Skip to main content

Gmail Toolkit

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.

Setupโ€‹

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.

Installationโ€‹

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: ")

Instantiationโ€‹

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 Authenticationโ€‹

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)

Toolsโ€‹

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 agentโ€‹

Below we show how to incorporate the toolkit into an agent.

We will need a LLM or chat model:

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")
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()
================================ Human Message =================================

Draft an email to fake@fake.com thanking them for coffee.
================================== Ai Message ==================================
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
================================= Tool Message =================================
Name: create_gmail_draft

Draft created. Draft Id: r-7233782721440261513
================================== Ai Message ==================================

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 referenceโ€‹

For detailed documentation of all GmailToolkit features and configurations head to the API reference.


Was this page helpful?


You can also leave detailed feedback on GitHub.