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Skills ZICQ category:Agent Workflows developing-genkit-python

Developing Genkit Python

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 errors, import issues, or API problems.

878 installs

Official URL:skills.sh

What this skill does

Intro in this page language first. The official description stays in its original wording; we do not rewrite SKILL.md.

What it does

Develop AI-powered applications using Genkit in Python

When to use it

the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems

How agents load it

Per Agent Skills progressive disclosure: name and description load at startup (~100 tokens); the full SKILL.md body loads when the skill activates; scripts/, references/, and assets/ load only as needed. This file's sections: Genkit Python; Prerequisites; Hello World; Agents (Beta); Imports; Workflow. It includes spec-recommended sections: step-by-step instructions.

File analysis

File analysis: besides SKILL.md, the body references references/setup.md, references/examples.md, references/agents.md, references/agents-sessions.md, references/agents-human-in-the-loop.md, references/agents-branching.md. Those resources load on demand.

Genkit PythonPrerequisitesHello WorldAgents (Beta)ImportsWorkflowGenkit CLI (recommended)References

Source category:skills.sh agent-skill

SKILL.md & Agent activation

Official spec ↗
name
developing-genkit-python
description
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 errors, import issues, or API problems.
  1. DiscoverThe client exposes names and descriptions to the agent.
  2. ActivateYour request or the task context selects the skill and loads its instructions.
  3. Load resourcesReferenced scripts, documentation and assets are used when needed.
Files referenced by the instructions · 11
  • references/setup.md
  • references/examples.md
  • references/agents.md
  • references/agents-sessions.md
  • references/agents-human-in-the-loop.md
  • references/agents-branching.md
  • references/agents-background.md
  • references/agents-state.md
  • references/agents-artifacts.md
  • references/agents-custom.md
  • references/agents-http.md

These paths are extracted from the text. Check the upstream package to verify the files exist.

Invocation syntax and available tools depend on your Agent client. Client integration guide ↗

Install this skill

Skills CLI ↗

Choose the target agent and installation scope, keep referenced package files, then verify the skill appears in the client's catalog.

This skill references supporting files. Retrieve the complete directory from the source; copying SKILL.md alone may leave missing dependencies.

Ask your Agent to install

Copy these instructions to a compatible agent and confirm the target directory matches your client.

Install the agent skill "developing-genkit-python" into my project. The full SKILL.md and official description are at https://zicq.com/en/skills/skl-129b20d03bd2e601-Developing-Genkit-Python.html
Save it as .cursor/skills/developing-genkit-python/SKILL.md or .claude/skills/developing-genkit-python/SKILL.md and keep the frontmatter name and description exactly as-is.
This skill also ships scripts/, references/, or assets/ — fetch the whole folder from https://github.com/genkit-ai/skills instead of creating only a SKILL.md.

Full package on GitHub ↗

Install from the terminal · Skills CLI

Requires Node.js and npx. First inspect the repository's skill list to confirm the name.

npx skills add 'https://github.com/genkit-ai/skills' --list

npx skills add 'https://github.com/genkit-ai/skills' --skill 'developing-genkit-python'

The CLI lets you choose the agent interactively. The default scope is the project; use -g for user scope. Confirm package availability with the discovery command, then use npx skills list to inspect installed skills.

Readable layout
--- name: developing-genkit-python description: 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 errors, import issues, or API problems. metadata: category: AiAndMachineLearning --- # Genkit Python Build AI features in Python — generate, stream, tools, flows, and multi-turn agents — with one SDK. ## Prerequisites - Python **3.10+** and **`uv`** ([install](https://docs.astral.sh/uv/getting-started/installation/)) - Genkit CLI: `npm install -g genkit-cli` if `genkit --version` is missing New app? [Setup](references/setup.md). Patterns? [Examples](references/examples.md). ## Hello World ```python from genkit import Genkit from genkit_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()) ``` ## Agents (Beta) Multi-turn chats with history, typed state, human approval, branching, and background work. Start here: [Agents](references/agents.md). ```python chat = agent.chat() res = await chat.send('Hello') # AgentResponse turn = chat.send_stream('Hello') # AgentTurn — .stream / .response ``` More: [sessions](references/agents-sessions.md) · [HITL](references/agents-human-in-the-loop.md) · [branching](references/agents-branching.md) · [background](references/agents-background.md) · [state](references/agents-state.md) · [artifacts](references/agents-artifacts.md) · [custom](references/agents-custom.md) · [HTTP](references/agents-http.md) ## Imports - Google AI: `from genkit_google_genai import GoogleAI` - Agents: `from genkit.agent import InMemorySessionStore, ...` - Middleware: `from genkit_middleware import Middleware, ToolApproval, ...` - FastAPI: `from genkit_fastapi import serve_agent, serve_flow` - Evals: `from genkit_evaluators import register_genkit_evaluators` ## Workflow 1. **Agent or flow?** If the task is conversational, multi-turn, or described as "an agent", "assistant", or "chatbot", build it with `ai.define_agent` (see [Agents](references/agents.md)) rather than hand-rolling a `generate` + tools loop inside a flow. Reach for a plain flow only for single-shot, stateless generation. 2. Set **`GEMINI_API_KEY`**. Use prefixed model ids (`googleai/gemini-flash-latest`). 3. Enter via **`ai.run_main(main())`** for Genkit apps (especially under `genkit start`). See [Common Errors](references/common-errors.md). 4. Run with [Dev Workflow](references/dev-workflow.md) (`genkit start` + Dev UI). 5. Verify with traces, not a blind run. Running the app directly (`uv run`) does **not** capture dev traces. See [Genkit CLI](#genkit-cli-recommended) for how to run your app and capture traces. 6. Stuck? [Common Errors](references/common-errors.md) first. ## Genkit CLI (recommended) `genkit start` unintrusively wraps any Python program that uses the Genkit library, running it unchanged while capturing traces from every Genkit action so you can prove tools were actually called and inspect model I/O from the terminal, even for headless checks. It forwards stdio, so interactive CLI tools that rely on stdin/stdout work without issues. Running the app directly (`uv run`) skips trace capture, so you're debugging blind. **Primary pattern (default):** prefix `genkit start --` to your normal run command. This collects telemetry from any Genkit code your program runs, whether triggered from the dev UI, your own web server/web UI, or a plain script: ```bash genkit start -- uv run src/main.py genkit start --noui -- uv run src/main.py # same, without the Dev UI (still a persistent server) ``` `genkit start` runs until you stop it with Ctrl+C. That is expected and correct for the common cases: a server your web/mobile app calls, or an interactive CLI you exit yourself. `--noui` only drops the Dev UI; it is **not** a one-shot command and will not exit on its own. Do **not** use `genkit start` as a blocking step in automated/non-interactive contexts; use `flow:run` (below) for that. **Non-interactive use (agents/CI):** add the global `--non-interactive` flag before `--` so the CLI uses defaults and never blocks on a prompt (e.g. the first-run analytics notice): `genkit start --non-interactive -- uv run src/main.py` (works with `flow:run` too). **Run a flow (`flow:run`):** invoke a specific flow by name from the CLI. Append your run command after `--` to spin up the runtime just for this run (the command runs as-is to register your flows): ```bash genkit flow:run myFlow '{"data": "input"}' -- uv run src/main.py ``` This is **self-terminating**: it runs the flow once, prints a `Trace ID`, then exits, so it's the right choice for a quick, non-interactive check (unlike `genkit start`). Note: `flow:run` runs **flows** (`@ai.flow()`), not agents; you can't `flow:run` an agent (`ai.define_agent`) directly. To exercise an agent from the CLI, wrap one turn in a throwaway flow and run that (see [Agents](references/agents.md)). **Debugging with traces:** the fastest way to see prompts, model inputs/outputs, tool calls, latencies, and errors. Inspect from the terminal after any run under `genkit start`: ```bash genkit trace:list # find recent trace IDs genkit trace:get # full trace details (inputs, outputs, tool calls, errors) genkit trace:get --format json # machine-readable JSON, safe to pipe into jq or other parsers ``` For machine-readable output, pass `--format json` to get clean JSON you can pipe into `jq` or other parsers. The **default** output is human-oriented (banner/log lines, possible truncation on large traces), so don't pipe that form directly; use `--format json`, grep, or the Dev UI trace viewer. See [Dev Workflow](references/dev-workflow.md) for the full checklist and Dev UI walkthrough. ## References - [Examples](references/examples.md): Structured output, streaming, flows, tools, embeddings. - [Setup](references/setup.md): New project bootstrap and plugins. - [Common Errors](references/common-errors.md): Read first when something breaks. - [FastAPI](references/fastapi.md): HTTP, `genkit_fastapi_handler`, parallel flows. - [Dotprompt](references/dotprompt.md): `.prompt` files and helpers. - [Evals](references/evals.md): Evaluators and datasets. - [Dev Workflow](references/dev-workflow.md): `genkit start`, Dev UI, checklist. - [Agents (Beta)](references/agents.md): Multi-turn API.

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