---
description: New guides show how to use an AI Search instance from the Cloudflare Agents SDK, the Vercel AI SDK, and LangChain.
title: Use AI Search with the Agents SDK, AI SDK, and LangChain
image: https://developers.cloudflare.com/changelog/post/2026-07-30-ai-search-agent-sdks/og.png?v=c5c7cc670a0a3d20
---

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July 30, 2026

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

[AI Search](https://developers.cloudflare.com/ai-search/)

Copy as Markdown| [View as Markdown](https://developers.cloudflare.com/changelog/post/2026-07-30-ai-search-agent-sdks/index.md)| [Agent setup](https://developers.cloudflare.com/agent-setup/)

You can now use [AI Search](https://developers.cloudflare.com/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](https://developers.cloudflare.com/ai-search/agent-sdks/) section has guides for the [Vercel AI SDK](https://developers.cloudflare.com/ai-search/agent-sdks/ai-sdk/), [LangChain](https://developers.cloudflare.com/ai-search/agent-sdks/langchain/), and the [Cloudflare Agents SDK](https://developers.cloudflare.com/ai-search/agent-sdks/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 SDK

The [`ai-search-provider` ↗︎](https://www.npmjs.com/package/ai-search-provider) package connects AI Search to the AI SDK, and targets AI SDK v6 (`ai@^6`). Pass `instance.chat()` to `generateText` or `streamText` to generate a response grounded in your indexed content, with the retrieved chunks returned as `sources`. You can also expose `instance.search()` as a tool for agent loops.

```js
import { createAISearchNamespace } from "ai-search-provider";
import { generateText } from "ai";

const aiSearch = createAISearchNamespace({ binding: env.AI_SEARCH });

const { text, sources } = await generateText({
	model: aiSearch.get("knowledge-base").chat(),
	messages: [{ role: "user", content: "How does caching work?" }],
});
```

```ts
import { createAISearchNamespace } from "ai-search-provider";
import { generateText } from "ai";

const aiSearch = createAISearchNamespace({ binding: env.AI_SEARCH });

const { text, sources } = await generateText({
	model: aiSearch.get("knowledge-base").chat(),
	messages: [{ role: "user", content: "How does caching work?" }],
});
```

#### LangChain

The `langchain-cloudflare` package ([PyPI ↗︎](https://pypi.org/project/langchain-cloudflare/), [GitHub ↗︎](https://github.com/cloudflare/langchain-cloudflare)) provides `CloudflareAISearchRetriever`, a standard LangChain retriever backed by AI Search. Use it on its own, wrap it with `create_retriever_tool` to give an agent a search tool, or drop it into a RAG chain. It works with REST credentials or a Worker binding inside a Python Worker.

```python
from langchain_cloudflare import CloudflareAISearchRetriever

retriever = CloudflareAISearchRetriever(
    account_id=ACCOUNT_ID,
    api_token=API_TOKEN,
    instance_name="knowledge-base",
    retrieval_type="hybrid",
)

docs = retriever.invoke("How do I configure Workers AI?")
```

#### Cloudflare Agents SDK

The [Cloudflare Agents SDK](https://developers.cloudflare.com/agents/) could already reach AI Search through the Workers binding. The new [guide](https://developers.cloudflare.com/ai-search/agent-sdks/agents-sdk/) walks through building a stateful chat agent that provisions its own instance, indexes content, and searches it from a tool.

```js
import { tool } from "ai";
import { z } from "zod";

const instance = env.AI_SEARCH.get("knowledge-base");

// Expose AI Search to the agent's model as a tool it can call.
const searchKnowledgeBase = tool({
	description: "Search the knowledge base for relevant content.",
	inputSchema: z.object({ query: z.string() }),
	execute: ({ query }) => instance.search({ query }),
});
```

```ts
import { tool } from "ai";
import { z } from "zod";

const instance = env.AI_SEARCH.get("knowledge-base");

// Expose AI Search to the agent's model as a tool it can call.
const searchKnowledgeBase = tool({
	description: "Search the knowledge base for relevant content.",
	inputSchema: z.object({ query: z.string() }),
	execute: ({ query }) => instance.search({ query }),
});
```

For the full walkthroughs, including creating an instance and indexing content, refer to the [Agents](https://developers.cloudflare.com/ai-search/agent-sdks/) guides.

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