Developing Genkit Python

Dev Workflow — Genkit Python

Agent responsibility

After generating code, always give the developer:

1. The full pre-run checklist with copy-paste commands using absolute paths

2. The genkit start command to run in their terminal (foreground — it's expected to block)

3. Step-by-step Dev UI instructions so they can test without guessing

Do not offer to run it for them. Give them the commands and let them run it.

Step 1 — Get a Gemini API key

If the developer doesn't have one:

Get a free key at https://aistudio.google.com/apikey — click "Create API key", copy it.

Step 2 — Set the API key

Open a terminal and run:

export GEMINI_API_KEY=your-api-key-here

To persist across sessions, add it to your shell profile:

echo 'export GEMINI_API_KEY=your-api-key-here' >> ~/.zshrc && source ~/.zshrc

Step 3 — Install dependencies

Replace /path/to/your-project with the actual full path to the project (e.g. /Users/yourname/projects/my-genkit-app):

cd /path/to/your-project
uv add genkit genkit-google-genai

(Requires a project with pyproject.toml — run uv init in an empty directory first if needed.)

Step 4 — Start the Dev UI

Run this in your terminal. It will block — that's expected. Leave this terminal open while you use the Dev UI.

cd /path/to/your-project
GEMINI_API_KEY=your-api-key-here genkit start -- uv run src/main.py

You'll see output like:

Genkit Tools UI: http://localhost:4000

The Dev UI is now running at http://localhost:4000

To stop it: press Ctrl+C in the terminal.

Step 5 — Test in the Dev UI

1. Open http://localhost:4000 in your browser

2. Click "Run" in the left sidebar

3. Find your flow by name (e.g. summarize, chat, joke_generator)

4. In the input box, paste your input as JSON — e.g:

   Copy

   {"text": "hello world"}

5. Click the "Run" button — the output appears on the right

6. Click "Traces" in the left sidebar to inspect every step, model call, token count, and latency

CLI Commands

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). Traces for this run can be inspected using the trace commands below.

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.

Documentation:

genkit docs:search "streaming" python
genkit docs:list python
genkit docs:read python/flows.md

Troubleshooting

• genkit: command not found — npm install -g genkit-cli

• GEMINI_API_KEY not set — export GEMINI_API_KEY=your-key

• Port 4000 already in use — genkit start --port 4001 -- uv run src/main.py

• uv: command not found — curl -LsSf https://astral.sh/uv/install.sh | sh

• Flow not showing in Dev UI — check genkit start output for errors