diff --git a/packages/firebase_ai/firebase_ai/lib/firebase_ai.dart b/packages/firebase_ai/firebase_ai/lib/firebase_ai.dart
index 0587c156f9a5..ef907861a6d3 100644
--- a/packages/firebase_ai/firebase_ai/lib/firebase_ai.dart
+++ b/packages/firebase_ai/firebase_ai/lib/firebase_ai.dart
@@ -76,5 +76,6 @@ export 'src/tool.dart'
         FunctionCallingConfig,
         FunctionCallingMode,
         FunctionDeclaration,
+        RAGEngineGrounding,
         Tool,
         ToolConfig;
diff --git a/packages/firebase_ai/firebase_ai/lib/src/tool.dart b/packages/firebase_ai/firebase_ai/lib/src/tool.dart
index 394cb555e7af..fb934d5d8439 100644
--- a/packages/firebase_ai/firebase_ai/lib/src/tool.dart
+++ b/packages/firebase_ai/firebase_ai/lib/src/tool.dart
@@ -21,12 +21,13 @@ import 'schema.dart';
 /// knowledge and scope of the model.
 final class Tool {
   // ignore: public_member_api_docs
-  Tool._(this._functionDeclarations, this._googleSearch);
+  Tool._(
+      this._functionDeclarations, this._googleSearch, this._ragEngineGrounding);
 
   /// Returns a [Tool] instance with list of [FunctionDeclaration].
   static Tool functionDeclarations(
       List<FunctionDeclaration> functionDeclarations) {
-    return Tool._(functionDeclarations, null);
+    return Tool._(functionDeclarations, null, null);
   }
 
   /// Creates a tool that allows the model to use Grounding with Google Search.
@@ -47,7 +48,18 @@ final class Tool {
   ///
   /// Returns a `Tool` configured for Google Search.
   static Tool googleSearch({GoogleSearch googleSearch = const GoogleSearch()}) {
-    return Tool._(null, googleSearch);
+    return Tool._(null, googleSearch, null);
+  }
+
+  /// Creates a tool that allows the model to use RAG Engine Grounding.
+  ///
+  /// RAG Engine Grounding can be used to allow the model to connect to Vertex AI
+  /// RAG Engine for retrieving and incorporating external knowledge into its
+  /// responses.
+  ///
+  /// Only available in Vertex AI
+  static Tool ragEngine(RAGEngineGrounding ragEngineGrounding) {
+    return Tool._(null, null, ragEngineGrounding);
   }
 
   /// A list of `FunctionDeclarations` available to the model that can be used
@@ -65,13 +77,74 @@ final class Tool {
   /// responses.
   final GoogleSearch? _googleSearch;
 
+  /// A tool that allows the generative model to connect to RAG Engine for
+  /// retrieving and incorporating external knowledge into its responses.
+  final RAGEngineGrounding? _ragEngineGrounding;
+
   /// Convert to json object.
   Map<String, Object> toJson() => {
         if (_functionDeclarations case final _functionDeclarations?)
           'functionDeclarations':
               _functionDeclarations.map((f) => f.toJson()).toList(),
         if (_googleSearch case final _googleSearch?)
-          'googleSearch': _googleSearch.toJson()
+          'googleSearch': _googleSearch.toJson(),
+        if (_ragEngineGrounding case final _ragEngineGrounding?)
+          'retrieval': _ragEngineGrounding.toJson()
+      };
+}
+
+/// Configuration for RAG (Retrieval-Augmented Generation) Engine Grounding.
+///
+/// This tool allows grounding a model's response in a specific corpus of data
+/// stored in Vertex AI. It helps the model generate more accurate and
+/// contextually relevant answers by retrieving information from your own data sources.
+///
+/// Use this to configure the grounding settings for a generative model tool.
+final class RAGEngineGrounding {
+  /// Creates a new instance of [RAGEngineGrounding].
+  ///
+  /// [projectId] is the ID of the Vertex AI project.
+  /// [location] is the location of the corpus (e.g., 'us-central1').
+  /// [corpusId] is the specific ID of the corpus to use for grounding.
+  /// [topK] specifies the number of top contexts to retrieve, defaulting to 20.
+  const RAGEngineGrounding({
+    required this.projectId,
+    required this.location,
+    required this.corpusId,
+    this.topK = 20,
+  });
+
+  /// The project ID of the Vertex AI project that contains the corpus.
+  final String projectId;
+
+  /// The location of the corpus.
+  final String location;
+
+  /// The ID of the corpus to use for RAG Engine Grounding.
+  final String corpusId;
+
+  /// The number of top contexts to retrieve.
+  ///
+  /// Must be between 1 and 20.
+  final int topK;
+
+  /// The path to the corpus in the format:
+  /// `projects/{projectId}/locations/{location}/ragCorpora/{corpusId}`
+  String get corpusPath =>
+      'projects/$projectId/locations/$location/ragCorpora/$corpusId';
+
+  /// Converts this [RAGEngineGrounding] object into a JSON-compatible Map.
+  Map<String, Object> toJson() => {
+        'vertexRagStore': {
+          'ragResources': [
+            {
+              'ragCorpus': corpusPath,
+            },
+          ],
+          'ragRetrievalConfig': {
+            'topK': topK,
+          },
+        },
       };
 }
 
diff --git a/packages/firebase_ai/firebase_ai/test/model_test.dart b/packages/firebase_ai/firebase_ai/test/model_test.dart
index 860b8e19ba7c..634051e5c70f 100644
--- a/packages/firebase_ai/firebase_ai/test/model_test.dart
+++ b/packages/firebase_ai/firebase_ai/test/model_test.dart
@@ -268,6 +268,47 @@ void main() {
         );
       });
 
+      test('can pass rag engine grounding tool', () async {
+        final (client, model) = createModel(
+          tools: [
+            Tool.ragEngine(
+              const RAGEngineGrounding(
+                corpusId: 'corpusId',
+                projectId: 'project-12345',
+                location: 'global',
+                topK: 15,
+              ),
+            ),
+          ],
+        );
+
+        const prompt = 'Some prompt';
+
+        await client.checkRequest(
+          () => model.generateContent([Content.text(prompt)]),
+          verifyRequest: (_, request) {
+            expect(request['tools'], [
+              {
+                'retrieval': {
+                  'vertexRagStore': {
+                    'ragResources': [
+                      {
+                        'ragCorpus':
+                            'projects/project-12345/locations/global/ragCorpora/corpusId',
+                      }
+                    ],
+                    'ragRetrievalConfig': {
+                      'topK': 15,
+                    }
+                  }
+                }
+              },
+            ]);
+          },
+          response: arbitraryGenerateContentResponse,
+        );
+      });
+
       test('can pass a google search tool', () async {
         final (client, model) = createModel(
           tools: [Tool.googleSearch()],