How to beginner · 3 min read

How to use LangChain with Gemini

Q: How to use LangChain with Gemini

Use the ChatOpenAI class from langchain_openai with the model name gemini-1.5-pro or gemini-2.0-flash to access Gemini chat models. For embeddings, use OpenAIEmbeddings with the same model family. Configure your environment with the GOOGLE_API_KEY and install LangChain and dependencies.

Quick answer

Use the ChatOpenAI class from langchain_openai with the model name gemini-1.5-pro or gemini-2.0-flash to access Gemini chat models. For embeddings, use OpenAIEmbeddings with the same model family. Configure your environment with the GOOGLE_API_KEY and install LangChain and dependencies.

PREREQUISITES

Python 3.8+
Google Cloud API key with access to Gemini models
pip install langchain_openai>=0.2.0
pip install openai>=1.0

Setup

Install the necessary packages and set your Google API key as an environment variable. LangChain uses the langchain_openai package to interface with Gemini models.

bash

pip install langchain_openai openai

Step by step

Use the ChatOpenAI class from LangChain to create a chat completion with Gemini. Below is a complete example that sends a user message and prints the assistant's reply.

python

import os
from langchain_openai import ChatOpenAI

# Ensure your Google API key is set in the environment
# export GOOGLE_API_KEY='your_google_api_key'

# Initialize the Gemini chat model
chat = ChatOpenAI(model_name="gemini-1.5-pro", temperature=0.7)

# Define the conversation
messages = [{"role": "user", "content": "Hello, how can I use LangChain with Gemini?"}]

# Get the response
response = chat(messages)

print("Assistant:", response.content)

output

Assistant: You can integrate LangChain with Gemini by using the ChatOpenAI class and specifying a Gemini model like gemini-1.5-pro. Set your Google API key in the environment and call the model with your messages.

Common variations

You can use different Gemini models such as gemini-1.5-flash or gemini-2.0-flash for faster or more advanced responses. For embeddings, use OpenAIEmbeddings with the gemini-1.5-pro model. LangChain supports async calls and streaming if needed.

python

from langchain_openai import OpenAIEmbeddings

# Initialize embeddings with Gemini
embeddings = OpenAIEmbeddings(model_name="gemini-1.5-pro")

# Example text to embed
text = "LangChain integration with Gemini models"

# Get embeddings vector
vector = embeddings.embed_query(text)

print("Embedding vector length:", len(vector))

output

Embedding vector length: 1536

Troubleshooting

If you get authentication errors, verify your GOOGLE_API_KEY environment variable is set correctly.
Ensure you have access to Gemini models in your Google Cloud project.
Check that your LangChain and OpenAI packages are up to date to avoid compatibility issues.

✅

Key Takeaways

Use ChatOpenAI with model_name set to Gemini models for chat completions.
Set your Google API key in GOOGLE_API_KEY environment variable before running code.
LangChain supports Gemini embeddings via OpenAIEmbeddings with Gemini models.
Keep LangChain and OpenAI SDKs updated for best compatibility with Gemini.
Troubleshoot authentication and access issues by verifying API keys and permissions.

Verified 2026-04 · gemini-1.5-pro, gemini-1.5-flash, gemini-2.0-flash

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