GmailToolkit
This will help you getting started with the GMail toolkit. This toolkit interacts with the GMail API to read messages, draft and send messages, and more. For detailed documentation of all GmailToolkit features and configurations head to the API reference.
Setup
To use this toolkit, you will need to set up your credentials explained in the Gmail API docs. Once you've downloaded the credentials.json
file, you can start using the Gmail API.
Installation
This toolkit lives in the langchain-google-community
package. We'll need the gmail
extra:
%pip install -qU langchain-google-community\[gmail\]
If you want to get automated tracing from runs of individual tools, you can also set your LangSmith API key by uncommenting below:
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
Instantiation
By default the toolkit reads the local credentials.json
file. You can also manually provide a Credentials
object.
from langchain_google_community import GmailToolkit
toolkit = GmailToolkit()
Customizing Authentication
Behind the scenes, a googleapi
resource is created using the following methods.
you can manually build a googleapi
resource for more auth control.
from langchain_google_community.gmail.utils import (
build_resource_service,
get_gmail_credentials,
)
# Can review scopes here https://developers.google.com/gmail/api/auth/scopes
# For instance, readonly scope is 'https://www.googleapis.com/auth/gmail.readonly'
credentials = get_gmail_credentials(
token_file="token.json",
scopes=["https://mail.google.com/"],
client_secrets_file="credentials.json",
)
api_resource = build_resource_service(credentials=credentials)
toolkit = GmailToolkit(api_resource=api_resource)
Tools
View available tools:
tools = toolkit.get_tools()
tools
[GmailCreateDraft(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
GmailSendMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
GmailSearch(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
GmailGetMessage(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>),
GmailGetThread(api_resource=<googleapiclient.discovery.Resource object at 0x1094509d0>)]
Use within an agent
Below we show how to incorporate the toolkit into an agent.
We will need a LLM or chat model:
- OpenAI
- Anthropic
- Azure
- Cohere
- NVIDIA
- FireworksAI
- Groq
- MistralAI
- TogetherAI
pip install -qU langchain-openai
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini")
pip install -qU langchain-anthropic
import getpass
import os
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
pip install -qU langchain-openai
import getpass
import os
os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import AzureChatOpenAI
llm = AzureChatOpenAI(
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)
pip install -qU langchain-google-vertexai
import getpass
import os
os.environ["GOOGLE_API_KEY"] = getpass.getpass()
from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(model="gemini-1.5-flash")
pip install -qU langchain-cohere
import getpass
import os
os.environ["COHERE_API_KEY"] = getpass.getpass()
from langchain_cohere import ChatCohere
llm = ChatCohere(model="command-r-plus")
pip install -qU langchain-nvidia-ai-endpoints
import getpass
import os
os.environ["NVIDIA_API_KEY"] = getpass.getpass()
from langchain import ChatNVIDIA
llm = ChatNVIDIA(model="meta/llama3-70b-instruct")
pip install -qU langchain-fireworks
import getpass
import os
os.environ["FIREWORKS_API_KEY"] = getpass.getpass()
from langchain_fireworks import ChatFireworks
llm = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")
pip install -qU langchain-groq
import getpass
import os
os.environ["GROQ_API_KEY"] = getpass.getpass()
from langchain_groq import ChatGroq
llm = ChatGroq(model="llama3-8b-8192")
pip install -qU langchain-mistralai
import getpass
import os
os.environ["MISTRAL_API_KEY"] = getpass.getpass()
from langchain_mistralai import ChatMistralAI
llm = ChatMistralAI(model="mistral-large-latest")
pip install -qU langchain-openai
import getpass
import os
os.environ["TOGETHER_API_KEY"] = getpass.getpass()
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
base_url="https://api.together.xyz/v1",
api_key=os.environ["TOGETHER_API_KEY"],
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)
from langgraph.prebuilt import create_react_agent
agent_executor = create_react_agent(llm, tools)
example_query = "Draft an email to fake@fake.com thanking them for coffee."
events = agent_executor.stream(
{"messages": [("user", example_query)]},
stream_mode="values",
)
for event in events:
event["messages"][-1].pretty_print()
================================[1m Human Message [0m=================================
Draft an email to fake@fake.com thanking them for coffee.
==================================[1m Ai Message [0m==================================
Tool Calls:
create_gmail_draft (call_slGkYKZKA6h3Mf1CraUBzs6M)
Call ID: call_slGkYKZKA6h3Mf1CraUBzs6M
Args:
message: Dear Fake,
I wanted to take a moment to thank you for the coffee yesterday. It was a pleasure catching up with you. Let's do it again soon!
Best regards,
[Your Name]
to: ['fake@fake.com']
subject: Thank You for the Coffee
=================================[1m Tool Message [0m=================================
Name: create_gmail_draft
Draft created. Draft Id: r-7233782721440261513
==================================[1m Ai Message [0m==================================
I have drafted an email to fake@fake.com thanking them for the coffee. You can review and send it from your email draft with the subject "Thank You for the Coffee".
API reference
For detailed documentation of all GmailToolkit
features and configurations head to the API reference.