langchain-google: the official LangChain integrations for Gemini and Vertex AI

What langchain-google centralizes

langchain-google is the repository that gathers LangChain's Google integrations in one place. It is maintained by the LangChain organization and exists so that a LangChain application can talk to Google's models through LangChain's own abstractions rather than Google's raw SDK. The README names three packages: langchain-google-genai for the Generative AI Gemini API, langchain-google-vertexai for generative AI on Google Cloud's Vertex AI, and langchain-google-community for Google products that fall outside those two. The audience is developers already building on LangChain who want Gemini or Vertex AI as the model behind their chains and agents. This is not a standalone tool and not a model; it is an integration layer, so it only earns its place if you are committed to LangChain and want Google as a provider inside it.

Three packages, split by Google surface

The design mirrors how Google exposes its models. langchain-google-genai targets the Gemini API at ai.google.dev, the path most individual developers use with an API key. langchain-google-vertexai targets Vertex AI, Google Cloud's managed surface, which brings the GCP project, authentication and enterprise features that come with it. langchain-google-community holds integrations for Google products that are neither, so the two focused packages stay clean. Each is published separately on PyPI and versioned on its own, which the release tags show, for example a genai package release adding agentic video understanding. That per-package split matters for adoption: you install and upgrade only the surface you use, and a change to the Vertex package does not force a bump of the Gemini one.

Installing the package you need

You install the specific integration from PyPI rather than the whole repository. For the Gemini API path:

pip install langchain-google-genai

For the Vertex AI path you install langchain-google-vertexai instead, and langchain-google-community for the remaining Google integrations. Once installed, you import the package's chat model or embeddings class and use it wherever a LangChain application expects a model, supplying your Google credentials, an API key for the genai path or Google Cloud authentication for Vertex. The README points to the Google provider page and the API reference in the LangChain docs for the exact class names and configuration, keeping the repository itself focused on the code rather than duplicating the guides. The first real use is wiring one of these classes into an existing chain.

Where an integration layer shows its seams

The honest limitations come from being a layer between two moving projects. langchain-google sits on top of Google's SDKs and inside LangChain's abstractions, so it inherits change from both directions: a Google model or API change and a LangChain interface change can each require an update here. You still need Google credentials and, for the Vertex path, a configured Google Cloud project, so the package removes none of the provider setup. And the abstraction is a trade: you gain LangChain interoperability, the ability to swap providers and reuse chains, but you add a layer over Google's own SDK, which can lag new Google features until the integration catches up. For an application that only ever calls Gemini, that layer may be more than you need.

langchain-google versus Google's own SDK

The direct alternative is to call Google's SDK yourself, the google-genai client for Gemini or the Vertex AI SDK, with no LangChain in the path. That gives you the newest Google features the moment they ship and one fewer layer to reason about, at the cost of writing provider-specific code that does not port if you later add or switch models. langchain-google's difference is interoperability: its classes plug into LangChain's chains, agents, retrievers and tooling, so Google becomes one interchangeable provider among many. The choice is about commitment. If you are not using LangChain, call Google's SDK directly; if your application is built on LangChain and you want Gemini or Vertex behind it with the option to swap providers, these packages are the maintained way to do it.

MIT license and per-package versioning

langchain-google is MIT-licensed, so it is freely reusable, and it is a monorepo under libs with each package released independently. The last push was on 2026-09-11, and the release tags are per-package and frequent, such as a langchain-google-genai 4.4.0 in early September 2026 adding agentic video understanding, which tells you the genai surface in particular is actively tracking Gemini's features. Because versioning is per-package, pin each integration you depend on and read that package's changelog rather than assuming a single project version. Treat the LangChain docs the README links as the source of truth for class names and configuration, since the repository defers to them instead of maintaining a parallel copy.

Editorial conclusion

Adopt langchain-google if you build on LangChain and want Gemini or Vertex AI as an interchangeable provider inside your chains and agents. Do not add it if you are not using LangChain, where Google's own SDK is the leaner path, and remember it removes none of the Google credential or GCP setup. Install only the package for your surface, langchain-google-genai for the Gemini API or langchain-google-vertexai for Vertex, pin its version, and follow the LangChain Google docs for class names.

Frequently asked questions

Official sources

1. langchain-ai/langchain-google on GitHub
2. License: MIT
3. Project website
4. README
5. Releases