---
title: AI Search
description: Give agents retrieval capabilities with Cloudflare AI Search.
image: https://developers.cloudflare.com/dev-products-preview.png
---

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# AI Search

Agents can use [AI Search](https://developers.cloudflare.com/ai-search/) to retrieve relevant information from indexed content and use it to augment [calls to AI models](https://developers.cloudflare.com/agents/runtime/operations/using-ai-models/). AI Search manages the retrieval pipeline for you, including indexing, search, and optional chat completions over your content.

Use AI Search when you want an agent to:

* Search product docs, support content, user files, or internal knowledge bases.
* Retrieve relevant chunks before calling a model.
* Use managed indexing instead of building retrieval infrastructure yourself.
* Query content from an R2 bucket, website, or uploaded files.

## Basic pattern

Bind AI Search to your Worker, then query an instance from an agent method.

* [  JavaScript ](#tab-panel-6731)
* [  TypeScript ](#tab-panel-6732)

JavaScript

```
import { Agent, callable } from "agents";
export class SearchAgent extends Agent {  @callable()  async searchKnowledge(query) {    const instance = this.env.AI_SEARCH.get("my-instance");
    const results = await instance.search({      messages: [{ role: "user", content: query }],    });
    return results;  }}
```

TypeScript

```
import { Agent, callable } from "agents";
type Env = {  AI_SEARCH: AiSearchNamespace;};
export class SearchAgent extends Agent<Env> {  @callable()  async searchKnowledge(query: string) {    const instance = this.env.AI_SEARCH.get("my-instance");
    const results = await instance.search({      messages: [{ role: "user", content: query }],    });
    return results;  }}
```

For answer generation, use `chatCompletions()` to retrieve relevant content and generate a response in one call.

* [  JavaScript ](#tab-panel-6729)
* [  TypeScript ](#tab-panel-6730)

JavaScript

```
const instance = this.env.AI_SEARCH.get("my-instance");
const response = await instance.chatCompletions({  messages: [{ role: "user", content: "How do I deploy an Agent?" }],  model: "@cf/meta/llama-3.3-70b-instruct-fp8-fast",  ai_search_options: {    retrieval: {      max_num_results: 5,    },  },});
```

TypeScript

```
const instance = this.env.AI_SEARCH.get("my-instance");
const response = await instance.chatCompletions({  messages: [{ role: "user", content: "How do I deploy an Agent?" }],  model: "@cf/meta/llama-3.3-70b-instruct-fp8-fast",  ai_search_options: {    retrieval: {      max_num_results: 5,    },  },});
```

## Configuration

Use an `ai_search_namespaces` binding when the agent needs to access AI Search instances by name.

* [  wrangler.jsonc ](#tab-panel-6727)
* [  wrangler.toml ](#tab-panel-6728)

JSONC

```
{  "ai_search_namespaces": [    {      "binding": "AI_SEARCH",      "namespace": "default",      "remote": true    }  ]}
```

TOML

```
[[ai_search_namespaces]]binding = "AI_SEARCH"namespace = "default"remote = true
```

Use `remote: true` to query deployed AI Search instances during local development with `wrangler dev`.

## Related resources

[ AI Search ](https://developers.cloudflare.com/ai-search/) Create managed retrieval pipelines over websites, R2 buckets, and uploaded files. 

[ Workers binding ](https://developers.cloudflare.com/ai-search/api/search/workers-binding/) Query AI Search directly from Workers code. 

[ Create an AI Search instance ](https://developers.cloudflare.com/ai-search/get-started/) Create your first AI Search instance and run your first query.

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