Genkit Python 0.11: Video generation, typed model options, and unified error handling
Genkit | Blog ·
Genkit Python 0.11 brings video generation, improved DX for model configuration, unified error handling across providers, and structured debug logs in the Developer UI.
Genkit Python 0.11 brings video generation, improved DX for model configuration, unified error handling across providers, and structured debug logs in the Developer UI. Video models now run as background operations you can poll or cancel, family constructors provide autocomplete for model options directly in your editor, and provider HTTP errors normalize into a single GenkitError with classified statuses. Calls to generate also preserve intermediate message history when a tool loop ends early, allowing you to resume execution without repeating completed tool calls.
To install or update to the new version, run the following command in your terminal:
uv add genkit genkit-google-genai Generate video content Genkit Python now supports video generation with models like Veo. Because rendering high-resolution video takes time, generation runs as an asynchronous background operation using generate_operation , returning an Operation you can poll until the video is ready:
import asyncio from genkit import Genkit from genkit_google_genai import GoogleAI ai = Genkit( plugins = [GoogleAI()]) operation = await ai.generate_operation( model = GoogleAI.veo_model( 'veo-3.1-fast-generate-preview' ), prompt = 'A paper airplane gliding through a bright classroom' , ) while not operation.done: await asyncio.sleep( 2 ) operation = await ai.check_operation(operation) print (operation.output.media[ 0 ].url) You can also test video models interactively in the Developer UI. Submitting a prompt from the model runner tracks the background generation and plays the finished video directly in the browser:
Genkit Python now provides strongly typed model references. Family constructors like GoogleAI.gemini_model , Anthropic.claude_model , and OpenAI.gpt_model bind each model directly to its supported configuration schema:
from genkit import Genkit from genkit_anthropic import Anthropic, AnthropicConfig from genkit_google_genai import GeminiConfigSchema, GoogleAI from genkit_openai import OpenAI, OpenAIConfig ai = Genkit( plugins = [GoogleAI(), Anthropic(), OpenAI()]) # Pass typed model references and provider configs directly inline res1 = await ai.generate( model = GoogleAI.gemini_model( 'gemini-flash-latest' , config = GeminiConfigSchema( temperature = 0.2 ), ), prompt = 'Say hi in one word.' , ) res2 = await ai.generate( model = Anthropic.claude_model( 'claude-sonnet-4-6' , config = AnthropicConfig( max_output_tokens = 1024 ), ), prompt = 'Say hi in one word.' , ) res3 = await ai.generate( model = OpenAI.gpt_model( 'gpt-5.2' , config = OpenAIConfig( temperature = 0.2 ), ), prompt = 'Say hi in one word.' , ) Typed references provide autocomplete, inline parameter hints, and type safety in your editor, helping you discover supported options and catch provider mismatches before running your code.
Genkit Python now normalizes provider exceptions into a unified GenkitError . A single error handling block gives you consistent status codes across Gemini, Anthropic, and OpenAI:
from genkit import Genkit, GenkitError from genkit_google_genai import GoogleAI from genkit_middleware import Retry ai = Genkit( plugins = [GoogleAI()]) try : response = await ai.generate( model = GoogleAI.gemini_model( 'gemini-flash-latest' ), prompt = 'Draft a travel itinerary for a weekend in Kyoto.' , use = [Retry()], ) print (response.text) except GenkitError as err: # Unified exception type across Gemini, Claude, and GPT print (err.status, err.original_message) if err.status == 'RESOURCE_EXHAUSTED' and err.response_metadata: print ( 'Retry after' , err.response_metadata.get( 'retry_after_ms' ), 'ms' ) Standardized status codes make it straightforward to inspect rate limits, configure retries, and build consistent error recovery across every provider.
When iterating on your AI application locally, calls to generate now stream structured debug logs directly into the Developer UI trace viewer.
Execution spans capture lifecycle milestones like model calls, tool executions, and middleware behavior in the Logs panel. This gives you a detailed diagnostic trail right beside your trace spans while keeping your terminal output clean.
When a multi-turn tool loop ends early due to errors or turn limits, generate now returns the intermediate state instead of raising an exception. This lets you inspect progress and resume from where you left off, without repeating completed tool calls:
from pydantic import BaseModel from genkit import Genkit from genkit_google_genai import GoogleAI ai = Genkit( plugins = [GoogleAI()], model = GoogleAI.gemini_model( 'gemini-flash-latest' ), ) class TripQuery ( BaseModel ): destination: str @ai.tool () async def search_flights (query: TripQuery) -> list[ str ]: """Find available flights to a destination.""" return [ f 'Flight 101 to { query.destination } ' ] @ai.tool () async def book_hotel (query: TripQuery) -> str : """Reserve hotel accommodations in a destination.""" return f 'Hotel in { query.destination } reserved' response = await ai.generate( prompt = 'Search flights to Kyoto, then reserve a hotel.' , tools = [search_flights, book_hotel], ) if response.finish_reason in ( 'failed' , 'aborted' ): # Completed tool rounds are preserved in response.messages. # Resume the turn without repeating earlier tool calls: response = await ai.generate( messages = response.messages, tools = [search_flights, book_hotel], ) print (response.text) The messages history preserves completed tool rounds and omits pending requests, so you can pass messages directly back into generate to resume execution without duplicate calls.
Upgrade to Genkit Python 0.11 today to take advantage of video generation, improved model configuration, and resumable generation. Explore the Genkit Python documentation , experiment with the Python samples , or review the complete v0.11.0 release notes .