MCP Terminal Server

# Defining AI workflows The core of your app's AI features are generative model requests, but it's rare that you can simply take user input, pass it to the model, and display the model output back to the user. Usually, there are pre- and post-processing steps that must accompany the model call. For example: * Retrieving contextual information to send with the model call * Retrieving the history of the user's current session, for example in a chat app * Using one model to reformat the user input in a way that's suitable to pass to another model * Evaluating the "safety" of a model's output before presenting it to the user * Combining the output of several models Every step of this workflow must work together for any AI-related task to succeed. In Genkit, you represent this tightly-linked logic using a construction called a flow. Flows are written just like functions, using ordinary TypeScript code, but they add additional capabilities intended to ease the development of AI features: * **Type safety**: Input and output schemas defined using Zod, which provides both static and runtime type checking * **Integration with developer UI**: Debug flows independently of your application code using the developer UI. In the developer UI, you can run flows and view traces for each step of the flow. * **Simplified deployment**: Deploy flows directly as web API endpoints, using Cloud Functions for Firebase or any platform that can host a web app. Unlike similar features in other frameworks, Genkit's flows are lightweight and unobtrusive, and don't force your app to conform to any specific abstraction. All of the flow's logic is written in standard TypeScript, and code inside a flow doesn't need to be flow-aware. ## Defining and calling flows In its simplest form, a flow just wraps a function. The following example wraps a function that calls `generate()`: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex01" adjust_indentation="auto" %} ``` 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 using Zod, in much the same way as you define the output schema of a `generate()` call; however, unlike with `generate()`, you can also specify an input schema. Here's a refinement of the last example, which defines a flow that takes a string as input and outputs an object: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex02" adjust_indentation="auto" %} ``` Note that the schema of a flow does not necessarily have to line up with the schema of the `generate()` calls within the flow (in fact, a flow might not even contain `generate()` calls). Here's a variation of the example that passes a schema to `generate()`, but uses the structured output to format a simple string, which the flow returns. ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex03" adjust_indentation="auto" %} ``` ### Calling flows Once you've defined a flow, you can call it from your Node.js code: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex04" adjust_indentation="auto" %} ``` The argument to the flow must conform to the input schema, if you defined one. 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: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex05" adjust_indentation="auto" %} ``` ## Streaming flows Flows support streaming using an interface similar to `generate()`'s 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: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex06" adjust_indentation="auto" %} ``` * The `streamSchema` option specifies the type of values your flow streams. This does not necessarily need to be the same type as the `outputSchema`, which is the type of the flow's complete output. * The second parameter to your flow definition is called `sideChannel`. It provides features such as request context and the `sendChunk` callback. The `sendChunk` callback takes a single parameter, of the type specified by `streamSchema`. Whenever data becomes available within your flow, send the data to the output stream by calling this function. In the above example, the values streamed by the flow are directly coupled to the values streamed by the `generate()` 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. ### Calling streaming flows Streaming flows are also callable, but they immediately return a response object rather than a promise: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex07" adjust_indentation="auto" %} ``` The response object has a stream property, which you can use to iterate over the streaming output of the flow as it's generated: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex08" adjust_indentation="auto" %} ``` You can also get the complete output of the flow, as you can with a non-streaming flow: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex09" adjust_indentation="auto" %} ``` Note that the streaming output of a flow might not be the same type as the complete output; the streaming output conforms to `streamSchema`, whereas the complete output conforms to `outputSchema`. ## Running flows from the command line You can run flows from the command line using the Genkit CLI tool: ```posix-terminal genkit flow:run menuSuggestionFlow '"French"' ``` For streaming flows, you can print the streaming output to the console by adding the `-s` flag: ```posix-terminal genkit flow:run menuSuggestionFlow '"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. To start the developer UI, run the following commands from your project directory: ```posix-terminal genkit start -- tsx --watch src/your-code.ts ``` From the **Run** tab of developer UI, you can run any of the flows defined in your project:  After you've run a flow, you can inspect a trace of the flow invocation by either clicking **View trace** or looking on the **Inspect** tab. In the trace viewer, you can see details about the execution of the entire flow, as well as details for each of the individual steps within the flow. For example, consider the following flow, which contains several generation requests: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex10" adjust_indentation="auto" %} ``` When you run this flow, the trace viewer shows you details about each generation request including its output:  ### Flow steps In the last example, you saw that each `generate()` call showed up as a separate step in the trace viewer. Each of Genkit's fundamental actions show up as separate steps of a flow: * `generate()` * `Chat.send()` * `embed()` * `index()` * `retrieve()` If you want to include code other than the above in your traces, you can do so by wrapping the code in a `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 `generate()` call. ```ts import { run } from 'genkit'; {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/index.ts" region_tag="ex11" adjust_indentation="auto" %} ``` Because the retrieval step is wrapped in a `run()` call, it's included as a step in the trace viewer:  ## 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. ### Cloud Functions for Firebase To deploy flows with Cloud Functions for Firebase, use the `onCallGenkit` feature of `firebase-functions/https`. `onCallGenkit` wraps your flow in a callable function. You may set an auth policy and configure App Check. ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/firebase.ts" region_tag="ex" adjust_indentation="auto" %} ``` For more information, see the following pages: * [Deploy with Firebase](/docs/genkit/firebase) * [Authorization and integrity](/docs/genkit/auth#cloud_functions_for_firebase_integration) * [Firebase plugin](/docs/genkit/plugins/firebase) ### Express.js To deploy flows using any Node.js hosting platform, such as Cloud Run, define your flows using `defineFlow()` and then call `startFlowServer()`: ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/express.ts" region_tag="ex01" adjust_indentation="auto" %} ``` By default, `startFlowServer` will serve all the flows defined in your codebase as HTTP endpoints (for example, `http://localhost:3400/menuSuggestionFlow`). You can call a flow with a POST request as follows: ```posix-terminal curl -X POST "http://localhost:3400/menuSuggestionFlow" \ -H "Content-Type: application/json" -d '{"data": "banana"}' ``` If needed, you can customize the flows server to serve a specific list of flows, as shown below. You can also specify a custom port (it will use the PORT environment variable if set) or specify CORS settings. ```ts {% includecode github_path="firebase/genkit/js/doc-snippets/src/flows/express.ts" region_tag="ex02" adjust_indentation="auto" %} ``` For information on deploying to specific platforms, see [Deploy with Cloud Run](/docs/genkit/cloud-run) and [Deploy flows to any Node.js platform](/docs/genkit/deploy-node).