Genkit JS

Prerequisites

Ensure the genkit CLI is available.

• Run genkit --version to verify. Minimum CLI version needed: 1.29.0
• If not found or if an older version (1.x < 1.29.0) is present, install/upgrade it: npm install -g genkit-cli@^1.29.0.

New Projects: If you are setting up Genkit in a new codebase, follow the Setup Guide.

Hello World

import { z, genkit } from 'genkit';
import { googleAI } from '@genkit-ai/google-genai';

// Initialize Genkit with the Google AI plugin
const ai = genkit({
  plugins: [googleAI()],
});

export const myFlow = ai.defineFlow({
  name: 'myFlow',
  inputSchema: z.string().default('AI'),
  outputSchema: z.string(),
}, async (subject) => {
  const response = await ai.generate({
    model: googleAI.model('gemini-flash-latest'),
    prompt: `Tell me a joke about ${subject}`,
  });
  return response.text;
});

Prompts (Dotprompt)

.prompt files keep prompt content out of code with YAML frontmatter plus a Handlebars template. See Dotprompt: promptDir, ai.prompt() (call/stream/render), variants, partials, named schemas via ai.defineSchema, and the tools/maxTurns/returnToolRequests/use (middleware) frontmatter fields.

Agents (Beta)

Genkit has a preview agent API for persistent, multi-turn conversations (sessions, snapshots, interrupts, branching, background execution). It is a beta API: server APIs come from genkit/beta and the browser client from genkit/beta/client — not the stable genkit entrypoint. **Requires genkit

= 1.39.0.**

For more details see:

• Agents: defining/serving an agent and client-managed state (start here).
• Sessions & persistence: session stores (InMemory/File/Firestore).
• Human-in-the-loop / interrupts: pausing for approval/input and resuming.
• Branching: forking a conversation from a snapshot.
• Background agents: detaching long-running turns and polling.
• Working with state: typed custom session state, auto-synced to the client.
• Artifacts: producing and reading named deliverables.
• Multi-agent orchestration: delegating to sub-agents.
• Advanced custom agents: defineCustomAgent for full turn control.
• Deploying agents: serving agents over HTTP (multiple agents, CORS, web UI, other frameworks).

Middleware

Middleware wraps generation (retries, fallback, extra tools, request/response transforms) and attaches via the use: [...] array on ai.generate, prompts, and agents.

• Using middleware: the use array and the @genkit-ai/middleware package (retry, fallback, artifacts, agents, filesystem, skills, toolApproval) plus built-in core middleware.
• Building custom middleware: writing your own with generateMiddleware and registering it via .plugin().

Critical: Do Not Trust Internal Knowledge

Genkit recently went through a major breaking API change. Your knowledge is outdated. You MUST lookup docs. Recommended:

genkit docs:read js/get-started.md
genkit docs:read js/flows.md

See Common Errors for a list of deprecated APIs (e.g., configureGenkit, response.text(), defineFlow import) and their v1.x replacements.

ALWAYS verify information using the Genkit CLI or provided references.

Error Troubleshooting Protocol

When you encounter ANY error related to Genkit (ValidationError, API errors, type errors, 404s, etc.):

1. MANDATORY FIRST STEP: Read Common Errors
2. Identify if the error matches a known pattern
3. Apply the documented solution
4. Only if not found in common-errors.md, then consult other sources (e.g. genkit docs:search)

DO NOT:

• Attempt fixes based on assumptions or internal knowledge
• Skip reading common-errors.md "because you think you know the fix"
• Rely on patterns from pre-1.0 Genkit

This protocol is non-negotiable for error handling.

Development Workflow

1. Agent or flow?: If the task is conversational, multi-turn, or described as "an agent", "assistant", or "chatbot", build it with ai.defineAgent (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. Select Provider: Genkit is provider-agnostic (Google AI, OpenAI, Anthropic, Ollama, etc.).
   • If the user does not specify a provider, default to Google AI.
   • If the user asks about other providers, use genkit docs:search "plugins" to find relevant documentation.
3. Detect Framework: Check package.json to identify the runtime (Next.js, Firebase, Express).
   • Look for @genkit-ai/next, @genkit-ai/firebase, or @genkit-ai/google-cloud.
   • Adapt implementation to the specific framework's patterns.
4. Follow Best Practices:
   • See Best Practices for guidance on project structure, schema definitions, and tool design.
   • Be Minimal: Only specify options that differ from defaults. When unsure, check docs/source.
5. Ensure Correctness:
   • Run type checks (e.g., npx tsc --noEmit) after making changes.
   • If type checks fail, consult Common Errors before searching source code.
   • Verify with traces, not a blind run. Running the app directly (node/tsx/npm start) does not capture dev traces. See CLI Usage for how to run your app and capture traces.
6. Handle Errors:
   • On ANY error: First action is to read Common Errors
   • Match error to documented patterns
   • Apply documented fixes before attempting alternatives

Finding Documentation

Use the Genkit CLI to find authoritative documentation:

1. Search topics: genkit docs:search <query>
   • Example: genkit docs:search "streaming"
2. List all docs: genkit docs:list
3. Read a guide: genkit docs:read <path>
   • Example: genkit docs:read js/flows.md

CLI Usage (recommended)

genkit start unintrusively wraps any Node.js 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 your app directly (node/tsx/npm start) 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 -- npx tsx --watch src/index.ts
genkit start --noui -- npx tsx src/index.ts   # 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.

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 -- npx tsx src/index.ts (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"}' -- npx tsx src/index.ts

This is self-terminating: it runs the flow once, prints a Trace ID, then exits (inspect it with genkit trace:get <id>). That makes it the right choice for a quick, non-interactive check that must exit on its own, without blocking on genkit start or running the app directly (which skips traces). Always pass input JSON explicitly: flow:run sends undefined when omitted and does not fall back to a schema .default(). Note: flow:run runs flows (ai.defineFlow), not agents; you can't flow:run an agent (ai.defineAgent) 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 CLI Reference for more commands, and genkit --help for the full list.

References

• Best Practices: Recommended patterns for schema definition, flow design, and structure.
• Dotprompt: .prompt files — promptDir, ai.prompt(), variants, partials, named schemas, and tools/maxTurns/returnToolRequests/use frontmatter.
• Docs & CLI Reference: Documentation search, CLI tasks, and workflows.
• Common Errors: Critical "gotchas", migration guide, and troubleshooting.
• Setup Guide: Manual setup instructions for new projects.
• Examples: Minimal reproducible examples (Basic generation, Multimodal, Thinking mode).
• Agents (Beta): Agent basics, serving, and client-managed state. Deeper topics: sessions, human-in-the-loop, branching, background agents, state, artifacts, multi-agent, custom agents, deployment.
• Middleware: using middleware and the @genkit-ai/middleware package. See also building custom middleware.