> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Google integration

> Integrate with Google Gemini tools using LangChain JavaScript.

The `@langchain/google` package supports Gemini's built-in tools, which provide capabilities like web search grounding, code execution, URL context retrieval, and more. These tools are passed as Gemini-native objects to `ChatGoogle` via `bindTools()` or the `tools` call option.

<Warning>
  You cannot mix Gemini native tools (Google Search, Code Execution, etc.) with standard LangChain tools (Zod-based function tools) in the same request. See the [ChatGoogle](/oss/javascript/integrations/chat/google) page for standard tool calling usage.
</Warning>

### Google Search

The `googleSearch` tool grounds model responses with real-time Google Search results. This is useful for questions about current events or specific facts.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatGoogle } from "@langchain/google";

const llm = new ChatGoogle("gemini-2.5-flash")
  .bindTools([
    {
      googleSearch: {},
    },
  ]);

const res = await llm.invoke("Who won the latest World Series?");
console.log(res.text);
```

You can optionally filter search results to a specific time range:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const llm = new ChatGoogle("gemini-2.5-flash")
  .bindTools([
  {
    googleSearch: {
      timeRangeFilter: {
        startTime: "2025-01-01T00:00:00Z",
        endTime: "2025-12-31T23:59:59Z",
      },
    },
  },
]);
```

<Note>
  The `googleSearchRetrieval` tool is maintained for backwards compatibility, but `googleSearch` is preferred.
</Note>

For more information, see [Google's Grounding with Google Search documentation](https://ai.google.dev/gemini-api/docs/grounding).

### Code execution

The `codeExecution` tool allows Gemini to generate and run Python code to solve complex problems. The model writes the code, executes it, and returns the results.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatGoogle } from "@langchain/google";

const llm = new ChatGoogle("gemini-2.5-flash")
  .bindTools([
    {
      codeExecution: {},
    },
  ]);

const res = await llm.invoke("Calculate the 100th Fibonacci number.");
console.log(res.contentBlocks);
```

The response includes both the generated code and its execution result in the `contentBlocks` field:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
for (const block of res.contentBlocks) {
  if (block.type === "tool_code") {
    console.log("Code:", block.toolCode);
  } else if (block.type === "tool_result") {
    console.log("Result:", block.toolResult);
  }
}
```

For more information, see [Google's Code Execution documentation](https://ai.google.dev/gemini-api/docs/code-execution).

### URL context

The `urlContext` tool allows Gemini to fetch and use content from URLs to ground its responses.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatGoogle } from "@langchain/google";

const llm = new ChatGoogle("gemini-2.5-flash")
  .bindTools([
    {
      urlContext: {},
    },
  ]);

const res = await llm.invoke("Summarize this page: https://js.langchain.com/");
console.log(res.text);
```

For more information, see [Google's URL Context documentation](https://ai.google.dev/gemini-api/docs/url-context).

### Google Maps

The `googleMaps` tool grounds responses with geospatial context from Google Maps. This is useful for place-related queries.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatGoogle } from "@langchain/google";

const llm = new ChatGoogle("gemini-2.5-flash")
  .bindTools([
    {
      googleMaps: {},
    },
  ]);

const res = await llm.invoke("What are the best coffee shops near Times Square?");
console.log(res.text);
```

You can enable a widget context token for rendering a Google Maps widget:

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const llm = new ChatGoogle("gemini-2.5-flash")
  .bindTools([
    {
      googleMaps: {
        enableWidget: true,
    },
  },
]);

const res = await llm.invoke("Find Italian restaurants in downtown Chicago");

// Access the widget context token from grounding metadata
const groundingMetadata = res.response_metadata?.groundingMetadata;
console.log(groundingMetadata?.googleMapsWidgetContextToken);
```

For more information, see [Google's Google Maps grounding documentation](https://ai.google.dev/gemini-api/docs/grounding/google-maps).

### File search

The `fileSearch` tool performs semantic retrieval from file search stores. Files must first be imported using the Gemini File API.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatGoogle } from "@langchain/google";

const llm = new ChatGoogle("gemini-2.5-flash")
  .bindTools([
    {
      fileSearch: {
      fileSearchStoreNames: ["fileSearchStores/my-store-123"],
    },
  },
]);

const res = await llm.invoke("What does the report say about Q4 revenue?");
console.log(res.text);
```

Configuration options:

* `fileSearchStoreNames` (required) -- the names of the file search stores to retrieve from
* `metadataFilter` (optional) -- metadata filter to apply to the retrieval
* `topK` (optional) -- the number of semantic retrieval chunks to return

For more information, see [Google's File Search documentation](https://ai.google.dev/gemini-api/docs/file-search).

### Computer use

The `computerUse` tool enables Gemini to interact with a browser environment. The model can view screenshots and perform actions like clicking, typing, and scrolling.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatGoogle } from "@langchain/google";

const llm = new ChatGoogle("gemini-2.5-flash")
  .bindTools([
    {
      computerUse: {
      environment: "ENVIRONMENT_BROWSER",
    },
  },
]);
```

Configuration options:

* `environment` (required) -- the environment being operated (e.g. `"ENVIRONMENT_BROWSER"`)
* `excludedPredefinedFunctions` (optional) -- predefined functions to exclude from the action space

For more information, see [Google's Computer Use documentation](https://ai.google.dev/gemini-api/docs/computer-use).

### MCP servers

The `mcpServers` field allows Gemini to connect to remote MCP (Model Context Protocol) servers. Unlike other native tools, MCP servers are specified as an array on the tool object.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatGoogle } from "@langchain/google";

const llm = new ChatGoogle("gemini-2.5-flash")
  .bindTools([
    {
      mcpServers: [
      {
        name: "my-mcp-server",
        streamableHttpTransport: {
          url: "https://my-mcp-server.example.com/mcp",
        },
      },
    ],
  },
]);

const res = await llm.invoke("Use the tools from the MCP server to help me.");
console.log(res.text);
```

For more information, see [Google's MCP documentation](https://ai.google.dev/gemini-api/docs/mcp).

### Vertex AI Search data store

If you are using Vertex AI (`platformType: "gcp"`), you can ground responses using a Vertex AI Search data store.

```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { ChatGoogle } from "@langchain/google";

const projectId = "YOUR_PROJECT_ID";
const datastoreId = "YOUR_DATASTORE_ID";

const llm = new ChatGoogle({
  model: "gemini-2.5-pro",
  platformType: "gcp",
}).bindTools([
  {
    retrieval: {
      vertexAiSearch: {
        datastore: `projects/${projectId}/locations/global/collections/default_collection/dataStores/${datastoreId}`,
      },
      disableAttribution: false,
    },
  },
]);

const res = await llm.invoke(
  "What is the score of Argentina vs Bolivia football game?"
);
console.log(res.text);
```

For more information, see [Google's Vertex AI Search grounding documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/ground-with-vertex-ai-search).

***

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