---
title: AI Search
description: Give agents retrieval capabilities with Cloudflare AI Search.
image: https://edgetunnel-b2h.pages.dev/dev-products-preview.png
---

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# AI Search

Agents can use [AI Search](https://edgetunnel-b2h.pages.dev/ai-search/) to retrieve relevant information from indexed content and use it to augment [calls to AI models](https://edgetunnel-b2h.pages.dev/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-7021)
* [  TypeScript ](#tab-panel-7022)

**JavaScript**

```js
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**

```ts
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-7019)
* [  TypeScript ](#tab-panel-7020)

**JavaScript**

```js
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**

```ts
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-7017)
* [  wrangler.toml ](#tab-panel-7018)

**JSONC**

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

**TOML**

```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://edgetunnel-b2h.pages.dev/ai-search/) Create managed retrieval pipelines over websites, R2 buckets, and uploaded files. 

[ Workers binding ](https://edgetunnel-b2h.pages.dev/ai-search/api/search/workers-binding/) Query AI Search directly from Workers code. 

[ Create an AI Search instance ](https://edgetunnel-b2h.pages.dev/ai-search/get-started/) Create your first AI Search instance and run your first query.

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```