Plugins overview

Genkit’s plugin system lets you swap in any model provider, vector store, or telemetry backend without changing your application code. A plugin registers one or more actions — models, embedders, retrievers, indexers, evaluators — into the Genkit registry. Your flows call those actions by name and never depend on a specific provider’s SDK directly.

How plugins extend Genkit

Every plugin follows the same lifecycle:

Register — pass the plugin to genkit({ plugins: [...] }) (JS/TS), genkit.Init(ctx, genkit.WithPlugins(...)) (Go), or Genkit(plugins=[...]) (Python).
Lazy init — the registry calls init() / Init() on first use, not at startup, so cold-start cost is minimised.
Resolve — when your code references googleai/gemini-2.5-flash, the registry asks the googleai plugin to materialise that action on demand.
List — the Dev UI calls list() / list_actions() to enumerate every available model or embedder for the action explorer.

You can configure multiple plugins in the same Genkit instance — for example googleAI() for prototyping and vertexAI() for production — and choose between them at call time by qualifying the model name with its provider prefix.

Official plugins

JavaScript / TypeScript

Plugin	Package	Category	Notes
Google AI (Gemini)	`@genkit-ai/google-genai`	Model provider	Gemini, Imagen, Veo, TTS via AI Studio API
Vertex AI	`@genkit-ai/google-genai`	Model provider	Gemini + Imagen on GCP (same package, different export)
Ollama	`@genkit-ai/ollama`	Model provider	Local models via Ollama server
Firebase	`@genkit-ai/firebase`	Vector store / telemetry	Firestore vector search, Firebase telemetry
Anthropic	`genkitx-anthropic`	Model provider	Claude 3.5 / 4 (community)
OpenAI-compatible	`genkitx-openai`	Model provider	OpenAI, OpenRouter, together.ai (community)
Pinecone	`genkitx-pinecone`	Vector store	Pinecone vector database (community)
Chroma	`genkitx-chromadb`	Vector store	ChromaDB (community)
LangChain	`genkitx-langchain`	Integration	Use LangChain tools inside Genkit (community)
Google Cloud	`@genkit-ai/google-cloud`	Telemetry	Cloud Trace + Cloud Logging

Go

Plugin	Import path	Category
Google AI / Vertex AI	`github.com/firebase/genkit/go/plugins/googlegenai`	Model provider
Ollama	`github.com/firebase/genkit/go/plugins/ollama`	Model provider
Firebase	`github.com/firebase/genkit/go/plugins/firebase`	Vector store / telemetry
Google Cloud	`github.com/firebase/genkit/go/plugins/googlecloud`	Telemetry

Python

Plugin	Package	Category
Google AI (Gemini)	`genkit-google-genai-plugin`	Model provider
Vertex AI	`genkit-google-genai-plugin`	Model provider
Ollama	`genkit-ollama-plugin`	Model provider
Anthropic	`genkit-anthropic-plugin`	Model provider
OpenAI-compatible	`genkit-compat-oai-plugin`	Model provider
DeepSeek	`genkit-deepseek-plugin`	Model provider
xAI (Grok)	`genkit-xai-plugin`	Model provider
Mistral	`genkit-mistral-plugin`	Model provider
Hugging Face	`genkit-huggingface-plugin`	Model provider
Firebase	`genkit-firebase-plugin`	Vector store / telemetry
Google Cloud	`genkit-google-cloud-plugin`	Telemetry

Quick start

TypeScript
Go
Python

import { genkit } from 'genkit';
import { googleAI } from '@genkit-ai/google-genai';

const ai = genkit({
  plugins: [googleAI()],
});

const { text } = await ai.generate({
  model: googleAI.model('gemini-2.5-flash'),
  prompt: 'Hello, Genkit!',
});

import (
  "github.com/firebase/genkit/go/genkit"
  "github.com/firebase/genkit/go/plugins/googlegenai"
)

g := genkit.Init(ctx,
  genkit.WithPlugins(&googlegenai.GoogleAI{}),
)

resp, _ := genkit.Generate(ctx, g,
  ai.WithModelName("googleai/gemini-2.5-flash"),
  ai.WithPrompt("Hello, Genkit!"),
)

from genkit import Genkit
from genkit.plugins.google_genai import GoogleAI

ai = Genkit(plugins=[GoogleAI()])

response = await ai.generate(
    model='googleai/gemini-2.5-flash',
    prompt='Hello, Genkit!',
)

The plugin interface

At its core, a plugin implements three methods:

Method	Purpose
`init()`	One-time setup — create API clients, register known actions. Called lazily on first use.
`resolve(actionType, name)`	Materialise a single action by kind and name (e.g. a model not registered at init time).
`list()`	Return metadata for all available actions so the Dev UI can enumerate them.

In TypeScript the modern interface is GenkitPluginV2 (from genkit/plugin). In Go plugins implement the api.Plugin interface. In Python plugins extend the abstract Plugin base class from genkit._core.plugin. See Writing plugins for a complete walkthrough.

Google AI plugin

Gemini, Imagen, Veo, and embeddings via the Gemini Developer API.

Vertex AI plugin

Enterprise access to Google models on Google Cloud.

Ollama plugin

Run Llama, Mistral, Gemma, and other models fully locally.

Firebase plugin

Firestore vector search and Firebase telemetry.

Writing plugins

Build your own plugin and publish it to npm.

RAG guide

Use retrievers and embedders together for retrieval-augmented generation.

Get Started

Core Concepts

Guides

Plugins

Deployment

Plugins overview

How plugins extend Genkit

Official plugins

JavaScript / TypeScript

Go

Python

Quick start

The plugin interface

Google AI plugin

Vertex AI plugin

Ollama plugin

Firebase plugin

Writing plugins

RAG guide

Build docs developers (and LLMs) love

Get Started

Core Concepts

Guides

Plugins

Deployment

Documentation Index

​How plugins extend Genkit

​Official plugins

​JavaScript / TypeScript

​Go

​Python

​Quick start

​The plugin interface

​Related pages

Google AI plugin

Vertex AI plugin

Ollama plugin

Firebase plugin

Writing plugins

RAG guide

Build docs developers (and LLMs) love

How plugins extend Genkit

Official plugins

JavaScript / TypeScript

Go

Python

Quick start

The plugin interface

Related pages