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Changelog

New updates and improvements at Cloudflare.

Agents SDK v0.8.0: readable state, idempotent schedules, typed AgentClient, and Zod 4

The latest release of the Agents SDK exposes agent state as a readable property, prevents duplicate schedule rows across Durable Object restarts, brings full TypeScript inference to AgentClient, and migrates to Zod 4.

Readable state on useAgent and AgentClient

Both useAgent (React) and AgentClient (vanilla JS) now expose a state property that reflects the current agent state. Previously, reading state required manually tracking it through the onStateUpdate callback.

React (useAgent)

const agent = useAgent({
	agent: "game-agent",
	name: "room-123",
});

// Read state directly — no separate useState + onStateUpdate needed
return <div>Score: {agent.state?.score}</div>;

// Spread for partial updates
agent.setState({ ...agent.state, score: (agent.state?.score ?? 0) + 10 });
const agent = useAgent<GameAgent, GameState>({
	agent: "game-agent",
	name: "room-123",
});

// Read state directly — no separate useState + onStateUpdate needed
return <div>Score: {agent.state?.score}</div>;

// Spread for partial updates
agent.setState({ ...agent.state, score: (agent.state?.score ?? 0) + 10 });

agent.state is reactive — the component re-renders when state changes from either the server or a client-side setState() call.

Vanilla JS (AgentClient)

const client = new AgentClient({
	agent: "game-agent",
	name: "room-123",
	host: "your-worker.workers.dev",
});

client.setState({ score: 100 });
console.log(client.state); // { score: 100 }
const client = new AgentClient<GameAgent>({
	agent: "game-agent",
	name: "room-123",
	host: "your-worker.workers.dev",
});

client.setState({ score: 100 });
console.log(client.state); // { score: 100 }

State starts as undefined and is populated when the server sends the initial state on connect (from initialState) or when setState() is called. Use optional chaining (agent.state?.field) for safe access. The onStateUpdate callback continues to work as before — the new state property is additive.

Idempotent schedule()

schedule() now supports an idempotent option that deduplicates by (type, callback, payload), preventing duplicate rows from accumulating when called in places that run on every Durable Object restart such as onStart().

Cron schedules are idempotent by default. Calling schedule("0 * * * *", "tick") multiple times with the same callback, expression, and payload returns the existing schedule row instead of creating a new one. Pass { idempotent: false } to override.

Delayed and date-scheduled types support opt-in idempotency:

import { Agent } from "agents";

class MyAgent extends Agent {
	async onStart() {
		// Safe across restarts — only one row is created
		await this.schedule(60, "maintenance", undefined, { idempotent: true });
	}
}
import { Agent } from "agents";

class MyAgent extends Agent {
	async onStart() {
		// Safe across restarts — only one row is created
		await this.schedule(60, "maintenance", undefined, { idempotent: true });
	}
}

Two new warnings help catch common foot-guns:

  • Calling schedule() inside onStart() without { idempotent: true } emits a console.warn with actionable guidance (once per callback; skipped for cron and when idempotent is set explicitly).
  • If an alarm cycle processes 10 or more stale one-shot rows for the same callback, the SDK emits a console.warn and a schedule:duplicate_warning diagnostics channel event.

Typed AgentClient with call inference and stub proxy

AgentClient now accepts an optional agent type parameter for full type inference on RPC calls, matching the typed experience already available with useAgent.

const client = new AgentClient({
	agent: "my-agent",
	host: window.location.host,
});

// Typed call — method name autocompletes, args and return type inferred
const value = await client.call("getValue");

// Typed stub — direct RPC-style proxy
await client.stub.getValue();
await client.stub.add(1, 2);
const client = new AgentClient<MyAgent>({
	agent: "my-agent",
	host: window.location.host,
});

// Typed call — method name autocompletes, args and return type inferred
const value = await client.call("getValue");

// Typed stub — direct RPC-style proxy
await client.stub.getValue();
await client.stub.add(1, 2);

State is automatically inferred from the agent type, so onStateUpdate is also typed:

const client = new AgentClient({
	agent: "my-agent",
	host: window.location.host,
	onStateUpdate: (state) => {
		// state is typed as MyAgent's state type
	},
});
const client = new AgentClient<MyAgent>({
	agent: "my-agent",
	host: window.location.host,
	onStateUpdate: (state) => {
		// state is typed as MyAgent's state type
	},
});

Existing untyped usage continues to work without changes. The RPC type utilities (AgentMethods, AgentStub, RPCMethods) are now exported from agents/client for advanced typing scenarios. agents, @cloudflare/ai-chat, and @cloudflare/codemode now require zod ^4.0.0. Zod v3 is no longer supported.

@cloudflare/ai-chat fixes

  • Turn serializationonChatMessage() and _reply() work is now queued so user requests, tool continuations, and saveMessages() never stream concurrently.
  • Duplicate messages on stop — Clicking stop during an active stream no longer splits the assistant message into two entries.
  • Duplicate messages after tool calls — Orphaned client IDs no longer leak into persistent storage.

keepAlive() and keepAliveWhile() are no longer experimental

keepAlive() now uses a lightweight in-memory ref count instead of schedule rows. Multiple concurrent callers share a single alarm cycle. The @experimental tag has been removed from both keepAlive() and keepAliveWhile().

@cloudflare/codemode: TanStack AI integration

A new entry point @cloudflare/codemode/tanstack-ai adds support for TanStack AI's chat() as an alternative to the Vercel AI SDK's streamText():

import {
	createCodeTool,
	tanstackTools,
} from "@cloudflare/codemode/tanstack-ai";
import { chat } from "@tanstack/ai";

const codeTool = createCodeTool({
	tools: [tanstackTools(myServerTools)],
	executor,
});

const stream = chat({ adapter, tools: [codeTool], messages });
import { createCodeTool, tanstackTools } from "@cloudflare/codemode/tanstack-ai";
import { chat } from "@tanstack/ai";

const codeTool = createCodeTool({
	tools: [tanstackTools(myServerTools)],
	executor,
});

const stream = chat({ adapter, tools: [codeTool], messages });

Upgrade

To update to the latest version:

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

New AI Search REST API endpoints for /search and /chat/completions

AI Search now offers new REST API endpoints for search and chat that use an OpenAI compatible format. This means you can use the familiar messages array structure that works with existing OpenAI SDKs and tools. The messages array also lets you pass previous messages within a session, so the model can maintain context across multiple turns.

Endpoint Path
Chat Completions POST /accounts/{account_id}/ai-search/instances/{name}/chat/completions
Search POST /accounts/{account_id}/ai-search/instances/{name}/search

Here is an example request to the Chat Completions endpoint using the new messages array format:

curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai-search/instances/{NAME}/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {API_TOKEN}" \
  -d '{
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful documentation assistant."
      },
      {
        "role": "user",
        "content": "How do I get started?"
      }
    ]
  }'

For more details, refer to the AI Search REST API guide.

If you are using the previous AutoRAG API endpoints (/autorag/rags/), we recommend migrating to the new endpoints. The previous AutoRAG API endpoints will continue to be fully supported.

Refer to the migration guide for step-by-step instructions.

AI Search UI snippets and MCP support

AI Search now supports public endpoints, UI snippets, and MCP, making it easy to add search to your website or connect AI agents.

Public endpoints allow you to expose AI Search capabilities without requiring API authentication. To enable public endpoints:

  1. Go to AI Search in the Cloudflare dashboard. Go to AI Search ↗
  2. Select your instance, and turn on Public Endpoint in Settings. For more details, refer to Public endpoint configuration.

UI snippets

UI snippets are pre-built search and chat components you can embed in your website. Visit search.ai.cloudflare.com to configure and preview components for your AI Search instance.

Example of the search-modal-snippet component

To add a search modal to your page:

<script
	type="module"
	src="https://<PUBLIC_ENDPOINT_ID>.search.ai.cloudflare.com/assets/v0.0.25/search-snippet.es.js"
></script>

<search-modal-snippet
	api-url="https://<PUBLIC_ENDPOINT_ID>.search.ai.cloudflare.com/"
	placeholder="Search..."
>
</search-modal-snippet>

For more details, refer to the UI snippets documentation.

MCP

The MCP endpoint allows AI agents to search your content via the Model Context Protocol. Connect your MCP client to:

https://<PUBLIC_ENDPOINT_ID>.search.ai.cloudflare.com/mcp

For more details, refer to the MCP documentation.

Custom metadata filtering for AI Search

AI Search now supports custom metadata filtering, allowing you to define your own metadata fields and filter search results based on attributes like category, version, or any custom field you define.

Define a custom metadata schema

You can define up to 5 custom metadata fields per AI Search instance. Each field has a name and data type (text, number, or boolean):

curl -X POST https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai-search/instances \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {API_TOKEN}" \
  -d '{
    "id": "my-instance",
    "type": "r2",
    "source": "my-bucket",
    "custom_metadata": [
      { "field_name": "category", "data_type": "text" },
      { "field_name": "version", "data_type": "number" },
      { "field_name": "is_public", "data_type": "boolean" }
    ]
  }'

Add metadata to your documents

How you attach metadata depends on your data source:

  • R2 bucket: Set metadata using S3-compatible custom headers (x-amz-meta-*) when uploading objects. Refer to R2 custom metadata for examples.
  • Website: Add <meta> tags to your HTML pages. Refer to Website custom metadata for details.

Filter search results

Use custom metadata fields in your search queries alongside built-in attributes like folder and timestamp:

curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai-search/instances/{NAME}/search \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {API_TOKEN}" \
  -d '{
    "messages": [
      {
        "content": "How do I configure authentication?",
        "role": "user"
      }
    ],
    "ai_search_options": {
      "retrieval": {
        "filters": {
          "category": "documentation",
          "version": { "$gte": 2.0 }
        }
      }
    }
  }'

Learn more in the metadata filtering documentation.

R2 SQL now supports over 190 new functions, expressions, and complex types

R2 SQL now supports an expanded SQL grammar so you can write richer analytical queries without exporting data. This release adds CASE expressions, column aliases, arithmetic in clauses, 163 scalar functions, 33 aggregate functions, EXPLAIN, Common Table Expressions (CTEs),and full struct/array/map access. R2 SQL is Cloudflare's serverless, distributed, analytics query engine for querying Apache Iceberg tables stored in R2 Data Catalog. This page documents the supported SQL syntax.

Highlights

  • Column aliasesSELECT col AS alias now works in all clauses
  • CASE expressions — conditional logic directly in SQL (searched and simple forms)
  • Scalar functions — 163 new functions across math, string, datetime, regex, crypto, encoding, and type inspection categories
  • Aggregate functions — statistical (variance, stddev, correlation, regression), bitwise, boolean, and positional aggregates join the existing basic and approximate functions
  • Complex types — query struct fields with bracket notation, use 46 array functions, and extract map keys/values
  • Common table expressions (CTEs) — use WITH ... AS to define named temporary result sets. Chained CTEs are supported. All CTEs must reference the same single table.
  • Full expression support — arithmetic, type casting (CAST, TRY_CAST, :: shorthand), and EXTRACT in SELECT, WHERE, GROUP BY, HAVING, and ORDER BY

Examples

CASE expressions with statistical aggregates

SELECT source,
    CASE
        WHEN AVG(price) > 30 THEN 'premium'
        WHEN AVG(price) > 10 THEN 'mid-tier'
        ELSE 'budget'
    END AS tier,
    round(stddev(price), 2) AS price_volatility,
    approx_percentile_cont(price, 0.95) AS p95_price
FROM my_namespace.sales_data
GROUP BY source

Struct and array access

SELECT product_name,
    pricing['price'] AS price,
    array_to_string(tags, ', ') AS tag_list
FROM my_namespace.products
WHERE array_has(tags, 'Action')
ORDER BY pricing['price'] DESC
LIMIT 10

Chained CTEs with time-series analysis

WITH monthly AS (
    SELECT date_trunc('month', sale_timestamp) AS month,
        department,
        COUNT(*) AS transactions,
        round(AVG(total_amount), 2) AS avg_amount
    FROM my_namespace.sales_data
    WHERE sale_timestamp BETWEEN '2025-01-01T00:00:00Z' AND '2025-12-31T23:59:59Z'
    GROUP BY date_trunc('month', sale_timestamp), department
),
ranked AS (
    SELECT month, department, transactions, avg_amount,
        CASE
            WHEN avg_amount > 1000 THEN 'high-value'
            WHEN avg_amount > 500 THEN 'mid-value'
            ELSE 'standard'
        END AS tier
    FROM monthly
    WHERE transactions > 100
)
SELECT * FROM ranked
ORDER BY month, avg_amount DESC

For the full function reference and syntax details, refer to the SQL reference. For limitations and best practices, refer to Limitations and best practices.

Stream logs from multiple replicas of Cloudflare Tunnel simultaneously

In the Cloudflare One dashboard, the overview page for a specific Cloudflare Tunnel now shows all replicas of that tunnel and supports streaming logs from multiple replicas at once.

View replicas and stream logs from multiple connectors

Previously, you could only stream logs from one replica at a time. With this update:

  • Replicas on the tunnel overview — All active replicas for the selected tunnel now appear on that tunnel's overview page under Connectors. Select any replica to stream its logs.
  • Multi-connector log streaming — Stream logs from multiple replicas simultaneously, making it easier to correlate events across your infrastructure during debugging or incident response. To try it out, log in to Cloudflare One and go to Networks > Connectors > Cloudflare Tunnels. Select View logs next to the tunnel you want to monitor.

For more information, refer to Tunnel log streams and Deploy replicas.

Observability for Workers VPC Services

Each VPC Service now has a Metrics tab so you can monitor connection health and debug failures without leaving the dashboard.

Workers VPC Metrics dashboard showing connections, latency, and errors charts
  • Connections — See successful and failed connections over time, broken down by what is responsible: your origin (Bad Upstream), your configuration (Client), or Cloudflare (Internal).
  • Latency — Track connection and DNS resolution latency trends.
  • Errors — Drill into specific error codes grouped by category, with filters to isolate upstream, client, or internal failures.

You can also view and edit your VPC Service configuration, host details, and port assignments from the Settings tab.

For a full list of error codes and what they mean, refer to Troubleshooting.

Hyperdrive now supports custom TLS/SSL certificates for MySQL

Hyperdrive now supports custom TLS/SSL certificates for MySQL databases, bringing the same certificate options previously available for PostgreSQL to MySQL connections.

You can now configure:

  • Server certificate verification with VERIFY_CA or VERIFY_IDENTITY SSL modes to verify that your MySQL database server's certificate is signed by the expected certificate authority (CA).
  • Client certificates (mTLS) for Hyperdrive to authenticate itself to your MySQL database with credentials beyond username and password.

Create a Hyperdrive configuration with custom certificates for MySQL:

# Upload a CA certificate
npx wrangler cert upload certificate-authority --ca-cert your-ca-cert.pem --name your-custom-ca-name

# Create a Hyperdrive with VERIFY_IDENTITY mode
npx wrangler hyperdrive create your-hyperdrive-config \
  --connection-string="mysql://user:password@hostname:port/database" \
  --ca-certificate-id <CA_CERT_ID> \
  --sslmode VERIFY_IDENTITY

For more information, refer to SSL/TLS certificates for Hyperdrive and MySQL TLS/SSL modes.

Manage Cloudflare Tunnels with Wrangler

You can now manage Cloudflare Tunnels directly from Wrangler, the CLI for the Cloudflare Developer Platform. The new wrangler tunnel commands let you create, run, and manage tunnels without leaving your terminal.

Wrangler tunnel commands demo

Available commands:

  • wrangler tunnel create — Create a new remotely managed tunnel.
  • wrangler tunnel list — List all tunnels in your account.
  • wrangler tunnel info — Display details about a specific tunnel.
  • wrangler tunnel delete — Delete a tunnel.
  • wrangler tunnel run — Run a tunnel using the cloudflared daemon.
  • wrangler tunnel quick-start — Start a free, temporary tunnel without an account using Quick Tunnels.

Wrangler handles downloading and managing the cloudflared binary automatically. On first use, you will be prompted to download cloudflared to a local cache directory.

These commands are currently experimental and may change without notice.

To get started, refer to the Wrangler tunnel commands documentation.

Moonshot AI Kimi K2.5 now available on Workers AI

Workers AI is officially in the big models game. @cf/moonshotai/kimi-k2.5 is the first frontier-scale open-source model on our AI inference platform — a large model with a full 256k context window, multi-turn tool calling, vision inputs, and structured outputs. By bringing a frontier-scale model directly onto the Cloudflare Developer Platform, you can now run the entire agent lifecycle on a single, unified platform.

The model has proven to be a fast, efficient alternative to larger proprietary models without sacrificing quality. As AI adoption increases, the volume of inference is skyrocketing — now you can access frontier intelligence at a fraction of the cost.

Key capabilities

  • 256,000 token context window for retaining full conversation history, tool definitions, and entire codebases across long-running agent sessions
  • Multi-turn tool calling for building agents that invoke tools across multiple conversation turns
  • Vision inputs for processing images alongside text
  • Structured outputs with JSON mode and JSON Schema support for reliable downstream parsing
  • Function calling for integrating external tools and APIs into agent workflows

Prefix caching and session affinity

When an agent sends a new prompt, it resends all previous prompts, tools, and context from the session. The delta between consecutive requests is usually just a few new lines of input. Prefix caching avoids reprocessing the shared context, saving time and compute from the prefill stage. This means faster Time to First Token (TTFT) and higher Tokens Per Second (TPS) throughput.

Workers AI has done prefix caching, but we are now surfacing cached tokens as a usage metric and offering a discount on cached tokens compared to input tokens (pricing is listed on the model page).

curl -X POST \
  "https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/@cf/moonshotai/kimi-k2.5" \
  -H "Authorization: Bearer {api_token}" \
  -H "Content-Type: application/json" \
  -H "x-session-affinity: ses_12345678" \
  -d '{
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "What is prefix caching and why does it matter?"
      }
    ],
    "max_tokens": 2400,
    "stream": true
  }'

Some clients like OpenCode implement session affinity automatically. The Agents SDK starter also sets up the wiring for you.

Redesigned asynchronous API

For volumes of requests that exceed synchronous rate limits, you can submit batches of inferences to be completed asynchronously. We have revamped the Asynchronous Batch API with a pull-based system that processes queued requests as soon as capacity is available. With internal testing, async requests usually execute within 5 minutes, but this depends on live traffic.

The async API is the best way to avoid capacity errors in durable workflows. It is ideal for use cases that are not real-time, such as code scanning agents or research agents.

To use the asynchronous API, pass queueRequest: true:

// 1. Push a batch of requests into the queue
const res = await env.AI.run(
	"@cf/moonshotai/kimi-k2.5",
	{
		requests: [
			{
				messages: [{ role: "user", content: "Tell me a joke" }],
			},
			{
				messages: [{ role: "user", content: "Explain the Pythagoras theorem" }],
			},
		],
	},
	{ queueRequest: true },
);

// 2. Grab the request ID
const requestId = res.request_id;

// 3. Poll for the result
const result = await env.AI.run("@cf/moonshotai/kimi-k2.5", {
	request_id: requestId,
});

if (result.status === "queued" || result.status === "running") {
	// Retry by polling again
} else {
	return Response.json(result);
}

You can also set up event notifications to know when inference is complete instead of polling.

Get started

Use Kimi K2.5 through the Workers AI binding (env.AI.run()), the REST API at /run or /v1/chat/completions, AI Gateway, or via the OpenAI-compatible endpoint.

For more information, refer to the Kimi K2.5 model page, pricing, and prompt caching.

Media Transformations binding for Workers

You can now use a Workers binding to transform videos with Media Transformations. This allows you to resize, crop, extract frames, and extract audio from videos stored anywhere, even in private locations like R2 buckets.

The Media Transformations binding is useful when you want to:

  • Transform videos stored in private or protected sources
  • Optimize videos and store the output directly back to R2 for re-use
  • Extract still frames for classification or description with Workers AI
  • Extract audio tracks for transcription using Workers AI

To get started, add the Media binding to your Wrangler configuration:

{
  "$schema": "./node_modules/wrangler/config-schema.json",
  "media": {
    "binding": "MEDIA"
  }
}
[media]
binding = "MEDIA"

Then use the binding in your Worker to transform videos:

export default {
	async fetch(request, env) {
		const video = await env.R2_BUCKET.get("input.mp4");

		const result = env.MEDIA.input(video.body)
			.transform({ width: 480, height: 270 })
			.output({ mode: "video", duration: "5s" });

		return await result.response();
	},
};
export default {
	async fetch(request, env) {
		const video = await env.R2_BUCKET.get("input.mp4");

		const result = env.MEDIA.input(video.body)
			.transform({ width: 480, height: 270 })
			.output({ mode: "video", duration: "5s" });

		return await result.response();
	},
};

Output modes include video for optimized MP4 clips, frame for still images, spritesheet for multiple frames, and audio for M4A extraction.

For more information, refer to the Media Transformations binding documentation.

@cloudflare/codemode v0.2.1: MCP barrel export, zero-dependency main entry point, and custom sandbox modules

The latest releases of @cloudflare/codemode add a new MCP barrel export, remove ai and zod as required peer dependencies from the main entry point, and give you more control over the sandbox.

New @cloudflare/codemode/mcp export

A new @cloudflare/codemode/mcp entry point provides two functions that wrap MCP servers with Code Mode:

  • codeMcpServer({ server, executor }) — wraps an existing MCP server with a single code tool where each upstream tool becomes a typed codemode.* method.
  • openApiMcpServer({ spec, executor, request }) — creates search and execute MCP tools from an OpenAPI spec with host-side request proxying and automatic $ref resolution.
import { codeMcpServer } from "@cloudflare/codemode/mcp";
import { DynamicWorkerExecutor } from "@cloudflare/codemode";

const executor = new DynamicWorkerExecutor({ loader: env.LOADER });

// Wrap an existing MCP server — all its tools become
// typed methods the LLM can call from generated code
const server = await codeMcpServer({ server: upstreamMcp, executor });
import { codeMcpServer } from "@cloudflare/codemode/mcp";
import { DynamicWorkerExecutor } from "@cloudflare/codemode";

const executor = new DynamicWorkerExecutor({ loader: env.LOADER });

// Wrap an existing MCP server — all its tools become
// typed methods the LLM can call from generated code
const server = await codeMcpServer({ server: upstreamMcp, executor });

Zero-dependency main entry point

Breaking change in v0.2.0: generateTypes and the ToolDescriptor / ToolDescriptors types have moved to @cloudflare/codemode/ai:

// Before
import { generateTypes } from "@cloudflare/codemode";

// After
import { generateTypes } from "@cloudflare/codemode/ai";
// Before
import { generateTypes } from "@cloudflare/codemode";

// After
import { generateTypes } from "@cloudflare/codemode/ai";

The main entry point (@cloudflare/codemode) no longer requires the ai or zod peer dependencies. It now exports:

Export Description
sanitizeToolName Sanitize tool names into valid JS identifiers
normalizeCode Normalize LLM-generated code into async arrow functions
generateTypesFromJsonSchema Generate TypeScript type definitions from plain JSON Schema
jsonSchemaToType Convert a single JSON Schema to a TypeScript type string
DynamicWorkerExecutor Sandboxed code execution via Dynamic Worker Loader
ToolDispatcher RPC target for dispatching tool calls from sandbox to host

The ai and zod peer dependencies are now optional — only required when importing from @cloudflare/codemode/ai.

Custom sandbox modules

DynamicWorkerExecutor now accepts an optional modules option to inject custom ES modules into the sandbox:

const executor = new DynamicWorkerExecutor({
	loader: env.LOADER,
	modules: {
		"utils.js": `export function add(a, b) { return a + b; }`,
	},
});

// Sandbox code can then: import { add } from "utils.js"
const executor = new DynamicWorkerExecutor({
	loader: env.LOADER,
	modules: {
		"utils.js": `export function add(a, b) { return a + b; }`,
	},
});

// Sandbox code can then: import { add } from "utils.js"

Internal normalization and sanitization

DynamicWorkerExecutor now normalizes code and sanitizes tool names internally. You no longer need to call normalizeCode() or sanitizeToolName() before passing code and functions to execute().

Upgrade

npm i @cloudflare/codemode@latest

See the Code Mode documentation for the full API reference.

Log AI Gateway request metadata without storing payloads

AI Gateway now supports the cf-aig-collect-log-payload header, which controls whether request and response bodies are stored in logs. By default, this header is set to true and payloads are stored alongside metadata. Set this header to false to skip payload storage while still logging metadata such as token counts, model, provider, status code, cost, and duration.

This is useful when you need usage metrics but do not want to persist sensitive prompt or response data.

curl https://gateway.ai.cloudflare.com/v1/$ACCOUNT_ID/$GATEWAY_ID/openai/chat/completions \
  --header "Authorization: Bearer $TOKEN" \
  --header 'Content-Type: application/json' \
  --header 'cf-aig-collect-log-payload: false' \
  --data '{
    "model": "gpt-4o-mini",
    "messages": [
      {
        "role": "user",
        "content": "What is the email address and phone number of user123?"
      }
    ]
  }'

For more information, refer to Logging.

Return up to 50 query results with values or metadata

You can now set topK up to 50 when a Vectorize query returns values or full metadata. This raises the previous limit of 20 for queries that use returnValues: true or returnMetadata: "all".

Use the higher limit when you need more matches in a single query response without dropping values or metadata. Refer to the Vectorize API reference for query options and current topK limits.

Access Durable Object name via `ctx.id.name`

When your Worker accesses a Durable Object via idFromName() or getByName(), the same name is now available on ctx.id.name inside the object — no need to pass it through method arguments or persist it in storage. This brings the runtime behavior in line with the Workers runtime types.

This is especially useful for alarms, where there is no calling client to pass the name as an argument. When an alarm handler runs, ctx.id.name will hold the same name the object was originally accessed with.

import { DurableObject } from "cloudflare:workers";

export class ChatRoom extends DurableObject {
  async getRoomName() {
    // ctx.id.name returns the name passed to getByName() or idFromName()
    return this.ctx.id.name;
  }
}

// Worker
export default {
  async fetch(request, env) {
    const stub = env.CHAT_ROOM.getByName("general");
    const roomName = await stub.getRoomName();
    return new Response(`Welcome to ${roomName}!`);
  },
};

ctx.id.name is undefined in the following cases:

  • For Durable Objects created with newUniqueId().
  • When accessed via idFromString(), even if the ID was originally created from a name.
  • For names longer than 1,024 bytes.

This works the same way in local development with wrangler dev as it does in production. Run npm update wrangler to ensure you are on a version with this support.

For more information, refer to the Durable Object ID documentation.

SSH into running Container instances

You can now SSH into running Container instances using Wrangler. This is useful for debugging, inspecting running processes, or executing one-off commands inside a Container.

To connect, enable wrangler_ssh in your Container configuration and add your ssh-ed25519 public key to authorized_keys:

{
	"containers": [
		{
			"wrangler_ssh": {
				"enabled": true
			},
			"authorized_keys": [
				{
					"name": "<NAME>",
					"public_key": "<YOUR_PUBLIC_KEY_HERE>"
				}
			]
		}
	]
}
[[containers]]
[containers.wrangler_ssh]
enabled = true

[[containers.authorized_keys]]
name = "<NAME>"
public_key = "<YOUR_PUBLIC_KEY_HERE>"

Then connect with:

wrangler containers ssh <INSTANCE_ID>

You can also run a single command without opening an interactive shell:

wrangler containers ssh <INSTANCE_ID> -- ls -al

Use wrangler containers instances <APPLICATION> to find the instance ID for a running Container.

For more information, refer to the SSH documentation.

List Container instances with `wrangler containers instances`

A new wrangler containers instances command lists all instances for a given Container application. This mirrors the instances view in the Cloudflare dashboard.

The command displays each instance's ID, name, state, location, version, and creation time:

wrangler containers instances <APPLICATION_ID>

Use the --json flag for machine-readable output, which is also the default format in non-interactive environments such as CI pipelines.

For the full list of options, refer to the containers instances command reference.

NVIDIA Nemotron 3 Super now available on Workers AI

We're excited to partner with NVIDIA to bring @cf/nvidia/nemotron-3-120b-a12b to Workers AI. NVIDIA Nemotron 3 Super is a Mixture-of-Experts (MoE) model with a hybrid Mamba-transformer architecture, 120B total parameters, and 12B active parameters per forward pass.

The model is optimized for running many collaborating agents per application. It delivers high accuracy for reasoning, tool calling, and instruction following across complex multi-step tasks.

Key capabilities:

  • Hybrid Mamba-transformer architecture delivers over 50% higher token generation throughput compared to leading open models, reducing latency for real-world applications
  • Tool calling support for building AI agents that invoke tools across multiple conversation turns
  • Multi-Token Prediction (MTP) accelerates long-form text generation by predicting several future tokens simultaneously in a single forward pass
  • 32,000 token context window for retaining conversation history and plan states across multi-step agent workflows

Use Nemotron 3 Super through the Workers AI binding (env.AI.run()), the REST API at /run or /v1/chat/completions, or the OpenAI-compatible endpoint.

For more information, refer to the Nemotron 3 Super model page.

Crawl entire websites with a single API call using Browser Rendering

Edit: this post has been edited to clarify crawling behavior with respect to site guidance.

You can now crawl an entire website with a single API call using Browser Rendering's new /crawl endpoint, available in open beta. Submit a starting URL, and pages are automatically discovered, rendered in a headless browser, and returned in multiple formats, including HTML, Markdown, and structured JSON. The endpoint is a verified bot (intermediary agent) that respects robots.txt and AI Crawl Control by default, making it easy for developers to comply with website rules, and making it less likely for crawlers to ignore web-owner guidance. This is great for training models, building RAG pipelines, and researching or monitoring content across a site.

Crawl jobs run asynchronously. You submit a URL, receive a job ID, and check back for results as pages are processed.

# Initiate a crawl
curl -X POST 'https://api.cloudflare.com/client/v4/accounts/{account_id}/browser-rendering/crawl' \
  -H 'Authorization: Bearer <apiToken>' \
  -H 'Content-Type: application/json' \
  -d '{
    "url": "https://blog.cloudflare.com/"
  }'

# Check results
curl -X GET 'https://api.cloudflare.com/client/v4/accounts/{account_id}/browser-rendering/crawl/{job_id}' \
  -H 'Authorization: Bearer <apiToken>'

Key features:

  • Multiple output formats - Return crawled content as HTML, Markdown, and structured JSON (powered by Workers AI)
  • Crawl scope controls - Configure crawl depth, page limits, and wildcard patterns to include or exclude specific URL paths
  • Automatic page discovery - Discovers URLs from sitemaps, page links, or both
  • Incremental crawling - Use modifiedSince and maxAge to skip pages that haven't changed or were recently fetched, saving time and cost on repeated crawls
  • Static mode - Set render: false to fetch static HTML without spinning up a browser, for faster crawling of static sites
  • Well-behaved bot - Honors robots.txt directives, including crawl-delay

Available on both the Workers Free and Paid plans.

Note: the /crawl endpoint cannot bypass Cloudflare bot detection or captchas, and self-identifies as a bot.

To get started, refer to the crawl endpoint documentation. If you are setting up your own site to be crawled, review the robots.txt and sitemaps best practices.

Workflow steps now expose retry attempt number via step context

Cloudflare Workflows allows you to configure specific retry logic for each step in your workflow execution. Now, you can access which retry attempt is currently executing for calls to step.do():

await step.do("my-step", async (ctx) => {
	// ctx.attempt is 1 on first try, 2 on first retry, etc.
	console.log(`Attempt ${ctx.attempt}`);
});

You can use the step context for improved logging & observability, progressive backoff, or conditional logic in your workflow definition.

Note that the current attempt number is 1-indexed. For more information on retry behavior, refer to Sleeping and Retrying.

Real-time transcription in RealtimeKit now supports 10 languages with regional variants

Real-time transcription in RealtimeKit now supports 10 languages with regional variants, powered by Deepgram Nova-3 running on Workers AI.

During a meeting, participant audio is routed through AI Gateway to Nova-3 on Workers AI — so transcription runs on Cloudflare's network end-to-end, reducing latency compared to routing through external speech-to-text services.

Set the language when creating a meeting via ai_config.transcription.language:

{
	"ai_config": {
		"transcription": {
			"language": "fr"
		}
	}
}

Supported languages include English, Spanish, French, German, Hindi, Russian, Portuguese, Japanese, Italian, and Dutch — with regional variants like en-AU, en-GB, en-IN, en-NZ, es-419, fr-CA, de-CH, pt-BR, and pt-PT. Use multi for automatic multilingual detection.

If you are building voice agents or real-time translation workflows, your agent can now transcribe in the caller's language natively — no extra services or routing logic needed.

Browser Rendering: 3x higher REST API request rate

Browser Rendering REST API rate limits for Workers Paid plans have been increased from 3 requests per second (180/min) to 10 requests per second (600/min). No action is needed to benefit from the higher limit.

Browser Rendering REST API rate limit increased from 3 to 10 requests per second

The REST API lets you perform common browser tasks with a single API call, and you can now do it at a higher rate.

If you use the Browser Sessions method, increases to concurrent browser and new browser limits are coming soon. Stay tuned.

For full details, refer to the Browser Rendering limits page.

New conversion options for Markdown Conversion

You can now customize how the Markdown Conversion service processes different file types by passing a conversionOptions object.

Available options:

  • Images: Set the language for AI-generated image descriptions
  • HTML: Use CSS selectors to extract specific content, or provide a hostname to resolve relative links
  • PDF: Exclude metadata from the output

Use the env.AI binding:

await env.AI.toMarkdown(
	{ name: "page.html", blob: new Blob([html]) },
	{
		conversionOptions: {
			html: { cssSelector: "article.content" },
			image: { descriptionLanguage: "es" },
		},
	},
);
await env.AI.toMarkdown(
	{ name: "page.html", blob: new Blob([html]) },
	{
		conversionOptions: {
			html: { cssSelector: "article.content" },
			image: { descriptionLanguage: "es" },
		},
	},
);

Or call the REST API:

curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/tomarkdown \
  -H 'Authorization: Bearer {API_TOKEN}' \
  -F 'files=@index.html' \
  -F 'conversionOptions={"html": {"cssSelector": "article.content"}}'

For more details, refer to Conversion Options.

Workflows step limit increased to 25,000 steps per instance

Each Workflow on Workers Paid now supports 10,000 steps by default, configurable up to 25,000 steps in your wrangler.jsonc file:

{
	"workflows": [
		{
			"name": "my-workflow",
			"binding": "MY_WORKFLOW",
			"class_name": "MyWorkflow",
			"limits": {
				"steps": 25000
			}
		}
	]
}

Previously, each instance was limited to 1,024 steps. Now, Workflows can support more complex, long-running executions without the additional complexity of recursive or child workflow calls.

Note that the maximum persisted state limit per Workflow instance remains 100 MB for Workers Free and 1 GB for Workers Paid. Refer to Workflows limits for more information.

Real-time file watching in Sandboxes

Sandboxes now support real-time filesystem watching via sandbox.watch(). The method returns a Server-Sent Events stream backed by native inotify, so your Worker receives create, modify, delete, and move events as they happen inside the container.

sandbox.watch(path, options)

Pass a directory path and optional filters. The returned stream is a standard ReadableStream you can proxy directly to a browser client or consume server-side.

// Stream events to a browser client
const stream = await sandbox.watch("/workspace/src", {
	recursive: true,
	include: ["*.ts", "*.js"],
});

return new Response(stream, {
	headers: { "Content-Type": "text/event-stream" },
});
// Stream events to a browser client
const stream = await sandbox.watch("/workspace/src", {
	recursive: true,
	include: ["*.ts", "*.js"],
});

return new Response(stream, {
	headers: { "Content-Type": "text/event-stream" },
});

Server-side consumption with parseSSEStream

Use parseSSEStream to iterate over events inside a Worker without forwarding them to a client.

import { parseSSEStream } from "@cloudflare/sandbox";

const stream = await sandbox.watch("/workspace/src", { recursive: true });

for await (const event of parseSSEStream(stream)) {
	console.log(event.type, event.path);
}
import { parseSSEStream } from "@cloudflare/sandbox";
import type { FileWatchSSEEvent } from "@cloudflare/sandbox";

const stream = await sandbox.watch("/workspace/src", { recursive: true });

for await (const event of parseSSEStream<FileWatchSSEEvent>(stream)) {
	console.log(event.type, event.path);
}

Each event includes a type field (create, modify, delete, or move) and the affected path. Move events also include a from field with the original path.

Options

Option Type Description
recursive boolean Watch subdirectories. Defaults to false.
include string[] Glob patterns to filter events. Omit to receive all events.

Upgrade

To update to the latest version:

npm i @cloudflare/sandbox@latest

For full API details, refer to the Sandbox file watching reference.