Developing genkit dart

Genkit Dart

Genkit Dart is an AI SDK for Dart that provides a unified interface for code generation, structured outputs, tools, flows, and AI agents.

Core Features and Usage

If you need help with initializing Genkit (Genkit()), Generation (ai.generate), Tooling (ai.defineTool), Flows (ai.defineFlow), Embeddings (ai.embedMany), streaming, or calling remote flow endpoints, please load the core framework reference: references/genkit.md

Prompts (Dotprompt)

.prompt files keep prompt content out of Dart code with YAML frontmatter plus a Handlebars template. See references/dotprompt.md: promptDir, ai.prompt() (call/stream/render), variants, partials, named schemas via defineSchema, and the tools/maxTurns/returnToolRequests/use (middleware) frontmatter fields. A .prompt file can also back an agent directly via definePromptAgent.

Agents

Genkit Dart has an agent API for persistent, multi-turn conversations (sessions, snapshots, interrupts, branching, background execution, custom state, artifacts, and multi-agent delegation). Server APIs come from package:genkit/genkit.dart and the browser/HTTP client from package:genkit/client.dart. The remoteAgent client works from any Dart app, including Flutter, and the backend is fully interchangeable — it can talk to a Genkit agent implemented in Dart, JS/TypeScript, or Go over the same HTTP protocol. A few Dart specifics: interrupts are modeled as tools that call ctx.interrupt(...) (there is no defineInterrupt), sub-agent delegation uses the agents() middleware from package:genkit_middleware, and there is no artifacts() middleware yet (define artifact tools directly).

For more details see:

• Agents: defining/serving an agent and client-managed state (start here).
• Sessions & persistence: session stores (InMemorySessionStore/FileSessionStore/FirestoreSessionStore).
• Human-in-the-loop / interrupts: pausing for approval/input via ctx.interrupt 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 with the agents() middleware.
• Advanced custom agents: defineCustomAgent for full turn control.
• Deploying agents: serving agents over HTTP with genkit_shelf (multiple agents, CORS).

Genkit CLI (recommended)

The Genkit CLI provides a local development UI for running Flow, tracing executions, playing with models, and evaluating outputs.

check if the user has it installed: genkit --version

Installation:

curl -sL cli.genkit.dev | bash # Native CLI
# OR
npm install -g genkit-cli # Via npm

Usage: Wrap your run command with genkit start to attach the Genkit developer UI and tracing:

genkit start -- dart run main.dart

# Run a flow directly from the CLI
genkit flow:run myFlow '{"data": "input"}' -- dart run main.dart

# Tracing
genkit trace:list                 # list recent traces to find trace IDs
genkit trace:get <traceId>        # view trace details (useful for debugging)

# Documentation
genkit docs:search "streaming" dart
genkit docs:list dart
genkit docs:read dart/flows.md

Plugin Ecosystem

Genkit relies on a large suite of plugins to perform generative AI actions, interface with external LLMs, or host web servers.

When asked to use any given plugin, always verify usage by referring to its corresponding reference below. You should load the reference when you need to know the specific initialization arguments, tools, models, and usage patterns for the plugin:

Plugin Name	Reference Link	Description
genkit_google_genai	references/genkit_google_genai.md	Load for Google Gemini plugin interface usage.
genkit_anthropic	references/genkit_anthropic.md	Load for Anthropic plugin interface for Claude models.
genkit_openai	references/genkit_openai.md	Load for OpenAI plugin interface for GPT models, Groq, and custom compatible endpoints.
genkit_middleware	references/genkit_middleware.md	Load for Tooling for specific agentic behavior: filesystem, skills, and toolApproval interrupts.
genkit_mcp	references/genkit_mcp.md	Load for Model Context Protocol integration (Server, Host, and Client capabilities).
genkit_chrome	references/genkit_chrome.md	Load for Running Gemini Nano locally inside the Chrome browser using the Prompt API.
genkit_shelf	references/genkit_shelf.md	Load for Integrating Genkit Flow actions over HTTP using Dart Shelf.
genkit_firebase_ai	references/genkit_firebase_ai.md	Load for Firebase AI plugin interface (Gemini API via Vertex AI).

External Dependencies

Whenever you define schemas mapping inside of Tools, Flows, and Prompts, you must use the schemantic library. To learn how to use schemantic, ensure you read references/schemantic.md for how to implement type safe generated Dart code. This is particularly relevant when you encounter symbols like @Schema(), SchemanticType, or classes with the $ prefix. Genkit Dart uses schemantic for all of its data models so it's a CRITICAL skill to understand for using Genkit Dart.

Best Practices

• Always check that code cleanly compiles using dart analyze before generating the final response.
• Always use the Genkit CLI for local development and debugging.