> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/genkit-ai/genkit/llms.txt
> Use this file to discover all available pages before exploring further.

# Plugins overview

> Extend Genkit with official and community plugins for model providers, vector stores, telemetry, and more.

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:

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

<Note>
  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.
</Note>

## Official plugins

### JavaScript / TypeScript

| Plugin                                      | Package                   | Category                 | Notes                                                   |
| ------------------------------------------- | ------------------------- | ------------------------ | ------------------------------------------------------- |
| [Google AI (Gemini)](/plugins/google-genai) | `@genkit-ai/google-genai` | Model provider           | Gemini, Imagen, Veo, TTS via AI Studio API              |
| [Vertex AI](/plugins/vertex-ai)             | `@genkit-ai/google-genai` | Model provider           | Gemini + Imagen on GCP (same package, different export) |
| [Ollama](/plugins/ollama)                   | `@genkit-ai/ollama`       | Model provider           | Local models via Ollama server                          |
| [Firebase](/plugins/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](/plugins/google-genai) | `github.com/firebase/genkit/go/plugins/googlegenai` | Model provider           |
| [Ollama](/plugins/ollama)                      | `github.com/firebase/genkit/go/plugins/ollama`      | Model provider           |
| [Firebase](/plugins/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)](/plugins/google-genai) | `genkit-google-genai-plugin` | Model provider           |
| [Vertex AI](/plugins/vertex-ai)             | `genkit-google-genai-plugin` | Model provider           |
| [Ollama](/plugins/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](/plugins/firebase)               | `genkit-firebase-plugin`     | Vector store / telemetry |
| Google Cloud                                | `genkit-google-cloud-plugin` | Telemetry                |

## Quick start

<Tabs>
  <Tab title="TypeScript">
    ```ts theme={null}
    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!',
    });
    ```
  </Tab>

  <Tab title="Go">
    ```go theme={null}
    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!"),
    )
    ```
  </Tab>

  <Tab title="Python">
    ```python theme={null}
    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!',
    )
    ```
  </Tab>
</Tabs>

## 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](/plugins/writing-plugins) for a complete walkthrough.

## Related pages

<CardGroup cols={2}>
  <Card title="Google AI plugin" href="/plugins/google-genai">
    Gemini, Imagen, Veo, and embeddings via the Gemini Developer API.
  </Card>

  <Card title="Vertex AI plugin" href="/plugins/vertex-ai">
    Enterprise access to Google models on Google Cloud.
  </Card>

  <Card title="Ollama plugin" href="/plugins/ollama">
    Run Llama, Mistral, Gemma, and other models fully locally.
  </Card>

  <Card title="Firebase plugin" href="/plugins/firebase">
    Firestore vector search and Firebase telemetry.
  </Card>

  <Card title="Writing plugins" href="/plugins/writing-plugins">
    Build your own plugin and publish it to npm.
  </Card>

  <Card title="RAG guide" href="/concepts/rag">
    Use retrievers and embedders together for retrieval-augmented generation.
  </Card>
</CardGroup>