diff --git a/.github/workflows/_compile_integration_test.yml b/.github/workflows/_compile_integration_test.yml
index e002a2414..756a372fa 100644
--- a/.github/workflows/_compile_integration_test.yml
+++ b/.github/workflows/_compile_integration_test.yml
@@ -20,7 +20,6 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - "3.9"
           - "3.10"
           - "3.11"
           - "3.12"
diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml
index 07d6bc618..9b362ee68 100644
--- a/.github/workflows/_lint.yml
+++ b/.github/workflows/_lint.yml
@@ -35,7 +35,7 @@ jobs:
         # Starting new jobs is also relatively slow,
         # so linting on fewer versions makes CI faster.
         python-version:
-          - "3.9"
+          - "3.10"
           - "3.12"
     steps:
       - uses: actions/checkout@v4
diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yml
index d3e78008f..c19a12ca6 100644
--- a/.github/workflows/_test.yml
+++ b/.github/workflows/_test.yml
@@ -24,7 +24,6 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - "3.9"
           - "3.10"
           - "3.11"
           - "3.12"
diff --git a/libs/community/langchain_google_community/__init__.py b/libs/community/langchain_google_community/__init__.py
index 2012fbb61..452ac72f4 100644
--- a/libs/community/langchain_google_community/__init__.py
+++ b/libs/community/langchain_google_community/__init__.py
@@ -1,5 +1,4 @@
 from langchain_google_community.bigquery import BigQueryLoader
-from langchain_google_community.bigquery_vector_search import BigQueryVectorSearch
 from langchain_google_community.bq_storage_vectorstores.bigquery import (
     BigQueryVectorStore,
 )
@@ -64,7 +63,6 @@
 __all__ = [
     "BigQueryLoader",
     "BigQueryVectorStore",
-    "BigQueryVectorSearch",
     "CalendarCreateEvent",
     "CalendarDeleteEvent",
     "CalendarMoveEvent",
diff --git a/libs/community/langchain_google_community/bigquery_vector_search.py b/libs/community/langchain_google_community/bigquery_vector_search.py
deleted file mode 100644
index 8164df585..000000000
--- a/libs/community/langchain_google_community/bigquery_vector_search.py
+++ /dev/null
@@ -1,866 +0,0 @@
-"""Vector Store in Google Cloud BigQuery."""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import sys
-import uuid
-from datetime import datetime
-from functools import partial
-from threading import Lock, Thread
-from typing import Any, Callable, Dict, List, Optional, Tuple, Type
-
-import numpy as np
-from google.api_core.exceptions import ClientError
-from langchain_community.vectorstores.utils import (
-    DistanceStrategy,
-    maximal_marginal_relevance,
-)
-from langchain_core._api.deprecation import deprecated
-from langchain_core.documents import Document
-from langchain_core.embeddings import Embeddings
-from langchain_core.vectorstores import VectorStore
-
-from langchain_google_community._utils import get_client_info
-
-DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.EUCLIDEAN_DISTANCE
-DEFAULT_DOC_ID_COLUMN_NAME = "doc_id"  # document id
-DEFAULT_TEXT_EMBEDDING_COLUMN_NAME = "text_embedding"  # embeddings vectors
-DEFAULT_METADATA_COLUMN_NAME = "metadata"  # document metadata
-DEFAULT_CONTENT_COLUMN_NAME = "content"  # text content, do not rename
-DEFAULT_TOP_K = 4  # default number of documents returned from similarity search
-
-_MIN_INDEX_ROWS = 5000  # minimal number of rows for creating an index
-_INDEX_CHECK_PERIOD_SECONDS = 60  # Do not check for index more often that this.
-_vector_table_lock = Lock()  # process-wide BigQueryVectorSearch table lock
-
-
-@deprecated(
-    since="1.0.5",
-    removal="2.0.0",
-    alternative_import="langchain_google_community.BigQueryVectorStore",
-)
-class BigQueryVectorSearch(VectorStore):
-    """Google Cloud BigQuery vector store.
-
-    To use, you need the following packages installed:
-        google-cloud-bigquery
-    """
-
-    def __init__(
-        self,
-        embedding: Embeddings,
-        project_id: str,
-        dataset_name: str,
-        table_name: str,
-        location: str = "US",
-        content_field: str = DEFAULT_CONTENT_COLUMN_NAME,
-        metadata_field: str = DEFAULT_METADATA_COLUMN_NAME,
-        text_embedding_field: str = DEFAULT_TEXT_EMBEDDING_COLUMN_NAME,
-        doc_id_field: str = DEFAULT_DOC_ID_COLUMN_NAME,
-        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
-        credentials: Optional[Any] = None,
-    ):
-        """Constructor for BigQueryVectorSearch.
-
-        Args:
-            embedding (Embeddings): Text Embedding model to use.
-            project_id (str): GCP project.
-            dataset_name (str): BigQuery dataset to store documents and embeddings.
-            table_name (str): BigQuery table name.
-            location (str, optional): BigQuery region. Defaults to
-                                      `US`(multi-region).
-            content_field (str): Specifies the column to store the content.
-                                 Defaults to `content`.
-            metadata_field (str): Specifies the column to store the metadata.
-                                  Defaults to `metadata`.
-            text_embedding_field (str): Specifies the column to store
-                                        the embeddings vector.
-                                        Defaults to `text_embedding`.
-            doc_id_field (str): Specifies the column to store the document id.
-                                Defaults to `doc_id`.
-            distance_strategy (DistanceStrategy, optional):
-                Determines the strategy employed for calculating
-                the distance between vectors in the embedding space.
-                Defaults to EUCLIDEAN_DISTANCE.
-                Available options are:
-                - COSINE: Measures the similarity between two vectors of an inner
-                    product space.
-                - EUCLIDEAN_DISTANCE: Computes the Euclidean distance between
-                    two vectors. This metric considers the geometric distance in
-                    the vector space, and might be more suitable for embeddings
-                    that rely on spatial relationships. This is the default behavior
-            credentials (Credentials, optional): Custom Google Cloud credentials
-                to use. Defaults to None.
-        """
-        try:
-            from google.cloud import bigquery  # type: ignore[attr-defined]
-
-            client_info = get_client_info(module="bigquery-vector-search")
-            self.bq_client = bigquery.Client(
-                project=project_id,
-                location=location,
-                credentials=credentials,
-                client_info=client_info,
-            )
-        except ModuleNotFoundError:
-            raise ImportError(
-                "Could not import google-cloud-bigquery python package. "
-                "Please, install bigquery dependency group: "
-                "`pip install langchain-google-community[bigquery]`"
-            )
-        self._logger = logging.getLogger(__name__)
-        self._creating_index = False
-        self._have_index = False
-        self.embedding_model = embedding
-        self.project_id = project_id
-        self.dataset_name = dataset_name
-        self.table_name = table_name
-        self.location = location
-        self.content_field = content_field
-        self.metadata_field = metadata_field
-        self.text_embedding_field = text_embedding_field
-        self.doc_id_field = doc_id_field
-        self.distance_strategy = distance_strategy
-        self._full_table_id = f"{self.project_id}.{self.dataset_name}.{self.table_name}"
-        self._logger.debug("Using table `%s`", self.full_table_id)
-        with _vector_table_lock:
-            self.vectors_table = self._initialize_table()
-        self._last_index_check = datetime.min
-        self._initialize_vector_index()
-
-    def _initialize_table(self) -> Any:
-        """Validates or creates the BigQuery table."""
-        from google.cloud import bigquery  # type: ignore[attr-defined]
-
-        table_ref = bigquery.TableReference.from_string(self._full_table_id)
-        table = self.bq_client.create_table(table_ref, exists_ok=True)
-        changed_schema = False
-        schema = table.schema.copy()
-        columns = {c.name: c for c in schema}
-        if self.doc_id_field not in columns:
-            changed_schema = True
-            schema.append(
-                bigquery.SchemaField(name=self.doc_id_field, field_type="STRING")
-            )
-        elif (
-            columns[self.doc_id_field].field_type != "STRING"
-            or columns[self.doc_id_field].mode == "REPEATED"
-        ):
-            raise ValueError(f"Column {self.doc_id_field} must be of STRING type")
-        if self.metadata_field not in columns:
-            changed_schema = True
-            schema.append(
-                bigquery.SchemaField(name=self.metadata_field, field_type="JSON")
-            )
-        elif (
-            columns[self.metadata_field].field_type not in ["JSON", "STRING"]
-            or columns[self.metadata_field].mode == "REPEATED"
-        ):
-            raise ValueError(
-                f"Column {self.metadata_field} must be of STRING or JSON type"
-            )
-        if self.content_field not in columns:
-            changed_schema = True
-            schema.append(
-                bigquery.SchemaField(name=self.content_field, field_type="STRING")
-            )
-        elif (
-            columns[self.content_field].field_type != "STRING"
-            or columns[self.content_field].mode == "REPEATED"
-        ):
-            raise ValueError(f"Column {self.content_field} must be of STRING type")
-        if self.text_embedding_field not in columns:
-            changed_schema = True
-            schema.append(
-                bigquery.SchemaField(
-                    name=self.text_embedding_field,
-                    field_type="FLOAT64",
-                    mode="REPEATED",
-                )
-            )
-        elif (
-            columns[self.text_embedding_field].field_type not in ("FLOAT", "FLOAT64")
-            or columns[self.text_embedding_field].mode != "REPEATED"
-        ):
-            raise ValueError(
-                f"Column {self.text_embedding_field} must be of ARRAY<FLOAT64> type"
-            )
-        if changed_schema:
-            self._logger.debug("Updated table `%s` schema.", self.full_table_id)
-            table.schema = schema
-            table = self.bq_client.update_table(table, fields=["schema"])
-        return table
-
-    def _initialize_vector_index(self) -> Any:
-        """
-        A vector index in BigQuery table enables efficient
-        approximate vector search.
-        """
-        from google.cloud import bigquery  # type: ignore[attr-defined]
-
-        if self._have_index or self._creating_index:
-            # Already have an index or in the process of creating one.
-            return
-        table = self.bq_client.get_table(self.vectors_table)
-        if (table.num_rows or 0) < _MIN_INDEX_ROWS:
-            # Not enough rows to create index.
-            self._logger.debug("Not enough rows to create a vector index.")
-            return
-        if (
-            datetime.utcnow() - self._last_index_check
-        ).total_seconds() < _INDEX_CHECK_PERIOD_SECONDS:
-            return
-        with _vector_table_lock:
-            if self._creating_index or self._have_index:
-                return
-            self._last_index_check = datetime.utcnow()
-            # Check if index exists, create if necessary
-            check_query = (
-                f"SELECT 1 FROM `{self.project_id}.{self.dataset_name}"
-                ".INFORMATION_SCHEMA.VECTOR_INDEXES` WHERE"
-                f" table_name = '{self.table_name}'"
-            )
-            job = self.bq_client.query(
-                check_query, api_method=bigquery.enums.QueryApiMethod.QUERY
-            )
-            if job.result().total_rows == 0:
-                # Need to create an index. Make it in a separate thread.
-                self._create_index_in_background()
-            else:
-                self._logger.debug("Vector index already exists.")
-                self._have_index = True
-
-    def _create_index_in_background(self):  # type: ignore[no-untyped-def]
-        if self._have_index or self._creating_index:
-            # Already have an index or in the process of creating one.
-            return
-        self._creating_index = True
-        self._logger.debug("Trying to create a vector index.")
-        thread = Thread(target=self._create_index, daemon=True)
-        thread.start()
-
-    def _create_index(self):  # type: ignore[no-untyped-def]
-        table = self.bq_client.get_table(self.vectors_table)
-        if (table.num_rows or 0) < _MIN_INDEX_ROWS:
-            # Not enough rows to create index.
-            return
-        if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
-            distance_type = "EUCLIDEAN"
-        elif self.distance_strategy == DistanceStrategy.COSINE:
-            distance_type = "COSINE"
-        # Default to EUCLIDEAN_DISTANCE
-        else:
-            distance_type = "EUCLIDEAN"
-        index_name = f"{self.table_name}_langchain_index"
-        try:
-            sql = f"""
-                CREATE VECTOR INDEX IF NOT EXISTS
-                `{index_name}`
-                ON `{self.full_table_id}`({self.text_embedding_field})
-                OPTIONS(distance_type="{distance_type}", index_type="IVF")
-            """
-            self.bq_client.query(sql).result()
-            self._have_index = True
-        except ClientError as ex:
-            self._logger.debug("Vector index creation failed (%s).", ex.args[0])
-        finally:
-            self._creating_index = False
-
-    def _persist(self, data: Dict[str, Any]) -> None:
-        """Saves documents and embeddings to BigQuery."""
-        from google.cloud import bigquery  # type: ignore[attr-defined]
-
-        data_len = len(data[list(data.keys())[0]])
-        if data_len == 0:
-            return
-
-        list_of_dicts = [dict(zip(data, t)) for t in zip(*data.values())]
-
-        job_config = bigquery.LoadJobConfig()
-        job_config.schema = self.vectors_table.schema
-        job_config.schema_update_options = (
-            bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION
-        )
-        job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND
-        job = self.bq_client.load_table_from_json(
-            list_of_dicts, self.vectors_table, job_config=job_config
-        )
-        job.result()
-
-    @property
-    def embeddings(self) -> Optional[Embeddings]:
-        return self.embedding_model
-
-    @property
-    def full_table_id(self) -> str:
-        return self._full_table_id
-
-    def add_texts(  # type: ignore[override]
-        self,
-        texts: List[str],
-        metadatas: Optional[List[dict]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more texts through the embeddings and add to the vectorstore.
-
-        Args:
-            texts: List of strings to add to the vectorstore.
-            metadatas: Optional list of metadata associated with the texts.
-
-        Returns:
-            List of ids from adding the texts into the vectorstore.
-        """
-        embs = self.embedding_model.embed_documents(texts)
-        return self.add_texts_with_embeddings(texts, embs, metadatas, **kwargs)
-
-    def add_texts_with_embeddings(
-        self,
-        texts: List[str],
-        embs: List[List[float]],
-        metadatas: Optional[List[dict]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more texts through the embeddings and add to the vectorstore.
-
-        Args:
-            texts: List of strings to add to the vectorstore.
-            embs: List of lists of floats with text embeddings for texts.
-            metadatas: Optional list of metadata associated with the texts.
-
-        Returns:
-            List of ids from adding the texts into the vectorstore.
-        """
-        ids = [uuid.uuid4().hex for _ in texts]
-        values_dict: Dict[str, List[Any]] = {
-            self.content_field: texts,
-            self.doc_id_field: ids,
-        }
-        if not metadatas:
-            metadatas = []
-        len_diff = len(ids) - len(metadatas)
-        add_meta = [None for _ in range(0, len_diff)]
-        metadatas = [m if m is not None else {} for m in metadatas + add_meta]
-        values_dict[self.metadata_field] = metadatas
-        values_dict[self.text_embedding_field] = embs
-        self._persist(values_dict)
-        return ids
-
-    def get_documents(
-        self, ids: Optional[List[str]] = None, filter: Optional[Dict[str, Any]] = None
-    ) -> List[Document]:
-        """Search documents by their ids or metadata values.
-
-        Args:
-            ids: List of ids of documents to retrieve from the vectorstore.
-            filter: Filter on metadata properties, e.g.
-                            {
-                                "str_property": "foo",
-                                "int_property": 123
-                            }
-        Returns:
-            List of ids from adding the texts into the vectorstore.
-        """
-        if ids and len(ids) > 0:
-            from google.cloud import bigquery  # type: ignore[attr-defined]
-
-            job_config = bigquery.QueryJobConfig(
-                query_parameters=[
-                    bigquery.ArrayQueryParameter("ids", "STRING", ids),
-                ]
-            )
-            id_expr = f"{self.doc_id_field} IN UNNEST(@ids)"
-        else:
-            job_config = None
-            id_expr = "TRUE"
-        if filter:
-            filter_expressions = []
-            for i in filter.items():
-                if isinstance(i[1], float):
-                    expr = (
-                        "ABS(CAST(JSON_VALUE("
-                        f"`{self.metadata_field}`,'$.{i[0]}') "
-                        f"AS FLOAT64) - {i[1]}) "
-                        f"<= {sys.float_info.epsilon}"
-                    )
-                else:
-                    val = str(i[1]).replace('"', '\\"')
-                    expr = f"JSON_VALUE(`{self.metadata_field}`,'$.{i[0]}') = \"{val}\""
-                filter_expressions.append(expr)
-            filter_expression_str = " AND ".join(filter_expressions)
-            where_filter_expr = f" AND ({filter_expression_str})"
-        else:
-            where_filter_expr = ""
-
-        job = self.bq_client.query(
-            f"""
-                    SELECT * FROM `{self.full_table_id}` WHERE {id_expr}
-                    {where_filter_expr}
-                    """,
-            job_config=job_config,
-        )
-        docs: List[Document] = []
-        for row in job:
-            metadata = None
-            if self.metadata_field:
-                metadata = row[self.metadata_field]
-            if metadata:
-                if not isinstance(metadata, dict):
-                    metadata = json.loads(metadata)
-            else:
-                metadata = {}
-            metadata["__id"] = row[self.doc_id_field]
-            doc = Document(page_content=row[self.content_field], metadata=metadata)
-            docs.append(doc)
-        return docs
-
-    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
-        """Delete by vector ID or other criteria.
-
-        Args:
-            ids: List of ids to delete.
-            **kwargs: Other keyword arguments that subclasses might use.
-
-        Returns:
-            Optional[bool]: True if deletion is successful,
-            False otherwise, None if not implemented.
-        """
-        if not ids or len(ids) == 0:
-            return True
-        from google.cloud import bigquery  # type: ignore[attr-defined]
-
-        job_config = bigquery.QueryJobConfig(
-            query_parameters=[
-                bigquery.ArrayQueryParameter("ids", "STRING", ids),
-            ]
-        )
-        self.bq_client.query(
-            f"""
-                    DELETE FROM `{self.full_table_id}` WHERE {self.doc_id_field}
-                    IN UNNEST(@ids)
-                    """,
-            job_config=job_config,
-        ).result()
-        return True
-
-    async def adelete(
-        self, ids: Optional[List[str]] = None, **kwargs: Any
-    ) -> Optional[bool]:
-        """Delete by vector ID or other criteria.
-
-        Args:
-            ids: List of ids to delete.
-            **kwargs: Other keyword arguments that subclasses might use.
-
-        Returns:
-            Optional[bool]: True if deletion is successful,
-            False otherwise, None if not implemented.
-        """
-        return await asyncio.get_running_loop().run_in_executor(
-            None, partial(self.delete, **kwargs), ids
-        )
-
-    def _search_with_score_and_embeddings_by_vector(
-        self,
-        embedding: List[float],
-        k: int = DEFAULT_TOP_K,
-        filter: Optional[Dict[str, Any]] = None,
-        brute_force: bool = False,
-        fraction_lists_to_search: Optional[float] = None,
-    ) -> List[Tuple[Document, List[float], float]]:
-        from google.cloud import bigquery  # type: ignore[attr-defined]
-
-        # Create an index if no index exists.
-        if not self._have_index and not self._creating_index:
-            self._initialize_vector_index()
-        # Prepare filter
-        filter_expr = "TRUE"
-        if filter:
-            filter_expressions = []
-            for i in filter.items():
-                if isinstance(i[1], float):
-                    expr = (
-                        "ABS(CAST(JSON_VALUE("
-                        f"base.`{self.metadata_field}`,'$.{i[0]}') "
-                        f"AS FLOAT64) - {i[1]}) "
-                        f"<= {sys.float_info.epsilon}"
-                    )
-                else:
-                    val = str(i[1]).replace('"', '\\"')
-                    expr = (
-                        f"JSON_VALUE(base.`{self.metadata_field}`,'$.{i[0]}')"
-                        f' = "{val}"'
-                    )
-                filter_expressions.append(expr)
-            filter_expression_str = " AND ".join(filter_expressions)
-            filter_expr += f" AND ({filter_expression_str})"
-        # Configure and run a query job.
-        job_config = bigquery.QueryJobConfig(
-            query_parameters=[
-                bigquery.ArrayQueryParameter("v", "FLOAT64", embedding),
-            ],
-            use_query_cache=False,
-            priority=bigquery.QueryPriority.BATCH,
-        )
-        if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
-            distance_type = "EUCLIDEAN"
-        elif self.distance_strategy == DistanceStrategy.COSINE:
-            distance_type = "COSINE"
-        # Default to EUCLIDEAN_DISTANCE
-        else:
-            distance_type = "EUCLIDEAN"
-        if brute_force:
-            options_string = ",options => '{\"use_brute_force\":true}'"
-        elif fraction_lists_to_search:
-            if fraction_lists_to_search == 0 or fraction_lists_to_search >= 1.0:
-                raise ValueError(
-                    "`fraction_lists_to_search` must be between 0.0 and 1.0"
-                )
-            options_string = (
-                ',options => \'{"fraction_lists_to_search":'
-                f"{fraction_lists_to_search}}}'"
-            )
-        else:
-            options_string = ""
-        query = f"""
-            SELECT
-                base.*,
-                distance AS _vector_search_distance
-            FROM VECTOR_SEARCH(
-                TABLE `{self.full_table_id}`,
-                "{self.text_embedding_field}",
-                (SELECT @v AS {self.text_embedding_field}),
-                distance_type => "{distance_type}",
-                top_k => {k}
-                {options_string}
-            )
-            WHERE {filter_expr}
-            LIMIT {k}
-        """
-        document_tuples: List[Tuple[Document, List[float], float]] = []
-        # TODO(vladkol): Use jobCreationMode=JOB_CREATION_OPTIONAL when available.
-        job = self.bq_client.query(
-            query, job_config=job_config, api_method=bigquery.enums.QueryApiMethod.QUERY
-        )
-        # Process job results.
-        for row in job:
-            metadata = row[self.metadata_field]
-            if metadata:
-                if not isinstance(metadata, dict):
-                    metadata = json.loads(metadata)
-            else:
-                metadata = {}
-            metadata["__id"] = row[self.doc_id_field]
-            metadata["__job_id"] = job.job_id
-            doc = Document(page_content=row[self.content_field], metadata=metadata)
-            document_tuples.append(
-                (doc, row[self.text_embedding_field], row["_vector_search_distance"])
-            )
-        return document_tuples
-
-    def similarity_search_with_score_by_vector(
-        self,
-        embedding: List[float],
-        k: int = DEFAULT_TOP_K,
-        filter: Optional[Dict[str, Any]] = None,
-        brute_force: bool = False,
-        fraction_lists_to_search: Optional[float] = None,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, float]]:
-        """Return docs most similar to embedding vector.
-
-        Args:
-            embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            filter: Filter on metadata properties, e.g.
-                            {
-                                "str_property": "foo",
-                                "int_property": 123
-                            }
-            brute_force: Whether to use brute force search. Defaults to False.
-            fraction_lists_to_search: Optional percentage of lists to search,
-                must be in range 0.0 and 1.0, exclusive.
-                If Node, uses service's default which is 0.05.
-
-        Returns:
-            List of Documents most similar to the query vector with distance.
-        """
-        del kwargs
-        document_tuples = self._search_with_score_and_embeddings_by_vector(
-            embedding, k, filter, brute_force, fraction_lists_to_search
-        )
-        return [(doc, distance) for doc, _, distance in document_tuples]
-
-    def similarity_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = DEFAULT_TOP_K,
-        filter: Optional[Dict[str, Any]] = None,
-        brute_force: bool = False,
-        fraction_lists_to_search: Optional[float] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs most similar to embedding vector.
-
-        Args:
-            embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            filter: Filter on metadata properties, e.g.
-                            {
-                                "str_property": "foo",
-                                "int_property": 123
-                            }
-            brute_force: Whether to use brute force search. Defaults to False.
-            fraction_lists_to_search: Optional percentage of lists to search,
-                must be in range 0.0 and 1.0, exclusive.
-                If Node, uses service's default which is 0.05.
-
-        Returns:
-            List of Documents most similar to the query vector.
-        """
-        tuples = self.similarity_search_with_score_by_vector(
-            embedding, k, filter, brute_force, fraction_lists_to_search, **kwargs
-        )
-        return [i[0] for i in tuples]
-
-    def similarity_search_with_score(
-        self,
-        query: str,
-        k: int = DEFAULT_TOP_K,
-        filter: Optional[Dict[str, Any]] = None,
-        brute_force: bool = False,
-        fraction_lists_to_search: Optional[float] = None,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, float]]:
-        """Run similarity search with score.
-
-        Args:
-            query: search query text.
-            k: Number of Documents to return. Defaults to 4.
-            filter: Filter on metadata properties, e.g.
-                            {
-                                "str_property": "foo",
-                                "int_property": 123
-                            }
-            brute_force: Whether to use brute force search. Defaults to False.
-            fraction_lists_to_search: Optional percentage of lists to search,
-                must be in range 0.0 and 1.0, exclusive.
-                If Node, uses service's default which is 0.05.
-
-        Returns:
-            List of Documents most similar to the query vector, with similarity scores.
-        """
-        emb = self.embedding_model.embed_query(query)  # type: ignore
-        return self.similarity_search_with_score_by_vector(
-            emb, k, filter, brute_force, fraction_lists_to_search, **kwargs
-        )
-
-    def similarity_search(
-        self,
-        query: str,
-        k: int = DEFAULT_TOP_K,
-        filter: Optional[Dict[str, Any]] = None,
-        brute_force: bool = False,
-        fraction_lists_to_search: Optional[float] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Run similarity search.
-
-        Args:
-            query: search query text.
-            k: Number of Documents to return. Defaults to 4.
-            filter: Filter on metadata properties, e.g.
-                            {
-                                "str_property": "foo",
-                                "int_property": 123
-                            }
-            brute_force: Whether to use brute force search. Defaults to False.
-            fraction_lists_to_search: Optional percentage of lists to search,
-                must be in range 0.0 and 1.0, exclusive.
-                If Node, uses service's default which is 0.05.
-
-        Returns:
-            List of Documents most similar to the query vector.
-        """
-        tuples = self.similarity_search_with_score(
-            query, k, filter, brute_force, fraction_lists_to_search, **kwargs
-        )
-        return [i[0] for i in tuples]
-
-    def _select_relevance_score_fn(self) -> Callable[[float], float]:
-        if self.distance_strategy == DistanceStrategy.COSINE:
-            return BigQueryVectorSearch._cosine_relevance_score_fn
-        else:
-            raise ValueError(
-                "Relevance score is not supported "
-                f"for `{self.distance_strategy}` distance."
-            )
-
-    def max_marginal_relevance_search(
-        self,
-        query: str,
-        k: int = DEFAULT_TOP_K,
-        fetch_k: int = DEFAULT_TOP_K * 5,
-        lambda_mult: float = 0.5,
-        filter: Optional[Dict[str, Any]] = None,
-        brute_force: bool = False,
-        fraction_lists_to_search: Optional[float] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance.
-
-        Maximal marginal relevance optimizes for similarity to query AND diversity
-        among selected documents.
-
-        Args:
-            query: search query text.
-            k: Number of Documents to return. Defaults to 4.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-            lambda_mult: Number between 0 and 1 that determines the degree
-                        of diversity among the results with 0 corresponding
-                        to maximum diversity and 1 to minimum diversity.
-                        Defaults to 0.5.
-            filter: Filter on metadata properties, e.g.
-                            {
-                                "str_property": "foo",
-                                "int_property": 123
-                            }
-            brute_force: Whether to use brute force search. Defaults to False.
-            fraction_lists_to_search: Optional percentage of lists to search,
-                must be in range 0.0 and 1.0, exclusive.
-                If Node, uses service's default which is 0.05.
-        Returns:
-            List of Documents selected by maximal marginal relevance.
-        """
-        query_embedding = self.embedding_model.embed_query(  # type: ignore
-            query
-        )
-        doc_tuples = self._search_with_score_and_embeddings_by_vector(
-            query_embedding, fetch_k, filter, brute_force, fraction_lists_to_search
-        )
-        doc_embeddings = [d[1] for d in doc_tuples]
-        mmr_doc_indexes = maximal_marginal_relevance(
-            np.array(query_embedding), doc_embeddings, lambda_mult=lambda_mult, k=k
-        )
-        return [doc_tuples[i][0] for i in mmr_doc_indexes]
-
-    def max_marginal_relevance_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = DEFAULT_TOP_K,
-        fetch_k: int = DEFAULT_TOP_K * 5,
-        lambda_mult: float = 0.5,
-        filter: Optional[Dict[str, Any]] = None,
-        brute_force: bool = False,
-        fraction_lists_to_search: Optional[float] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance.
-
-        Maximal marginal relevance optimizes for similarity to query AND diversity
-        among selected documents.
-
-        Args:
-            embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-            lambda_mult: Number between 0 and 1 that determines the degree
-                        of diversity among the results with 0 corresponding
-                        to maximum diversity and 1 to minimum diversity.
-                        Defaults to 0.5.
-            filter: Filter on metadata properties, e.g.
-                            {
-                                "str_property": "foo",
-                                "int_property": 123
-                            }
-            brute_force: Whether to use brute force search. Defaults to False.
-            fraction_lists_to_search: Optional percentage of lists to search,
-                must be in range 0.0 and 1.0, exclusive.
-                If Node, uses service's default which is 0.05.
-        Returns:
-            List of Documents selected by maximal marginal relevance.
-        """
-        doc_tuples = self._search_with_score_and_embeddings_by_vector(
-            embedding, fetch_k, filter, brute_force, fraction_lists_to_search
-        )
-        doc_embeddings = [d[1] for d in doc_tuples]
-        mmr_doc_indexes = maximal_marginal_relevance(
-            np.array(embedding), doc_embeddings, lambda_mult=lambda_mult, k=k
-        )
-        return [doc_tuples[i][0] for i in mmr_doc_indexes]
-
-    async def amax_marginal_relevance_search(
-        self,
-        query: str,
-        k: int = DEFAULT_TOP_K,
-        fetch_k: int = DEFAULT_TOP_K * 5,
-        lambda_mult: float = 0.5,
-        filter: Optional[Dict[str, Any]] = None,
-        brute_force: bool = False,
-        fraction_lists_to_search: Optional[float] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance."""
-
-        func = partial(
-            self.max_marginal_relevance_search,
-            query,
-            k=k,
-            fetch_k=fetch_k,
-            lambda_mult=lambda_mult,
-            filter=filter,
-            brute_force=brute_force,
-            fraction_lists_to_search=fraction_lists_to_search,
-            **kwargs,
-        )
-        return await asyncio.get_event_loop().run_in_executor(None, func)
-
-    async def amax_marginal_relevance_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = DEFAULT_TOP_K,
-        fetch_k: int = DEFAULT_TOP_K * 5,
-        lambda_mult: float = 0.5,
-        filter: Optional[Dict[str, Any]] = None,
-        brute_force: bool = False,
-        fraction_lists_to_search: Optional[float] = None,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance."""
-        return await asyncio.get_running_loop().run_in_executor(
-            None,
-            partial(self.max_marginal_relevance_search_by_vector, **kwargs),
-            embedding,
-            k,
-            fetch_k,
-            lambda_mult,
-            filter,
-            brute_force,
-            fraction_lists_to_search,
-        )
-
-    @classmethod
-    def from_texts(
-        cls: Type["BigQueryVectorSearch"],
-        texts: List[str],
-        embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
-        **kwargs: Any,
-    ) -> "BigQueryVectorSearch":
-        """Return VectorStore initialized from texts and embeddings."""
-        vs_obj = BigQueryVectorSearch(embedding=embedding, **kwargs)
-        vs_obj.add_texts(texts, metadatas)
-        return vs_obj
-
-    def explore_job_stats(self, job_id: str) -> Dict:
-        """Return the statistics for a single job execution.
-
-        Args:
-            job_id: The BigQuery Job id.
-
-        Returns:
-            A dictionary of job statistics for a given job.
-        """
-        return self.bq_client.get_job(job_id)._properties["statistics"]
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index 601e6fdd1..2c4e0c371 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -8,13 +8,13 @@ version = "2.0.10"
 description = "An integration package connecting miscellaneous Google's products and LangChain"
 authors = []
 license = {text = "MIT"}
-requires-python = ">=3.9.0,<3.13.0"
+requires-python = ">=3.10.0,<3.13.0"
 readme = "README.md"
 repository = "https://github.com/langchain-ai/langchain-google"
 
 dependencies = [
-    "langchain-core>=0.3.0,<2.0.0",
-    "langchain-community>=0.3.0,<2.0.0",
+    "langchain-core>=1.0.0a3,<2.0.0",
+    "langchain-community>=0.3.29,<2.0.0",
     "google-api-core>=2.25.0,<3.0.0",
     "google-api-python-client>=2.161.0,<3.0.0",
     "google-cloud-core>=2.4.3,<3.0.0",
@@ -87,6 +87,7 @@ dev = [
 ]
 
 [tool.ruff]
+target-version = "py39"
 fix = true
 
 [tool.ruff.format]
@@ -103,6 +104,11 @@ select = [
 disallow_untyped_defs = "True"
 ignore_missing_imports = "True"
 
+[tool.uv]
+constraint-dependencies = [
+    "llvmlite!=0.45.0; sys_platform == 'darwin' and platform_machine == 'arm64'"
+]
+
 [tool.coverage.run]
 omit = ["tests/*"]
 
diff --git a/libs/community/tests/integration_tests/test_bigquery_vector_search.py b/libs/community/tests/integration_tests/test_bigquery_vector_search.py
deleted file mode 100644
index 67b0e1ab0..000000000
--- a/libs/community/tests/integration_tests/test_bigquery_vector_search.py
+++ /dev/null
@@ -1,108 +0,0 @@
-"""Test BigQuery Vector Search.
-In order to run this test, you need to install Google Cloud BigQuery SDK
-pip install google-cloud-bigquery
-Your end-user credentials would be used to make the calls (make sure you've run
-`gcloud auth login` first).
-"""
-
-import os
-import uuid
-
-import pytest
-
-from langchain_google_community import BigQueryVectorSearch
-from tests.integration_tests.fake import FakeEmbeddings
-
-TEST_TABLE_NAME = "langchain_test_table"
-
-
-@pytest.fixture(scope="class")
-def store(request: pytest.FixtureRequest) -> BigQueryVectorSearch:
-    """BigQueryVectorStore tests context.
-
-    In order to run this test, you define PROJECT_ID environment variable
-    with GCP project id.
-
-    Example:
-    export PROJECT_ID=...
-    """
-    from google.cloud import bigquery  # type: ignore[attr-defined]
-
-    bigquery.Client(location="US").create_dataset(
-        TestBigQueryVectorStore.dataset_name, exists_ok=True
-    )
-    TestBigQueryVectorStore.store = BigQueryVectorSearch(
-        project_id=os.environ.get("PROJECT_ID", None),  # type: ignore[arg-type]
-        embedding=FakeEmbeddings(),  # type: ignore[call-arg]
-        dataset_name=TestBigQueryVectorStore.dataset_name,
-        table_name=TEST_TABLE_NAME,
-    )
-    TestBigQueryVectorStore.store.add_texts(
-        TestBigQueryVectorStore.texts, TestBigQueryVectorStore.metadatas
-    )
-
-    def teardown() -> None:
-        bigquery.Client(location="US").delete_dataset(
-            TestBigQueryVectorStore.dataset_name,
-            delete_contents=True,
-            not_found_ok=True,
-        )
-
-    request.addfinalizer(teardown)
-    return TestBigQueryVectorStore.store
-
-
-class TestBigQueryVectorStore:
-    """BigQueryVectorStore tests class."""
-
-    dataset_name = uuid.uuid4().hex
-    store: BigQueryVectorSearch
-    texts = ["apple", "ice cream", "Saturn", "candy", "banana"]
-    metadatas = [
-        {
-            "kind": "fruit",
-        },
-        {
-            "kind": "treat",
-        },
-        {
-            "kind": "planet",
-        },
-        {
-            "kind": "treat",
-        },
-        {
-            "kind": "fruit",
-        },
-    ]
-
-    @pytest.mark.skip(reason="investigating")
-    @pytest.mark.extended
-    def test_semantic_search(self, store: BigQueryVectorSearch) -> None:
-        """Test on semantic similarity."""
-        docs = store.similarity_search("food", k=4)
-        print(docs)  # noqa: T201
-        kinds = [d.metadata["kind"] for d in docs]
-        assert "fruit" in kinds
-        assert "treat" in kinds
-        assert "planet" not in kinds
-
-    @pytest.mark.skip(reason="investigating")
-    @pytest.mark.extended
-    def test_semantic_search_filter_fruits(self, store: BigQueryVectorSearch) -> None:
-        """Test on semantic similarity with metadata filter."""
-        docs = store.similarity_search("food", filter={"kind": "fruit"})
-        kinds = [d.metadata["kind"] for d in docs]
-        assert "fruit" in kinds
-        assert "treat" not in kinds
-        assert "planet" not in kinds
-
-    @pytest.mark.skip(reason="investigating")
-    @pytest.mark.extended
-    def test_get_doc_by_filter(self, store: BigQueryVectorSearch) -> None:
-        """Test on document retrieval with metadata filter."""
-        docs = store.get_documents(filter={"kind": "fruit"})
-        kinds = [d.metadata["kind"] for d in docs]
-        assert "fruit" in kinds
-        assert "treat" not in kinds
-        assert "planet" not in kinds
diff --git a/libs/community/uv.lock b/libs/community/uv.lock
index 889c1a996..862bc046a 100644
--- a/libs/community/uv.lock
+++ b/libs/community/uv.lock
@@ -1,13 +1,18 @@
 version = 1
 revision = 3
-requires-python = ">=3.9.0, <3.13.0"
+requires-python = ">=3.10.0, <3.13.0"
 resolution-markers = [
-    "python_full_version >= '3.12'",
-    "python_full_version == '3.11.*'",
-    "python_full_version == '3.10.*'",
-    "python_full_version < '3.10'",
+    "python_full_version >= '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin'",
+    "(python_full_version >= '3.12' and platform_machine != 'arm64') or (python_full_version >= '3.12' and sys_platform != 'darwin')",
+    "python_full_version == '3.11.*' and platform_machine == 'arm64' and sys_platform == 'darwin'",
+    "(python_full_version == '3.11.*' and platform_machine != 'arm64') or (python_full_version == '3.11.*' and sys_platform != 'darwin')",
+    "python_full_version < '3.11' and platform_machine == 'arm64' and sys_platform == 'darwin'",
+    "(python_full_version < '3.11' and platform_machine != 'arm64') or (python_full_version < '3.11' and sys_platform != 'darwin')",
 ]
 
+[manifest]
+constraints = [{ name = "llvmlite", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'", specifier = "!=0.45.0" }]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -84,23 +89,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/52/b0/4ff3abd81aa7d929b27d2e1403722a65fc87b763e3a97b3a2a494bfc63bc/aiohttp-3.12.15-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b2acbbfff69019d9014508c4ba0401822e8bae5a5fdc3b6814285b71231b60f3", size = 1726045, upload-time = "2025-07-29T05:51:13.689Z" },
     { url = "https://files.pythonhosted.org/packages/71/16/949225a6a2dd6efcbd855fbd90cf476052e648fb011aa538e3b15b89a57a/aiohttp-3.12.15-cp312-cp312-win32.whl", hash = "sha256:d849b0901b50f2185874b9a232f38e26b9b3d4810095a7572eacea939132d4e1", size = 423591, upload-time = "2025-07-29T05:51:15.452Z" },
     { url = "https://files.pythonhosted.org/packages/2b/d8/fa65d2a349fe938b76d309db1a56a75c4fb8cc7b17a398b698488a939903/aiohttp-3.12.15-cp312-cp312-win_amd64.whl", hash = "sha256:b390ef5f62bb508a9d67cb3bba9b8356e23b3996da7062f1a57ce1a79d2b3d34", size = 450266, upload-time = "2025-07-29T05:51:17.239Z" },
-    { url = "https://files.pythonhosted.org/packages/18/8d/da08099af8db234d1cd43163e6ffc8e9313d0e988cee1901610f2fa5c764/aiohttp-3.12.15-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:691d203c2bdf4f4637792efbbcdcd157ae11e55eaeb5e9c360c1206fb03d4d98", size = 706829, upload-time = "2025-07-29T05:51:54.434Z" },
-    { url = "https://files.pythonhosted.org/packages/4e/94/8eed385cfb60cf4fdb5b8a165f6148f3bebeb365f08663d83c35a5f273ef/aiohttp-3.12.15-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:8e995e1abc4ed2a454c731385bf4082be06f875822adc4c6d9eaadf96e20d406", size = 481806, upload-time = "2025-07-29T05:51:56.355Z" },
-    { url = "https://files.pythonhosted.org/packages/38/68/b13e1a34584fbf263151b3a72a084e89f2102afe38df1dce5a05a15b83e9/aiohttp-3.12.15-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:bd44d5936ab3193c617bfd6c9a7d8d1085a8dc8c3f44d5f1dcf554d17d04cf7d", size = 469205, upload-time = "2025-07-29T05:51:58.277Z" },
-    { url = "https://files.pythonhosted.org/packages/38/14/3d7348bf53aa4af54416bc64cbef3a2ac5e8b9bfa97cc45f1cf9a94d9c8d/aiohttp-3.12.15-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:46749be6e89cd78d6068cdf7da51dbcfa4321147ab8e4116ee6678d9a056a0cf", size = 1644174, upload-time = "2025-07-29T05:52:00.23Z" },
-    { url = "https://files.pythonhosted.org/packages/ba/ed/fd9b5b22b0f6ca1a85c33bb4868cbcc6ae5eae070a0f4c9c5cad003c89d7/aiohttp-3.12.15-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:0c643f4d75adea39e92c0f01b3fb83d57abdec8c9279b3078b68a3a52b3933b6", size = 1618672, upload-time = "2025-07-29T05:52:02.272Z" },
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diff --git a/libs/genai/langchain_google_genai/_compat.py b/libs/genai/langchain_google_genai/_compat.py
new file mode 100644
index 000000000..84e661270
--- /dev/null
+++ b/libs/genai/langchain_google_genai/_compat.py
@@ -0,0 +1,248 @@
+"""Go from v1 content blocks to generativelanguage_v1beta format."""
+
+import json
+from typing import Any, Optional
+
+from langchain_core.messages import content as types
+
+
+def translate_citations_to_grounding_metadata(
+    citations: list[types.Citation], web_search_queries: Optional[list[str]] = None
+) -> dict[str, Any]:
+    """Translate LangChain Citations to Google AI grounding metadata format.
+
+    Args:
+        citations: List of Citation content blocks.
+        web_search_queries: Optional list of search queries that generated
+            the grounding data.
+
+    Returns:
+        Google AI grounding metadata dictionary.
+
+    Example:
+        >>> citations = [
+        ...     create_citation(
+        ...         url="https://uefa.com/euro2024",
+        ...         title="UEFA Euro 2024 Results",
+        ...         start_index=0,
+        ...         end_index=47,
+        ...         cited_text="Spain won the UEFA Euro 2024 championship",
+        ...     )
+        ... ]
+        >>> metadata = translate_citations_to_grounding_metadata(citations)
+        >>> len(metadata["groundingChunks"])
+        1
+        >>> metadata["groundingChunks"][0]["web"]["uri"]
+        'https://uefa.com/euro2024'
+    """
+    if not citations:
+        return {}
+
+    # Group citations by text segment (start_index, end_index, cited_text)
+    segment_to_citations: dict[
+        tuple[Optional[int], Optional[int], Optional[str]], list[types.Citation]
+    ] = {}
+
+    for citation in citations:
+        key = (
+            citation.get("start_index"),
+            citation.get("end_index"),
+            citation.get("cited_text"),
+        )
+        if key not in segment_to_citations:
+            segment_to_citations[key] = []
+        segment_to_citations[key].append(citation)
+
+    # Build grounding chunks from unique URLs
+    url_to_chunk_index: dict[str, int] = {}
+    grounding_chunks: list[dict[str, Any]] = []
+
+    for citation in citations:
+        url = citation.get("url")
+        if url and url not in url_to_chunk_index:
+            url_to_chunk_index[url] = len(grounding_chunks)
+            grounding_chunks.append(
+                {"web": {"uri": url, "title": citation.get("title", "")}}
+            )
+
+    # Build grounding supports
+    grounding_supports: list[dict[str, Any]] = []
+
+    for (
+        start_index,
+        end_index,
+        cited_text,
+    ), citations_group in segment_to_citations.items():
+        if start_index is not None and end_index is not None and cited_text:
+            chunk_indices = []
+            confidence_scores = []
+
+            for citation in citations_group:
+                url = citation.get("url")
+                if url and url in url_to_chunk_index:
+                    chunk_indices.append(url_to_chunk_index[url])
+
+                    # Extract confidence scores from extras if available
+                    extras = citation.get("extras", {})
+                    google_metadata = extras.get("google_ai_metadata", {})
+                    scores = google_metadata.get("confidence_scores", [])
+                    confidence_scores.extend(scores)
+
+            support = {
+                "segment": {
+                    "startIndex": start_index,
+                    "endIndex": end_index,
+                    "text": cited_text,
+                },
+                "groundingChunkIndices": chunk_indices,
+            }
+
+            if confidence_scores:
+                support["confidenceScores"] = confidence_scores
+
+            grounding_supports.append(support)
+
+    # Extract search queries from extras if not provided
+    if web_search_queries is None:
+        web_search_queries = []
+        for citation in citations:
+            extras = citation.get("extras", {})
+            google_metadata = extras.get("google_ai_metadata", {})
+            queries = google_metadata.get("web_search_queries", [])
+            web_search_queries.extend(queries)
+        # Remove duplicates while preserving order
+        web_search_queries = list(dict.fromkeys(web_search_queries))
+
+    return {
+        "webSearchQueries": web_search_queries,
+        "groundingChunks": grounding_chunks,
+        "groundingSupports": grounding_supports,
+    }
+
+
+def _convert_from_v1_to_generativelanguage_v1beta(
+    content: list[types.ContentBlock], model_provider: str | None
+) -> list[dict[str, Any]]:
+    """Convert v1 content blocks to `google.ai.generativelanguage_v1beta.types.Content`.
+
+    Args:
+        content: List of v1 `ContentBlock` objects.
+        model_provider: The model provider name that generated the v1 content.
+
+    Returns:
+        List of dictionaries in `google.ai.generativelanguage_v1beta.types.Content`
+        format, ready to be sent to the API.
+    """
+    new_content: list = []
+    for block in content:
+        if not isinstance(block, dict) or "type" not in block:
+            continue
+
+        block_dict = dict(block)  # (For typing)
+
+        # TextContentBlock
+        if block_dict["type"] == "text":
+            new_block = {"text": block_dict.get("text", "")}
+            new_content.append(new_block)
+            # Citations are only handled on output. Can't pass them back :/
+
+        # ReasoningContentBlock -> thinking
+        elif block_dict["type"] == "reasoning" and model_provider == "google_genai":
+            # Google requires passing back the thought_signature when available.
+            # Signatures are only provided when function calling is enabled.
+            # If no signature is available, we skip the reasoning block as it cannot
+            # be properly serialized back to the API.
+            if "extras" in block_dict and isinstance(block_dict["extras"], dict):
+                extras = block_dict["extras"]
+                if "signature" in extras:
+                    new_block = {
+                        "thought": True,
+                        "text": block_dict.get("reasoning", ""),
+                        "thought_signature": extras["signature"],
+                    }
+                    new_content.append(new_block)
+                # else: skip reasoning blocks without signatures
+                # TODO: log a warning?
+            # else: skip reasoning blocks without extras
+            # TODO: log a warning?
+
+        # ImageContentBlock
+        elif block_dict["type"] == "image":
+            if base64 := block_dict.get("base64"):
+                new_block = {
+                    "inline_data": {
+                        "mime_type": block_dict.get("mime_type", "image/jpeg"),
+                        "data": base64.encode("utf-8")
+                        if isinstance(base64, str)
+                        else base64,
+                    }
+                }
+                new_content.append(new_block)
+            elif url := block_dict.get("url") and model_provider == "google_genai":
+                # Google file service
+                new_block = {
+                    "file_data": {
+                        "mime_type": block_dict.get("mime_type", "image/jpeg"),
+                        "file_uri": block_dict[str(url)],
+                    }
+                }
+                new_content.append(new_block)
+
+        # TODO: AudioContentBlock -> audio once models support passing back in
+
+        # FileContentBlock (documents)
+        elif block_dict["type"] == "file":
+            if base64 := block_dict.get("base64"):
+                new_block = {
+                    "inline_data": {
+                        "mime_type": block_dict.get(
+                            "mime_type", "application/octet-stream"
+                        ),
+                        "data": base64.encode("utf-8")
+                        if isinstance(base64, str)
+                        else base64,
+                    }
+                }
+                new_content.append(new_block)
+            elif url := block_dict.get("url") and model_provider == "google_genai":
+                # Google file service
+                new_block = {
+                    "file_data": {
+                        "mime_type": block_dict.get(
+                            "mime_type", "application/octet-stream"
+                        ),
+                        "file_uri": block_dict[str(url)],
+                    }
+                }
+                new_content.append(new_block)
+
+        # ToolCall -> FunctionCall
+        elif block_dict["type"] == "tool_call":
+            function_call = {
+                "function_call": {
+                    "name": block_dict.get("name", ""),
+                    "args": block_dict.get("args", {}),
+                }
+            }
+            new_content.append(function_call)
+
+        # ToolCallChunk -> FunctionCall
+        elif block_dict["type"] == "tool_call_chunk":
+            try:
+                args_str = block_dict.get("args") or "{}"
+                input_ = json.loads(args_str) if isinstance(args_str, str) else args_str
+            except json.JSONDecodeError:
+                input_ = {}
+
+            function_call = {
+                "function_call": {
+                    "name": block_dict.get("name", "no_tool_name_present"),
+                    "args": input_,
+                }
+            }
+            new_content.append(function_call)
+
+        # NonStandardContentBlock
+        # TODO: Handle new server tools
+
+    return new_content
diff --git a/libs/genai/langchain_google_genai/chat_models.py b/libs/genai/langchain_google_genai/chat_models.py
index f8725b9c6..a40bc99bd 100644
--- a/libs/genai/langchain_google_genai/chat_models.py
+++ b/libs/genai/langchain_google_genai/chat_models.py
@@ -58,11 +58,16 @@
     ResourceExhausted,
     ServiceUnavailable,
 )
+from google.protobuf.json_format import MessageToDict
 from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
-from langchain_core.language_models import LangSmithParams, LanguageModelInput
+from langchain_core.language_models import (
+    LangSmithParams,
+    LanguageModelInput,
+    is_openai_data_block,
+)
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.messages import (
     AIMessage,
@@ -74,6 +79,7 @@
     ToolMessage,
     is_data_content_block,
 )
+from langchain_core.messages import content as types
 from langchain_core.messages.ai import UsageMetadata, add_usage, subtract_usage
 from langchain_core.messages.tool import invalid_tool_call, tool_call, tool_call_chunk
 from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
@@ -110,6 +116,9 @@
     _BaseGoogleGenerativeAI,
     get_client_info,
 )
+from langchain_google_genai._compat import (
+    _convert_from_v1_to_generativelanguage_v1beta,
+)
 from langchain_google_genai._function_utils import (
     _dict_to_gapic_schema,
     _tool_choice_to_tool_config,
@@ -287,56 +296,78 @@ async def _achat_with_retry(**kwargs: Any) -> Any:
     return await _achat_with_retry(**params)
 
 
-def _is_lc_content_block(part: dict) -> bool:
-    return "type" in part
-
-
-def _is_openai_image_block(block: dict) -> bool:
-    """Check if the block contains image data in OpenAI Chat Completions format."""
-    if block.get("type") == "image_url":
-        if (
-            (set(block.keys()) <= {"type", "image_url", "detail"})
-            and (image_url := block.get("image_url"))
-            and isinstance(image_url, dict)
-        ):
-            url = image_url.get("url")
-            if isinstance(url, str):
-                return True
-    else:
-        return False
-
-    return False
-
-
 def _convert_to_parts(
     raw_content: Union[str, Sequence[Union[str, dict]]],
 ) -> List[Part]:
-    """Converts a list of LangChain messages into a Google parts."""
-    parts = []
+    """Converts LangChain message content into generativelanguage_v1beta parts.
+
+    Used when preparing Human, System and AI messages for sending to the API.
+
+    Handles both legacy (pre-v1) dict-based content blocks and v1 ContentBlock objects.
+    """
     content = [raw_content] if isinstance(raw_content, str) else raw_content
     image_loader = ImageBytesLoader()
+
+    parts = []
+    # Iterate over each item in the content list, constructing a list of Parts
     for part in content:
         if isinstance(part, str):
             parts.append(Part(text=part))
         elif isinstance(part, Mapping):
-            if _is_lc_content_block(part):
+            if "type" in part:
                 if part["type"] == "text":
-                    parts.append(Part(text=part["text"]))
+                    # Either old dict-style CC text block or new TextContentBlock
+                    # Check if there's a signature attached to this text block
+                    thought_sig = None
+                    if "extras" in part and isinstance(part["extras"], dict):
+                        sig = part["extras"].get("signature")
+                        if sig and isinstance(sig, str):
+                            # Decode base64-encoded signature back to bytes
+                            thought_sig = base64.b64decode(sig)
+                    if thought_sig:
+                        parts.append(
+                            Part(text=part["text"], thought_signature=thought_sig)
+                        )
+                    else:
+                        parts.append(Part(text=part["text"]))
                 elif is_data_content_block(part):
-                    if part["source_type"] == "url":
+                    # Handle both legacy LC blocks (with `source_type`) and blocks >= v1
+
+                    if "source_type" in part:
+                        # Catch legacy v0 formats
+                        # Safe since v1 content blocks don't have `source_type` key
+                        if part["source_type"] == "url":
+                            bytes_ = image_loader._bytes_from_url(part["url"])
+                        elif part["source_type"] == "base64":
+                            bytes_ = base64.b64decode(part["data"])
+                        else:
+                            # Unable to support IDContentBlock
+                            msg = "source_type must be url or base64."
+                            raise ValueError(msg)
+                    elif "url" in part:
+                        # v1 multimodal block w/ URL
                         bytes_ = image_loader._bytes_from_url(part["url"])
-                    elif part["source_type"] == "base64":
-                        bytes_ = base64.b64decode(part["data"])
+                    elif "base64" in part:
+                        # v1 multimodal block w/ base64
+                        bytes_ = base64.b64decode(part["base64"])
                     else:
-                        msg = "source_type must be url or base64."
+                        msg = (
+                            "Data content block must contain 'url', 'base64', or "
+                            "'data' field."
+                        )
                         raise ValueError(msg)
                     inline_data: dict = {"data": bytes_}
                     if "mime_type" in part:
                         inline_data["mime_type"] = part["mime_type"]
                     else:
-                        source = cast("str", part.get("url") or part.get("data"))
+                        # Guess mime type based on data field if not provided
+                        source = cast(
+                            "str",
+                            part.get("url") or part.get("base64") or part.get("data"),
+                        )
                         mime_type, _ = mimetypes.guess_type(source)
                         if not mime_type:
+                            # Last resort - try to guess based on file bytes
                             kind = filetype.guess(bytes_)
                             if kind:
                                 mime_type = kind.mime
@@ -344,6 +375,7 @@ def _convert_to_parts(
                             inline_data["mime_type"] = mime_type
                     parts.append(Part(inline_data=inline_data))
                 elif part["type"] == "image_url":
+                    # Chat Completions image format
                     img_url = part["image_url"]
                     if isinstance(img_url, dict):
                         if "url" not in img_url:
@@ -351,9 +383,9 @@ def _convert_to_parts(
                             raise ValueError(msg)
                         img_url = img_url["url"]
                     parts.append(image_loader.load_part(img_url))
-                # Handle media type like LangChain.js
-                # https://github.com/langchain-ai/langchainjs/blob/e536593e2585f1dd7b0afc187de4d07cb40689ba/libs/langchain-google-common/src/utils/gemini.ts#L93-L106
                 elif part["type"] == "media":
+                    # Handle `media` following pattern established in LangChain.js
+                    # https://github.com/langchain-ai/langchainjs/blob/e536593e2585f1dd7b0afc187de4d07cb40689ba/libs/langchain-google-common/src/utils/gemini.ts#L93-L106
                     if "mime_type" not in part:
                         msg = f"Missing mime_type in media part: {part}"
                         raise ValueError(msg)
@@ -361,10 +393,12 @@ def _convert_to_parts(
                     media_part = Part()
 
                     if "data" in part:
+                        # Embedded media
                         media_part.inline_data = Blob(
                             data=part["data"], mime_type=mime_type
                         )
                     elif "file_uri" in part:
+                        # Referenced files (e.g. stored in GCS)
                         media_part.file_data = FileData(
                             file_uri=part["file_uri"], mime_type=mime_type
                         )
@@ -375,7 +409,59 @@ def _convert_to_parts(
                         metadata = VideoMetadata(part["video_metadata"])
                         media_part.video_metadata = metadata
                     parts.append(media_part)
+                elif part["type"] == "function_call_signature":
+                    # Signature for function_call Part - skip it here as it should be
+                    # attached to the actual function_call Part
+                    # This is handled separately in the history parsing logic
+                    pass
+                elif part["type"] == "thinking":
+                    # Pre-existing thinking block format that we continue to store as
+                    thought_sig = None
+                    if "signature" in part:
+                        sig = part["signature"]
+                        if sig and isinstance(sig, str):
+                            # Decode base64-encoded signature back to bytes
+                            thought_sig = base64.b64decode(sig)
+                    parts.append(
+                        Part(
+                            text=part["thinking"],
+                            thought=True,
+                            thought_signature=thought_sig,
+                        )
+                    )
+                elif part["type"] == "reasoning":
+                    # ReasoningContentBlock (when output_version = "v1")
+                    extras = part.get("extras", {}) or {}
+                    sig = extras.get("signature")
+                    thought_sig = None
+                    if sig and isinstance(sig, str):
+                        # Decode base64-encoded signature back to bytes
+                        thought_sig = base64.b64decode(sig)
+                    parts.append(
+                        Part(
+                            text=part["reasoning"],
+                            thought=True,
+                            thought_signature=thought_sig,
+                        )
+                    )
+                elif part["type"] == "server_tool_call":
+                    if part.get("name") == "code_interpreter":
+                        args = part.get("args", {})
+                        code = args.get("code", "")
+                        language = args.get("language", "python")
+                        executable_code_part = Part(
+                            executable_code=ExecutableCode(language=language, code=code)
+                        )
+                        parts.append(executable_code_part)
+                    else:
+                        warnings.warn(
+                            f"Server tool call with name '{part.get('name')}' is not "
+                            "currently supported by Google GenAI. Only "
+                            "'code_interpreter' is supported.",
+                            stacklevel=2,
+                        )
                 elif part["type"] == "executable_code":
+                    # Legacy executable_code format (backward compat)
                     if "executable_code" not in part or "language" not in part:
                         msg = (
                             "Executable code part must have 'code' and 'language' "
@@ -388,7 +474,22 @@ def _convert_to_parts(
                         )
                     )
                     parts.append(executable_code_part)
+                elif part["type"] == "server_tool_result":
+                    output = part.get("output", "")
+                    status = part.get("status", "success")
+                    # Map status to outcome: success → 1 (OUTCOME_OK), error → 2
+                    outcome = 1 if status == "success" else 2
+                    # Check extras for original outcome if available
+                    if "extras" in part and "outcome" in part["extras"]:
+                        outcome = part["extras"]["outcome"]
+                    code_execution_result_part = Part(
+                        code_execution_result=CodeExecutionResult(
+                            output=str(output), outcome=outcome
+                        )
+                    )
+                    parts.append(code_execution_result_part)
                 elif part["type"] == "code_execution_result":
+                    # Legacy code_execution_result format (backward compat)
                     if "code_execution_result" not in part:
                         msg = (
                             "Code execution result part must have "
@@ -406,24 +507,17 @@ def _convert_to_parts(
                         )
                     )
                     parts.append(code_execution_result_part)
-                elif part["type"] == "thinking":
-                    parts.append(Part(text=part["thinking"], thought=True))
                 else:
-                    msg = (
-                        f"Unrecognized message part type: {part['type']}. Only text, "
-                        f"image_url, and media types are supported."
-                    )
+                    msg = f"Unrecognized message part type: {part['type']}."
                     raise ValueError(msg)
             else:
-                # Yolo
+                # Yolo. The input message content doesn't have a `type` key
                 logger.warning(
                     "Unrecognized message part format. Assuming it's a text part."
                 )
                 parts.append(Part(text=str(part)))
         else:
-            # TODO: Maybe some of Google's native stuff
-            # would hit this branch.
-            msg = "Gemini only supports text and inline_data parts."
+            msg = "Unknown error occurred while converting LC message content to parts."
             raise ChatGoogleGenerativeAIError(msg)
     return parts
 
@@ -441,7 +535,7 @@ def _convert_tool_message_to_parts(
         other_blocks = []
         for block in message.content:
             if isinstance(block, dict) and (
-                is_data_content_block(block) or _is_openai_image_block(block)
+                is_data_content_block(block) or is_openai_data_block(block)
             ):
                 media_blocks.append(block)
             else:
@@ -496,7 +590,20 @@ def _get_ai_message_tool_messages_parts(
 def _parse_chat_history(
     input_messages: Sequence[BaseMessage], convert_system_message_to_human: bool = False
 ) -> Tuple[Optional[Content], List[Content]]:
-    messages: List[Content] = []
+    """Parses sequence of `BaseMessage` into system instruction and formatted messages.
+
+    Args:
+        input_messages: Sequence of `BaseMessage` objects representing the chat history.
+        convert_system_message_to_human: Whether to convert the first system message
+            into a human message. Deprecated, use system instructions instead.
+
+    Returns:
+        A tuple containing:
+        - An optional `google.ai.generativelanguage_v1beta.types.Content` representing
+            the system instruction (if any).
+        - A list of `google.ai.generativelanguage_v1beta.types.Content` representing the
+            formatted messages.
+    """
 
     if convert_system_message_to_human:
         warnings.warn(
@@ -505,6 +612,28 @@ def _parse_chat_history(
             DeprecationWarning,
             stacklevel=2,
         )
+    input_messages = list(input_messages)  # Make a mutable copy
+
+    # Case where content was serialized to v1 format
+    for idx, message in enumerate(input_messages):
+        if (
+            isinstance(message, AIMessage)
+            and message.response_metadata.get("output_version") == "v1"
+        ):
+            # Unpack known v1 content to v1beta format for the request
+            #
+            # Old content types and any previously serialized messages passed back in to
+            # history will skip this, but hit and processed in `_convert_to_parts`
+            input_messages[idx] = message.model_copy(
+                update={
+                    "content": _convert_from_v1_to_generativelanguage_v1beta(
+                        cast(list[types.ContentBlock], message.content),
+                        message.response_metadata.get("model_provider"),
+                    )
+                }
+            )
+
+    formatted_messages: List[Content] = []
 
     system_instruction: Optional[Content] = None
     messages_without_tool_messages = [
@@ -527,19 +656,43 @@ def _parse_chat_history(
             role = "model"
             if message.tool_calls:
                 ai_message_parts = []
-                for tool_call in message.tool_calls:
+                # Extract any function_call_signature blocks from content
+                function_call_sigs: dict[int, bytes] = {}
+                if isinstance(message.content, list):
+                    for idx, item in enumerate(message.content):
+                        if (
+                            isinstance(item, dict)
+                            and item.get("type") == "function_call_signature"
+                        ):
+                            sig_str = item.get("signature", "")
+                            if sig_str and isinstance(sig_str, str):
+                                # Decode base64-encoded signature back to bytes
+                                sig_bytes = base64.b64decode(sig_str)
+                                function_call_sigs[idx] = sig_bytes
+
+                for tool_call_idx, tool_call in enumerate(message.tool_calls):
                     function_call = FunctionCall(
                         {
                             "name": tool_call["name"],
                             "args": tool_call["args"],
                         }
                     )
-                    ai_message_parts.append(Part(function_call=function_call))
+                    # Check if there's a signature for this function call
+                    # (We use the index to match signature to function call)
+                    sig = function_call_sigs.get(tool_call_idx)
+                    if sig:
+                        ai_message_parts.append(
+                            Part(function_call=function_call, thought_signature=sig)
+                        )
+                    else:
+                        ai_message_parts.append(Part(function_call=function_call))
                 tool_messages_parts = _get_ai_message_tool_messages_parts(
                     tool_messages=tool_messages, ai_message=message
                 )
-                messages.append(Content(role=role, parts=ai_message_parts))
-                messages.append(Content(role="user", parts=tool_messages_parts))
+                formatted_messages.append(Content(role=role, parts=ai_message_parts))
+                formatted_messages.append(
+                    Content(role="user", parts=tool_messages_parts)
+                )
                 continue
             if raw_function_call := message.additional_kwargs.get("function_call"):
                 function_call = FunctionCall(
@@ -550,7 +703,12 @@ def _parse_chat_history(
                 )
                 parts = [Part(function_call=function_call)]
             else:
-                parts = _convert_to_parts(message.content)
+                if message.response_metadata.get("output_version") == "v1":
+                    # Already converted to v1beta format above
+                    parts = message.content  # type: ignore[assignment]
+                else:
+                    # Prepare request content parts from message.content field
+                    parts = _convert_to_parts(message.content)
         elif isinstance(message, HumanMessage):
             role = "user"
             parts = _convert_to_parts(message.content)
@@ -564,8 +722,11 @@ def _parse_chat_history(
             msg = f"Unexpected message with type {type(message)} at the position {i}."
             raise ValueError(msg)
 
-        messages.append(Content(role=role, parts=parts))
-    return system_instruction, messages
+        # Final step; assemble the Content object to pass to the API
+        # If version = "v1", the parts are already in v1beta format and will be
+        # automatically converted using protobuf's auto-conversion
+        formatted_messages.append(Content(role=role, parts=parts))
+    return system_instruction, formatted_messages
 
 
 # Helper function to append content consistently
@@ -589,13 +750,20 @@ def _append_to_content(
 
 
 def _parse_response_candidate(
-    response_candidate: Candidate, streaming: bool = False
+    response_candidate: Candidate,
+    streaming: bool = False,
+    model_name: Optional[str] = None,
 ) -> AIMessage:
     content: Union[None, str, List[Union[str, dict]]] = None
     additional_kwargs: Dict[str, Any] = {}
+    response_metadata: Dict[str, Any] = {"model_provider": "google_genai"}
+    if model_name:
+        response_metadata["model_name"] = model_name
     tool_calls = []
     invalid_tool_calls = []
     tool_call_chunks = []
+    # Track function call signatures separately to handle them conditionally
+    function_call_signatures: List[dict] = []
 
     for part in response_candidate.content.parts:
         text: Optional[str] = None
@@ -608,21 +776,49 @@ def _parse_response_candidate(
         except AttributeError:
             pass
 
+        # Extract thought signature if present (can be on any Part type)
+        # Signatures are binary data, encode to base64 string for JSON serialization
+        thought_sig: Optional[str] = None
+        if hasattr(part, "thought_signature") and part.thought_signature:
+            try:
+                # Encode binary signature to base64 string
+                thought_sig = base64.b64encode(part.thought_signature).decode("ascii")
+                if not thought_sig:  # Empty string
+                    thought_sig = None
+            except (AttributeError, TypeError):
+                thought_sig = None
+
         if hasattr(part, "thought") and part.thought:
             thinking_message = {
                 "type": "thinking",
                 "thinking": part.text,
             }
+            # Include signature if present
+            if thought_sig:
+                thinking_message["signature"] = thought_sig
             content = _append_to_content(content, thinking_message)
         elif text is not None and text:
-            content = _append_to_content(content, text)
+            # Check if this text Part has a signature attached
+            if thought_sig:
+                # Text with signature needs structured block to preserve signature
+                # We use a v1 TextContentBlock
+                text_with_sig = {
+                    "type": "text",
+                    "text": text,
+                    "extras": {"signature": thought_sig},
+                }
+                content = _append_to_content(content, text_with_sig)
+            else:
+                content = _append_to_content(content, text)
 
         if hasattr(part, "executable_code") and part.executable_code is not None:
             if part.executable_code.code and part.executable_code.language:
+                code_id = str(uuid.uuid4())  # Generate ID if not present, needed later
                 code_message = {
                     "type": "executable_code",
                     "executable_code": part.executable_code.code,
                     "language": part.executable_code.language,
+                    "id": code_id,
                 }
                 content = _append_to_content(content, code_message)
 
@@ -630,14 +826,21 @@ def _parse_response_candidate(
             hasattr(part, "code_execution_result")
             and part.code_execution_result is not None
         ) and part.code_execution_result.output:
+            # outcome: 1 = OUTCOME_OK (success), else = error
+            outcome = part.code_execution_result.outcome
             execution_result = {
                 "type": "code_execution_result",
                 "code_execution_result": part.code_execution_result.output,
-                "outcome": part.code_execution_result.outcome,
+                "outcome": outcome,
+                "tool_call_id": "",  # Linked via block translator
             }
             content = _append_to_content(content, execution_result)
 
-        if part.inline_data.mime_type.startswith("audio/"):
+        if (
+            hasattr(part, "inline_data")
+            and part.inline_data
+            and part.inline_data.mime_type.startswith("audio/")
+        ):
             buffer = io.BytesIO()
 
             with wave.open(buffer, "wb") as wf:
@@ -647,9 +850,17 @@ def _parse_response_candidate(
                 wf.setframerate(24000)
                 wf.writeframes(part.inline_data.data)
 
-            additional_kwargs["audio"] = buffer.getvalue()
+            audio_data = buffer.getvalue()
+            additional_kwargs["audio"] = audio_data
 
-        if part.inline_data.mime_type.startswith("image/"):
+            # For backwards compatibility, audio stays in additional_kwargs by default
+            # and is accessible via .content_blocks property
+
+        if (
+            hasattr(part, "inline_data")
+            and part.inline_data
+            and part.inline_data.mime_type.startswith("image/")
+        ):
             image_format = part.inline_data.mime_type[6:]
             image_message = {
                 "type": "image_url",
@@ -708,6 +919,23 @@ def _parse_response_candidate(
                             id=tool_call_dict.get("id", str(uuid.uuid4())),
                         )
                     )
+
+            # If this function_call Part has a signature, track it separately
+            # We'll add it to content only if there's other content present
+            if thought_sig:
+                sig_block = {
+                    "type": "function_call_signature",
+                    "signature": thought_sig,
+                }
+                function_call_signatures.append(sig_block)
+
+    # Add function call signatures to content only if there's already other content
+    # This preserves backward compatibility where content is "" for
+    # function-only responses
+    if function_call_signatures and content is not None:
+        for sig_block in function_call_signatures:
+            content = _append_to_content(content, sig_block)
+
     if content is None:
         content = ""
     if isinstance(content, list) and any(
@@ -722,17 +950,18 @@ def _parse_response_candidate(
         Validate before using in production.
 """
         )
-
     if streaming:
         return AIMessageChunk(
             content=content,
             additional_kwargs=additional_kwargs,
+            response_metadata=response_metadata,
             tool_call_chunks=tool_call_chunks,
         )
 
     return AIMessage(
         content=content,
         additional_kwargs=additional_kwargs,
+        response_metadata=response_metadata,
         tool_calls=tool_calls,
         invalid_tool_calls=invalid_tool_calls,
     )
@@ -741,8 +970,11 @@ def _parse_response_candidate(
 def _extract_grounding_metadata(candidate: Any) -> Dict[str, Any]:
     """Extract grounding metadata from candidate.
 
-    Uses `proto.Message.to_dict()` for complete unfiltered extraction first,
-    falls back to custom field extraction in cases of failure for robustness.
+    core's block translator converts this metadata into citation annotations.
+
+    Uses `MessageToDict` for complete unfiltered extraction.
+
+    Falls back to custom field extraction in cases of failure for robustness.
     """
     if not hasattr(candidate, "grounding_metadata") or not candidate.grounding_metadata:
         return {}
@@ -750,12 +982,26 @@ def _extract_grounding_metadata(candidate: Any) -> Dict[str, Any]:
     grounding_metadata = candidate.grounding_metadata
 
     try:
-        return proto.Message.to_dict(grounding_metadata)
-    except (AttributeError, TypeError):
-        # Fallback: field extraction
+        # proto-plus wraps protobuf messages - access ._pb to get the raw protobuf
+        # message that MessageToDict expects
+        pb_message = (
+            grounding_metadata._pb
+            if hasattr(grounding_metadata, "_pb")
+            else grounding_metadata
+        )
+
+        return MessageToDict(  # type: ignore[call-arg]
+            pb_message,
+            preserving_proto_field_name=True,
+            always_print_fields_with_no_presence=True,
+            # type stub issue - ensures that protobuf fields with default values
+            # (like start_index=0) are included in the output
+        )
+    except (AttributeError, TypeError, ImportError):
+        # Attempt manual extraction of known fields
         result: Dict[str, Any] = {}
 
-        # Extract grounding chunks
+        # Grounding chunks
         if hasattr(grounding_metadata, "grounding_chunks"):
             grounding_chunks = []
             for chunk in grounding_metadata.grounding_chunks:
@@ -768,7 +1014,7 @@ def _extract_grounding_metadata(candidate: Any) -> Dict[str, Any]:
                 grounding_chunks.append(chunk_data)
             result["grounding_chunks"] = grounding_chunks
 
-        # Extract grounding supports
+        # Grounding supports
         if hasattr(grounding_metadata, "grounding_supports"):
             grounding_supports = []
             for support in grounding_metadata.grounding_supports:
@@ -791,7 +1037,7 @@ def _extract_grounding_metadata(candidate: Any) -> Dict[str, Any]:
                 grounding_supports.append(support_data)
             result["grounding_supports"] = grounding_supports
 
-        # Extract web search queries
+        # Web search queries
         if hasattr(grounding_metadata, "web_search_queries"):
             result["web_search_queries"] = list(grounding_metadata.web_search_queries)
 
@@ -863,6 +1109,7 @@ def _response_to_result(
         grounding_metadata = _extract_grounding_metadata(candidate)
         generation_info["grounding_metadata"] = grounding_metadata
         message = _parse_response_candidate(candidate, streaming=stream)
+
         message.usage_metadata = lc_usage
 
         if not hasattr(message, "response_metadata"):
@@ -1725,12 +1972,12 @@ def invoke(
         code_execution: Optional[bool] = None,
         stop: Optional[list[str]] = None,
         **kwargs: Any,
-    ) -> BaseMessage:
-        """Enable code execution. Supported on: gemini-1.5-pro, gemini-1.5-flash,
-        gemini-2.0-flash, and gemini-2.0-pro. When enabled, the model can execute
-        code to solve problems.
+    ) -> AIMessage:
+        """Override invoke to add code_execution parameter.
+
+        Supported on: gemini-1.5-pro, gemini-1.5-flash, gemini-2.0-flash, and
+        gemini-2.0-pro. When enabled, the model can execute code to solve problems.
         """
-        """Override invoke to add code_execution parameter."""
 
         if code_execution is not None:
             if not self._supports_code_execution:
@@ -2129,7 +2376,7 @@ def _prepare_request(
             ]
         request = GenerateContentRequest(
             model=self.model,
-            contents=history,
+            contents=history,  # google.ai.generativelanguage_v1beta.types.Content
             tools=formatted_tools,
             tool_config=formatted_tool_config,
             safety_settings=formatted_safety_settings,
@@ -2146,7 +2393,7 @@ def _prepare_request(
         return request
 
     def get_num_tokens(self, text: str) -> int:
-        """Get the number of tokens present in the text.
+        """Get the number of tokens present in the text. Uses the model's tokenizer.
 
         Useful for checking if an input will fit in a model's context window.
 
@@ -2178,7 +2425,8 @@ def with_structured_output(
 
         parser: OutputParserLike
 
-        if method in ("json_mode", "json_schema"):  # `json_schema` preferred
+        # `json_schema` preferred, but `json_mode` kept for backwards compatibility
+        if method in ("json_mode", "json_schema"):
             if isinstance(schema, type) and is_basemodel_subclass(schema):
                 if issubclass(schema, BaseModelV1):
                     schema_json = schema.schema()
@@ -2246,7 +2494,7 @@ def bind_tools(
         *,
         tool_choice: Optional[Union[_ToolChoiceType, bool]] = None,
         **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, BaseMessage]:
+    ) -> Runnable[LanguageModelInput, AIMessage]:
         """Bind tool-like objects to this chat model.
 
         Assumes model is compatible with google-generativeAI tool-calling API.
diff --git a/libs/genai/pyproject.toml b/libs/genai/pyproject.toml
index 26be19600..d6e62616d 100644
--- a/libs/genai/pyproject.toml
+++ b/libs/genai/pyproject.toml
@@ -4,16 +4,16 @@ build-backend = "pdm.backend"
 
 [project]
 name = "langchain-google-genai"
-version = "2.1.12"
+version = "3.0.0a1"
 description = "An integration package connecting Google's genai package and LangChain"
 authors = []
 license = {text = "MIT"}
-requires-python = ">=3.9.0,<4.0.0"
+requires-python = ">=3.10.0,<4.0.0"
 readme = "README.md"
 repository = "https://github.com/langchain-ai/langchain-google"
 
 dependencies = [
-    "langchain-core>=0.3.75,<2.0.0",
+    "langchain-core>=1.0.0a6,<2.0.0",
     "google-ai-generativelanguage>=0.7.0,<1.0.0",
     "pydantic>=2.0.0,<3.0.0",
     "filetype>=1.2.0,<2.0.0",
@@ -25,7 +25,7 @@ dependencies = [
 repository = "https://github.com/langchain-ai/langchain-google"
 
 [dependency-groups]
-lint = ["ruff>=0.12.2,<0.13.0"]
+lint = ["ruff>=0.13.1,<0.14.0"]
 typing = [
     "mypy>=1.18.1,<1.19.0",
     "types-requests>=2.31.0,<3.0.0",
@@ -46,7 +46,7 @@ test = [
     "pytest-socket>=0.7.0,<1.0.0",
     "numpy>=1.26.4; python_version<'3.13'",
     "numpy>=2.1.0; python_version>='3.13'",
-    "langchain-tests>=0.3.0,<2.0.0",
+    "langchain-tests>=1.0.0a1,<2.0.0",
 ]
 
 test_integration = [
@@ -55,6 +55,10 @@ test_integration = [
 
 dev = []
 
+[tool.uv.sources]
+#langchain-core = { path = "../../../langchain/libs/core", editable = true }
+#langchain-tests = { path = "../../../langchain/libs/standard-tests", editable = true }
+
 [tool.ruff]
 fix = true
 
@@ -86,7 +90,6 @@ ignore = [
     # 3.9 needs
     "UP007",
     "UP006",
-    "UP038",
     "UP045",
     "FA100",
 
@@ -139,4 +142,4 @@ markers = [
     "asyncio: mark tests as requiring asyncio",
     "compile: mark placeholder test used to compile integration tests without running them",
 ]
-asyncio_mode = "auto"
\ No newline at end of file
+asyncio_mode = "auto"
diff --git a/libs/genai/tests/integration_tests/test_chat_models.py b/libs/genai/tests/integration_tests/test_chat_models.py
index 8c350d57b..2942334d7 100644
--- a/libs/genai/tests/integration_tests/test_chat_models.py
+++ b/libs/genai/tests/integration_tests/test_chat_models.py
@@ -141,8 +141,6 @@ async def test_chat_google_genai_invoke(is_async: bool) -> None:
     _check_usage_metadata(result)
 
 
-# TODO: assert calling .content_blocks translates outcome to ImageContentBlock
-# but do this in unit tests in langchain-core
 @pytest.mark.flaky(retries=3, delay=1)
 def test_chat_google_genai_invoke_with_image() -> None:
     """Test generating an image and then text from ChatGoogleGenerativeAI.
@@ -179,9 +177,23 @@ def test_chat_google_genai_invoke_with_image() -> None:
     assert not result.content[0].startswith(" ")
     _check_usage_metadata(result)
 
+    # Test we can pass back in
+    next_message = {"role": "user", "content": "Thanks!"}
+    _ = llm.invoke([result, next_message])
+
+    # Test content_blocks property
+    content_blocks = result.content_blocks
+    assert isinstance(content_blocks, list)
+    assert len(content_blocks) == 2
+    assert isinstance(content_blocks[0], dict)
+    assert content_blocks[0].get("type") == "text"
+    assert isinstance(content_blocks[1], dict)
+    assert content_blocks[1].get("type") == "image"
+
+    # Test we can pass back in content_blocks
+    _ = llm.invoke(["What's this?", {"role": "assistant", "content": content_blocks}])
+
 
-# TODO: assert calling .content_blocks translates outcome to AudioContentBlock
-# but do this in unit tests in langchain-core
 def test_chat_google_genai_invoke_with_audio() -> None:
     """Test generating audio from ChatGoogleGenerativeAI."""
     llm = ChatGoogleGenerativeAI(
@@ -198,6 +210,16 @@ def test_chat_google_genai_invoke_with_audio() -> None:
     assert get_wav_type_from_bytes(audio_data)
     _check_usage_metadata(result)
 
+    # Test content_blocks property
+    content_blocks = result.content_blocks
+    assert isinstance(content_blocks, list)
+    assert len(content_blocks) == 1
+    assert isinstance(content_blocks[0], dict)
+    assert content_blocks[0].get("type") == "audio"
+
+    # Test we can pass back in
+    # TODO: no model currently supports audio input
+
 
 def test_chat_google_genai_invoke_thinking_default() -> None:
     """Test invoke thinking model with default thinking config."""
@@ -241,11 +263,14 @@ def test_chat_google_genai_invoke_thinking() -> None:
         assert result.usage_metadata["output_token_details"]["reasoning"] > 0
 
 
-# TODO: parametrize this test to use output_version=v1 and then assert `content` is
-# proper ReasoningContentBlock with google-specific thinking fields in `extras`
-def test_chat_google_genai_invoke_thinking_include_thoughts() -> None:
+@pytest.mark.parametrize("output_version", ["v0", "v1"])
+def test_chat_google_genai_invoke_thinking_include_thoughts(
+    output_version: str,
+) -> None:
     """Test invoke thinking model with `include_thoughts` on the chat model."""
-    llm = ChatGoogleGenerativeAI(model=_THINKING_MODEL, include_thoughts=True)
+    llm = ChatGoogleGenerativeAI(
+        model=_THINKING_MODEL, include_thoughts=True, output_version=output_version
+    )
 
     input_message = {
         "role": "user",
@@ -260,24 +285,163 @@ def test_chat_google_genai_invoke_thinking_include_thoughts() -> None:
     assert isinstance(result, AIMessage)
     content = result.content
 
-    assert isinstance(content[0], dict)
-    assert content[0].get("type") == "thinking"
-    assert isinstance(content[0].get("thinking"), str)
+    response_metadata = result.response_metadata
+    model_provider = response_metadata.get("model_provider", "google_genai")
+    assert model_provider == "google_genai"
 
-    assert isinstance(content[1], str)
+    if output_version == "v0":
+        assert isinstance(content[0], dict)
+        assert content[0].get("type") == "thinking"
+        assert isinstance(content[0].get("thinking"), str)
 
-    _check_usage_metadata(result)
+        assert isinstance(content[1], str)
 
-    assert result.usage_metadata is not None
-    if (
-        "output_token_details" in result.usage_metadata
-        and "reasoning" in result.usage_metadata["output_token_details"]
-    ):
-        assert result.usage_metadata["output_token_details"]["reasoning"] > 0
+        _check_usage_metadata(result)
 
-    # Test we can pass back in
-    next_message = {"role": "user", "content": "Thanks!"}
-    _ = llm.invoke([input_message, result, next_message])
+        assert result.usage_metadata is not None
+        if (
+            "output_token_details" in result.usage_metadata
+            and "reasoning" in result.usage_metadata["output_token_details"]
+        ):
+            assert result.usage_metadata["output_token_details"]["reasoning"] > 0
+
+        # We don't test passing back in here as it's covered in the next test
+        # (Google requires function declaration)
+    else:
+        # v1
+        assert isinstance(content, list)
+        assert len(content) == 2
+        assert isinstance(content[0], dict)
+        assert content[0].get("type") == "reasoning"
+        assert isinstance(content[0].get("reasoning"), str)
+
+        assert isinstance(content[1], dict)
+        assert content[1].get("type") == "text"
+        assert isinstance(content[1].get("text"), str)
+
+        _check_usage_metadata(result)
+
+        # We don't test passing back in here as it's covered in the next test
+        # (Google requires function declaration)
+
+
+@pytest.mark.flaky(retries=5, delay=1)
+@pytest.mark.parametrize("output_version", ["v0", "v1"])
+def test_chat_google_genai_invoke_thinking_with_tools(
+    output_version: str,
+) -> None:
+    """Test thinking with function calling to get thought signatures.
+
+    Ensure we can pass the response back in.
+    """
+
+    @tool
+    def analyze_weather(location: str, date: str) -> dict:
+        """Analyze weather patterns for a location on a specific date.
+
+        Args:
+            location: The city or region to analyze.
+            date: The date in YYYY-MM-DD format.
+
+        Returns:
+            A dictionary with weather analysis.
+        """
+        return {
+            "location": location,
+            "date": date,
+            "temperature": "72F",
+            "conditions": "sunny",
+            "analysis": "Pleasant weather expected",
+        }
+
+    llm = ChatGoogleGenerativeAI(
+        model=_THINKING_MODEL, include_thoughts=True, output_version=output_version
+    )
+    llm_with_tools = llm.bind_tools([analyze_weather])
+
+    input_message = {
+        "role": "user",
+        "content": (
+            "I'm planning a trip to Paris. Should I check the weather for "
+            "tomorrow (2025-01-22) before deciding what to pack? "
+            "Please think through whether you need to use the weather tool, "
+            "and if so, use it to help me decide."
+        ),
+    }
+
+    result = llm_with_tools.invoke([input_message])
+
+    assert isinstance(result, AIMessage)
+    content = result.content
+
+    response_metadata = result.response_metadata
+    model_provider = response_metadata.get("model_provider", "google_genai")
+    assert model_provider == "google_genai"
+
+    if output_version == "v0":
+        # v0 format:
+        # Signatures are attached to function_call Parts, not thinking Parts.
+        # They appear as separate function_call_signature blocks in content.
+        # [{"type": "thinking", "thinking": "..."},
+        #  {"type": "function_call_signature", "signature": "..."}]
+
+        # Check for thinking blocks (should exist)
+        thinking_blocks = [
+            item
+            for item in content
+            if isinstance(item, dict) and item.get("type") == "thinking"
+        ]
+        assert thinking_blocks, "Should have thinking blocks when include_thoughts=True"
+
+        # Check for function_call_signature blocks (not in thinking blocks)
+        signature_blocks = [
+            item
+            for item in content
+            if isinstance(item, dict) and item.get("type") == "function_call_signature"
+        ]
+        if signature_blocks:
+            # Signature should be present when using function calling
+            assert "signature" in signature_blocks[0]
+            assert isinstance(signature_blocks[0]["signature"], str)
+            assert len(signature_blocks[0]["signature"]) > 0
+
+        # Test we can pass the result back in (with signature)
+        next_message = {"role": "user", "content": "Thanks!"}
+        _ = llm_with_tools.invoke([input_message, result, next_message])
+    else:
+        # v1 format:
+        # Signatures are attached to function_call Parts, not reasoning Parts.
+        # They appear as separate function_call_signature blocks in content.
+        # [{"type": "reasoning", "reasoning": "..."},
+        #  {"type": "function_call_signature", "signature": "..."}]
+        assert isinstance(content, list)
+
+        # Check for reasoning blocks (should exist)
+        reasoning_blocks = [
+            block
+            for block in content
+            if isinstance(block, dict) and block.get("type") == "reasoning"
+        ]
+        assert reasoning_blocks, (
+            "Should have reasoning blocks when include_thoughts=True"
+        )
+
+        # Check for function_call_signature blocks (not in reasoning blocks)
+        signature_blocks = [
+            block
+            for block in content
+            if isinstance(block, dict)
+            and block.get("type") == "function_call_signature"
+        ]
+        if signature_blocks:
+            # Signature should be present when using function calling
+            assert "signature" in signature_blocks[0]
+            assert isinstance(signature_blocks[0]["signature"], str)
+            assert len(signature_blocks[0]["signature"]) > 0
+
+        # Test we can pass the result back in (with signature)
+        next_message = {"role": "user", "content": "Thanks!"}
+        _ = llm_with_tools.invoke([input_message, result, next_message])
 
 
 def test_chat_google_genai_invoke_thinking_disabled() -> None:
@@ -522,7 +686,6 @@ def search(
     assert "brown" in content_str.lower()
 
 
-# TODO: check .content_blocks result
 def test_chat_vertexai_gemini_function_calling() -> None:
     """Test function calling with Gemini models.
 
@@ -585,6 +748,13 @@ def my_tool(name: str, age: int, likes: list[str]) -> None:
     arguments = json.loads(str(arguments_str))
     _check_tool_call_args(arguments)
 
+    # Test .content_blocks property
+    content_blocks = response.content_blocks
+    assert isinstance(content_blocks, list)
+    assert len(content_blocks) == 1
+    assert isinstance(content_blocks[0], dict)
+    assert content_blocks[0].get("type") == "tool_call"
+
 
 @pytest.mark.parametrize(
     ("model_name", "method"),
@@ -726,9 +896,6 @@ async def model_ainvoke(context: str) -> BaseMessage:
         assert isinstance(invoke_result, AIMessage)
 
 
-# TODO: in unit tests, translate to new content blocks in langchain-core
-
-
 @pytest.mark.parametrize("use_streaming", [False, True])
 def test_search_builtin(use_streaming: bool) -> None:
     llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-001").bind_tools(
@@ -767,6 +934,128 @@ def test_search_builtin(use_streaming: bool) -> None:
         _ = llm.invoke([input_message, response, next_message])
 
 
+@pytest.mark.parametrize("use_streaming", [False, True])
+def test_search_builtin_with_citations(use_streaming: bool) -> None:
+    """Test that citations are properly extracted from grounding metadata."""
+    llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash").bind_tools(
+        [{"google_search": {}}]
+    )
+    input_message = {
+        "role": "user",
+        "content": "Who won the 2024 UEFA Euro championship? Use search/citations.",
+    }
+
+    if use_streaming:
+        full: Optional[BaseMessageChunk] = None
+        for chunk in llm.stream([input_message]):
+            assert isinstance(chunk, AIMessageChunk)
+            full = chunk if full is None else full + chunk
+        assert isinstance(full, AIMessageChunk)
+
+        assert "grounding_metadata" in full.response_metadata
+        grounding = full.response_metadata["grounding_metadata"]
+
+        assert "grounding_chunks" in grounding or "grounding_chunks" in grounding
+        assert "grounding_supports" in grounding or "grounding_supports" in grounding
+
+        content_blocks = full.content_blocks
+        text_blocks_with_citations = [
+            block
+            for block in content_blocks
+            if block.get("type") == "text" and block.get("annotations")
+        ]
+
+        assert len(text_blocks_with_citations) > 0, (
+            "Expected citations in text block if grounding metadata present"
+        )
+
+        for block in text_blocks_with_citations:
+            annotations = block.get("annotations", [])
+            citations = [
+                ann
+                for ann in annotations  # type: ignore[attr-defined]
+                if annotations and ann.get("type") == "citation"
+            ]
+
+            for citation in citations:
+                # Required fields
+                assert citation.get("type") == "citation"
+                assert "id" in citation
+
+                # Optional but expected fields from Google AI
+                if "url" in citation:
+                    assert isinstance(citation["url"], str)
+                    assert citation["url"].startswith("http")
+                if "title" in citation:
+                    assert isinstance(citation["title"], str)
+                if "start_index" in citation:
+                    assert isinstance(citation["start_index"], int)
+                    assert citation["start_index"] >= 0
+                if "end_index" in citation:
+                    assert isinstance(citation["end_index"], int)
+                    assert citation["end_index"] > citation.get("start_index", 0)
+                if "cited_text" in citation:
+                    assert isinstance(citation["cited_text"], str)
+                if "extras" in citation:
+                    google_metadata = citation["extras"].get("google_ai_metadata", {})
+                    if google_metadata:
+                        assert isinstance(google_metadata, dict)
+    else:
+        # Test invoke
+        response = llm.invoke([input_message])
+        assert isinstance(response, AIMessage)
+
+        assert "grounding_metadata" in response.response_metadata
+        grounding = response.response_metadata["grounding_metadata"]
+
+        assert "grounding_chunks" in grounding or "grounding_chunks" in grounding
+        assert "grounding_supports" in grounding or "grounding_supports" in grounding
+
+        content_blocks = response.content_blocks
+        text_blocks_with_citations = [
+            block
+            for block in content_blocks
+            if block.get("type") == "text" and block.get("annotations")
+        ]
+
+        assert len(text_blocks_with_citations) > 0, (
+            "Expected citations in text blocks if grounding metadata present"
+        )
+
+        for block in text_blocks_with_citations:
+            block_annotations = block.get("annotations", [])
+            citations = [
+                ann
+                for ann in block_annotations  # type: ignore[attr-defined]
+                if ann.get("type") == "citation"
+            ]
+
+            for citation in citations:
+                # Required fields
+                assert citation.get("type") == "citation"
+                assert "id" in citation
+
+                # Optional but expected fields from Google AI
+                if "url" in citation:
+                    assert isinstance(citation["url"], str)
+                    assert citation["url"].startswith("http")
+                if "title" in citation:
+                    assert isinstance(citation["title"], str)
+                if "start_index" in citation:
+                    assert isinstance(citation["start_index"], int)
+                    assert citation["start_index"] >= 0
+                if "end_index" in citation:
+                    assert isinstance(citation["end_index"], int)
+                    assert citation["end_index"] > citation.get("start_index", 0)
+                if "cited_text" in citation:
+                    assert isinstance(citation["cited_text"], str)
+                if "extras" in citation:
+                    google_metadata = citation["extras"].get("google_ai_metadata", {})
+                    if google_metadata:
+                        assert isinstance(google_metadata, dict)
+
+
+@pytest.mark.filterwarnings("ignore::UserWarning")
 @pytest.mark.parametrize("use_streaming", [False, True])
 def test_code_execution_builtin(use_streaming: bool) -> None:
     llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-001").bind_tools(
@@ -780,38 +1069,56 @@ def test_code_execution_builtin(use_streaming: bool) -> None:
     if use_streaming:
         # Test streaming mode
         full: Optional[BaseMessageChunk] = None
-        with pytest.warns(match="executable_code"):
-            for chunk in llm.stream([input_message]):
-                assert isinstance(chunk, AIMessageChunk)
-                full = chunk if full is None else full + chunk
+        for chunk in llm.stream([input_message]):
+            assert isinstance(chunk, AIMessageChunk)
+            full = chunk if full is None else full + chunk
         assert isinstance(full, AIMessageChunk)
 
-        # Should get both code and result blocks
+        # Check raw content still has legacy format (backward compatibility)
         blocks = [block for block in full.content if isinstance(block, dict)]
         expected_block_types = {"executable_code", "code_execution_result"}
         assert {block.get("type") for block in blocks} == expected_block_types
 
-        # Test we can process chat history without raising errors
+        content_blocks = full.content_blocks
+        standard_blocks = [block for block in content_blocks if isinstance(block, dict)]
+        standard_types = {block.get("type") for block in standard_blocks}
+        assert (
+            "server_tool_call" in standard_types or "executable_code" in standard_types
+        )
+        assert (
+            "server_tool_result" in standard_types
+            or "code_execution_result" in standard_types
+        )
+
+        # Test passing back in chat history without raising errors
         next_message = {
             "role": "user",
             "content": "Can you show me the calculation again with comments?",
         }
-        with pytest.warns(match="executable_code"):
-            _ = llm.invoke([input_message, full, next_message])
+        _ = llm.invoke([input_message, full, next_message])
     else:
-        # Test invoke mode
-        with pytest.warns(match="executable_code"):
-            response = llm.invoke([input_message])
+        # Invoke
+        response = llm.invoke([input_message])
         blocks = [block for block in response.content if isinstance(block, dict)]
 
-        # Should get both code and result blocks
+        # Check raw content still has legacy format (backward compatibility)
         expected_block_types = {"executable_code", "code_execution_result"}
         assert {block.get("type") for block in blocks} == expected_block_types
 
-        # Test we can process chat history without raising errors
+        content_blocks = response.content_blocks
+        standard_blocks = [block for block in content_blocks if isinstance(block, dict)]
+        standard_types = {block.get("type") for block in standard_blocks}
+        assert (
+            "server_tool_call" in standard_types or "executable_code" in standard_types
+        )
+        assert (
+            "server_tool_result" in standard_types
+            or "code_execution_result" in standard_types
+        )
+
+        # Test passing back in chat history without raising errors
         next_message = {
             "role": "user",
             "content": "Can you show me the calculation again with comments?",
         }
-        with pytest.warns(match="executable_code"):
-            _ = llm.invoke([input_message, response, next_message])
+        _ = llm.invoke([input_message, response, next_message])
diff --git a/libs/genai/tests/integration_tests/test_tools.py b/libs/genai/tests/integration_tests/test_tools.py
index 068e493eb..2b2643fbf 100644
--- a/libs/genai/tests/integration_tests/test_tools.py
+++ b/libs/genai/tests/integration_tests/test_tools.py
@@ -33,5 +33,5 @@ def test_multiple_tools() -> None:
     input_ = "What is the latest tennis score for Leonid?"
 
     result = model_with_tools.invoke(input_)
-    assert len(result.tool_calls) == 1  # type: ignore
-    assert result.tool_calls[0]["name"] == "check_tennis_score"  # type: ignore
+    assert len(result.tool_calls) == 1
+    assert result.tool_calls[0]["name"] == "check_tennis_score"
diff --git a/libs/genai/tests/unit_tests/test_chat_models.py b/libs/genai/tests/unit_tests/test_chat_models.py
index c1512d477..8dbd15c1a 100644
--- a/libs/genai/tests/unit_tests/test_chat_models.py
+++ b/libs/genai/tests/unit_tests/test_chat_models.py
@@ -27,12 +27,15 @@
     SystemMessage,
     ToolMessage,
 )
+from langchain_core.messages.block_translators.google_genai import (
+    _convert_to_v1_from_genai,
+)
 from langchain_core.messages.tool import tool_call as create_tool_call
 from langchain_core.outputs import ChatGeneration, ChatResult
 from pydantic import SecretStr
 from pydantic_core._pydantic_core import ValidationError
 
-from langchain_google_genai import HarmBlockThreshold, HarmCategory
+from langchain_google_genai import HarmBlockThreshold, HarmCategory, Modality
 from langchain_google_genai.chat_models import (
     ChatGoogleGenerativeAI,
     _chat_with_retry,
@@ -762,6 +765,53 @@ def test_parse_response_candidate(raw_candidate: dict, expected: AIMessage) -> N
                 assert res_kw == exp_kw
 
 
+def test_parse_response_candidate_includes_model_provider() -> None:
+    """Test `_parse_response_candidate` has `model_provider` in `response_metadata`."""
+    raw_candidate = {
+        "content": {"parts": [{"text": "Hello, world!"}]},
+        "finish_reason": 1,
+        "safety_ratings": [],
+    }
+
+    response_candidate = glm.Candidate(raw_candidate)
+    result = _parse_response_candidate(response_candidate)
+
+    assert hasattr(result, "response_metadata")
+    assert result.response_metadata["model_provider"] == "google_genai"
+
+    # Streaming
+    result = _parse_response_candidate(response_candidate, streaming=True)
+
+    assert hasattr(result, "response_metadata")
+    assert result.response_metadata["model_provider"] == "google_genai"
+
+
+def test_parse_response_candidate_includes_model_name() -> None:
+    """Test that _parse_response_candidate includes model_name in response_metadata."""
+    raw_candidate = {
+        "content": {"parts": [{"text": "Hello, world!"}]},
+        "finish_reason": 1,
+        "safety_ratings": [],
+    }
+
+    response_candidate = glm.Candidate(raw_candidate)
+    result = _parse_response_candidate(
+        response_candidate, model_name="gemini-2.5-flash"
+    )
+
+    assert hasattr(result, "response_metadata")
+    assert result.response_metadata["model_provider"] == "google_genai"
+    assert result.response_metadata["model_name"] == "gemini-2.5-flash"
+
+    # No name
+
+    result = _parse_response_candidate(response_candidate)
+
+    assert hasattr(result, "response_metadata")
+    assert result.response_metadata["model_provider"] == "google_genai"
+    assert "model_name" not in result.response_metadata
+
+
 def test_serialize() -> None:
     llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key="test-key")
     serialized = dumps(llm)
@@ -896,6 +946,7 @@ def test_retry_decorator_with_custom_parameters() -> None:
                                         "start_index": 0,
                                         "end_index": 13,
                                         "text": "Test response",
+                                        "part_index": 0,
                                     },
                                     "grounding_chunk_indices": [0],
                                     "confidence_scores": [0.95],
@@ -968,6 +1019,271 @@ def test_response_to_result_grounding_metadata(
         assert grounding_metadata == expected_grounding_metadata
 
 
+def test_grounding_metadata_to_citations_conversion() -> None:
+    """Test grounding metadata is properly converted to citations in content blocks."""
+
+    raw_response = {
+        "candidates": [
+            {
+                "content": {
+                    "parts": [
+                        {
+                            "text": (
+                                "Spain won the UEFA Euro 2024 championship by "
+                                "defeating England 2-1 in the final."
+                            )
+                        }
+                    ]
+                },
+                "grounding_metadata": {
+                    "grounding_chunks": [
+                        {
+                            "web": {
+                                "uri": "https://uefa.com/euro2024",
+                                "title": "UEFA Euro 2024 Results",
+                            }
+                        },
+                        {
+                            "web": {
+                                "uri": "https://bbc.com/sport/football",
+                                "title": "BBC Sport Football",
+                            }
+                        },
+                    ],
+                    "grounding_supports": [
+                        {
+                            "segment": {
+                                "start_index": 0,
+                                "end_index": 40,
+                                "text": "Spain won the UEFA Euro 2024 championship",
+                                "part_index": 0,
+                            },
+                            "grounding_chunk_indices": [0],
+                            "confidence_scores": [0.95],
+                        },
+                        {
+                            "segment": {
+                                "start_index": 41,
+                                "end_index": 78,
+                                "text": "by defeating England 2-1 in the final",
+                                "part_index": 0,
+                            },
+                            "grounding_chunk_indices": [0, 1],
+                            "confidence_scores": [0.92, 0.88],
+                        },
+                    ],
+                    "web_search_queries": [
+                        "UEFA Euro 2024 winner",
+                        "Euro 2024 final score",
+                    ],
+                },
+            }
+        ],
+        "prompt_feedback": {"block_reason": 0, "safety_ratings": []},
+        "usage_metadata": {
+            "prompt_token_count": 10,
+            "candidates_token_count": 20,
+            "total_token_count": 30,
+        },
+    }
+
+    response = GenerateContentResponse(raw_response)
+    result = _response_to_result(response, stream=False)
+
+    assert len(result.generations) == 1
+    message = result.generations[0].message
+
+    assert "grounding_metadata" in message.response_metadata
+
+    # Verify grounding metadata structure uses snake_case
+    gm = message.response_metadata["grounding_metadata"]
+    assert "grounding_chunks" in gm
+    assert "grounding_supports" in gm
+    assert "web_search_queries" in gm
+
+    # Verify all required fields are present with correct casing
+    assert len(gm["grounding_chunks"]) == 2
+    assert len(gm["grounding_supports"]) == 2
+
+    # Verify first support has all fields including start_index
+    first_support = gm["grounding_supports"][0]
+    assert "segment" in first_support
+    assert "start_index" in first_support["segment"]
+    assert first_support["segment"]["start_index"] == 0
+    assert "end_index" in first_support["segment"]
+    assert "grounding_chunk_indices" in first_support
+    assert "confidence_scores" in first_support
+
+    content_blocks = message.content_blocks
+    text_blocks_with_citations = [
+        block
+        for block in content_blocks
+        if block.get("type") == "text" and block.get("annotations")
+    ]
+    assert len(text_blocks_with_citations) > 0, "Expected citations in text blocks"
+
+    for block in text_blocks_with_citations:
+        annotations = block.get("annotations", [])
+        citations = [ann for ann in annotations if ann.get("type") == "citation"]  # type: ignore[attr-defined]
+        assert len(citations) > 0, "Expected at least one citation"
+
+        for citation in citations:
+            assert citation.get("type") == "citation"
+            assert "id" in citation
+            if "url" in citation:
+                assert isinstance(citation["url"], str)
+
+
+def test_empty_grounding_metadata_no_citations() -> None:
+    """Test that empty grounding metadata doesn't create citations."""
+    raw_response = {
+        "candidates": [
+            {
+                "content": {
+                    "parts": [{"text": "This is a response without grounding."}]
+                },
+                "grounding_metadata": {},
+            }
+        ],
+        "prompt_feedback": {"block_reason": 0, "safety_ratings": []},
+        "usage_metadata": {
+            "prompt_token_count": 5,
+            "candidates_token_count": 8,
+            "total_token_count": 13,
+        },
+    }
+
+    response = GenerateContentResponse(raw_response)
+    result = _response_to_result(response, stream=False)
+
+    message = result.generations[0].message
+    content_blocks = message.content_blocks
+
+    text_blocks_with_citations = [
+        block
+        for block in content_blocks
+        if block.get("type") == "text" and block.get("annotations")
+    ]
+
+    assert len(text_blocks_with_citations) == 0
+
+
+def test_grounding_metadata_missing_optional_fields() -> None:
+    """Test handling of grounding metadata with missing optional fields."""
+    raw_response = {
+        "candidates": [
+            {
+                "content": {"parts": [{"text": "Sample text"}]},
+                "grounding_metadata": {
+                    "grounding_chunks": [
+                        {
+                            "web": {
+                                "uri": "https://example.com",
+                                # Missing 'title'
+                            }
+                        }
+                    ],
+                    "grounding_supports": [
+                        {
+                            "segment": {
+                                # Missing 'text'
+                                "start_index": 0,
+                                "end_index": 11,
+                                "part_index": 0,
+                            },
+                            "grounding_chunk_indices": [0],
+                        }
+                    ],
+                    # Missing 'web_search_queries' (optional field)
+                },
+            }
+        ],
+        "prompt_feedback": {"block_reason": 0, "safety_ratings": []},
+        "usage_metadata": {
+            "prompt_token_count": 5,
+            "candidates_token_count": 3,
+            "total_token_count": 8,
+        },
+    }
+
+    response = GenerateContentResponse(raw_response)
+    result = _response_to_result(response, stream=False)
+
+    message = result.generations[0].message
+
+    # Verify grounding metadata is present even with missing optional fields
+    assert "grounding_metadata" in message.response_metadata
+    gm = message.response_metadata["grounding_metadata"]
+
+    # Verify structure is correct (snake_case from MessageToDict)
+    assert "grounding_chunks" in gm
+    assert "grounding_supports" in gm
+
+    # Verify optional fields can be missing
+    chunk = gm["grounding_chunks"][0]
+    assert "web" in chunk
+    assert "uri" in chunk["web"]
+    # Title is missing, which is OK
+
+    support = gm["grounding_supports"][0]
+    assert "segment" in support
+    # Text is missing from segment, which is OK
+    assert "start_index" in support["segment"]
+    assert "end_index" in support["segment"]
+
+    # web_search_queries is optional and missing in this test
+
+
+def test_grounding_metadata_multiple_parts() -> None:
+    """Test grounding metadata with multiple content parts."""
+    raw_response = {
+        "candidates": [
+            {
+                "content": {
+                    "parts": [
+                        {"text": "First part. "},
+                        {"text": "Second part with citation."},
+                    ]
+                },
+                "grounding_metadata": {
+                    "grounding_chunks": [
+                        {"web": {"uri": "https://example.com", "title": "Example"}}
+                    ],
+                    "grounding_supports": [
+                        {
+                            "segment": {
+                                "start_index": 12,  # Points to second part
+                                "end_index": 38,
+                                "text": "Second part with citation",
+                                "part_index": 1,  # Indicates which part
+                            },
+                            "grounding_chunk_indices": [0],
+                        }
+                    ],
+                },
+            }
+        ],
+        "prompt_feedback": {"block_reason": 0, "safety_ratings": []},
+        "usage_metadata": {
+            "prompt_token_count": 10,
+            "candidates_token_count": 10,
+            "total_token_count": 20,
+        },
+    }
+
+    response = GenerateContentResponse(raw_response)
+    result = _response_to_result(response, stream=False)
+
+    message = result.generations[0].message
+
+    # Verify grounding metadata is present
+    assert "grounding_metadata" in message.response_metadata
+    grounding = message.response_metadata["grounding_metadata"]
+    # grounding metadata from proto.Message.to_dict() uses snake_case
+    assert len(grounding["grounding_supports"]) == 1
+    assert grounding["grounding_supports"][0]["segment"]["part_index"] == 1
+
+
 @pytest.mark.parametrize(
     "is_async,mock_target,method_name",
     [
@@ -1361,3 +1677,185 @@ def test_modalities_override_in_generation_config() -> None:
 
         # Verify that the _generate method was called
         mock_generate.assert_called_once()
+
+
+def test_chat_google_genai_image_content_blocks() -> None:
+    """Test generating an image with mocked response and content_blocks translation."""
+    mock_response = GenerateContentResponse(
+        {
+            "candidates": [
+                {
+                    "content": {
+                        "parts": [
+                            {"text": "Meow!"},
+                            {
+                                "inline_data": {
+                                    "mime_type": "image/png",
+                                    "data": base64.b64decode(
+                                        "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAf"
+                                        "FcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
+                                    ),
+                                }
+                            },
+                        ]
+                    },
+                    "finish_reason": "STOP",
+                }
+            ],
+            "usage_metadata": {
+                "prompt_token_count": 10,
+                "candidates_token_count": 5,
+                "total_token_count": 15,
+            },
+        }
+    )
+
+    llm = ChatGoogleGenerativeAI(
+        model="models/gemini-2.5-flash",
+        google_api_key=SecretStr("test-key"),
+    )
+
+    with patch.object(llm.client, "generate_content", return_value=mock_response):
+        result = llm.invoke(
+            "Say 'meow!' and then Generate an image of a cat.",
+            generation_config={
+                "top_k": 2,
+                "top_p": 1,
+                "temperature": 0.7,
+                "response_modalities": ["TEXT", "IMAGE"],
+            },
+        )
+
+    assert isinstance(result, AIMessage)
+    assert isinstance(result.content, list)
+    assert isinstance(result.content[0], str)
+    assert isinstance(result.content[1], dict)
+    assert result.content[1].get("type") == "image_url"
+    assert not result.content[0].startswith(" ")
+
+    content_blocks = result.content_blocks
+    assert len(content_blocks) == 2
+
+    text_block = content_blocks[0]
+    assert text_block["type"] == "text"
+    assert isinstance(text_block["text"], str)
+
+    image_block = content_blocks[1]
+    assert isinstance(image_block, dict)
+    assert image_block["type"] == "image"
+    assert "base64" in image_block
+    assert "mime_type" in image_block
+    assert image_block["mime_type"] == "image/png"
+    assert image_block["base64"] == (
+        "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDw"
+        "AChwGA60e6kgAAAABJRU5ErkJggg=="
+    )
+
+    # Pre-v1 we returned images using chat completions format. This translation logic
+    # is already covered by unit tests in langchain core, but we add a simple test here
+    # to ensure our translator function is wired up correctly
+    image_blocks_v1 = _convert_to_v1_from_genai(result)
+    assert len(image_blocks_v1) == 2
+    assert image_blocks_v1[0] == text_block
+    assert image_blocks_v1[1] == image_block
+
+
+def test_content_blocks_translation_with_mixed_image_content() -> None:
+    """Test converting with mixed image and text content."""
+    mixed_content = [
+        "Here is the image you requested:",
+        {
+            "type": "image_url",
+            "image_url": {
+                "url": base64.b64encode(
+                    base64.b64decode(
+                        "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk"
+                        "YPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
+                    )
+                ).decode(),
+            },
+        },
+    ]
+    msg = AIMessage(content=mixed_content)  # type: ignore[arg-type]
+    msg.response_metadata = {"model_provider": "google_genai"}
+
+    content_blocks = msg.content_blocks
+
+    assert len(content_blocks) == 2
+
+    # First block should be text
+    text_block = content_blocks[0]
+    assert text_block["type"] == "text"
+    assert text_block["text"] == "Here is the image you requested:"
+
+    # Second block should be ImageContentBlock
+    image_block = content_blocks[1]
+    assert image_block["type"] == "image"
+    assert "base64" in image_block
+
+
+def test_chat_google_genai_invoke_with_audio_mocked() -> None:
+    """Test generating audio with mocked response and content_blocks translation."""
+    mock_response = GenerateContentResponse(
+        {
+            "candidates": [
+                {
+                    # Empty content when audio is in additional_kwargs
+                    "content": {"parts": []},
+                    "finish_reason": "STOP",
+                }
+            ],
+            "usage_metadata": {
+                "prompt_token_count": 10,
+                "candidates_token_count": 5,
+                "total_token_count": 15,
+            },
+        }
+    )
+
+    wav_bytes = (  # (minimal WAV header)
+        b"RIFF$\x00\x00\x00WAVEfmt \x10\x00\x00\x00\x01\x00\x01\x00"
+        b"\x44\xac\x00\x00\x88X\x01\x00\x02\x00\x10\x00data\x00\x00\x00\x00"
+    )
+
+    llm = ChatGoogleGenerativeAI(
+        model="models/gemini-2.5-flash-preview-tts",
+        google_api_key=SecretStr("test-key"),
+        response_modalities=[Modality.AUDIO],
+    )
+
+    with patch.object(llm.client, "generate_content", return_value=mock_response):
+        with patch(
+            "langchain_google_genai.chat_models._parse_response_candidate"
+        ) as mock_parse:
+            mock_parse.return_value = AIMessage(
+                content="",
+                additional_kwargs={"audio": wav_bytes},
+                usage_metadata={
+                    "input_tokens": 10,
+                    "output_tokens": 5,
+                    "total_tokens": 15,
+                },
+                response_metadata={"model_provider": "google_genai"},
+            )
+
+            result = llm.invoke(
+                "Please say The quick brown fox jumps over the lazy dog",
+            )
+
+    assert isinstance(result, AIMessage)
+    assert result.content == ""
+    audio_data = result.additional_kwargs.get("audio")
+    assert isinstance(audio_data, bytes)
+    assert len(audio_data) >= 12
+    assert audio_data[0:4] == b"RIFF"
+    assert audio_data[8:12] == b"WAVE"
+
+    # Test content_blocks translation to AudioContentBlock
+    blocks = result.content_blocks
+    audio_blocks = [block for block in blocks if block["type"] == "audio"]
+    assert len(audio_blocks) >= 1
+    audio_block = audio_blocks[0]
+    assert audio_block["type"] == "audio"
+    assert "base64" in audio_block
+    assert audio_block["base64"] == base64.b64encode(wav_bytes).decode()
diff --git a/libs/genai/uv.lock b/libs/genai/uv.lock
index e1710a7a5..bff567794 100644
--- a/libs/genai/uv.lock
+++ b/libs/genai/uv.lock
@@ -1,15 +1,13 @@
 version = 1
 revision = 3
-requires-python = ">=3.9.0, <4.0.0"
+requires-python = ">=3.10.0, <4.0.0"
 resolution-markers = [
     "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'",
     "python_full_version >= '3.13' and platform_python_implementation != 'PyPy'",
     "python_full_version >= '3.11' and python_full_version < '3.13' and platform_python_implementation == 'PyPy'",
     "python_full_version >= '3.11' and python_full_version < '3.13' and platform_python_implementation != 'PyPy'",
-    "python_full_version == '3.10.*' and platform_python_implementation == 'PyPy'",
-    "python_full_version == '3.10.*' and platform_python_implementation != 'PyPy'",
-    "python_full_version < '3.10' and platform_python_implementation == 'PyPy'",
-    "python_full_version < '3.10' and platform_python_implementation != 'PyPy'",
+    "python_full_version < '3.11' and platform_python_implementation == 'PyPy'",
+    "python_full_version < '3.11' and platform_python_implementation != 'PyPy'",
 ]
 
 [[package]]
@@ -23,7 +21,7 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.10.0"
+version = "4.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
@@ -31,18 +29,18 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" },
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 [[package]]
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+sdist = { url = "https://files.pythonhosted.org/packages/9d/61/e4fad8155db4a04bfb4734c7c8ff0882f078f24294d42798b3568eb63bff/cachetools-6.2.0.tar.gz", hash = "sha256:38b328c0889450f05f5e120f56ab68c8abaf424e1275522b138ffc93253f7e32", size = 30988, upload-time = "2025-08-25T18:57:30.924Z" }
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+    { url = "https://files.pythonhosted.org/packages/6c/56/3124f61d37a7a4e7cc96afc5492c78ba0cb551151e530b54669ddd1436ef/cachetools-6.2.0-py3-none-any.whl", hash = "sha256:1c76a8960c0041fcc21097e357f882197c79da0dbff766e7317890a65d7d8ba6", size = 11276, upload-time = "2025-08-25T18:57:29.684Z" },
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diff --git a/libs/vertexai/langchain_google_vertexai/__init__.py b/libs/vertexai/langchain_google_vertexai/__init__.py
index ff18bb288..405e6e25b 100644
--- a/libs/vertexai/langchain_google_vertexai/__init__.py
+++ b/libs/vertexai/langchain_google_vertexai/__init__.py
@@ -56,14 +56,6 @@
 from langchain_google_vertexai.functions_utils import (
     PydanticFunctionsOutputParser,
 )
-from langchain_google_vertexai.gemma import (
-    GemmaChatLocalHF,
-    GemmaChatLocalKaggle,
-    GemmaChatVertexAIModelGarden,
-    GemmaLocalHF,
-    GemmaLocalKaggle,
-    GemmaVertexAIModelGarden,
-)
 from langchain_google_vertexai.llms import VertexAI
 from langchain_google_vertexai.model_garden import VertexAIModelGarden
 from langchain_google_vertexai.model_garden_maas import get_vertex_maas_model
@@ -89,12 +81,6 @@
     "FunctionCallingConfig",
     "FunctionDeclaration",
     "GCSDocumentStorage",
-    "GemmaChatLocalHF",
-    "GemmaChatLocalKaggle",
-    "GemmaChatVertexAIModelGarden",
-    "GemmaLocalHF",
-    "GemmaLocalKaggle",
-    "GemmaVertexAIModelGarden",
     "HarmBlockThreshold",
     "HarmCategory",
     "Modality",
diff --git a/libs/vertexai/langchain_google_vertexai/chat_models.py b/libs/vertexai/langchain_google_vertexai/chat_models.py
index 5dc5bca3a..fc69daede 100644
--- a/libs/vertexai/langchain_google_vertexai/chat_models.py
+++ b/libs/vertexai/langchain_google_vertexai/chat_models.py
@@ -16,6 +16,7 @@
     Dict,
     List,
     Optional,
+    Sequence,
     Type,
     Union,
     cast,
@@ -24,7 +25,7 @@
     TypedDict,
     overload,
 )
-from collections.abc import AsyncIterator, Iterator, Sequence
+from collections.abc import AsyncIterator, Iterator
 
 import proto  # type: ignore[import-untyped]
 
@@ -75,6 +76,7 @@
 from langchain_core.utils.pydantic import is_basemodel_subclass
 from langchain_core.utils.utils import _build_model_kwargs
 from vertexai.generative_models import (
+    Candidate as VertexCandidate,
     Tool as VertexTool,  # TODO: migrate to google-genai since this is deprecated
 )
 from vertexai.generative_models._generative_models import (
@@ -610,18 +612,19 @@ def _append_to_content(
 
 @overload
 def _parse_response_candidate(
-    response_candidate: Candidate, streaming: Literal[False] = False
+    response_candidate: Union[Candidate, VertexCandidate],
+    streaming: Literal[False] = False,
 ) -> AIMessage: ...
 
 
 @overload
 def _parse_response_candidate(
-    response_candidate: Candidate, streaming: Literal[True]
+    response_candidate: Union[Candidate, VertexCandidate], streaming: Literal[True]
 ) -> AIMessageChunk: ...
 
 
 def _parse_response_candidate(
-    response_candidate: Candidate, streaming: bool = False
+    response_candidate: Union[Candidate, VertexCandidate], streaming: bool = False
 ) -> AIMessage:
     content: Union[None, str, List[Union[str, dict[str, Any]]]] = None
     additional_kwargs = {}
@@ -637,7 +640,7 @@ def _parse_response_candidate(
         except AttributeError:
             pass
 
-        if part.thought:
+        if hasattr(part, "thought") and part.thought:
             thinking_message = {
                 "type": "thinking",
                 "thinking": part.text,
@@ -696,7 +699,9 @@ def _parse_response_candidate(
 
             if getattr(part, "thought_signature", None):
                 # store dict of {tool_call_id: thought_signature}
-                if isinstance(part.thought_signature, bytes):
+                if hasattr(part, "thought_signature") and isinstance(
+                    part.thought_signature, bytes
+                ):
                     thought_signature = _bytes_to_base64(part.thought_signature)
                     if (
                         _FUNCTION_CALL_THOUGHT_SIGNATURES_MAP_KEY
@@ -2044,8 +2049,7 @@ def _prepare_request_gemini(
             tool_config = _tool_choice_to_tool_config(tool_choice, all_names)
         else:
             pass
-        # Type conversion handled internally
-        safety_settings = self._safety_settings_gemini(safety_settings)  # type: ignore[assignment]
+        formatted_safety_settings = self._safety_settings_gemini(safety_settings)
         logprobs = logprobs if logprobs is not None else self.logprobs
         logprobs = logprobs if isinstance(logprobs, (int, bool)) else False
         generation_config = self._generation_config_gemini(
@@ -2113,17 +2117,14 @@ def _content_to_v1(contents: list[Content]) -> list[v1Content]:
                     )
                 )
 
-            if safety_settings:
+            if formatted_safety_settings:
                 v1_safety_settings = [
                     v1SafetySetting(
-                        # Ignores needed due to complex union type where mypy cannot
-                        # infer which variant of SafetySetting is being used
-                        # (Different SDK e.g. vertexai vs gapic has same name)
-                        category=s.category,  # type: ignore[union-attr]
-                        method=s.method,  # type: ignore[union-attr]
-                        threshold=s.threshold,  # type: ignore[union-attr]
+                        category=s.category,
+                        method=s.method,
+                        threshold=s.threshold,
                     )
-                    for s in safety_settings
+                    for s in formatted_safety_settings
                 ]
 
         if (self.cached_content is not None) or (cached_content is not None):
@@ -2148,7 +2149,7 @@ def _content_to_v1(contents: list[Content]) -> list[v1Content]:
             return GenerateContentRequest(
                 contents=contents,
                 model=self.full_model_name,
-                safety_settings=safety_settings,
+                safety_settings=formatted_safety_settings,
                 generation_config=generation_config,
                 cached_content=full_cache_name,
             )
@@ -2170,7 +2171,7 @@ def _content_to_v1(contents: list[Content]) -> list[v1Content]:
             system_instruction=system_instruction,
             tools=formatted_tools,
             tool_config=tool_config,
-            safety_settings=safety_settings,
+            safety_settings=formatted_safety_settings,
             generation_config=generation_config,
             model=self.full_model_name,
             labels=self.labels,
@@ -2623,7 +2624,7 @@ def bind_tools(
         *,
         tool_choice: Optional[Union[_ToolChoiceType, bool]] = None,
         **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, BaseMessage]:
+    ) -> Runnable[LanguageModelInput, AIMessage]:
         """Bind tool-like objects to this chat model.
 
         Assumes model is compatible with Vertex tool-calling API.
@@ -2666,8 +2667,7 @@ def _gemini_response_to_chat_result(
             info = get_generation_info(
                 candidate, usage_metadata=usage, logprobs=logprobs
             )
-            # Using default streaming=False, but mypy can't verify due to type mismatch
-            message = _parse_response_candidate(candidate)  # type: ignore[call-overload]
+            message = _parse_response_candidate(candidate)
             message.response_metadata["model_name"] = self.model_name
             if isinstance(message, AIMessage):
                 message.usage_metadata = lc_usage
@@ -2715,8 +2715,7 @@ def _gemini_chunk_to_generation_chunk(
             generation_info = {}
         else:
             top_candidate = response_chunk.candidates[0]
-            # Type ignore since mypy infers literal as bool
-            message = _parse_response_candidate(top_candidate, streaming=True)  # type: ignore[call-overload]
+            message = _parse_response_candidate(top_candidate, streaming=True)
             if lc_usage:
                 message.usage_metadata = lc_usage
             generation_info = get_generation_info(
diff --git a/libs/vertexai/langchain_google_vertexai/embeddings.py b/libs/vertexai/langchain_google_vertexai/embeddings.py
index 016333ae3..0e2b9874d 100644
--- a/libs/vertexai/langchain_google_vertexai/embeddings.py
+++ b/libs/vertexai/langchain_google_vertexai/embeddings.py
@@ -2,7 +2,6 @@
 import re
 import string
 import threading
-import warnings
 from concurrent.futures import ThreadPoolExecutor, wait
 from enum import Enum, auto
 from functools import cached_property
@@ -17,7 +16,6 @@
     ServiceUnavailable,
 )
 from google.cloud.aiplatform import telemetry
-from langchain_core._api.deprecation import deprecated
 from langchain_core.embeddings import Embeddings
 from langchain_core.language_models.llms import create_base_retry_decorator
 from pydantic import ConfigDict, model_validator
@@ -146,6 +144,8 @@ def validate_environment(self) -> Self:
             "default_metadata": self.default_metadata,
         }
         self._init_vertexai(values)
+        if not self.model_name:
+            raise ValueError("model_name must be provided")
         _, user_agent = get_user_agent(f"{self.__class__.__name__}_{self.model_name}")
         with telemetry.tool_context_manager(user_agent):
             if (
@@ -498,46 +498,6 @@ def embed_query(
         """
         return self.embed([text], 1, embeddings_task_type)[0]
 
-    @deprecated(
-        since="2.0.1", removal="3.0.0", alternative="VertexAIEmbeddings.embed_images()"
-    )
-    def embed_image(
-        self,
-        image_path: str,
-        contextual_text: Optional[str] = None,
-        dimensions: Optional[int] = None,
-    ) -> List[float]:
-        """Embed an image.
-
-        Args:
-            image_path: Path to image (Google Cloud Storage or web) to generate
-            embeddings for.
-            contextual_text: Text to generate embeddings for.
-
-        Returns:
-            Embedding for the image.
-        """
-        warnings.warn(
-            "The `embed_image()` API will be deprecated and replaced by \
-            `embed_images()`. Change your usage to \
-            `embed_images([image_path1, image_path2])` and note\
-            that the result returned will be a list of image embeddings."
-        )
-        if self.model_type != GoogleEmbeddingModelType.MULTIMODAL:
-            msg = "Only supported for multimodal models"
-            raise NotImplementedError(msg)
-
-        image_loader = self._image_bytes_loader_client
-        bytes_image = image_loader.load_bytes(image_path)
-        image = Image(bytes_image)
-        result: MultiModalEmbeddingResponse = self.instance[
-            "get_embeddings_with_retry"
-        ](image=image, contextual_text=contextual_text, dimension=dimensions)
-        if result.image_embedding is None:
-            msg = "Failed to generate image embedding"
-            raise ValueError(msg)
-        return result.image_embedding
-
     def embed_images(
         self,
         uris: List[str],
@@ -560,7 +520,7 @@ def embed_images(
 
         image_loader = self._image_bytes_loader_client
 
-        embeddings = []
+        embeddings: List[List[float]] = []
         for image_path in uris:
             bytes_image = image_loader.load_bytes(image_path)
             image = Image(bytes_image)
diff --git a/libs/vertexai/langchain_google_vertexai/gemma.py b/libs/vertexai/langchain_google_vertexai/gemma.py
deleted file mode 100644
index cec88e347..000000000
--- a/libs/vertexai/langchain_google_vertexai/gemma.py
+++ /dev/null
@@ -1,415 +0,0 @@
-import os
-from typing import Any, Dict, List, Optional, cast
-
-from langchain_core.callbacks import (
-    AsyncCallbackManagerForLLMRun,
-    CallbackManagerForLLMRun,
-)
-from langchain_core.language_models.chat_models import (
-    BaseChatModel,
-)
-from langchain_core.language_models.llms import BaseLLM
-from langchain_core.messages import (
-    AIMessage,
-    BaseMessage,
-    HumanMessage,
-    SystemMessage,
-)
-from langchain_core.outputs import (
-    ChatGeneration,
-    ChatResult,
-    Generation,
-    LLMResult,
-)
-from pydantic import BaseModel, ConfigDict, Field, model_validator
-from typing_extensions import Self
-
-from langchain_google_vertexai._base import _BaseVertexAIModelGarden
-from langchain_google_vertexai._utils import enforce_stop_tokens
-from langchain_google_vertexai.model_garden import VertexAIModelGarden
-
-USER_CHAT_TEMPLATE = "<start_of_turn>user\n{prompt}<end_of_turn>\n"
-MODEL_CHAT_TEMPLATE = "<start_of_turn>model\n{prompt}<end_of_turn>\n"
-
-
-def gemma_messages_to_prompt(history: List[BaseMessage]) -> str:
-    """Converts a list of messages to a chat prompt for Gemma."""
-    messages: List[str] = []
-    if len(messages) == 1:
-        content = cast("str", history[0].content)
-        if isinstance(history[0], SystemMessage):
-            msg = "Gemma currently doesn't support system message!"
-            raise ValueError(msg)
-        return content
-    for message in history:
-        content = cast("str", message.content)
-        if isinstance(message, SystemMessage):
-            msg = "Gemma currently doesn't support system message!"
-            raise ValueError(msg)
-        if isinstance(message, AIMessage):
-            messages.append(MODEL_CHAT_TEMPLATE.format(prompt=content))
-        elif isinstance(message, HumanMessage):
-            messages.append(USER_CHAT_TEMPLATE.format(prompt=content))
-        else:
-            msg = f"Unexpected message with type {type(message)}"
-            raise ValueError(msg)
-    messages.append("<start_of_turn>model\n")
-    return "".join(messages)
-
-
-def _parse_gemma_chat_response(response: str) -> str:
-    """Removes chat history from the response."""
-    pattern = "<start_of_turn>model\n"
-    pos = response.rfind(pattern)
-    if pos == -1:
-        return response
-    text = response[(pos + len(pattern)) :]
-    pos = text.find("<start_of_turn>user\n")
-    if pos > 0:
-        return text[:pos]
-    return text
-
-
-class _GemmaBase(BaseModel):
-    max_tokens: Optional[int] = None
-    """The maximum number of tokens to generate."""
-    temperature: Optional[float] = None
-    """The temperature to use for sampling."""
-    top_p: Optional[float] = None
-    """The top-p value to use for sampling."""
-    top_k: Optional[int] = None
-    """The top-k value to use for sampling."""
-
-    model_config = ConfigDict(protected_namespaces=())
-
-    @property
-    def _default_params(self) -> Dict[str, Any]:
-        """Get the default parameters for calling gemma."""
-        params = {
-            "max_tokens": self.max_tokens,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "top_k": self.top_k,
-        }
-        return dict(params.items())
-
-    def _get_params(self, **kwargs) -> Dict[str, Any]:
-        params = {k: kwargs.get(k, v) for k, v in self._default_params.items()}
-        return {k: v for k, v in params.items() if v is not None}
-
-
-class GemmaVertexAIModelGarden(VertexAIModelGarden):
-    allowed_model_args: Optional[List[str]] = [
-        "temperature",
-        "top_p",
-        "top_k",
-        "max_tokens",
-    ]
-
-    @property
-    def _llm_type(self) -> str:
-        return "gemma_vertexai_model_garden"
-
-    # Needed so that mypy doesn't flag missing aliased init args.
-    def __init__(self, **kwargs: Any) -> None:
-        super().__init__(**kwargs)
-
-
-class GemmaChatVertexAIModelGarden(_GemmaBase, _BaseVertexAIModelGarden, BaseChatModel):
-    allowed_model_args: Optional[List[str]] = [
-        "temperature",
-        "top_p",
-        "top_k",
-        "max_tokens",
-        "max_length",
-    ]
-    parse_response: bool = False
-    """Whether to post-process the chat response and clean repeations """
-    """or multi-turn statements."""
-
-    def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None:
-        """Needed for mypy typing to recognize model_name as a valid arg."""
-        if model_name:
-            kwargs["model_name"] = model_name
-        super().__init__(**kwargs)
-
-    model_config = ConfigDict(
-        populate_by_name=True,
-        protected_namespaces=(),
-    )
-
-    @property
-    def _llm_type(self) -> str:
-        return "gemma_vertexai_model_garden"
-
-    @property
-    def _default_params(self) -> Dict[str, Any]:
-        """Get the default parameters for calling gemma."""
-        # support both Gemma 1B and 2B
-        params = super()._default_params
-        params["max_length"] = self.max_tokens
-        return params
-
-    def _generate(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> ChatResult:
-        request = self._get_params(**kwargs)
-        request["prompt"] = gemma_messages_to_prompt(messages)
-        output = self.client.predict(endpoint=self.endpoint_path, instances=[request])
-        text = output.predictions[0]
-        if self.parse_response or kwargs.get("parse_response"):
-            text = _parse_gemma_chat_response(text)
-        if stop:
-            text = enforce_stop_tokens(text, stop)
-        generations = [
-            ChatGeneration(
-                message=AIMessage(content=text),
-            )
-        ]
-        return ChatResult(generations=generations)
-
-    async def _agenerate(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> ChatResult:
-        """Top Level call."""
-        request = self._get_params(**kwargs)
-        request["prompt"] = gemma_messages_to_prompt(messages)
-        output = await self.async_client.predict(
-            endpoint=self.endpoint_path, instances=[request]
-        )
-        text = output.predictions[0]
-        if self.parse_response or kwargs.get("parse_response"):
-            text = _parse_gemma_chat_response(text)
-        if stop:
-            text = enforce_stop_tokens(text, stop)
-        generations = [
-            ChatGeneration(
-                message=AIMessage(content=text),
-            )
-        ]
-        return ChatResult(generations=generations)
-
-
-class _GemmaLocalKaggleBase(_GemmaBase):
-    """Local gemma model loaded from Kaggle."""
-
-    client: Any = Field(default=None, exclude=True)  #: :meta private:
-    keras_backend: str = "jax"
-    model_name: str = Field(default="gemma_2b_en", alias="model")
-    """Gemma model name."""
-
-    model_config = ConfigDict(
-        populate_by_name=True,
-    )
-
-    def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None:
-        """Needed for mypy typing to recognize model_name as a valid arg."""
-        if model_name:
-            kwargs["model_name"] = model_name
-        super().__init__(**kwargs)
-
-    @model_validator(mode="after")
-    def validate_environment(self) -> Self:
-        """Validate that llama-cpp-python library is installed."""
-        try:
-            os.environ["KERAS_BACKEND"] = self.keras_backend
-            from keras_nlp.models import GemmaCausalLM  # type: ignore
-        except ImportError:
-            msg = (
-                "Could not import GemmaCausalLM library. "
-                "Please install the GemmaCausalLM library to "
-                "use this  model: pip install keras-nlp keras>=3 kaggle"
-            )
-            raise ImportError(msg)
-
-        self.client = GemmaCausalLM.from_preset(self.model_name)
-        return self
-
-    @property
-    def _default_params(self) -> Dict[str, Any]:
-        """Get the default parameters for calling gemma."""
-        params = {"max_length": self.max_tokens}
-        return {k: v for k, v in params.items() if v is not None}
-
-    def _get_params(self, **kwargs) -> Dict[str, Any]:
-        mapping = {"max_tokens": "max_length"}
-        params = {mapping[k]: v for k, v in kwargs.items() if k in mapping}
-        return {**self._default_params, **params}
-
-
-class GemmaLocalKaggle(_GemmaLocalKaggleBase, BaseLLM):
-    """Local gemma chat model loaded from Kaggle."""
-
-    def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None:
-        """Only needed for typing."""
-        if model_name:
-            kwargs["model_name"] = model_name
-        super().__init__(**kwargs)
-
-    def _generate(
-        self,
-        prompts: List[str],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> LLMResult:
-        """Run the LLM on the given prompt and input."""
-        params = self._get_params(**kwargs)
-        results = self.client.generate(prompts, **params)
-        results = [results] if isinstance(results, str) else results
-        if stop:
-            results = [enforce_stop_tokens(text, stop) for text in results]
-        return LLMResult(generations=[[Generation(text=result)] for result in results])
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of llm."""
-        return "gemma_local_kaggle"
-
-
-class GemmaChatLocalKaggle(_GemmaLocalKaggleBase, BaseChatModel):
-    parse_response: bool = False
-    """Whether to post-process the chat response and clean repeations """
-    """or multi-turn statements."""
-
-    def __init__(self, *, model_name: Optional[str] = None, **kwargs: Any) -> None:
-        """Needed for mypy typing to recognize model_name as a valid arg."""
-        if model_name:
-            kwargs["model_name"] = model_name
-        super().__init__(**kwargs)
-
-    def _generate(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> ChatResult:
-        params = self._get_params(**kwargs)
-        prompt = gemma_messages_to_prompt(messages)
-        text = self.client.generate(prompt, **params)
-        if self.parse_response or kwargs.get("parse_response"):
-            text = _parse_gemma_chat_response(text)
-        if stop:
-            text = enforce_stop_tokens(text, stop)
-        generation = ChatGeneration(message=AIMessage(content=text))
-        return ChatResult(generations=[generation])
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of llm."""
-        return "gemma_local_chat_kaggle"
-
-
-class _GemmaLocalHFBase(_GemmaBase):
-    """Local gemma model loaded from HuggingFace."""
-
-    tokenizer: Any = None  #: :meta private:
-    client: Any = Field(default=None, exclude=True)  #: :meta private:
-    hf_access_token: str
-    cache_dir: Optional[str] = None
-    model_name: str = Field(default="google/gemma-2b", alias="model")
-    """Gemma model name."""
-
-    model_config = ConfigDict(
-        populate_by_name=True,
-    )
-
-    @model_validator(mode="after")
-    def validate_environment(self) -> Self:
-        """Validate that llama-cpp-python library is installed."""
-        try:
-            from transformers import AutoTokenizer, GemmaForCausalLM  # type: ignore
-        except ImportError:
-            msg = (
-                "Could not import GemmaForCausalLM library. "
-                "Please install the GemmaForCausalLM library to "
-                "use this  model: pip install transformers>=4.38.1"
-            )
-            raise ImportError(msg)
-
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            self.model_name, token=self.hf_access_token
-        )
-        self.client = GemmaForCausalLM.from_pretrained(
-            self.model_name,
-            token=self.hf_access_token,
-            cache_dir=self.cache_dir,
-        )
-        return self
-
-    @property
-    def _default_params(self) -> Dict[str, Any]:
-        """Get the default parameters for calling gemma."""
-        params = {"max_length": self.max_tokens}
-        return {k: v for k, v in params.items() if v is not None}
-
-    def _get_params(self, **kwargs) -> Dict[str, Any]:
-        mapping = {"max_tokens": "max_length"}
-        params = {mapping[k]: v for k, v in kwargs.items() if k in mapping}
-        return {**self._default_params, **params}
-
-    def _run(self, prompt: str, **kwargs: Any) -> str:
-        inputs = self.tokenizer(prompt, return_tensors="pt")
-        params = self._get_params(**kwargs)
-        generate_ids = self.client.generate(inputs.input_ids, **params)
-        return self.tokenizer.batch_decode(
-            generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
-        )[0]
-
-
-class GemmaLocalHF(_GemmaLocalHFBase, BaseLLM):
-    """Local gemma model loaded from HuggingFace."""
-
-    def _generate(
-        self,
-        prompts: List[str],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> LLMResult:
-        """Run the LLM on the given prompt and input."""
-        results = [self._run(prompt, **kwargs) for prompt in prompts]
-        if stop:
-            results = [enforce_stop_tokens(text, stop) for text in results]
-        return LLMResult(generations=[[Generation(text=text)] for text in results])
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of llm."""
-        return "gemma_local_hf"
-
-
-class GemmaChatLocalHF(_GemmaLocalHFBase, BaseChatModel):
-    parse_response: bool = False
-    """Whether to post-process the chat response and clean repeations """
-    """or multi-turn statements."""
-
-    def _generate(
-        self,
-        messages: List[BaseMessage],
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> ChatResult:
-        prompt = gemma_messages_to_prompt(messages)
-        text = self._run(prompt, **kwargs)
-        if self.parse_response or kwargs.get("parse_response"):
-            text = _parse_gemma_chat_response(text)
-        if stop:
-            text = enforce_stop_tokens(text, stop)
-        generation = ChatGeneration(message=AIMessage(content=text))
-        return ChatResult(generations=[generation])
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of llm."""
-        return "gemma_local_chat_hf"
diff --git a/libs/vertexai/langchain_google_vertexai/model_garden.py b/libs/vertexai/langchain_google_vertexai/model_garden.py
index b658af592..eb2385628 100644
--- a/libs/vertexai/langchain_google_vertexai/model_garden.py
+++ b/libs/vertexai/langchain_google_vertexai/model_garden.py
@@ -448,7 +448,7 @@ def bind_tools(
             Union[Dict[str, str], Literal["any", "auto"], str]
         ] = None,
         **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, BaseMessage]:
+    ) -> Runnable[LanguageModelInput, AIMessage]:
         """Bind tool-like objects to this chat model."""
         formatted_tools = [convert_to_anthropic_tool(tool) for tool in tools]
         if not tool_choice:
diff --git a/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py b/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py
index 62e8e6597..472869a26 100644
--- a/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py
+++ b/libs/vertexai/langchain_google_vertexai/model_garden_maas/llama.py
@@ -518,7 +518,7 @@ def bind_tools(
         self,
         tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
         **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, BaseMessage]:
+    ) -> Runnable[LanguageModelInput, AIMessage]:
         """Bind tool-like objects to this chat model."""
         formatted_tools = [convert_to_openai_function(tool) for tool in tools]
         return super().bind(tools=formatted_tools, **kwargs)
diff --git a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py
index 87d77db79..d18885a07 100644
--- a/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py
+++ b/libs/vertexai/langchain_google_vertexai/vectorstores/vectorstores.py
@@ -8,7 +8,6 @@
     NumericNamespace,
 )
 from google.oauth2.service_account import Credentials
-from langchain_core._api.deprecation import deprecated
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.vectorstores import VectorStore
@@ -32,7 +31,6 @@ def __init__(
         self,
         searcher: Searcher,
         document_storage: DocumentStorage,
-        embbedings: Optional[Embeddings] = None,  # Deprecated parameter
         embeddings: Optional[Embeddings] = None,
     ) -> None:
         """Constructor.
@@ -40,31 +38,13 @@ def __init__(
         Args:
             searcher: Object in charge of searching and storing the index.
             document_storage: Object in charge of storing and retrieving documents.
-            embbedings: Object in charge of transforming text to embbeddings.
-                Deprecated: Use 'embeddings' instead.
             embeddings: Object in charge of transforming text to embeddings.
         """
         super().__init__()
         self._searcher = searcher
         self._document_storage = document_storage
 
-        # Add explicit warning when the misspelled parameter is used
-        if embbedings is not None:
-            warnings.warn(
-                message=(
-                    "The parameter `embbedings` is deprecated due to a spelling error. "
-                    "Please use `embeddings` instead. "
-                    "Support for `embbedings` will be removed in a future version."
-                ),
-                category=DeprecationWarning,
-            )
-        self._embeddings = embeddings or embbedings or self._get_default_embeddings()
-
-    @property
-    @deprecated(since="0.1.0", removal="3.0.0", alternative="embeddings")
-    def embbedings(self) -> Embeddings:
-        """Returns the embeddings object."""
-        return self._embeddings
+        self._embeddings = embeddings or self._get_default_embeddings()
 
     @property
     def embeddings(self) -> Embeddings:
diff --git a/libs/vertexai/pyproject.toml b/libs/vertexai/pyproject.toml
index f139a17f8..b65f0710b 100644
--- a/libs/vertexai/pyproject.toml
+++ b/libs/vertexai/pyproject.toml
@@ -8,12 +8,12 @@ version = "2.1.2"
 description = "An integration package connecting Google VertexAI and LangChain"
 authors = []
 license = {text = "MIT"}
-requires-python = ">=3.9.0,<4.0.0"
+requires-python = ">=3.10.0,<4.0.0"
 readme = "README.md"
 repository = "https://github.com/langchain-ai/langchain-google"
 
 dependencies = [
-    "langchain-core>=0.3.76,<2.0.0",
+    "langchain-core>=1.0.0a4,<2.0.0",
     "google-cloud-aiplatform>=1.97.0,<2.0.0",
     "google-cloud-storage>=2.18.0,<3.0.0",
     "httpx>=0.28.0,<1.0.0",
@@ -35,7 +35,7 @@ anthropic = ["anthropic>=0.35.0,<1.0.0"]
 mistral = ["langchain-mistralai>=0.2.0,<2.0.0"]
 
 [dependency-groups]
-lint = ["ruff<0.13.0,>=0.12.2"]
+lint = ["ruff>=0.13.1,<0.14.0"]
 typing = [
     "mypy>=1.18.1,<1.19.0",
     "types-requests>=2.31.0,<3.0.0",
@@ -57,10 +57,10 @@ test = [
     "types-requests>=2.31.0,<3.0.0",
     "types-protobuf>=4.24.0.4,<7.0.0.0",
     "google-api-python-client>=2.117.0,<3.0.0",
-    "langchain>=0.3.27,<2.0.0",
+    "langchain>=1.0.0a10,<2.0.0",
     "anthropic>=0.64.0,<1.0.0",
     "validators>=0.35.0,<1.0.0",
-    "langchain-tests>=0.2.0,<2.0.0",
+    "langchain-tests>=1.0.0a1,<2.0.0",
 ]
 
 test_integration = [
@@ -69,8 +69,8 @@ test_integration = [
     "numpy>=2.1.0; python_version>='3.13'",
     "google-api-python-client>=2.114.0,<3.0.0",
     "google-cloud-datastore>=2.19.0,<3.0.0",
-    "langchain-mistralai>=0.2,<2.0.0",
-    "langchain-tests>=0.2.0,<2.0.0",
+    "langchain-mistralai>=0.2.12,<2.0.0",
+    "langchain-tests>=1.0.0a1,<2.0.0",
 ]
 
 dev = []
@@ -106,7 +106,6 @@ ignore = [
     # 3.9 needs
     "UP007",
     "UP006",
-    "UP038",
     "UP045",
     "FA100",
 
diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py
index 39b939519..b3c92e2d5 100644
--- a/libs/vertexai/tests/integration_tests/test_chat_models.py
+++ b/libs/vertexai/tests/integration_tests/test_chat_models.py
@@ -5,7 +5,12 @@
 import json
 import os
 import re
-from typing import List, Literal, Optional, cast
+from typing import Any, List, Literal, Optional, cast
+
+try:
+    from langgraph.graph.state import CompiledStateGraph
+except ImportError:
+    CompiledStateGraph = Any  # type: ignore[misc,assignment]
 
 import pytest
 import requests
@@ -317,7 +322,7 @@ def get_climate_info(query: str) -> str:
     )
     prompt_template = ChatPromptTemplate.from_messages(
         [
-            ("human", "{input}"),
+            ("placeholder", "{messages}"),
             ("placeholder", "{agent_scratchpad}"),
         ]
     )
@@ -336,20 +341,17 @@ def get_climate_info(query: str) -> str:
         "mime_type": mime_type,
     }
     text_message = {"type": "text", "text": "Describe the attached media in 5 words."}
-    message = [media_message, text_message]
     tools = [get_climate_info]
-    agent = agents.create_tool_calling_agent(
-        llm=llm,
+    agent: CompiledStateGraph[Any, Any] = agents.create_agent(
+        model=llm,
         tools=tools,
         prompt=prompt_template,
     )
-    agent_executor = agents.AgentExecutor(
-        agent=agent,
-        tools=tools,
-        verbose=False,
+    output = agent.invoke(
+        {"messages": [{"role": "user", "content": [text_message, media_message]}]}
     )
-    output = agent_executor.invoke({"input": message})
-    assert isinstance(output["output"], str)
+    assert "messages" in output
+    assert len(output["messages"]) > 0
 
 
 @pytest.mark.flaky(retries=3)
@@ -1247,18 +1249,20 @@ def get_secret_number() -> int:
 
     prompt = ChatPromptTemplate.from_messages(
         [
-            ("human", "{input}"),
+            ("placeholder", "{messages}"),
             ("placeholder", "{agent_scratchpad}"),
         ]
     )
-    agent = agents.create_tool_calling_agent(
-        llm=chat,
+    agent: CompiledStateGraph[Any, Any] = agents.create_agent(
+        model=chat,
         tools=tools,
         prompt=prompt,
     )
-    agent_executor = agents.AgentExecutor(agent=agent, tools=tools, verbose=False)
-    response = agent_executor.invoke({"input": "what is the secret number?"})
-    assert isinstance(response["output"], str)
+    response = agent.invoke(
+        {"messages": [{"role": "user", "content": "what is the secret number?"}]}
+    )
+    assert "messages" in response
+    assert len(response["messages"]) > 0
 
 
 @pytest.mark.release
@@ -1324,7 +1328,7 @@ def test_langgraph_example() -> None:
         [
             *messages,
             step1,
-            ToolMessage(content="9", tool_call_id=step1.tool_calls[0]["id"]),  # type: ignore[attr-defined]
+            ToolMessage(content="9", tool_call_id=step1.tool_calls[0]["id"]),
         ],
         tools=[{"function_declarations": [add_declaration, multiply_declaration]}],
     )
diff --git a/libs/vertexai/tests/integration_tests/test_gemma.py b/libs/vertexai/tests/integration_tests/test_gemma.py
deleted file mode 100644
index c86d2822c..000000000
--- a/libs/vertexai/tests/integration_tests/test_gemma.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import os
-
-import pytest
-from langchain_core.messages import (
-    AIMessage,
-    HumanMessage,
-)
-
-from langchain_google_vertexai import (
-    GemmaChatLocalKaggle,
-    GemmaChatVertexAIModelGarden,
-    GemmaLocalKaggle,
-    GemmaVertexAIModelGarden,
-)
-
-
-@pytest.mark.extended
-def test_gemma_model_garden() -> None:
-    """In order to run this test, you should provide endpoint names.
-
-    Example:
-    export GEMMA_ENDPOINT_ID=...
-    export PROJECT_ID=...
-    """
-    endpoint_id = os.environ["GEMMA_ENDPOINT_ID"]
-    project = os.environ["PROJECT_ID"]
-    location = "us-central1"
-    llm = GemmaVertexAIModelGarden(
-        endpoint_id=endpoint_id,
-        project=project,
-        location=location,
-    )
-    output = llm.invoke("What is the meaning of life?")
-    assert isinstance(output, str)
-    assert len(output) > 2
-    assert llm._llm_type == "gemma_vertexai_model_garden"
-
-
-@pytest.mark.extended
-def test_gemma_chat_model_garden() -> None:
-    """In order to run this test, you should provide endpoint names.
-
-    Example:
-    export GEMMA_ENDPOINT_ID=...
-    export PROJECT_ID=...
-    """
-    endpoint_id = os.environ["GEMMA_ENDPOINT_ID"]
-    project = os.environ["PROJECT_ID"]
-    location = "us-central1"
-    llm = GemmaChatVertexAIModelGarden(
-        endpoint_id=endpoint_id,
-        project=project,
-        location=location,
-    )
-    assert llm._llm_type == "gemma_vertexai_model_garden"
-
-    text_question1, text_answer1 = "How much is 2+2?", "4"
-    text_question2 = "How much is 3+3?"
-    message1 = HumanMessage(content=text_question1)
-    message2 = AIMessage(content=text_answer1)
-    message3 = HumanMessage(content=text_question2)
-    output = llm.invoke([message1])
-    assert isinstance(output, AIMessage)
-    assert len(output.content) > 2
-    output = llm.invoke([message1, message2, message3])
-    assert isinstance(output, AIMessage)
-    assert len(output.content) > 2
-
-
-@pytest.mark.gpu
-def test_gemma_kaggle() -> None:
-    llm = GemmaLocalKaggle(model_name="gemma_2b_en")
-    output = llm.invoke("What is the meaning of life?")
-    assert isinstance(output, str)
-    assert len(output) > 2
-
-
-@pytest.mark.gpu
-def test_gemma_chat_kaggle() -> None:
-    llm = GemmaChatLocalKaggle(model_name="gemma_2b_en")
-    text_question1, text_answer1 = "How much is 2+2?", "4"
-    text_question2 = "How much is 3+3?"
-    message1 = HumanMessage(content=text_question1)
-    message2 = AIMessage(content=text_answer1)
-    message3 = HumanMessage(content=text_question2)
-    output = llm.invoke([message1])
-    assert isinstance(output, AIMessage)
-    assert len(output.content) > 2
-    output = llm.invoke([message1, message2, message3])
-    assert isinstance(output, AIMessage)
-    assert len(output.content) > 2
diff --git a/libs/vertexai/tests/unit_tests/test_chat_models.py b/libs/vertexai/tests/unit_tests/test_chat_models.py
index 539fde32c..88e96cdb8 100644
--- a/libs/vertexai/tests/unit_tests/test_chat_models.py
+++ b/libs/vertexai/tests/unit_tests/test_chat_models.py
@@ -1128,6 +1128,7 @@ def test_safety_settings_gemini() -> None:
     )
     safety_settings = model._safety_settings_gemini([expected_safety_setting])
     assert safety_settings == [expected_safety_setting]
+    # Ignores for tests that intentionally use invalid dict types
     safety_settings = model._safety_settings_gemini(
         # Ignore since testing string conversion
         {"HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE"}  # type: ignore[dict-item]
diff --git a/libs/vertexai/tests/unit_tests/test_function_utils.py b/libs/vertexai/tests/unit_tests/test_function_utils.py
index 2a9100d4c..b8c2aab4d 100644
--- a/libs/vertexai/tests/unit_tests/test_function_utils.py
+++ b/libs/vertexai/tests/unit_tests/test_function_utils.py
@@ -42,6 +42,7 @@
     _format_vertex_to_function_declaration,
     _FunctionDeclarationLike,
     _tool_choice_to_tool_config,
+    _ToolType,
 )
 
 
@@ -418,20 +419,20 @@ def test_format_to_gapic_function_declaration() -> None:
 
 
 def test_format_to_gapic_tool() -> None:
-    src = [src for src, _, _, _ in SRC_EXP_MOCKS_DESC]
+    src: List[_FunctionDeclarationLike] = [src for src, _, _, _ in SRC_EXP_MOCKS_DESC]
     fds = [fd for _, fd, _, _ in SRC_EXP_MOCKS_DESC]
     expected = gapic.Tool(function_declarations=fds)
     result = _format_to_gapic_tool(src)
     assert result == expected
 
-    src_2 = [
-        *src,
+    additional_tools: List[_ToolType] = [
         gapic.Tool(function_declarations=[search_model_exp]),
         vertexai.Tool.from_function_declarations(
             [vertexai.FunctionDeclaration.from_func(search)]
         ),
         {"function_declarations": [search_model_dict]},
     ]
+    src_2 = src + additional_tools
     expected = gapic.Tool(
         function_declarations=[*fds, search_model_exp, search_vfd_exp, search_model_exp]
     )
@@ -466,7 +467,7 @@ def test_format_to_gapic_tool() -> None:
     assert result == src_5
 
     with pytest.raises(ValueError) as exc_info1:
-        # Intentional invalid tool for testing
+        # type ignore since we're testing invalid input
         _ = _format_to_gapic_tool(["fake_tool"])  # type: ignore[list-item]
     assert str(exc_info1.value).startswith("Unsupported tool")
 
diff --git a/libs/vertexai/tests/unit_tests/test_imports.py b/libs/vertexai/tests/unit_tests/test_imports.py
index feeec0ca2..9d7b0b4e9 100644
--- a/libs/vertexai/tests/unit_tests/test_imports.py
+++ b/libs/vertexai/tests/unit_tests/test_imports.py
@@ -7,12 +7,6 @@
     "FunctionCallingConfig",
     "FunctionDeclaration",
     "GCSDocumentStorage",
-    "GemmaChatLocalHF",
-    "GemmaChatLocalKaggle",
-    "GemmaChatVertexAIModelGarden",
-    "GemmaLocalHF",
-    "GemmaLocalKaggle",
-    "GemmaVertexAIModelGarden",
     "HarmBlockThreshold",
     "HarmCategory",
     "Modality",
diff --git a/libs/vertexai/uv.lock b/libs/vertexai/uv.lock
index b7664a429..0eef743f0 100644
--- a/libs/vertexai/uv.lock
+++ b/libs/vertexai/uv.lock
@@ -1,15 +1,13 @@
 version = 1
 revision = 3
-requires-python = ">=3.9.0, <4.0.0"
+requires-python = ">=3.10.0, <4.0.0"
 resolution-markers = [
     "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'",
     "python_full_version >= '3.13' and platform_python_implementation != 'PyPy'",
     "python_full_version >= '3.11' and python_full_version < '3.13' and platform_python_implementation == 'PyPy'",
     "python_full_version >= '3.11' and python_full_version < '3.13' and platform_python_implementation != 'PyPy'",
-    "python_full_version == '3.10.*' and platform_python_implementation == 'PyPy'",
-    "python_full_version == '3.10.*' and platform_python_implementation != 'PyPy'",
-    "python_full_version < '3.10' and platform_python_implementation == 'PyPy'",
-    "python_full_version < '3.10' and platform_python_implementation != 'PyPy'",
+    "python_full_version < '3.11' and platform_python_implementation != 'PyPy'",
+    "python_full_version < '3.11' and platform_python_implementation == 'PyPy'",
 ]
 
 [[package]]
@@ -55,15 +53,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6f/12/e5e0282d673bb9746bacfb6e2dba8719989d3660cdb2ea79aee9a9651afb/anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1", size = 107213, upload-time = "2025-08-04T08:54:24.882Z" },
 ]
 
-[[package]]
-name = "async-timeout"
-version = "4.0.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/87/d6/21b30a550dafea84b1b8eee21b5e23fa16d010ae006011221f33dcd8d7f8/async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f", size = 8345, upload-time = "2023-08-10T16:35:56.907Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/a7/fa/e01228c2938de91d47b307831c62ab9e4001e747789d0b05baf779a6488c/async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028", size = 5721, upload-time = "2023-08-10T16:35:55.203Z" },
-]
-
 [[package]]
 name = "backports-asyncio-runner"
 version = "1.2.0"
@@ -73,77 +62,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a0/59/76ab57e3fe74484f48a53f8e337171b4a2349e506eabe136d7e01d059086/backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5", size = 12313, upload-time = "2025-07-02T02:27:14.263Z" },
 ]
 
-[[package]]
-name = "bottleneck"
-version = "1.5.0"
-source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version < '3.10' and platform_python_implementation == 'PyPy'",
-    "python_full_version < '3.10' and platform_python_implementation != 'PyPy'",
-]
-dependencies = [
-    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/80/82/dd20e69b97b9072ed2d26cc95c0a573461986bf62f7fde7ac59143490918/bottleneck-1.5.0.tar.gz", hash = "sha256:c860242cf20e69d5aab2ec3c5d6c8c2a15f19e4b25b28b8fca2c2a12cefae9d8", size = 104177, upload-time = "2025-05-13T21:11:21.158Z" }
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