All functionality related to Google Cloud, Google Gemini and other Google products.
  1. Google Generative AI (Gemini API & AI Studio): Access Google Gemini models directly via the Gemini API. Use Google AI Studio for rapid prototyping and get started quickly with the langchain-google-genai package. This is often the best starting point for individual developers.
  2. Google Cloud (Vertex AI & other services): Access Gemini models, Vertex AI Model Garden and a wide range of cloud services (databases, storage, document AI, etc.) via the Google Cloud Platform. Use the langchain-google-vertexai package for Vertex AI models and specific packages (e.g., langchain-google-cloud-sql-pg, langchain-google-community) for other cloud services. This is ideal for developers already using Google Cloud or needing enterprise features like MLOps, specific model tuning or enterprise support.
See Google’s guide on migrating from the Gemini API to Vertex AI for more details on the differences. Integration packages for Gemini models and the Vertex AI platform are maintained in the langchain-google repository. You can find a host of LangChain integrations with other Google APIs and services in the googleapis Github organization and the langchain-google-community package.

​
Google Generative AI (Gemini API & AI Studio)

Access Google Gemini models directly using the Gemini API, best suited for rapid development and experimentation. Gemini models are available in Google AI Studio. 
pip install -U langchain-google-genai
Start for free and get your API key from Google AI Studio. 
export GOOGLE_API_KEY="YOUR_API_KEY"

​
Chat Models

Use the ChatGoogleGenerativeAI class to interact with Gemini models. See details in this guide. 
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage

llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash")

# Simple text invocation
result = llm.invoke("Sing a ballad of LangChain.")
print(result.content)

# Multimodal invocation with gemini-pro-vision
message = HumanMessage(
    content=[
        {
            "type": "text",
            "text": "What's in this image?",
        },
        {"type": "image_url", "image_url": "https://picsum.photos/seed/picsum/200/300"},
    ]
)
result = llm.invoke([message])
print(result.content)
The image_url can be a public URL, a GCS URI (gs://...), a local file path, a base64 encoded image string (data:image/png;base64,...), or a PIL Image object.

​
Embedding Models

Generate text embeddings using models like gemini-embedding-001 with the GoogleGenerativeAIEmbeddings class. See a usage example. 
from langchain_google_genai import GoogleGenerativeAIEmbeddings

embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-001")
vector = embeddings.embed_query("What are embeddings?")
print(vector[:5])

​
LLMs

Access the same Gemini models using the (legacy) LLM interface with the GoogleGenerativeAI class. See a usage example. 
from langchain_google_genai import GoogleGenerativeAI

llm = GoogleGenerativeAI(model="gemini-2.5-flash")
result = llm.invoke("Sing a ballad of LangChain.")
print(result)

​
Google Cloud

Access Gemini models, Vertex AI Model Garden and other Google Cloud services via Vertex AI and specific cloud integrations. Vertex AI models require the langchain-google-vertexai package. Other services might require additional packages like langchain-google-community, langchain-google-cloud-sql-pg, etc. 
pip install langchain-google-vertexai
# pip install langchain-google-community[...] # For other services
Google Cloud integrations typically use Application Default Credentials (ADC). Refer to the Google Cloud authentication documentation for setup instructions (e.g., using gcloud auth application-default login).

​
Chat Models

​
Vertex AI

Access chat models like Gemini via the Vertex AI platform. See a usage example. 
from langchain_google_vertexai import ChatVertexAI

​
Anthropic on Vertex AI Model Garden

See a usage example. 
from langchain_google_vertexai.model_garden import ChatAnthropicVertex

​
Llama on Vertex AI Model Garden

from langchain_google_vertexai.model_garden_maas.llama import VertexModelGardenLlama

​
Mistral on Vertex AI Model Garden

from langchain_google_vertexai.model_garden_maas.mistral import VertexModelGardenMistral

​
Gemma local from Hugging Face

Local Gemma model loaded from HuggingFace. Requires langchain-google-vertexai. 
from langchain_google_vertexai.gemma import GemmaChatLocalHF

​
Gemma local from Kaggle

Local Gemma model loaded from Kaggle. Requires langchain-google-vertexai. 
from langchain_google_vertexai.gemma import GemmaChatLocalKaggle

​
Gemma on Vertex AI Model Garden

Requires langchain-google-vertexai. 
from langchain_google_vertexai.gemma import GemmaChatVertexAIModelGarden

​
Vertex AI image captioning

Implementation of the Image Captioning model as a chat. Requires langchain-google-vertexai. 
from langchain_google_vertexai.vision_models import VertexAIImageCaptioningChat

​
Vertex AI image editor

Given an image and a prompt, edit the image. Currently only supports mask-free editing. Requires langchain-google-vertexai. 
from langchain_google_vertexai.vision_models import VertexAIImageEditorChat

​
Vertex AI image generator

Generates an image from a prompt. Requires langchain-google-vertexai. 
from langchain_google_vertexai.vision_models import VertexAIImageGeneratorChat

​
Vertex AI visual QnA

Chat implementation of a visual QnA model. Requires langchain-google-vertexai. 
from langchain_google_vertexai.vision_models import VertexAIVisualQnAChat

​
LLMs

You can also use the (legacy) string-in, string-out LLM interface.

​
Vertex AI Model Garden

Access Gemini, and hundreds of OSS models via Vertex AI Model Garden service. Requires langchain-google-vertexai. See a usage example. 
from langchain_google_vertexai import VertexAIModelGarden

​
Gemma local from Hugging Face

Local Gemma model loaded from HuggingFace. Requires langchain-google-vertexai. 
from langchain_google_vertexai.gemma import GemmaLocalHF

​
Gemma local from Kaggle

Local Gemma model loaded from Kaggle. Requires langchain-google-vertexai. 
from langchain_google_vertexai.gemma import GemmaLocalKaggle

​
Gemma on Vertex AI Model Garden

Requires langchain-google-vertexai. 
from langchain_google_vertexai.gemma import GemmaVertexAIModelGarden

​
Vertex AI image captioning

Implementation of the Image Captioning model as an LLM. Requires langchain-google-vertexai. 
from langchain_google_vertexai.vision_models import VertexAIImageCaptioning

​
Embedding Models

​
Vertex AI

Generate embeddings using models deployed on Vertex AI. Requires langchain-google-vertexai. See a usage example. 
from langchain_google_vertexai import VertexAIEmbeddings

​
Document Loaders

Load documents from various Google Cloud sources.

​
AlloyDB for PostgreSQL

Google Cloud AlloyDB is a fully managed PostgreSQL-compatible database service.
Install the python package: 
pip install langchain-google-alloydb-pg
See usage example. 
from langchain_google_alloydb_pg import AlloyDBLoader # AlloyDBEngine also available

​
BigQuery

Google Cloud BigQuery is a serverless data warehouse.
Install with BigQuery dependencies: 
pip install langchain-google-community[bigquery]
See a usage example. 
from langchain_google_community import BigQueryLoader

​
Bigtable

Google Cloud Bigtable is a fully managed NoSQL Big Data database service.
Install the python package: 
pip install langchain-google-bigtable
See usage example. 
from langchain_google_bigtable import BigtableLoader

​
Cloud SQL for MySQL

Google Cloud SQL for MySQL is a fully-managed MySQL database service.
Install the python package: 
pip install langchain-google-cloud-sql-mysql
See usage example. 
from langchain_google_cloud_sql_mysql import MySQLLoader # MySQLEngine also available

​
Cloud SQL for SQL Server

Google Cloud SQL for SQL Server is a fully-managed SQL Server database service.
Install the python package: 
pip install langchain-google-cloud-sql-mssql
See usage example. 
from langchain_google_cloud_sql_mssql import MSSQLLoader # MSSQLEngine also available

​
Cloud SQL for PostgreSQL

Google Cloud SQL for PostgreSQL is a fully-managed PostgreSQL database service.
Install the python package: 
pip install langchain-google-cloud-sql-pg
See usage example. 
from langchain_google_cloud_sql_pg import PostgresLoader # PostgresEngine also available

​
Cloud Storage

Cloud Storage is a managed service for storing unstructured data.
Install with GCS dependencies: 
pip install langchain-google-community[gcs]
Load from a directory or a specific file: See directory usage example. 
from langchain_google_community import GCSDirectoryLoader
See file usage example. 
from langchain_google_community import GCSFileLoader

​
Cloud Vision loader

Load data using Google Cloud Vision API. Install with Vision dependencies: 
pip install langchain-google-community[vision]

from langchain_google_community.vision import CloudVisionLoader

​
El Carro for Oracle Workloads

Google El Carro Oracle Operator runs Oracle databases in Kubernetes.
Install the python package: 
pip install langchain-google-el-carro
See usage example. 
from langchain_google_el_carro import ElCarroLoader

​
Firestore (Native Mode)

Google Cloud Firestore is a NoSQL document database.
Install the python package: 
pip install langchain-google-firestore
See usage example. 
from langchain_google_firestore import FirestoreLoader

​
Firestore (Datastore Mode)

Google Cloud Firestore in Datastore mode.
Install the python package: 
pip install langchain-google-datastore
See usage example. 
from langchain_google_datastore import DatastoreLoader

​
Memorystore for Redis

Google Cloud Memorystore for Redis is a fully managed Redis service.
Install the python package: 
pip install langchain-google-memorystore-redis
See usage example. 
from langchain_google_memorystore_redis import MemorystoreDocumentLoader

​
Spanner

Google Cloud Spanner is a fully managed, globally distributed relational database service.
Install the python package: 
pip install langchain-google-spanner
See usage example. 
from langchain_google_spanner import SpannerLoader

​
Speech-to-Text

Google Cloud Speech-to-Text transcribes audio files.
Install with Speech-to-Text dependencies: 
pip install langchain-google-community[speech]
See usage example and authorization instructions. 
from langchain_google_community import SpeechToTextLoader

​
Document Transformers

Transform documents using Google Cloud services.

​
Document AI

Google Cloud Document AI is a Google Cloud service that transforms unstructured data from documents into structured data, making it easier to understand, analyze, and consume.
We need to set up a GCS bucket and create your own OCR processor The GCS_OUTPUT_PATH should be a path to a folder on GCS (starting with gs://) and a processor name should look like projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID. We can get it either programmatically or copy from the Prediction endpoint section of the Processor details tab in the Google Cloud Console. 
pip install langchain-google-community[docai]
See a usage example. 
from langchain_core.document_loaders.blob_loaders import Blob
from langchain_google_community import DocAIParser

​
Google Translate

Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another.
The GoogleTranslateTransformer allows you to translate text and HTML with the Google Cloud Translation API. First, we need to install the langchain-google-community with translate dependencies. 
pip install langchain-google-community[translate]
See usage example and authorization instructions. 
from langchain_google_community import GoogleTranslateTransformer

​
Vector Stores

Store and search vectors using Google Cloud databases and Vertex AI Vector Search.

​
AlloyDB for PostgreSQL

Google Cloud AlloyDB is a fully managed relational database service that offers high performance, seamless integration, and impressive scalability on Google Cloud. AlloyDB is 100% compatible with PostgreSQL.
Install the python package: 
pip install langchain-google-alloydb-pg
See usage example. 
from langchain_google_alloydb_pg import AlloyDBVectorStore # AlloyDBEngine also available
Google Cloud BigQuery, BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud. Google Cloud BigQuery Vector Search BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast but approximate results, or using brute force for exact results.
It can calculate Euclidean or Cosine distance. With LangChain, we default to use Euclidean distance.
We need to install several python packages. 
pip install google-cloud-bigquery
See usage example. 
# Note: BigQueryVectorSearch might be in langchain or langchain_community depending on version
# Check imports in the usage example.
from langchain.vectorstores import BigQueryVectorSearch # Or langchain_community.vectorstores

​
Memorystore for Redis

Vector store using Memorystore for Redis.
Install the python package: 
pip install langchain-google-memorystore-redis
See usage example. 
from langchain_google_memorystore_redis import RedisVectorStore

​
Spanner

Vector store using Cloud Spanner.
Install the python package: 
pip install langchain-google-spanner
See usage example. 
from langchain_google_spanner import SpannerVectorStore

​
Firestore (Native Mode)

Vector store using Firestore.
Install the python package: 
pip install langchain-google-firestore
See usage example. 
from langchain_google_firestore import FirestoreVectorStore

​
Cloud SQL for MySQL

Vector store using Cloud SQL for MySQL.
Install the python package: 
pip install langchain-google-cloud-sql-mysql
See usage example. 
from langchain_google_cloud_sql_mysql import MySQLVectorStore # MySQLEngine also available

​
Cloud SQL for PostgreSQL

Vector store using Cloud SQL for PostgreSQL.
Install the python package: 
pip install langchain-google-cloud-sql-pg
See usage example. 
from langchain_google_cloud_sql_pg import PostgresVectorStore # PostgresEngine also available
Google Cloud Vertex AI Vector Search from Google Cloud, formerly known as Vertex AI Matching Engine, provides the industry’s leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.
Install the python package: 
pip install langchain-google-vertexai
See a usage example. 
from langchain_google_vertexai import VectorSearchVectorStore
With DataStore Backend
Vector search using Datastore for document storage.
See usage example. 
from langchain_google_vertexai import VectorSearchVectorStoreDatastore
With GCS Backend
Alias for VectorSearchVectorStore storing documents/index in GCS. 
from langchain_google_vertexai import VectorSearchVectorStoreGCS

​
Retrievers

Retrieve information using Google Cloud services.
Build generative AI powered search engines using Vertex AI Search. from Google Cloud allows developers to quickly build generative AI powered search engines for customers and employees.
See a usage example. Note: GoogleVertexAISearchRetriever is deprecated. Use the components below from langchain-google-community. Install the google-cloud-discoveryengine package for underlying access. 
pip install google-cloud-discoveryengine langchain-google-community
VertexAIMultiTurnSearchRetriever
from langchain_google_community import VertexAIMultiTurnSearchRetriever
VertexAISearchRetriever
# Note: The example code shows VertexAIMultiTurnSearchRetriever, confirm if VertexAISearchRetriever is separate or related.
# Assuming it might be related or a typo in the original doc:
from langchain_google_community import VertexAISearchRetriever # Verify class name if needed
VertexAISearchSummaryTool
from langchain_google_community import VertexAISearchSummaryTool

​
Document AI Warehouse

Search, store, and manage documents using Document AI Warehouse.
Note: GoogleDocumentAIWarehouseRetriever (from langchain) is deprecated. Use DocumentAIWarehouseRetriever from langchain-google-community. Requires installation of relevant Document AI packages (check specific docs). 
pip install langchain-google-community # Add specific docai dependencies if needed

from langchain_google_community.documentai_warehouse import DocumentAIWarehouseRetriever

​
Tools

Integrate agents with various Google services.

​
Text-to-Speech

Google Cloud Text-to-Speech is a Google Cloud service that enables developers to synthesize natural-sounding speech with 100+ voices, available in multiple languages and variants. It applies DeepMind’s groundbreaking research in WaveNet and Google’s powerful neural networks to deliver the highest fidelity possible.
Install required packages: 
pip install google-cloud-text-to-speech langchain-google-community
See usage example and authorization instructions. 
from langchain_google_community import TextToSpeechTool

​
Google Drive

Tools for interacting with Google Drive. Install required packages: 
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib langchain-googledrive
See usage example and authorization instructions. 
from langchain_googledrive.utilities.google_drive import GoogleDriveAPIWrapper
from langchain_googledrive.tools.google_drive.tool import GoogleDriveSearchTool

​
Google Finance

Query financial data. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_finance import GoogleFinanceQueryRun
from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper

​
Google Jobs

Query job listings. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_jobs import GoogleJobsQueryRun
# Note: Utilities might be shared, e.g., GoogleFinanceAPIWrapper was listed, verify correct utility
# from langchain_community.utilities.google_jobs import GoogleJobsAPIWrapper # If exists

​
Google Lens

Perform visual searches. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_lens import GoogleLensQueryRun
from langchain_community.utilities.google_lens import GoogleLensAPIWrapper

​
Google Places

Search for places information. Requires googlemaps package and a Google Maps API key. 
pip install googlemaps langchain # Requires base langchain
See usage example and authorization instructions. 
# Note: GooglePlacesTool might be in langchain or langchain_community depending on version
from langchain.tools import GooglePlacesTool # Or langchain_community.tools

​
Google Scholar

Search academic papers. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_scholar import GoogleScholarQueryRun
from langchain_community.utilities.google_scholar import GoogleScholarAPIWrapper
Perform web searches using Google Custom Search Engine (CSE). Requires GOOGLE_API_KEY and GOOGLE_CSE_ID. Install langchain-google-community: 
pip install langchain-google-community
Wrapper: 
from langchain_google_community import GoogleSearchAPIWrapper
Tools: 
from langchain_community.tools import GoogleSearchRun, GoogleSearchResults
Agent Loading: 
from langchain_community.agent_toolkits.load_tools import load_tools
tools = load_tools(["google-search"])
See detailed notebook. Query Google Trends data. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_trends import GoogleTrendsQueryRun
from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper

​
Toolkits

Collections of tools for specific Google services.

​
Gmail

Google Gmail is a free email service provided by Google. This toolkit works with emails through the Gmail API. 
pip install langchain-google-community[gmail]
See usage example and authorization instructions. 
# Load the whole toolkit
from langchain_google_community import GmailToolkit

# Or use individual tools
from langchain_google_community.gmail.create_draft import GmailCreateDraft
from langchain_google_community.gmail.get_message import GmailGetMessage
from langchain_google_community.gmail.get_thread import GmailGetThread
from langchain_google_community.gmail.search import GmailSearch
from langchain_google_community.gmail.send_message import GmailSendMessage

​
MCP Toolbox

MCP Toolbox provides a simple and efficient way to connect to your databases, including those on Google Cloud like Cloud SQL and AlloyDB. With MCP Toolbox, you can seamlessly integrate your database with LangChain to build powerful, data-driven applications.

​
Installation

To get started, install the Toolbox server and client. Configure a tools.yaml to define your tools, and then execute toolbox to start the server: 
toolbox --tools-file "tools.yaml"
Then, install the Toolbox client: 
pip install toolbox-langchain

​
Getting Started

Here is a quick example of how to use MCP Toolbox to connect to your database: 
from toolbox_langchain import ToolboxClient

async with ToolboxClient("http://127.0.0.1:5000") as client:

    tools = client.load_toolset()
See usage example and setup instructions.

​
Memory

Store conversation history using Google Cloud databases.

​
AlloyDB for PostgreSQL

Chat memory using AlloyDB.
Install the python package: 
pip install langchain-google-alloydb-pg
See usage example. 
from langchain_google_alloydb_pg import AlloyDBChatMessageHistory # AlloyDBEngine also available

​
Cloud SQL for PostgreSQL

Chat memory using Cloud SQL for PostgreSQL.
Install the python package: 
pip install langchain-google-cloud-sql-pg
See usage example. 
from langchain_google_cloud_sql_pg import PostgresChatMessageHistory # PostgresEngine also available

​
Cloud SQL for MySQL

Chat memory using Cloud SQL for MySQL.
Install the python package: 
pip install langchain-google-cloud-sql-mysql
See usage example. 
from langchain_google_cloud_sql_mysql import MySQLChatMessageHistory # MySQLEngine also available

​
Cloud SQL for SQL Server

Chat memory using Cloud SQL for SQL Server.
Install the python package: 
pip install langchain-google-cloud-sql-mssql
See usage example. 
from langchain_google_cloud_sql_mssql import MSSQLChatMessageHistory # MSSQLEngine also available

​
Spanner

Chat memory using Cloud Spanner.
Install the python package: 
pip install langchain-google-spanner
See usage example. 
from langchain_google_spanner import SpannerChatMessageHistory

​
Memorystore for Redis

Chat memory using Memorystore for Redis.
Install the python package: 
pip install langchain-google-memorystore-redis
See usage example. 
from langchain_google_memorystore_redis import MemorystoreChatMessageHistory

​
Bigtable

Chat memory using Cloud Bigtable.
Install the python package: 
pip install langchain-google-bigtable
See usage example. 
from langchain_google_bigtable import BigtableChatMessageHistory

​
Firestore (Native Mode)

Chat memory using Firestore.
Install the python package: 
pip install langchain-google-firestore
See usage example. 
from langchain_google_firestore import FirestoreChatMessageHistory

​
Firestore (Datastore Mode)

Chat memory using Firestore in Datastore mode.
Install the python package: 
pip install langchain-google-datastore
See usage example. 
from langchain_google_datastore import DatastoreChatMessageHistory

​
El Carro: The Oracle Operator for Kubernetes

Chat memory using Oracle databases run via El Carro.
Install the python package: 
pip install langchain-google-el-carro
See usage example. 
from langchain_google_el_carro import ElCarroChatMessageHistory

​
Callbacks

Track LLM/Chat model usage.

​
Vertex AI callback handler

Callback Handler that tracks VertexAI usage info.
Requires langchain-google-vertexai. 
from langchain_google_vertexai.callbacks import VertexAICallbackHandler

​
Evaluators

Evaluate model outputs using Vertex AI. Requires langchain-google-vertexai.

​
VertexPairWiseStringEvaluator

Pair-wise evaluation using Vertex AI models. 
from langchain_google_vertexai.evaluators.evaluation import VertexPairWiseStringEvaluator

​
VertexStringEvaluator

Evaluate a single prediction string using Vertex AI models. 
# Note: Original doc listed VertexPairWiseStringEvaluator twice. Assuming this class exists.
from langchain_google_vertexai.evaluators.evaluation import VertexStringEvaluator # Verify class name if needed

​
Other Google Products

Integrations with various Google services beyond the core Cloud Platform.

​
Document Loaders

​
Google Drive

Google Drive file storage. Currently supports Google Docs.
Install with Drive dependencies: 
pip install langchain-google-community[drive]
See usage example and authorization instructions. 
from langchain_google_community import GoogleDriveLoader

​
Vector Stores

​
ScaNN (Local Index)

Google ScaNN (Scalable Nearest Neighbors) is a python package. ScaNN is a method for efficient vector similarity search at scale.
ScaNN includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is optimized for x86 processors with AVX2 support. See its Google Research github for more details.
Install the scann package: 
pip install scann langchain-community # Requires langchain-community
See a usage example. 
from langchain_community.vectorstores import ScaNN

​
Retrievers

​
Google Drive

Retrieve documents from Google Drive. Install required packages: 
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib langchain-googledrive
See usage example and authorization instructions. 
from langchain_googledrive.retrievers import GoogleDriveRetriever

​
Tools

​
Google Drive

Tools for interacting with Google Drive. Install required packages: 
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib langchain-googledrive
See usage example and authorization instructions. 
from langchain_googledrive.utilities.google_drive import GoogleDriveAPIWrapper
from langchain_googledrive.tools.google_drive.tool import GoogleDriveSearchTool

​
Google Finance

Query financial data. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_finance import GoogleFinanceQueryRun
from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper

​
Google Jobs

Query job listings. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_jobs import GoogleJobsQueryRun
# Note: Utilities might be shared, e.g., GoogleFinanceAPIWrapper was listed, verify correct utility
# from langchain_community.utilities.google_jobs import GoogleJobsAPIWrapper # If exists

​
Google Lens

Perform visual searches. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_lens import GoogleLensQueryRun
from langchain_community.utilities.google_lens import GoogleLensAPIWrapper

​
Google Places

Search for places information. Requires googlemaps package and a Google Maps API key. 
pip install googlemaps langchain # Requires base langchain
See usage example and authorization instructions. 
# Note: GooglePlacesTool might be in langchain or langchain_community depending on version
from langchain.tools import GooglePlacesTool # Or langchain_community.tools

​
Google Scholar

Search academic papers. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_scholar import GoogleScholarQueryRun
from langchain_community.utilities.google_scholar import GoogleScholarAPIWrapper

​
Google Search

Perform web searches using Google Custom Search Engine (CSE). Requires GOOGLE_API_KEY and GOOGLE_CSE_ID. Install langchain-google-community: 
pip install langchain-google-community
Wrapper: 
from langchain_google_community import GoogleSearchAPIWrapper
Tools: 
from langchain_community.tools import GoogleSearchRun, GoogleSearchResults
Agent Loading: 
from langchain_community.agent_toolkits.load_tools import load_tools
tools = load_tools(["google-search"])
See detailed notebook. Query Google Trends data. Requires google-search-results package and SerpApi key. 
pip install google-search-results langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.tools.google_trends import GoogleTrendsQueryRun
from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper

​
Toolkits

​
Gmail

Google Gmail is a free email service provided by Google. This toolkit works with emails through the Gmail API. 
pip install langchain-google-community[gmail]
See usage example and authorization instructions. 
# Load the whole toolkit
from langchain_google_community import GmailToolkit

# Or use individual tools
from langchain_google_community.gmail.create_draft import GmailCreateDraft
from langchain_google_community.gmail.get_message import GmailGetMessage
from langchain_google_community.gmail.get_thread import GmailGetThread
from langchain_google_community.gmail.search import GmailSearch
from langchain_google_community.gmail.send_message import GmailSendMessage

​
Chat Loaders

​
Gmail

Load chat history from Gmail threads.
Install with Gmail dependencies: 
pip install langchain-google-community[gmail]
See usage example and authorization instructions. 
from langchain_google_community import GMailLoader

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3rd Party Integrations

Access Google services via third-party APIs.

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SearchApi

SearchApi provides API access to Google search, YouTube, etc. Requires langchain-community.
See usage examples and authorization instructions. 
from langchain_community.utilities import SearchApiAPIWrapper

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SerpApi

SerpApi provides API access to Google search results. Requires langchain-community.
See a usage example and authorization instructions. 
from langchain_community.utilities import SerpAPIWrapper

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Serper.dev

Google Serper provides API access to Google search results. Requires langchain-community.
See a usage example and authorization instructions. 
from langchain_community.utilities import GoogleSerperAPIWrapper

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YouTube

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YouTube Search Tool

Search YouTube videos without the official API. Requires youtube_search package. 
pip install youtube_search langchain # Requires base langchain
See a usage example. 
# Note: YouTubeSearchTool might be in langchain or langchain_community
from langchain.tools import YouTubeSearchTool # Or langchain_community.tools

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YouTube Audio Loader

Download audio from YouTube videos. Requires yt_dlp, pydub, librosa. 
pip install yt_dlp pydub librosa langchain-community # Requires langchain-community
See usage example and authorization instructions. 
from langchain_community.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
# Often used with whisper parsers:
# from langchain_community.document_loaders.parsers import OpenAIWhisperParser, OpenAIWhisperParserLocal

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YouTube Transcripts Loader

Load video transcripts. Requires youtube-transcript-api. 
pip install youtube-transcript-api langchain-community # Requires langchain-community
See a usage example. 
from langchain_community.document_loaders import YoutubeLoader