AI Search

AI Search billing postponed to December 1, 2026, and default sync interval is now 24 hours

Billing postponed to December 1, 2026 AI Search billing has been postponed by one month and now begins on December 1, 2026, instead of November 1, 2026. You will not be charged for AI Search usage before December 1, 2026. Pricing and included usage are unchanged. For rates and included usage, refer to Limits & pricing. Default sync interval is now 24 hours The default sync interval for AI Search…

AI Search keyword search supports Japanese, Chinese, and Korean queries

AI Search keyword search with the trigram tokenizer now matches Japanese, Chinese, and Korean queries, including full natural-language questions such as 経費精算の締め日はいつですか？. Previously, a query without spaces was treated as a single exact phrase and usually returned no keyword results. The porter tokenizer does not work well for these languages. If your content is in Japanese, Chinese, or Korean, set…

Cloudflare and OpenAI: Making AI discovery better for everyone Blog

How Cloudflare and OpenAI are working together to help AI find useful content, keep up with changes, and make fewer wasted requests — making the experience better for users and website owners alike.

AI Search is generally available

AI Search is now generally available. Usage-based billing begins on November 1, 2026, with included monthly ingestion, storage, semantic query, and full-text query usage. Cloudflare will send a reminder email the week before billing begins. Refer to Limits & pricing for rates and included usage. Hybrid search is on by default New AI Search instances use hybrid search by default. Hybrid search com…

AI Search supports extensionless R2 objects with Content-Type metadata

AI Search can index R2 objects without filename extensions when they include supported Content-Type metadata. This supports object keys that do not include file extensions while preserving file-type validation during indexing. For supported file types and Content-Type requirements, refer to R2 data sources.

AI Search now supports GLM-5.3 Flash

AI Search now supports @cf/zai-org/glm-5.3-flash for text generation. The model has a 1,048,576-token context window and runs on Workers AI. To configure the model for an AI Search instance, refer to Supported models.

New Workers AI text generation models in AI Search

AI Search now supports six additional Workers AI models for text generation: Model Context window (tokens) @cf/deepseek-ai/deepseek-v4-flash-0731 1,048,576 @cf/deepseek-ai/deepseek-v4-pro-0813 1,048,576 @cf/openai/gpt-oss-120b 128,000 @cf/openai/gpt-oss-20b 128,000 @cf/qwen/qwen3.8-27b 262,144 @cf/moonshotai/kimi-k2.7-code 262,144 These models run on Workers AI, so they do not require an addition…

Store larger custom metadata values in AI Search

AI Search supports larger custom metadata values within a shared 10 KiB metadata envelope for each vector. The envelope includes AI Search system metadata and JSON overhead, so it is not a per-field limit. The first 64 UTF-8 bytes of each indexed string remain filterable. For details, refer to Metadata attributes.

AI Search makes it easier to build a search engine for your data

AI Search gets you from a data source to a working search endpoint quickly. This release adds what you need to put that endpoint in front of real users: your own domain, authentication, and one endpoint across several instances. It also adds crawling for sites without a complete sitemap, so your index covers everything you want it to find. Each of the following is a new option. The previous behav…

Use AI Search with the Agents SDK, AI SDK, and LangChain

You can now use AI Search directly from popular agent frameworks, adding grounded retrieval to an existing app instead of calling the REST API by hand. The new Agents section has guides for the Vercel AI SDK, LangChain, and the Cloudflare Agents SDK. The AI SDK integration is a new package, and the LangChain integration is a new retriever in the existing langchain-cloudflare package. Vercel AI SD…

Filter AI Search list items by exact object key

In AI Search, you can upload files to an instance, or connect a data source such as an R2 bucket, to make your content searchable with natural language. Each file becomes an item identified by an object key (its filename or path). The list items endpoint returns the items in an instance. That endpoint now accepts a key query parameter, so you can look up a single item by its exact object key with…

Workers AI toMarkdown and AI Search now supports GIF and BMP image conversion

Workers AI Markdown conversion (toMarkdown) now supports .gif and .bmp image files, in addition to the JPEG, PNG, WebP, and SVG formats already supported. GIF and BMP files run through the same image pipeline as other formats. Each image is resized if needed (and for animated GIFs, only the first frame is used), then passed to an object-detection model to identify what it contains. Those detected…

Manage AI Search sync jobs with Wrangler CLI

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: wrangler ai-search jobs create my-instance This creates…

Control AI Search similarity cache freshness

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. Cache duration now defaults to 48 hours Previously, AI Search cached responses for a fixe…

Manage AI Search namespaces with Wrangler CLI

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: Command Description 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 wrang…

AI Search instances now include built-in storage and namespace Workers Bindings

New AI Search instances created after today will work differently. New instances come with built-in storage and a vector index, so you can upload a file, have it indexed immediately, and search it right away. Additionally new Workers Bindings are now available to use with AI Search. The new namespace binding lets you create and manage instances at runtime, and cross-instance search API lets you q…

AI Search now has hybrid search and relevance boosting

AI Search now supports hybrid search and relevance boosting, giving you more control over how results are found and ranked. Hybrid search Hybrid search combines vector (semantic) search with BM25 keyword search in a single query. Vector search finds chunks with similar meaning, even when the exact words differ. Keyword search matches chunks that contain your query terms exactly. When you enable h…

Website Source CSS content selectors for precise content extraction in AI Search

AI Search now supports CSS content selectors for website data sources. You can now define which parts of a crawled page are extracted and indexed by specifying CSS selectors paired with URL glob patterns. Content selectors solve the problem of indexing only relevant content while ignoring navigation, sidebars, footers, and other boilerplate. When a page URL matches a glob pattern, only elements m…

New Workers AI models for text generation and embedding in AI Search

AI Search now supports four additional Workers AI models across text generation and embedding. Text generation Model Context window (tokens) @cf/zai-org/glm-4.7-flash 131,072 @cf/qwen/qwen3-30b-a3b-fp8 32,000 GLM-4.7-Flash is a lightweight model from Zhipu AI with a 131,072 token context window, suitable for long-document summarization and retrieval tasks. Qwen3-30B-A3B is a mixture-of-experts mo…

Create, manage, search AI Search instances with Wrangler CLI

AI Search supports a wrangler ai-search command namespace. Use it to manage instances from the command line. The following commands are available: Command Description wrangler ai-search create Create a new instance with an interactive wizard wrangler ai-search list List all instances in your account wrangler ai-search get Get details of a specific instance wrangler ai-search update Update the con…

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 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: Go to AI Search in the Cloudflare dashboard. Go to AI Search ↗ Select your instance, and turn on Public Endpoint in Settings. For more details,…

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.cloudfla…

AI Search now with more granular controls over indexing

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…

AI Search path filtering for website and R2 data sources

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 experi…

Create AI Search instances programmatically via REST API

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.…

AI Search support for crawling login protected website content

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 authenticati…

Reranking and API-based system prompt configuration in AI Search

AI Search now supports reranking for improved retrieval quality and allows you to set the system prompt directly in your API requests. Rerank for more relevant results You can now enable reranking to reorder retrieved documents based on their semantic relevance to the user’s query. Reranking helps improve accuracy, especially for large or noisy datasets where vector similarity alone may not produ…

AI Search (formerly AutoRAG) now with More Models To Choose From

AutoRAG is now AI Search! The new name marks a new and bigger mission: to make world-class search infrastructure available to every developer and business. With AI Search you can now use models from different providers like OpenAI and Anthropic. By attaching your provider keys to the AI Gateway linked to your AI Search instance, you can use many more models for both embedding and inference. To us…

New Metrics View in AutoRAG

AutoRAG now includes a Metrics tab that shows how your data is indexed and searched. Get a clear view of the health of your indexing pipeline, compare usage between ai-search and search, and see which files are retrieved most often. You can find these metrics within each AutoRAG instance: Indexing: Track how files are ingested and see status changes over time. Search breakdown: Compare usage betw…

Faster indexing and new Jobs view in AutoRAG

You can now expect 3-5× faster indexing in AutoRAG, and with it, a brand new Jobs view to help you monitor indexing progress. With each AutoRAG, indexing jobs are automatically triggered to sync your data source (i.e. R2 bucket) with your Vectorize index, ensuring new or updated files are reflected in your query results. You can also trigger jobs manually via the Sync API or by clicking “Sync ind…

View custom metadata in responses and guide AI-search with context in AutoRAG

In AutoRAG, you can now view your object's custom metadata in the response from /search and /ai-search, and optionally add a context field in the custom metadata of an object to provide additional guidance for AI-generated answers. You can add custom metadata to an object when uploading it to your R2 bucket. Object's custom metadata in search responses When you run a search, AutoRAG now returns a…

Filter your AutoRAG search by file name

In AutoRAG, you can now filter by an object's file name using the filename attribute, giving you more control over which files are searched for a given query. This is useful when your application has already determined which files should be searched. For example, you might query a PostgreSQL database to get a list of files a user has access to based on their permissions, and then use that list to…

Metadata filtering and multitenancy support in AutoRAG

You can now filter AutoRAG search results by folder and timestamp using metadata filtering to narrow down the scope of your query. This makes it easy to build multitenant experiences where each user can only access their own data. By organizing your content into per-tenant folders and applying a folder filter at query time, you ensure that each tenant retrieves only their own documents. Example f…

Create fully-managed RAG pipelines for your AI applications with AutoRAG

AutoRAG is now in open beta, making it easy for you to build fully-managed retrieval-augmented generation (RAG) pipelines without managing infrastructure. Just upload your docs to R2, and AutoRAG handles the rest: embeddings, indexing, retrieval, and response generation via API. With AutoRAG, you can: Customize your pipeline: Choose from Workers AI models, configure chunking strategies, edit syst…