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The Genkit CLI provides powerful commands for developing, testing, and managing your AI applications. It includes tools for running flows, evaluating models, and launching the Developer UI.

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Installation

Install the Genkit CLI globally using npm: 
npm install -g genkit-cli
The CLI is distributed as the genkit-cli package and provides the genkit command for interacting with the Genkit AI framework.

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Core Commands

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start

Run your application in development mode with telemetry and Developer UI: 
genkit start -- <command to run your code>
Options:
  • -n, --noui - Do not start the Developer UI
  • -p, --port <port> - Port for the Developer UI (defaults to 4000-4099 range)
  • -o, --open - Open the browser on UI startup
  • --disable-realtime-telemetry - Disable real-time telemetry streaming
  • --cors-origin <origin> - Specify the allowed origin for CORS requests
Example: 
genkit start -- npm run dev
This command wraps your development server with telemetry collection and launches the interactive Developer UI for testing and debugging.

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ui:start

Start the Developer UI standalone (connects to runtimes in the same directory): 
genkit ui:start
Options:
  • -p, --port <number> - Port to serve on (defaults to 4000)
  • -o, --open - Open the browser on UI startup

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ui:stop

Stop the running Developer UI server: 
genkit ui:stop

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Flow Commands

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flow:run

Run a flow with provided input data: 
genkit flow:run <flowName> [data]
Arguments:
  • <flowName> - Name of the flow to run
  • [data] - JSON data to use as input (optional)
Options:
  • -w, --wait - Wait for the flow to complete
  • -s, --stream - Stream output
  • -c, --context <JSON> - JSON object passed to context
  • --output <filename> - Name of the output file to store results
Examples: 
# Run a flow with inline JSON data
genkit flow:run simpleGreeting '{"customerName":"Sam"}'

# Run a flow and save output to file
genkit flow:run simpleGreeting '{"customerName":"Sam"}' --output result.json

# Run a flow with streaming enabled
genkit flow:run menuQuestion '{"question":"What drinks do you have?"}' --stream

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flow:batchRun

Batch run a flow using a set of data from a file: 
genkit flow:batchRun <flowName> <inputFileName>
Arguments:
  • <flowName> - Name of the flow to run
  • <inputFileName> - JSON file containing batch data
Options:
  • -w, --wait - Wait for the flow to complete
  • -c, --context <JSON> - JSON object passed to context
  • --output <filename> - Name of the output file to store results
  • --label [label] - Label flow runs in this batch
Example: 
genkit flow:batchRun simpleGreeting inputs.json --output results.json
The input file should contain an array of inputs: 
[
  {"customerName": "Alice"},
  {"customerName": "Bob"},
  {"customerName": "Charlie"}
]

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Evaluation Commands

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eval:run

Evaluate a dataset against configured evaluators: 
genkit eval:run <dataset>
Arguments:
  • <dataset> - Dataset to evaluate (JSON file)
Options:
  • --output <filename> - Output file for evaluation results
  • --output-format <format> - Output file format (csv, json) - defaults to json
  • --evaluators <evaluators> - Comma-separated list of evaluators to use
  • --batchSize <batchSize> - Batch size for parallel evaluations (default: 1)
  • --force - Automatically accept all interactive prompts
Example: 
genkit eval:run dataset.json --evaluators faithfulness,relevance --output eval-results.json

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eval:flow

Evaluate a flow against configured evaluators: 
genkit eval:flow <flowName> [data]
Arguments:
  • <flowName> - Name of the flow to evaluate
  • [data] - JSON data to use as input (optional)
Options:
  • --input <input> - Input dataset ID or JSON file for evaluation
  • -c, --context <JSON> - JSON object passed to context
  • -o, --output <filename> - Output file for evaluation results
  • --output-format <format> - Output file format (csv, json)
  • -e, --evaluators <evaluators> - Comma-separated list of evaluators
  • --batchSize <batchSize> - Batch size for parallel evaluations
  • -f, --force - Automatically accept all interactive prompts
Example: 
genkit eval:flow menuQuestion --input test-dataset.json --evaluators answer-relevance

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eval:extractData

Extract evaluation data for a flow from the trace store: 
genkit eval:extractData <flowName>
Options:
  • --output <filename> - Output file for extracted data
  • --maxRows <maxRows> - Maximum number of rows (default: 100)
  • --label [label] - Filter by flow run label
Example: 
genkit eval:extractData simpleGreeting --output extracted-data.json --maxRows 50

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Configuration Commands

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config

Set development environment configuration: 
genkit config
This command guides you through configuring your Genkit development environment.

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init

Initialize a project directory with Genkit: 
genkit init [options]
This command sets up a new Genkit project with the necessary dependencies and configuration.

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Global Options

  • --no-update-notification - Do not show update notification
  • --non-interactive - Run in non-interactive mode (use default choices)
  • --version - Display the CLI version
  • --help - Display help information

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Tips

  • Use genkit start during development to automatically collect traces and test flows in the Developer UI
  • The CLI automatically detects your project root and runtime environment
  • Set the GENKIT_ENV environment variable to dev when running your application to enable development features
  • Use --stream flag with flow commands to see output as it’s generated
  • View evaluation results in the Developer UI at /evaluate/<evalRunId>

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Next Steps

  • Learn about the Developer UI for visual testing and debugging
  • Explore Testing strategies for AI applications
  • Read about Debugging techniques for flows

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