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This template creates an agent that uses Google Gemini function calling to communicate its decisions on what actions to take.

This example creates an agent that can optionally look up information on the internet using Tavily's search engine.

See an example LangSmith trace here

Environment Setup​

The following environment variables need to be set:

Set the TAVILY_API_KEY environment variable to access Tavily

Set the GOOGLE_API_KEY environment variable to access the Google Gemini APIs.


To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package gemini-functions-agent

If you want to add this to an existing project, you can just run:

langchain app add gemini-functions-agent

And add the following code to your file:

from gemini_functions_agent import agent_executor as gemini_functions_agent_chain

add_routes(app, gemini_functions_agent_chain, path="/openai-functions-agent")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/gemini-functions-agent")

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