docs(python): Update package names and imports for 0.8.0 SDK reorganization

src/content/docs/docs/backend-frameworks/django.mdx

@@ -9,7 +9,7 @@ import { Aside, FileTree, LinkButton } from '@astrojs/starlight/components';
 
 In this tutorial, you'll build **Bargain Chef**, a standalone Genkit backend on Django that exposes a recipe-generating flow over HTTP. It uses two AI patterns Genkit simplifies: streaming structured output and tool calling.
 
-The `genkit-plugin-django` package wires the flow into Django's async (ASGI) stack with a single decorator, exposing both a JSON endpoint and a Server-Sent Events stream so any client can consume the partial recipe as it arrives, no manual request parsing or streaming code required.
+The `genkit-django` package wires the flow into Django's async (ASGI) stack with a single decorator, exposing both a JSON endpoint and a Server-Sent Events stream so any client can consume the partial recipe as it arrives, no manual request parsing or streaming code required.
 
 ## What you'll build
 
@@ -41,7 +41,7 @@ uv run python manage.py startapp recipes
 ### Install packages
 
 ```bash
-uv add django django-cors-headers uvicorn genkit genkit-plugin-django genkit-plugin-google-genai
+uv add django django-cors-headers uvicorn genkit genkit-django genkit-googleai
 ```
 
 These packages include:
@@ -50,8 +50,8 @@ These packages include:
 - **`django-cors-headers`**: CORS middleware. Lets browser frontends served from a different origin call the Genkit endpoint.
 - **`uvicorn`**: ASGI server that runs Django with async support.
 - **`genkit`**: Core Genkit SDK.
-- **`genkit-plugin-django`**: Helper that exposes Genkit flows as Django views, including server-sent events for streaming.
-- **`genkit-plugin-google-genai`**: Plugin that connects Genkit to Google's Gemini models.
+- **`genkit-django`**: Helper that exposes Genkit flows as Django views, including server-sent events for streaming.
+- **`genkit-googleai`**: Plugin that connects Genkit to Google's Gemini models.
 
 Install the Genkit CLI, which enables Genkit testing and observability:
 
@@ -132,8 +132,8 @@ from typing import Literal
 from pydantic import BaseModel, Field
 
 from genkit import ActionRunContext, Genkit
-from genkit.plugins.django import genkit_django_handler
-from genkit.plugins.google_genai import GoogleAI
+from genkit_django import genkit_django_handler
+from genkit_googleai import GoogleAI
 
 ai = Genkit(
     plugins=[GoogleAI()],

src/content/docs/docs/backend-frameworks/fastapi.mdx

@@ -43,16 +43,16 @@ curl -sL cli.genkit.dev | bash
 Then install the packages you need in your project:
 
 ```bash
-uv add fastapi uvicorn genkit genkit-plugin-google-genai genkit-plugin-fastapi
+uv add fastapi uvicorn genkit genkit-googleai genkit-fastapi
 ```
 
 These packages include:
 
 - **`fastapi`**: The async Python web framework that serves your endpoint.
 - **`uvicorn`**: The ASGI server that runs your FastAPI app.
 - **`genkit`**: Core Genkit SDK.
-- **`genkit-plugin-google-genai`**: Plugin that connects Genkit to Google's Gemini models.
-- **`genkit-plugin-fastapi`**: Serves Genkit flows as FastAPI endpoints, including streaming.
+- **`genkit-googleai`**: Plugin that connects Genkit to Google's Gemini models.
+- **`genkit-fastapi`**: Serves Genkit flows as FastAPI endpoints, including streaming.
 
 ### Configure a model API key
 
@@ -100,8 +100,8 @@ from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel, Field
 
 from genkit import ActionRunContext, Genkit
-from genkit.plugins.fastapi import genkit_fastapi_handler
-from genkit.plugins.google_genai import GoogleAI
+from genkit_fastapi import genkit_fastapi_handler
+from genkit_googleai import GoogleAI
 
 ai = Genkit(
     plugins=[GoogleAI()],

src/content/docs/docs/backend-frameworks/flask.mdx

@@ -45,15 +45,15 @@ curl -sL cli.genkit.dev | bash
 Then install the packages you need in your project:
 
 ```bash
-uv add "flask[async]" flask-cors genkit genkit-plugin-google-genai genkit-plugin-flask
+uv add "flask[async]" flask-cors genkit genkit-googleai genkit-flask
 ```
 
 These packages include:
 
 - **`flask`**: The Flask web framework.
 - **`genkit`**: Core Genkit SDK for Python.
-- **`genkit-plugin-google-genai`**: Plugin that connects Genkit to Google's Gemini models.
-- **`genkit-plugin-flask`**: Helper that exposes Genkit flows as Flask routes, including server-sent events for streaming.
+- **`genkit-googleai`**: Plugin that connects Genkit to Google's Gemini models.
+- **`genkit-flask`**: Helper that exposes Genkit flows as Flask routes, including server-sent events for streaming.
 
 ### Configure a model API key
 
@@ -91,8 +91,8 @@ from flask_cors import CORS
 from pydantic import BaseModel, Field
 
 from genkit import ActionRunContext, Genkit
-from genkit.plugins.flask import genkit_flask_handler
-from genkit.plugins.google_genai import GoogleAI
+from genkit_flask import genkit_flask_handler
+from genkit_googleai import GoogleAI
 
 ai = Genkit(
     plugins=[GoogleAI()],

src/content/docs/docs/deployment/any-platform.mdx

@@ -555,7 +555,7 @@ mkdir -p ~/tmp/genkit-fastapi-project
 cd ~/tmp/genkit-fastapi-project
 
 uv init
-uv add genkit genkit-plugin-google-genai fastapi uvicorn
+uv add genkit genkit-googleai fastapi uvicorn
 ```
 
 ## 2. Define a flow
@@ -564,7 +564,7 @@ Create `flows.py`:
 
 ```python title="flows.py"
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 
 ai = Genkit(
     plugins=[GoogleAI()],

src/content/docs/docs/deployment/cloud-run.mdx

@@ -599,7 +599,7 @@ cd genkit-cloudrun
 uv init
 
 # Add dependencies
-uv add genkit genkit-plugin-google-genai fastapi uvicorn slowapi
+uv add genkit genkit-googleai fastapi uvicorn slowapi
 ```
 
 ### Create your FastAPI application with Genkit
@@ -616,7 +616,7 @@ from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel, Field
 
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 
 # Initialize Genkit
 ai = Genkit(

src/content/docs/docs/dotprompt.mdx

@@ -297,7 +297,7 @@ genkit start -- go run .
 
 ```python
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 
 ai = Genkit(
     plugins=[GoogleAI()],

src/content/docs/docs/evaluation.mdx

@@ -1147,7 +1147,7 @@ UI, located at `localhost:4000/evaluate`.
 
 ```python
 from genkit import Genkit, Document
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 from pydantic import BaseModel
 
 # Initialize Genkit
@@ -1315,7 +1315,7 @@ Here's an example of a custom evaluator that uses an LLM to check for "delicious
 
 ```python
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 from genkit.evaluator import (
     BaseEvalDataPoint,
     EvalFnResponse,
@@ -1380,7 +1380,7 @@ You can also run evaluations directly in your code using `ai.evaluate()`:
 ```python
 import asyncio
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 from genkit.evaluator import BaseEvalDataPoint, EvalResponse
 
 ai = Genkit(plugins=[GoogleAI()])
@@ -1643,7 +1643,7 @@ questions.
 ```python
 from pathlib import Path
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 from pydantic import BaseModel, Field
 import pypdf  # uv add pypdf
 

src/content/docs/docs/flows.mdx

@@ -172,7 +172,7 @@ void main() {
 
 ```python
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 from pydantic import BaseModel, Field
 
 ai = Genkit(
@@ -1320,7 +1320,7 @@ call.
 
 ```python
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 from pydantic import BaseModel, Field
 
 ai = Genkit(
@@ -1562,7 +1562,7 @@ from fastapi import FastAPI
 from pydantic import BaseModel, Field
 
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 
 # Initialize Genkit
 ai = Genkit(

src/content/docs/docs/integrations/anthropic.mdx

@@ -896,19 +896,19 @@ print('Name: \${person.name}, Age: \${person.age}');
 
 <Lang lang="python">
 
-The `genkit-plugin-anthropic` package provides access to Anthropic's Claude models through the Genkit framework.
+The `genkit-anthropic` package provides access to Anthropic's Claude models through the Genkit framework.
 
 ## Installation
 
 ```bash
-uv add genkit-plugin-anthropic
+uv add genkit-anthropic
 ```
 
 ## Configuration
 
 ```python
 from genkit import Genkit
-from genkit.plugins.anthropic import Anthropic, anthropic_name
+from genkit_anthropic import Anthropic, anthropic_name
 
 ai = Genkit(
     plugins=[Anthropic()],

src/content/docs/docs/integrations/deepseek.mdx

@@ -175,7 +175,7 @@ resp, err := genkit.Generate(ctx, g,
 
 <Lang lang="python">
 
-The `genkit-plugin-compat-oai` package handles integration for [DeepSeek](https://www.deepseek.com/) models, including the powerful V4-Pro model and the efficient V4-Flash model.
+The `genkit-openai` package handles integration for [DeepSeek](https://www.deepseek.com/) models, including the powerful V4-Pro model and the efficient V4-Flash model.
 
 :::note
 The DeepSeek plugin is built on top of the OpenAI-compatible plugin architecture. It is pre-configured for DeepSeek's API endpoints.
@@ -184,7 +184,7 @@ The DeepSeek plugin is built on top of the OpenAI-compatible plugin architecture
 ## Installation
 
 ```bash
-uv add genkit-plugin-compat-oai
+uv add genkit-openai
 ```
 
 ## Configuration
@@ -193,7 +193,7 @@ To use this plugin, import `OpenAI` and specify it when you initialize Genkit:
 
 ```python
 from genkit import Genkit
-from genkit.plugins.compat_oai import OpenAI
+from genkit_openai import OpenAI
 import os
 
 ai = Genkit(
@@ -220,7 +220,7 @@ Use the `openai_model()` helper to reference a DeepSeek model.
 
 ```python
 from genkit import Genkit
-from genkit.plugins.compat_oai import OpenAI, openai_model
+from genkit_openai import OpenAI, openai_model
 import os
 
 ai = Genkit(
@@ -434,7 +434,7 @@ You can pass configuration options that are not defined in the plugin's custom c
 
 ```python
 from genkit import Genkit
-from genkit.plugins.compat_oai import OpenAI, openai_model
+from genkit_openai import OpenAI, openai_model
 import os
 
 ai = Genkit(plugins=[OpenAI(base_url='https://api.deepseek.com/v1', api_key=os.getenv('DEEPSEEK_API_KEY'))])

src/content/docs/docs/integrations/google-cloud.mdx

@@ -193,7 +193,7 @@ If you want to locally run flows that use this plugin, you need the
 ## Installation
 
 ```bash
-uv add genkit-plugin-google-cloud
+uv add genkit-google-cloud
 ```
 
 ## Configuration
@@ -203,7 +203,7 @@ To enable exporting to Google Cloud Tracing and Monitoring, import
 telemetry gets automatically exported.
 
 ```python
-from genkit.plugins.google_cloud import add_gcp_telemetry
+from genkit_google_cloud import add_gcp_telemetry
 ```
 
 ```python

src/content/docs/docs/integrations/google-genai.mdx

@@ -1515,7 +1515,7 @@ The following features documented in other languages are not yet fully supported
 
 <Lang lang="python">
 
-The `genkit-plugin-google-genai` package provides the `GoogleAI` plugin for accessing Google's generative AI models via the Google Gemini API using API key authentication.
+The `genkit-googleai` package provides the `GoogleAI` plugin for accessing Google's generative AI models via the Google Gemini API using API key authentication.
 
 The plugin supports a wide range of capabilities:
 
@@ -1528,14 +1528,14 @@ The plugin supports a wide range of capabilities:
 ## Installation
 
 ```bash
-uv add genkit-plugin-google-genai
+uv add genkit-googleai
 ```
 
 ## Configuration
 
 ```python
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 
 ai = Genkit(
     plugins=[GoogleAI()],
@@ -1589,7 +1589,7 @@ This is useful for multi-tenant apps or routing requests to different keys. Mode
 
 ```python
 from genkit import Genkit
-from genkit.plugins.google_genai import GoogleAI
+from genkit_googleai import GoogleAI
 
 ai = Genkit(
     plugins=[GoogleAI()],
@@ -1787,7 +1787,7 @@ Veo models generate videos from text prompts using the background model pattern
 
 ```python
 import asyncio
-from genkit.plugins.google_genai import VeoVersion
+from genkit_googleai import VeoVersion
 
 # Start video generation (returns an Operation)
 response = await ai.generate(

src/content/docs/docs/integrations/ollama.mdx

@@ -293,7 +293,7 @@ For development, you can run Ollama on your development machine. Deployed apps u
 ## Installation
 
 ```bash
-uv add genkit-plugin-ollama
+uv add genkit-ollama
 ```
 
 ## Configuration
@@ -302,8 +302,8 @@ To use this plugin, import `Ollama` and specify it when you initialize Genkit:
 
 ```python
 from genkit import Genkit
-from genkit.plugins.ollama import Ollama, ollama_name
-from genkit.plugins.ollama.models import ModelDefinition
+from genkit_ollama import Ollama, ollama_name
+from genkit_ollama.models import ModelDefinition
 
 ai = Genkit(
     plugins=[
@@ -343,8 +343,8 @@ This plugin doesn't statically export model references. Specify one of the model
 
 ```python
 from genkit import Genkit
-from genkit.plugins.ollama import Ollama, ollama_name
-from genkit.plugins.ollama.models import ModelDefinition
+from genkit_ollama import Ollama, ollama_name
+from genkit_ollama.models import ModelDefinition
 
 ai = Genkit(
     plugins=[
@@ -388,8 +388,8 @@ The Ollama plugin supports embeddings, which can be used for similarity searches
 
 ```python
 from genkit import Genkit
-from genkit.plugins.ollama import Ollama
-from genkit.plugins.ollama.embedders import EmbeddingDefinition
+from genkit_ollama import Ollama
+from genkit_ollama.embedders import EmbeddingDefinition
 
 ai = Genkit(
     plugins=[