developing-genkit-pythonโดย firebase
Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit…
npx skills add https://github.com/firebase/agent-skills --skill developing-genkit-pythonGenkit Python
Prerequisites
- Runtime: Python 3.14+,
uvfor deps (install). - CLI:
genkit --version— install vianpm install -g genkit-cliif missing.
New projects: Setup (bootstrap + env). Patterns and code samples: Examples.
Hello World
from genkit import Genkit
from genkit.plugins.google_genai import GoogleAI
ai = Genkit(
plugins=[GoogleAI()],
model='googleai/gemini-flash-latest',
)
async def main():
response = await ai.generate(prompt='Tell me a joke about Python.')
print(response.text)
if __name__ == '__main__':
ai.run_main(main())
Critical: Do Not Trust Internal Knowledge
The Python SDK changes often — verify imports and APIs against the references here or upstream docs. On any error, read Common Errors first.
Development Workflow
- Default provider: Google AI (
GoogleAI()),GEMINI_API_KEYin the environment. - Model IDs: always prefixed, e.g.
googleai/gemini-flash-latest(always-on-latest Flash alias; same pattern as other skills). - Entrypoint:
ai.run_main(main())for Genkit-driven apps (notasyncio.run()for long-lived servers started withgenkit start— see Common Errors). - After generating code, follow Dev Workflow for
genkit startand the Dev UI. - On errors: step 1 is always Common Errors.
References
- Examples: Structured output, streaming, flows, tools, embeddings.
- Setup: New project bootstrap and plugins.
- Common Errors: Read first when something breaks.
- FastAPI: HTTP,
genkit_fastapi_handler, parallel flows. - Dotprompt:
.promptfiles and helpers. - Evals: Evaluators and datasets.
- Dev Workflow:
genkit start, Dev UI, checklist.
Skills เพิ่มเติมจาก firebase
developing-genkit-dart
by firebase
Unified AI SDK for Dart enabling code generation, structured outputs, tools, flows, and agents. Provides core APIs for generation, tool definition, flow orchestration, embeddings, and streaming with a single interface Includes 8+ plugins for LLM providers (Google Gemini, Anthropic Claude, OpenAI GPT), Firebase AI, Model Context Protocol, Chrome browser integration, and HTTP server hosting via Shelf Built-in CLI with local development UI for flow execution, tracing, model experimentation, and...
developing-genkit-go
by firebase
Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working…
developing-genkit-js
by firebase
Build AI-powered Node.js/TypeScript applications with Genkit flows, tools, and multi-model support. Genkit is provider-agnostic; supports Google AI, OpenAI, Anthropic, Ollama, and other LLM providers via plugins Define flows with type-safe schemas using Zod, execute generation requests, and compose multi-step AI workflows in TypeScript Requires Genkit CLI v1.29.0+; recent major API changes mean you must consult genkit docs:read and common-errors.md for current patterns, not prior knowledge...
firebase-ai-logic
by firebase
Client-side Gemini integration for web apps with multimodal inference, streaming, and on-device hybrid execution. Supports text-only and multimodal inputs (images, audio, video, PDFs); files over 20 MB route through Cloud Storage Includes chat sessions with automatic history, streaming responses for real-time display, and structured JSON output enforcement Offers hybrid on-device inference via Gemini Nano in Chrome, with automatic fallback to cloud execution Requires App Check for production...
firebase-ai-logic-basics
by firebase
Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
firebase-app-hosting-basics
by firebase
Deploy and manage full-stack web apps with Firebase App Hosting using Next.js, Angular, and other supported frameworks. Requires Firebase project on Blaze pricing plan; supports Server-Side Rendering (SSR) and Incremental Static Regeneration (ISR) workflows Deploy via firebase.json configuration with optional apphosting.yaml for backend setup, or enable automated "git push to deploy" through GitHub integration Includes secret management via CLI commands for secure access to sensitive keys...
firebase-auth-basics
by firebase
Set up Firebase Authentication with multiple identity providers and secure data access rules. Supports email/password, phone number, anonymous, federated providers (Google, Facebook, Twitter, GitHub, Microsoft, Apple), and custom auth integration Each authenticated user receives a unique ID and JWT-based tokens (short-lived ID tokens and long-lived refresh tokens) for accessing Firebase services Enable providers via CLI for Google Sign In, anonymous, and email/password; use Firebase Console...
firebase-basics
by firebase
Firebase project setup and CLI workflow for AI agent integration. Requires prior completion of firebase-local-env-setup skill and Firebase CLI installation Core workflow covers authentication via firebase login , project creation with unique IDs, and service initialization through the interactive firebase init command Supports feature selection during setup including Firestore, Functions, and Hosting with automatic configuration file generation Self-documenting CLI with --help flags for...