Defining AI workflows

AI workflows typically require more than just a model call. They need pre- and post-processing steps like retrieving context, managing session history, reformatting inputs, validating outputs, or combining multiple model responses.

A flow is a special Genkit function that wraps your AI logic to provide:

Type-safe inputs and outputs: Define schemas using Go structs for static and runtime validation
Streaming support: Stream partial responses or custom data
Developer UI integration: Test and debug flows with visual traces
Easy deployment: Deploy as HTTP endpoints to any platform

Flows are lightweight. They’re written like regular functions with minimal abstraction.

Defining and calling flows

In its simplest form, a flow just wraps a function. The following example wraps a function that makes a model generation request:

package main

import (
    "context"

    "github.com/firebase/genkit/go/ai"
    "github.com/firebase/genkit/go/genkit"
    "github.com/firebase/genkit/go/plugins/googlegenai"
)

func main() {
    ctx := context.Background()

    g := genkit.Init(ctx,
        genkit.WithPlugins(&googlegenai.GoogleAI{}),
    )

    type MenuSuggestionInput struct {
        Theme string `json:"theme"`
    }

    type MenuSuggestionOutput struct {
        MenuItem string `json:"menuItem"`
    }

    menuSuggestionFlow := genkit.DefineFlow(g, "menuSuggestionFlow",
        func(ctx context.Context, input MenuSuggestionInput) (MenuSuggestionOutput, error) {
            resp, err := genkit.Generate(ctx, g,
                ai.WithPrompt("Invent a menu item for a %s themed restaurant.", input.Theme),
            )
            if err != nil {
                return MenuSuggestionOutput{}, err
            }

            return MenuSuggestionOutput{MenuItem: resp.Text()}, nil
        })
}

Just by wrapping your generate calls like this, you add some functionality: doing so lets you run the flow from the Genkit CLI and from the developer UI, and is a requirement for several of Genkit’s features, including deployment and observability (later sections discuss these topics).

Input and output schemas

One of the most important advantages Genkit flows have over directly calling a model API is type safety of both inputs and outputs. When defining flows, you can define schemas for them.

You can define schemas using Go structs with JSON tags. While you can use primitive types directly as input and output parameters, it’s considered best practice to use struct-based schemas for these reasons:

Better developer experience: Struct-based schemas provide a better experience in the Developer UI by giving you labeled input fields.
Future-proof API design: Struct-based schemas allow for easy extensibility in the future. You can add new fields to your input or output schemas without breaking existing clients, which is a core principle of robust API design.

All examples in this documentation use struct-based schemas to follow these best practices.

Here’s a refinement of the last example, which defines a flow that takes a string as input and outputs an object:

type MenuSuggestionInput struct {
    Theme string `json:"theme"`
}

type MenuItem struct {
    Name        string `json:"name"`
    Description string `json:"description"`
}

menuSuggestionFlow := genkit.DefineFlow(g, "menuSuggestionFlow",
    func(ctx context.Context, input MenuSuggestionInput) (MenuItem, error) {
        item, _, err := genkit.GenerateData[MenuItem](ctx, g,
            ai.WithPrompt("Invent a menu item for a %s themed restaurant.", input.Theme),
        )
        return item, err
    })

Note that the schema of a flow does not necessarily have to line up with the schema of the model generation calls within the flow (in fact, a flow might not even contain model calls). Here’s a variation of the example that uses the structured output to format a simple string, which the flow returns.

Note how we pass MenuItem as a type parameter; this is the equivalent of passing the WithOutputType() option and getting a value of that type in response.

type MenuSuggestionInput struct {
    Theme string `json:"theme"`
}

type MenuItem struct {
    Name        string `json:"name"`
    Description string `json:"description"`
}

type FormattedMenuOutput struct {
    FormattedMenuItem string `json:"formattedMenuItem"`
}

menuSuggestionMarkdownFlow := genkit.DefineFlow(g, "menuSuggestionMarkdownFlow",
    func(ctx context.Context, input MenuSuggestionInput) (FormattedMenuOutput, error) {
        item, _, err := genkit.GenerateData[MenuItem](ctx, g,
            ai.WithPrompt("Invent a menu item for a %s themed restaurant.", input.Theme),
        )
        if err != nil {
            return FormattedMenuOutput{}, err
        }

        return FormattedMenuOutput{
            FormattedMenuItem: fmt.Sprintf("**%s**: %s", item.Name, item.Description),
        }, nil
    })

Calling flows

Once you’ve defined a flow, you can call it from your code:

output, err := menuSuggestionFlow.Run(context.Background(), MenuSuggestionInput{Theme: "bistro"})

The argument to the flow must conform to the input schema.

If you defined an output schema, the flow response will conform to it. For example, if you set the output schema to MenuItemSchema, the flow output will contain its properties:

item, err := menuSuggestionFlow.Run(context.Background(), MenuSuggestionInput{Theme: "bistro"})
if err != nil {
    log.Fatal(err)
}

log.Println(item.Name)
log.Println(item.Description)

Streaming flows

Flows support streaming using an interface similar to the model generation streaming interface. Streaming is useful when your flow generates a large amount of output, because you can present the output to the user as it’s being generated, which improves the perceived responsiveness of your app. As a familiar example, chat-based LLM interfaces often stream their responses to the user as they are generated.

Here’s an example of a flow that supports streaming:

Using iterator-based streaming

The recommended approach for streaming within flows is to use genkit.GenerateStream() or genkit.GenerateDataStream[T](), which return iterators that integrate naturally with Go’s range syntax:

type MenuItem struct {
    Name        string `json:"name"`
    Description string `json:"description"`
}

menuSuggestionFlow := genkit.DefineStreamingFlow(g, "menuSuggestionFlow",
    func(ctx context.Context, theme string, sendChunk core.StreamCallback[string]) (string, error) {
        stream := genkit.GenerateStream(ctx, g,
            ai.WithPrompt("Invent a menu item for a %s themed restaurant.", theme),
        )

        for result, err := range stream {
            if err != nil {
                return "", err
            }
            if result.Done {
                return result.Response.Text(), nil
            }
            // Pass each chunk to the flow's output stream
            sendChunk(ctx, result.Chunk.Text())
        }

        return "", nil
    })

For structured output with strong typing, use genkit.GenerateDataStream[T]():

type MenuItem struct {
    Name        string `json:"name"`
    Description string `json:"description"`
}

menuSuggestionFlow := genkit.DefineStreamingFlow(g, "menuSuggestionFlow",
    func(ctx context.Context, theme string, sendChunk core.StreamCallback[*MenuItem]) (*MenuItem, error) {
        stream := genkit.GenerateDataStream[*MenuItem](ctx, g,
            ai.WithPrompt("Invent a menu item for a %s themed restaurant.", theme),
        )

        for result, err := range stream {
            if err != nil {
                return nil, err
            }
            if result.Done {
                // result.Output is strongly typed as *MenuItem
                return result.Output, nil
            }
            // result.Chunk is also strongly typed as *MenuItem
            sendChunk(ctx, result.Chunk)
        }

        return nil, nil
    })

The iterator pattern makes the streaming logic clear and linear, avoiding nested callbacks and making error handling straightforward with Go’s standard patterns.

Using callback-based streaming

Alternatively, you can use the callback-based approach with ai.WithStreaming(). This is useful when you need to combine streaming with other Generate options or when integrating with existing callback-based code:

type MenuSuggestionInput struct {
    Theme string `json:"theme"`
}

type Menu struct {
    Theme  string     `json:"theme"`
    Items  []MenuItem `json:"items"`
}

type MenuItem struct {
    Name        string `json:"name"`
    Description string `json:"description"`
}

menuSuggestionFlow := genkit.DefineStreamingFlow(g, "menuSuggestionFlow",
    func(ctx context.Context, input MenuSuggestionInput, callback core.StreamCallback[string]) (Menu, error) {
        item, _, err := genkit.GenerateData[MenuItem](ctx, g,
            ai.WithPrompt("Invent a menu item for a %s themed restaurant.", input.Theme),
            ai.WithStreaming(func(ctx context.Context, chunk *ai.ModelResponseChunk) error {
                // Process the chunk and send it to the flow's output stream
                return callback(ctx, chunk.Text())
            }),
        )
        if err != nil {
            return Menu{}, err
        }

        return Menu{
            Theme: input.Theme,
            Items: []MenuItem{item},
        }, nil
    })

The string type in StreamCallback[string] specifies the type of values your flow streams. This does not necessarily need to be the same type as the return type, which is the type of the flow’s complete output (Menu in this example).

In both examples, the values streamed by the flow are directly coupled to the values streamed by the generation call inside the flow. Although this is often the case, it doesn’t have to be: you can output values to the stream using the callback as often as is useful for your flow.

Using channel-based streaming (experimental)

For a more idiomatic Go approach, you can use the experimental channel-based streaming API. Instead of a callback, your flow function receives a channel to which it writes stream chunks:

import (
    "context"

    "github.com/firebase/genkit/go/ai"
    "github.com/firebase/genkit/go/genkit"
    x "github.com/firebase/genkit/go/genkit/x"
)

jokeFlow := x.DefineStreamingFlow(g, "jokeFlow",
    func(ctx context.Context, topic string, streamCh chan<- string) (string, error) {
        for chunk, err := range genkit.GenerateStream(ctx, g,
            ai.WithPrompt("Tell me a joke about %s.", topic),
        ) {
            if err != nil {
                return "", err
            }
            if chunk.Done {
                return chunk.Response.Text(), nil
            }
            select {
            case streamCh <- chunk.Chunk.Text():
            case <-ctx.Done():
                return "", ctx.Err()
            }
        }
        return "", nil
    })

The channel-based API:

Passes a send-only channel (chan<- string) to your function instead of a callback
The channel is managed by the framework and closed automatically after your function returns
Your function should NOT close the channel
Use a select statement with ctx.Done() to handle cancellation gracefully

The returned flow works identically to callback-based flows and can be run with either Run() or Stream().

Calling streaming flows

Streaming flows can be run like non-streaming flows with menuSuggestionFlow.Run(ctx, MenuSuggestionInput{Theme: "bistro"}) or they can be streamed:

streamCh, err := menuSuggestionFlow.Stream(context.Background(), MenuSuggestionInput{Theme: "bistro"})
if err != nil {
    log.Fatal(err)
}

for result := range streamCh {
    if result.Err != nil {
        log.Fatalf("Stream error: %v", result.Err)
    }
    if result.Done {
        log.Printf("Menu with %s theme:\n", result.Output.Theme)
        for _, item := range result.Output.Items {
            log.Printf(" - %s: %s", item.Name, item.Description)
        }
    } else {
        log.Println("Stream chunk:", result.Stream)
    }
}

Running flows from the command line

You can run flows from the command line using the Genkit CLI tool:

genkit flow:run menuSuggestionFlow '{"theme": "French"}'

For streaming flows, you can print the streaming output to the console by adding the -s flag:

genkit flow:run menuSuggestionFlow '{"theme": "French"}' -s

Running a flow from the command line is useful for testing a flow, or for running flows that perform tasks needed on an ad hoc basis—for example, to run a flow that ingests a document into your vector database.

Debugging flows

One of the advantages of encapsulating AI logic within a flow is that you can test and debug the flow independently from your app using the Genkit developer UI.

The developer UI relies on the Go app continuing to run, even if the logic has completed. If you are just getting started and Genkit is not part of a broader app, add select {} as the last line of main() to prevent the app from shutting down so that you can inspect it in the UI.

To start the developer UI, run the following command from your project directory:

genkit start -- go run .

From the Run tab of developer UI, you can run any of the flows defined in your project:

Screenshot of the Flow runner

After you’ve run a flow, you can inspect a trace of the flow invocation by either clicking View trace or looking at the Inspect tab.

Flow steps

Each of Genkit’s fundamental actions show up as separate steps in the trace viewer:

genkit.Generate()
genkit.Embed()
genkit.Retrieve()

If you want to include code other than the above in your traces, you can do so by wrapping the code in a genkit.Run() call. You might do this for calls to third-party libraries that are not Genkit-aware, or for any critical section of code.

For example, here’s a flow with two steps: the first step retrieves a menu using some unspecified method, and the second step includes the menu as context for a genkit.Generate() call.

type MenuQuestionInput struct {
    Question string `json:"question"`
}

type MenuQuestionOutput struct {
    Answer string `json:"answer"`
}

menuQuestionFlow := genkit.DefineFlow(g, "menuQuestionFlow",
    func(ctx context.Context, input MenuQuestionInput) (MenuQuestionOutput, error) {
        menu, err := genkit.Run(ctx, "retrieve-daily-menu", func() (string, error) {
            // Retrieve today's menu. (This could be a database access or simply
            // fetching the menu from your website.)

            // ...

            return menu, nil
        })
        if err != nil {
            return MenuQuestionOutput{}, err
        }

        resp, err := genkit.Generate(ctx, g,
            ai.WithPrompt(input.Question),
            ai.WithSystem("Help the user answer questions about today's menu."),
            ai.WithDocs(ai.NewTextPart(menu)),
        )
        if err != nil {
            return MenuQuestionOutput{}, err
        }

        return MenuQuestionOutput{Answer: resp.Text()}, nil
    })

Because the retrieval step is wrapped in a genkit.Run() call, it’s included as a step in the trace viewer:

Genkit DevUI flows

Deploying flows

You can deploy your flows directly as web API endpoints, ready for you to call from your app clients. Deployment is discussed in detail on several other pages, but this section gives brief overviews of your deployment options.

`net/http` Server

To deploy a flow using any Go hosting platform, such as Cloud Run, define your flow using genkit.DefineFlow() and start a net/http server with the provided flow handler using genkit.Handler():

package main

import (
    "context"
    "log"
    "net/http"

    "github.com/firebase/genkit/go/ai"
    "github.com/firebase/genkit/go/genkit"
    "github.com/firebase/genkit/go/plugins/googlegenai"
    "github.com/firebase/genkit/go/plugins/server"
)

type MenuSuggestionInput struct {
    Theme string `json:"theme"`
}

type MenuItem struct {
    Name        string `json:"name"`
    Description string `json:"description"`
}

func main() {
    ctx := context.Background()

    g := genkit.Init(ctx, genkit.WithPlugins(&googlegenai.GoogleAI{}))

    menuSuggestionFlow := genkit.DefineFlow(g, "menuSuggestionFlow",
        func(ctx context.Context, input MenuSuggestionInput) (MenuItem, error) {
            item, _, err := genkit.GenerateData[MenuItem](ctx, g,
                ai.WithPrompt("Invent a menu item for a %s themed restaurant.", input.Theme),
            )
            return item, err
        })

    mux := http.NewServeMux()
    mux.HandleFunc("POST /menuSuggestionFlow", genkit.Handler(menuSuggestionFlow))
    log.Fatal(server.Start(ctx, "127.0.0.1:3400", mux))
}

server.Start() is an optional helper function that starts the server and manages its lifecycle, including capturing interrupt signals to ease local development, but you may use your own method.

To serve all the flows defined in your codebase, you can use genkit.ListFlows():

mux := http.NewServeMux()
for _, flow := range genkit.ListFlows(g) {
    mux.HandleFunc("POST /"+flow.Name(), genkit.Handler(flow))
}
log.Fatal(server.Start(ctx, "127.0.0.1:3400", mux))

Other server frameworks

You can also use other server frameworks to deploy your flows. For example, you can use Gin with just a few lines:

router := gin.Default()
for _, flow := range genkit.ListFlows(g) {
    router.POST("/"+flow.Name(), func(c *gin.Context) {
        genkit.Handler(flow)(c.Writer, c.Request)
    })
}
log.Fatal(router.Run(":3400"))

Calling deployed flows

Once your flow is deployed, you can call it with a POST request:

curl -X POST "http://localhost:3400/menuSuggestionFlow" \
  -H "Content-Type: application/json" -d '{"data": {"theme": "banana"}}'

For streaming responses, you can add the Accept: text/event-stream header:

curl -X POST "http://localhost:3400/menuSuggestionFlow" \
  -H "Content-Type: application/json" \
  -H "Accept: text/event-stream" \
  -d '{"data": {"theme": "banana"}}'

You can also use the Genkit web client library to call flows from web applications. See Accessing flows from the client for detailed examples of using the runFlow() and streamFlow() functions.

Learn more about deployment

For detailed deployment instructions and platform-specific guides, see:

Deploy with Cloud Run