textStream is used).
• MCP. McpAgent server-to-client requests can now be sent from callbacks that do not inherit the agent's async context, including callbacks reached through Worker Loader RPC.
• Experimental: server actions and channels. This release lays groundwork for guarded server actions (action() / getActions() with a durable replay ledger and approvals) and a unified channels surface (configureChannels(), deliverNotice()). Both are experimental and their APIs may change, so we don't recommend depending on them yet.

Upgrade

To update to the latest version:

npm i agents@latest @cloudflare/think@latest @cloudflare/ai-chat@latest @cloudflare/codemode@latest @cloudflare/voice@latest

Refer to the Think documentation, Code Mode documentation, and Agents documentation for more information.

Original source

Cache duration now defaults to 48 hours

AI Search now gives you more control over similarity cache freshness. Similarity cache helps reduce latency and inference cost by reusing responses for semantically similar queries. With these updates, you can choose how long responses are eligible for reuse and clear cached responses when they may be stale.

Previously, AI Search cached responses for a fixed duration of 30 days. Cached responses now use the instance's cache_ttl setting, and the default is 48 hours.

You can set cache_ttl when creating or updating an instance to choose a cache duration from 10 minutes to 6 days.

Use a shorter TTL when your source content changes frequently and freshness is more important. Use a longer TTL when your content is stable and you want more cache reuse.

For example, set cache_ttl to 518400 to retain cached responses for 6 days:

{
  "cache_ttl" : 518400
}

Purge cached responses

You can also purge all cached responses for an instance on demand. Purging cached responses does not delete indexed content or source files.

It prevents AI Search from reusing previous cached responses, so subsequent similar queries generate fresh answers and repopulate the cache.

Terminal window

curl -X POST "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/ai-search/instances/$INSTANCE_NAME/purge_cache" \
  -H "Authorization: Bearer $CLOUDFLARE_API_TOKEN"

You can also purge cached responses from the instance settings page in the Cloudflare dashboard.

Refer to similarity cache for the full list of supported cache_ttl values and more details about cache behavior.

Original source

The latest release of the Agents SDK makes it easier to build agents that can safely interact with real systems and keep working through interruptions.

Agents can now browse websites through Browser Run, write code against external tools through Codemode, use client-provided tools when delegating to Think sub-agents, and recover more reliably from deploys, Durable Object evictions, and connection churn.

Safer browser automation

Agents can now use Browser Run through a single durable browser_execute tool. Instead of choosing from a fixed list of actions, the model writes code against the Chrome DevTools Protocol (CDP) and can inspect pages, capture screenshots, read rendered content, debug frontend behavior, and interact with live browser sessions.

Browser sessions can be one-time, reused, or promoted from one-time to persistent during a run. This is useful when an agent needs a human to log in, complete MFA, or approve a sensitive action. The run can pause, keep the same tabs and cookies, and resume after approval.

The browser tools also add Live View URLs, optional session recording, and quick actions such as browser_markdown, browser_extract, browser_links, and browser_scrape for one-shot browsing tasks.

Resumable code execution with approvals

Codemode now uses createCodemodeRuntime, connectors, and a durable execution log. This lets you give a model one codemode tool instead of a large prompt full of tool definitions. The model can discover the capabilities it needs, write code against typed globals, and reuse saved snippets.

When the code reaches an approval-gated action, the runtime pauses execution and returns a pending approval. After approval, completed calls replay from the durable log, the approved action runs, and the same code continues. This makes it practical to build agents that create issues, update external systems, or perform other side effects without custom pause-and-resume logic for every tool.

Better Think delegation

Think sub-agents can now use client-defined tools over the RPC chat() path. A parent agent can pass tool schemas with clientTools and resolve tool calls through onClientToolCall. This lets delegated agents use caller-provided capabilities without requiring a browser WebSocket.

Think Workflows also improve step.prompt(). A prompt step now runs a full agentic turn before returning structured output, so the agent can call tools before producing the typed result. This makes Workflow steps more useful for durable triage, research, and approval flows.

The unified Think execute tool can also include cdp.* browser capabilities alongside state.* and tools.* when Browser Run is bound.

Voice output device selection

Voice clients can route assistant audio to a specific output device. Use outputDeviceId with useVoiceAgent, or call client.setOutputDevice() from the framework-agnostic client.

Browsers without speaker-selection support continue playing through the default output device and report a non-fatal outputDeviceError.

Reliability fixes

This release includes several fixes for production agents:

• useAgent and AgentClient handle WebSocket replacement more reliably during reconnects and configuration changes.
• Chat stream replay is more reliable after reconnects, deploys, and provider errors.
• Fiber recovery continues across multi-pass scans and backs off when recovery hooks keep failing.
• Agent teardown continues even when the request that started teardown is canceled.
• Large session histories use byte-budgeted reads to reduce memory pressure during startup.

Upgrade

To update to the latest version:

npm i agents@latest @cloudflare/think@latest @cloudflare/codemode@latest @cloudflare/ai-chat@latest @cloudflare/voice@latest

Refer to the Codemode documentation, Browser tools documentation, Think tools documentation, and Voice documentation for more information.

Original source

We are excited to announce GLM-5.2 on Workers AI, Z.ai's flagship agentic coding model.

@cf/zai-org/glm-5.2 is a text generation model built for agentic coding workflows. With function calling and reasoning support, it can handle long codebases, multi-step planning, and tool-augmented agents.

Key features and use cases:

• Agentic coding: Designed for autonomous coding tasks, long-horizon planning, and complex software engineering workflows
• Large context window: GLM-5.2 supports up to a 1,048,576 token context window. Workers AI is launching the model with a 262,144 token context window and plans to increase this in the future
• Function calling: Build agents that invoke tools and APIs across multiple conversation turns
• Reasoning: Tackles complex problem-solving and step-by-step reasoning tasks

Use GLM-5.2 through the Workers AI binding (env.AI.run()), the REST API at /run or /v1/chat/completions, or AI Gateway.

Pricing is available on the model page or pricing page.

Original source

AI Gateway logs now capture the user agent of the client that made each request, making it easier to identify which SDK, library, or application sent the traffic flowing through your gateway. For example, you can tell apart requests coming from openai-python versus a custom application or a Cloudflare Worker.

The user agent appears alongside the other details in each log entry, and you can filter logs by user agent (equals, does not equal, or contains) in the dashboard.

For more information, refer to Logging.

Original source

@cf/moonshotai/kimi-k2.7-code is now available on Workers AI

Kimi K2.7 Code is a code-optimized variant of the Kimi K2 family, built on a Mixture-of-Experts architecture with 1T total parameters and 32B active per token.

Improved coding and agent performance

K2.7 Code delivers meaningful gains over K2.6 on coding and agentic benchmarks:

• +21.8% on Kimi Code Bench v2
• +11.0% on Program Bench
• +31.5% on MLS Bench Lite

Reasoning efficiency

K2.7 Code uses 30% fewer reasoning tokens compared to K2.6, reducing overthinking and lowering inference cost for reasoning-heavy workloads.

Key capabilities

• 262.1k token context window for retaining full conversation history, tool definitions, and codebases across long-running agent sessions
• Long-horizon coding with improved instruction following and higher end-to-end coding task success rates
• Vision inputs for processing images alongside text
• Thinking mode with configurable reasoning depth via chat_template_kwargs.thinking
• Multi-turn tool calling for building agents that invoke tools across multiple conversation turns
• Structured outputs with JSON schema support

Differences from Kimi K2.6

If you are migrating from Kimi K2.6, note the following:

• K2.7 Code is optimized for coding tasks with improved benchmark performance and reasoning efficiency
• Cached input token pricing is $0.19 per M tokens (vs $0.16 for K2.6)
• API usage is identical — no parameter changes required

Get started

Use Kimi K2.7 Code through the Workers AI binding (env.AI.run()), the REST API at /ai/run, or the OpenAI-compatible endpoint at /v1/chat/completions. You can also use AI Gateway with any of these endpoints.

For more information, refer to the Kimi K2.7 Code model page and pricing.

Original source

Browser Run's /snapshot endpoint now supports a formats parameter that lets you return multiple page formats in a single API call. Previously, /snapshot returned only HTML content and a screenshot. You can now also include Markdown and the accessibility tree in the same response.

These formats are particularly useful for AI agent workflows:

• Markdown provides a token-efficient representation of page content that LLMs can process directly, without parsing HTML markup.
• The accessibility tree provides a structured representation of a page's elements, including roles, labels, and hierarchy, helping LLMs understand page structure and navigate its contents.

The following example returns a screenshot, Markdown, and the accessibility tree in one call:

[Example curl request shown]

You must request at least two formats. If you only need one, use the respective single-format endpoint such as /screenshot or /markdown.

Refer to the /snapshot documentation for the full list of accepted values.

Original source

AI Search now supports namespace-level Wrangler commands, making it easier to manage namespaces from your terminal, scripts, and agent workflows.

The following commands are available:

• wrangler ai-search namespace list - List AI Search namespaces
• wrangler ai-search namespace create - Create a new AI Search namespace
• wrangler ai-search namespace get - Get details for a namespace
• wrangler ai-search namespace update - Update a namespace description
• wrangler ai-search namespace delete - Delete an AI Search namespace

Create a namespace for a new application or tenant directly from the CLI. List namespaces with pagination or filter by name or description. Use --json with list, create, get, and update to return structured output that automation and AI agents can parse directly.

Instance-level commands also now support a --namespace flag, so you can interact with instances inside a specific namespace from the CLI.

For full usage details, refer to the AI Search Wrangler commands documentation.

Original source

Today we are announcing the deprecation of several features from the Sandbox SDK. The SDK has grown and matured substantially since it first launched. As agent workflows have developed, we have shipped many new features and experiments so developers can easily integrate secure, isolated code execution into their workflows.

We want the SDK to continue providing a stable foundation for agentic workflows while we iterate quickly on the codebase. These deprecated features have either been superseded by newer capabilities or seen low adoption. They will remain in the codebase until July 9, 2026, after which they will no longer be present in future Sandbox SDK versions.

HTTP and WebSocket transports

In April 2026, we released the new RPC transport and deprecated the WebSocket transport. This setting governs how the sandbox container talks to the Workers ecosystem. The RPC transport removes the limitations of both the HTTP and WebSocket transports. As of June 9, 2026, it is the recommended default. HTTP and WebSocket transports will no longer be present in Sandbox SDK versions released after July 9, 2026.

To migrate before July 9, 2026, update the SANDBOX_TRANSPORT variable to rpc or set the transport option when calling getSandbox(). For more information, refer to the transport configuration documentation.

Desktop

The desktop feature landed as a technical demonstration of what can be done with the Sandbox SDK — controlling a full browser environment from within a sandbox. With Cloudflare Browser Run now available, this feature saw very little use. We have removed it in 0.10.2.

Expose ports

We recently released support for Cloudflare Tunnel in the Sandbox SDK. This provides a robust API for exposing services running in your sandbox to the public internet. It fixes issues many were facing with local development and deployment to workers.dev domains. To migrate from exposePort() to tunnels, refer to the tunnels API documentation and the expose services guide.

Default sessions

By default, the exec() method in the Sandbox SDK maintains a default session across all calls, so a cd in one call is honored in the next. This convenience helped developers writing exec statements by hand, but confused agents and caused hard-to-trace bugs. As of 0.10.3, we have introduced the enableDefaultSession flag on the getSandbox() interface to turn this off. Default sessions as a concept — and the flag — will be removed in an upcoming release.

We recommend setting enableDefaultSession: false today and using the sandbox.createSession() API when you need the previous behavior.

Other changes

We are also consolidating all APIs that buffer data to support streaming by default. This includes readFile, writeFile, and exec. The stream equivalents will be removed.

We are exploring moving non-core features like the code interpreter, terminal, and git APIs into helpers. These features will retain their existing APIs, so migration should be simple.

Next steps

If you use any of these features, refer to the 2026 deprecation migration guide. We also provide an agent skill to help with the migration.

For any questions, ask in the Cloudflare Developers Discord Cloudflare Developers Discord.

Original source

AI Gateway now supports spend limits — cost-based budgets that track cumulative dollar spend and block requests when the budget is exceeded. Unlike rate limiting, which caps the number of requests, spend limits track actual cost based on token usage and model pricing.

You can scope limits by model, provider, or custom metadata dimensions. For example, give each user a $200/day budget, cap total gateway spend at $10,000/day, or limit a specific model to $50/day per user. Each rule uses a configurable time window with fixed or sliding enforcement.

Spend limits work with both

Unified Billing

and

BYOK

requests for models with known pricing.

For more details, refer to the

Spend limits documentation

.

Original source

Pay-as-you-go customers can now view billable usage and create budget alerts directly from the product overview pages for Workers & Pages, D1, R2, Workers KV, Queues, Vectorize, Durable Objects, and Containers. A new sidebar widget shows current-period spend and the billing cycle date range, alongside a button to create a budget alert.

The widget pulls from the same data as the Billable Usage dashboard and aligns to your billing cycle (or the current day on Free plans), so the numbers match your invoice. Enterprise contract accounts are not yet supported.

Selecting Create budget alert opens the budget alert flow inline so you can set a dollar threshold in the same place you are reviewing usage. Budget alerts apply to your total account-level spend across all products, not just the product page you create them from.

For more information, refer to the Usage-based billing documentation.

Original source

Durable chat turns have always been designed to survive a mid-turn deploy or Durable Object eviction. This release is a major hardening pass on that machinery for production.

Better recovery during deploys

• Turns now ride through continuous deploys and evictions without losing completed work or re-running tools that already ran.

A live "recovering…" signal

• useAgentChat exposes a new isRecovering flag, so a recovering turn shows progress instead of looking frozen. Most UIs render isStreaming || isRecovering as "busy".

Stalled streams recover

• Set chatStreamStallTimeoutMs to route a hung provider stream into the same recovery path instead of leaving an infinite spinner.

Sub-agents re-attach

• On parent recovery, an in-flight agentTool() child is re-attached to its result rather than abandoned and re-run, so long-running children no longer lose work under deploys.

Original source

• Resumable streams — In-flight tool calls over Server-Sent Events (SSE) survive a dropped connection. Clients reconnect with Last-Event-ID and replay anything they missed.

• Readable server IDs — addMcpServer accepts an optional id, so tools surface as readable keys (for example tool_github_create_pull_request) instead of opaque connection IDs.

• Better handling of concurrent requests — Overlapping JSON-RPC requests are now correctly correlated to their responses across the HTTP and RPC transports.

Original source