Developing Genkit Python

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)
• Genkit CLI: npm install -g genkit-cli if genkit --version is missing

New app? Setup. Patterns? Examples.

Hello World

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.

chat = agent.chat()
res = await chat.send('Hello')           # AgentResponse
turn = chat.send_stream('Hello')         # AgentTurn — .stream / .response

More: sessions · HITL · branching · background · state · artifacts · custom · HTTP

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) 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.
4. Run with Dev Workflow (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 for how to run your app and capture traces.
6. Stuck? Common Errors 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:

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):

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).

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:

genkit trace:list                        # find recent trace IDs
genkit trace:get <traceId>               # full trace details (inputs, outputs, tool calls, errors)
genkit trace:get <traceId> --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 for the full checklist and Dev UI walkthrough.

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: .prompt files and helpers.
• Evals: Evaluators and datasets.
• Dev Workflow: genkit start, Dev UI, checklist.
• Agents (Beta): Multi-turn API.