# Genkit Genkit is Google's open-source framework and SDK for building AI-powered and agentic applications across multiple programming languages, with developer tools such as a CLI and local UI. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building AI Agents in Dart with the Genkit Dart SDK DevFeed: [Building AI Agents in Dart with the Genkit Dart SDK]() Original publisher: [Read original article]() Author: Abhishek Doshi Published: 2026-08-10T23:16:05Z Content type: tutorial Language: en Sources: [Google Developer Experts - Medium]() Topics: [Dart](), [Genkit](), [AI Development](), [Flutter](), [Cloud Functions](), [Firebase](), [SDKs]() Tags: [ai](), [ai-agent](), [ai-agents](), [ai-development](), [cloud-functions](), [dart](), [firebase](), [flutter](), [generative-ai-tools](), [genkit](), [sdk]() ### AI overview This tutorial introduces the Genkit Dart SDK for building AI agents natively in Dart and deploying them to Cloud Functions for Firebase. It explains agent orchestration, conversational state, history, streams, models, tools, flows, and multi-agent delegation. ### Source excerpt Because your Flutter app deserves a brain, and you deserve to never look at a Python traceback again. Let's be honest. We all love Dart. It's clean, it's safe, and it powers our beloved Flutter. But until recently, if you wanted to build serious, multi-step AI agents, the industry basically told you to pack your bags, leave your cozy typed ecosystem, and go write Python. Gross 🤮 Thankfully, Google finally heard our collective sighs and dropped the Genkit Dart SDK. Now, you can build full-stack, hyper-intelligent AI agents natively in Dart and deploy them straight to Cloud Functions for Firebase. No context-switching. No spinning up a random Node.js microservice just to talk to an LLM. Grab your coffee (or your energy drink of choice). Let's look at how Genkit turns your Dart code into an absolute powerhouse. https://medium.com/media/91c2efa4198c043f3f400f216214650e/hrefThe Shift: From "Dumb Chatbots" to "Autonomous Overlords" Building traditional chatbots or strict multi-step UI flows often feels like playing one of those terrible, rigid video games that block you at a level until you do exactly what the developers scripted. Agentic AI, by contrast, is the ultimate open-ended sandbox. An agent doesn't just blindly answer a prompt. It analyzes what the user wants, figures out which tools it needs to achieve the goal, and dynamically orchestrates the steps to get there. With Genkit's new Agents API, all the messy plumbing: maintaining conversational state, keeping track of history, and parsing streams, is handled behind a single API. You just focus on giving it a brain. The Holy Trinity: Models, Tools, and Flows To stop your AI from just hallucinating fan-fiction, Genkit uses a few core primitives. Think of this as the toddler-proofing stage of AI development. Models (ai.generate()): The actual brain. Whether you are using Gemini, Claude, or OpenAI, the API stays exactly the same. Tools (defineTool): This is how you give your AI hands. By defining strict input schemas ## Beyond the Spinner: Building Responsive AI Apps with Genkit Streaming DevFeed: [Beyond the Spinner: Building Responsive AI Apps with Genkit Streaming]() Original publisher: [Read original article]() Author: Pavel J Published: 2025-10-28T10:35:17Z Content type: tutorial Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [Streaming](), [Large Language Model](), [User experience (UX)](), [Structured-data]() Tags: [ai](), [await](), [genkit](), [json](), [llm](), [process](), [streaming](), [ux]() ### AI overview A practical guide to using Genkit streaming for responsive AI applications. It covers streaming LLM response chunks with generateStream and callbacks, awaiting final response metadata, parsing incomplete streamed JSON, and streaming custom AI logic from flows. ### Source excerpt Better AI UXA practical guide to streaming LLM responses, partial JSON, and custom status messages to eliminate perceived latency. In the new age of AI, streaming structured data has suddenly become useful and mainstream. The main reason is that LLMs can be slow, and a good user experience (UX) strives to reduce this perceived latency. We could show a spinner for a few seconds, but that feels slow. Instead, if we start rendering content as soon as we receive the first few tokens from the LLM, the user can watch the response "grow in front of their eyes." Even if the end-to-end time is the same, it feels faster because the user sees activity immediately. BTW, this sample app (streaming side only) is available at: https://github.com/genkit-ai/samples/tree/main/simple-chatbot Streaming is foundational to Genkit's design. Everything in Genkit is built on top of actions -- simple, function-like constructs that, among other things, can stream. Models are actions, and flows are actions, so they can all stream. LLM streams Let's start with receiving streams from LLMs. The most common and useful way is to use the generateStream function: const { stream, response } = ai.generateStream({ prompt: 'Tell me a story about AI', }); for await (const chunk of stream) { process.stdout.write(chunk.text); } // optional const finalResponse = await response; console.log(finalResponse.usage); console.log(finalResponse.messages); // history In addition to the stream of "generate response chunks," we can optionally await the response promise. This is useful because it contains final usage data (like token counts), the complete message array (great for tracking history), and other metadata. If you don't like using the for await syntax and prefer callbacks, there's another less known way to stream using the generate function, by providing the onChunk callback: const response = await ai.generate({ prompt: 'Tell me a story aboud AI', onChunk: (chunk) => process.stdout.write(chunk.text), }); conso ## Dart Client for Genkit DevFeed: [Dart Client for Genkit]() Original publisher: [Read original article]() Author: Nozomi Koborinai Published: 2025-10-23T10:49:27Z Content type: tutorial Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [Dart](), [client library](), [Flutter](), [AI Development](), [Cloud Functions](), [Cloud Run]() Tags: [ai-development](), [apis](), [client-library](), [dart](), [firebase](), [flutter](), [genkit](), [google-cloud-platform](), [http](), [open-source](), [server-sent-events-sse](), [streaming]() ### AI overview This article introduces a Dart client library for calling Genkit flows deployed on a server from Flutter or other Dart applications. It explains that the library handles Genkit request and response conventions, reduces repeated HTTP client code, supports authentication mechanisms, and simplifies streaming responses through Server-Sent Events. The client can call flows implemented with Genkit JS, TypeScript, or Go when their server endpoints are accessible. ### Source excerpt Introducing the Dart Client for GenkitCalling Genkit Flows from Flutter/DartDart client for Genkit I'm a big fan of Firebase and often choose Flutter as my front-end framework. Since Google I/O 2024, I've been continuously exploring Genkit. Developed by the Firebase team, Genkit is an open-source framework from Google that simplifies the definition of AI-powered processing sequences on the server-side as Flows. Genkit | Open-source AI development framework by Google However, calling a server-defined flow from a Dart/Flutter client application required some extra effort. To bridge this gap, I developed the Dart client for Genkit package. genkit | Dart package Note: This library is not for defining Genkit flows themselves. It is specifically designed for easily and securely calling flows deployed on a server from a Dart client.Library overview As mentioned above, this library is a dedicated client for easily calling Genkit flows deployed on a server (e.g., as Web APIs on platforms like Cloud Functions for Firebase, Cloud Run, or GKE). It is not for implementing Genkit flows in Dart. The challenges before Previously, calling a Genkit flow from Dart/Flutter required manually implementing an HTTP client according to a set of conventions. For example, here's a typical piece of code using the dio package to call an image generation flow defined in Genkit. Future generateImage({required String imageDescription}) async { try { final response = await dio.post( 'https:///generateImage', data: { 'data': { 'imageDescription': imageDescription, }, }, ); if (response.statusCode == 200) { return response.data['result']['url'] as String; } throw Exception('Failed to generate image: ${response.statusCode}'); } on DioException catch (e) { throw Exception('Failed to generate image: ${e.message}'); } } This code presents several challenges: When sending a request, the input must be wrapped in {'data': ...}, and when receiving a response, the result must be ex ## A roundup of Firebase news from Brazil DevFeed: [A roundup of Firebase news from Brazil]() Original publisher: [Read original article]() Author: Arthur Thompson Published: 2025-09-10T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [AI Models](), [SDKs](), [Genkit](), [Cloud]() Tags: [ai](), [ai-logic](), [ai-models](), [android](), [app-hosting](), [brazil](), [browser](), [firebase](), [firebase-studio](), [flutter](), [gemini](), [genkit](), [go](), [image-editing](), [mcp](), [news](), [postgresql](), [sdk](), [sql-connect](), [updates]() ### AI overview Firebase's Brazil roundup describes updates to Firebase AI Logic, including thinking-budget controls, thought summaries, image editing for Android and Flutter, Gemini Live API support, and hybrid on-device and cloud model experiences in Chrome. It also highlights Firebase App Hosting, Data Connect, and Genkit Go 1.0 updates. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Fueld.AI's nutrition coach with Firebase and Google Cloud DevFeed: [Fueld.AI's nutrition coach with Firebase and Google Cloud]() Original publisher: [Read original article]() Author: Raïssa Tona Published: 2025-06-25T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Google Cloud Platform (GCP)](), [Artificial Intelligence](), [Cloud Functions](), [Firestore](), [Genkit]() Tags: [ai](), [authentication](), [case-study](), [cloud-functions](), [cloud-logging](), [cloud-storage](), [firebase](), [firestore](), [gemini](), [genkit](), [google-analytics](), [google-cloud](), [hosting](), [news](), [vertex-ai]() ### AI overview Fueld.AI used Firebase and Google Cloud services to build a health and nutrition app that analyzes meal photos, provides AI coaching, and securely shares data with health professionals. The article describes its use of Authentication, Cloud Storage, Cloud Functions, Genkit, Gemini, Cloud Firestore, Hosting, Analytics, and Cloud Logging. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Building AI-powered apps with Firebase AI Logic DevFeed: [Building AI-powered apps with Firebase AI Logic]() Original publisher: [Read original article]() Author: Thevi Sundaralingam; Dmitry Lyalin; Miguel Ramos Published: 2025-05-20T10:49:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Generative AI](), [SDKs](), [Genkit](), [API]() Tags: [ai](), [ai-logic](), [dashboards](), [firebase](), [firebase-ai-logic](), [gemini](), [genkit](), [google-i-o](), [hybrid-inference](), [inference](), [launch](), [node-js](), [on-device](), [on-device-ai](), [sdks](), [vertex-ai]() ### AI overview Firebase announces Firebase AI Logic, the evolution of Vertex AI in Firebase, with client-side and Genkit-based server-side integrations for generative AI. New capabilities include Gemini Developer API access, hybrid and on-device inference, Unity support, image generation and editing, enhanced observability, and dynamic model lookup in Genkit for Node.js. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Building Your First MCP Server with Genkit DevFeed: [Building Your First MCP Server with Genkit]() Original publisher: [Read original article]() Author: Pavel J Published: 2025-04-25T14:09:50Z Content type: tutorial Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [Model Context Protocol](), [MCP Server](), [backends](), [Firestore](), [Structured-data](), [AI Development](), [Testing](), [developer tooling]() Tags: [ai](), [backends](), [firebase](), [firestore](), [generative-ai](), [generative-ai-tools](), [genkit](), [guides](), [mcp](), [mcp-server](), [schema](), [testing]() ### AI overview This tutorial explains how to build a custom MCP server with Genkit. It covers defining order data schemas with zod, generating optional test data and storing it in Firestore, creating flows to retrieve orders, and exposing tools that can be called by AI models or external clients such as Cline. ### Source excerpt Generative AI Building robust backends for AI applications often involves integrating various components. The Model Context Protocol (MCP) offers a standardized communication layer, and Genkit provides a powerful framework to leverage it. This article guides you through creating a custom MCP server using Genkit, from defining data schemas to interacting with the server using development tools like Cline. This article assumes some familiarity with Genkit concepts like flows and tools. Learn more about Genkit: https://firebase.google.com/docs/genkit If you prefer this tutorial in video format, check out: https://medium.com/media/908bbf0df5ad1306b125a4c7411c203c/href1. Defining Your Data Schema Before building any application, you need to define the structure of your data. In this example, we're building a system to manage website orders for a consulting company. We use zod, a popular schema declaration library, integrated with Genkit. The OrderSchema defines the necessary fields for each order: const OrderSchema = z.object({ id: z.string().describe("Order Id. Format: A-XXX-XXX, ex. A-123-456"), name: z.string().describe("Name of the business"), colorScheme: z.object({ mainColor: z.string().describe("RGB color prefixed with #, ex. #FFAA66"), highlight: z.string().describe("RGB color prefixed with #, ex. #AA6611"), textColor: z.string().describe("RGB color prefixed with #, ex. #224455"), }), email: z.string(), jobDescription: z .string() .describe( "Detailed description of the website they need. Ex. if they are a construction " + "company describe what they want on the home, contact age, testimonials, etc. " + "pages. Not too crazy, usually static website." ), address: z.string(), }); This schema clearly outlines each piece of information associated with an order, including nested details like the colorScheme. 2. Generating Realistic Test Data (optional) During development, you need ways to both populate your system with test data and inspect that data. Manually creatin ## What's new in Firebase at Cloud Next 2025 DevFeed: [What's new in Firebase at Cloud Next 2025]() Original publisher: [Read original article]() Author: Kristen Richards; Andrea Wu Published: 2025-04-09T06:07:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [Cloud](), [Genkit](), [Next.js](), [Testing]() Tags: [2025](), [ai](), [ai-logic](), [android](), [angular](), [app-distribution](), [app-hosting](), [cloud-next](), [firebase](), [firebase-studio](), [flaky](), [gemini](), [genkit](), [go](), [graphql](), [ios](), [launch](), [next-js](), [postgresql](), [python](), [react](), [react-native](), [sql-connect](), [updates](), [vertex-ai]() ### AI overview Firebase announces product updates at Cloud Next 2025, including Firebase Studio, an App Testing agent, expanded Genkit language support, Vertex AI integrations, and general availability for Data Connect and App Hosting. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Introducing Firebase Studio DevFeed: [Introducing Firebase Studio]() Original publisher: [Read original article]() Author: Rody Davis; Kelvin Boateng Published: 2025-04-09T06:06:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [AI-assisted coding](), [Development](), [Genkit](), [Next.js](), [AI Chat]() Tags: [agentic](), [ai](), [app-development](), [app-hosting](), [build](), [cloud-next](), [coding](), [development](), [firebase](), [firebase-studio](), [gemini](), [genkit](), [launch](), [news](), [next-js](), [project-idx](), [updates](), [web-app]() ### AI overview Firebase is launching a preview of Firebase Studio, a cloud-based agentic development environment for building, testing, deploying, and running AI applications. It combines Project IDX, Genkit, and Gemini in Firebase with prototyping, coding workspaces, and deployment options. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Announcing Genkit for Python and Go DevFeed: [Announcing Genkit for Python and Go]() Original publisher: [Read original article]() Author: Chris Gill; Peter Friese Published: 2025-04-09T06:04:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Genkit](), [Go Language](), [Python](), [SDKs]() Tags: [ai](), [cloud-next](), [firebase](), [genkit](), [go](), [launch](), [news](), [open-source](), [python](), [sdk](), [updates]() ### AI overview Firebase announces Genkit SDKs for Python in Alpha and Go in Beta, expanding the open-source framework beyond its stable Node.js release. The SDKs provide Genkit's AI-building, tooling, debugging, observability, and plugin capabilities for additional developer ecosystems. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Monitor your Genkit features in production DevFeed: [Monitor your Genkit features in production]() Original publisher: [Read original article]() Author: Konstantin Mandrika; Alex Singer Published: 2025-03-18T15:42:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Genkit](), [Artificial Intelligence](), [observability](), [Monitoring](), [telemetry](), [dashboards](), [Firebase](), [Large Language Model](), [Node.js]() Tags: [ai](), [cloud-logging](), [cloud-monitoring](), [cloud-observability](), [firebase](), [genkit](), [latency](), [llms](), [metrics](), [monitoring](), [node-js](), [observability](), [telemetry]() ### AI overview This article introduces Firebase Genkit Monitoring for observing generative AI features in production. It explains how telemetry is exported and displayed in the Firebase console, including usage, success rates, p95 latency, token counts, and feature health metrics. ### Source excerpt Gain visibility and debug complex AI features with powerful observability tools, ensuring a seamless user experience. ## How to Develop Firebase Genkit Flows DevFeed: [How to Develop Firebase Genkit Flows]() Original publisher: [Read original article]() Author: Nozomi Koborinai Published: 2025-03-15T10:00:54Z Content type: tutorial Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [Firebase](), [Firestore](), [test]() Tags: [ai](), [emulator](), [emulator-suite](), [firebase](), [firestore](), [gemini](), [genkit](), [how-to](), [test](), [testing]() ### AI overview This tutorial explains how to test Firebase Genkit Flows locally. It describes the limitations of the Genkit Developer UI when flows need Firebase services such as Firestore or Cloud Storage, and presents the Firebase Local Emulator Suite as a way to simulate those services for local testing. ### Source excerpt AIHow to Develop Genkit Flows Genkit includes a powerful tool called the Genkit Developer UI that can significantly accelerate the development of Genkit Flows by enabling local testing. However, when integrating with other Firebase products, such as Firestore or Cloud Storage, the Genkit Developer UI may not be sufficient. In this article, I'll walk you through how to enhance your local testing environment by integrating the Firebase Local Emulator Suite to test Genkit Flows more effectively. Genkit Developer UI The Genkit Developer UI is an essential tool that allows you to verify the behavior of Genkit Flows locally before deploying them. (It has a great UI, and it's super exciting to use!!) While the Genkit Developer UI provides a fantastic developer experience, it does have some limitations. The Limitation The challenge comes when you need to integrate other Firebase products. For example, when you create a configuration where Genkit communicates with Firestore, such as the one I discussed in my previous article, "8 Genkit Architectures to Orchestrate Firebase & AI". To clarify this, let's look at two cases. Working with self-contained flowsOK case In this case, the entire processing is self-contained within Genkit, with no need to integrate Firebase products like Firestore or Cloud Storage, so you can run everything locally using the Genkit Developer UI. export const firebaseAuthFlow = onFlow( ai, { name: `firebaseAuthFlow`, outputSchema: z.string(), authPolicy: firebaseAuth((user) => { if (user.firebase?.sign_in_provider !== `anonymous`) { throw new Error(`Only anonymously authenticated users can access this function`) } }), httpsOptions: { secrets: [googleAIapiKey], cors: true, }, }, async () => {} ) Even though this function uses authPolicy with Firebase Authentication, Genkit Developer UI allows you to inject an authentication provider, so local testing is still possible. Working with Flows that require Firebase servicesFurther testing required case In this ## Build Responsive, AI-powered Apps with Cloud Functions for Firebase DevFeed: [Build Responsive, AI-powered Apps with Cloud Functions for Firebase]() Original publisher: [Read original article]() Author: Daniel Lee; Jeff Huleatt Published: 2025-03-06T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Cloud Functions](), [Firebase](), [Streaming](), [Genkit](), [Generative AI]() Tags: [ai](), [cloud-functions](), [firebase](), [gemini](), [generative-ai](), [genkit](), [javascript](), [node-js](), [streaming](), [web](), [web-app]() ### AI overview Firebase introduces streaming responses for callable Cloud Functions and a new Genkit trigger. Streaming lets servers send data to clients incrementally, improving responsiveness for AI-generated content and large or real-time datasets. ### Source excerpt Announcing streaming callables and a new Genkit trigger. ## Extracting structured data from PDFs using Gemini 2.0 and Genkit DevFeed: [Extracting structured data from PDFs using Gemini 2.0 and Genkit]() Original publisher: [Read original article]() Author: Peter Friese Published: 2025-02-20T09:41:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Genkit](), [Structured-data](), [Artificial Intelligence](), [Node.js](), [multimodal](), [SDKs]() Tags: [ai](), [firebase](), [gemini](), [genkit](), [guide](), [multimodal](), [node-js](), [snippet](), [structured-output](), [typescript]() ### AI overview A step-by-step tutorial showing Node.js developers how to use Gemini 2.0 and Genkit to extract structured invoice data from PDF files. It covers multimodal PDF analysis, OCR, and schema-constrained output with Zod. ### Source excerpt Extract structured data from PDFs using Gemini 2.0's vision and Genkit. Step-by-step guide with code examples for Node.js developers. ## internal AI: A Genkit-Based Internal AI Chat Released as Open Source! DevFeed: [internal AI: A Genkit-Based Internal AI Chat Released as Open Source!]() Original publisher: [Read original article]() Author: tanabee Published: 2025-02-17T17:14:41Z Content type: article Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [AI Chat](), [Open Source](), [Firebase](), [Retrieval-Augmented Generation](), [Generative AI]() Tags: [emulator](), [firebase](), [firestore](), [function-calling](), [gemini](), [genkit](), [google-cloud-platform](), [llm](), [open-source](), [rag](), [vertex-ai]() ### AI overview This article introduces internal AI, an open-source Genkit-based internal chat implementation. It describes its usage-based pricing, customizable RAG and function-calling support, Firebase deployment, monitoring, domain restrictions, and local testing with the Firebase Emulator. ### Source excerpt internal AI: An open-source Genkit-based internal AI chat implementation I have released internal AI, an AI chat solution you can easily and cost-effectively run internally, under an open-source license! Recently, Firebase Genkit reached version 1.0 and became stable for production use. With Genkit, you can quickly build applications that leverage generative AI. However, Genkit itself does not include a user interface. As a use case for an internal AI chat, I decided to make internal AI available as open source. Because internal AI uses a pay-as-you-go model instead of charging per user, you can optimize costs by only paying for the amount of usage. Moreover, thanks to Genkit's extensibility, you can combine RAG (Retrieval-Augmented Generation) and Function Calling to tailor AI features to your organization's needs. This opens the door to more practical AI applications, such as specialized internal information retrieval or automated workflow processes! GitHub - tanabee/internal-ai internal AI -- ChatKey Features Open-source project, allowing customization to fit your organization's needs Abstracted interface with generative AI via Genkit Deployable on Firebase Firebase AI Monitoring for monitoring requests to generative AI Restricting Usage to Specific Domains Usage-based pricing, independent of the number of users Open-source project, allowing customization to fit your organization's needs internal AI is provided as a boilerplate, which you can freely fork and customize according to your organization's requirements. Genkit can be extended in many ways, such as using RAG for accessing internal data or utilizing Google Search for grounding. To keep things simple for initial adoption, this example just communicates with Gemini 2.0. const ai = genkit({ // ... model: gemini20Flash001, }) export const chatFlow = ai.defineFlow( // ... async (messages) => { const response = await ai.generate({ messages }) return response.messages }, ) internal-ai/functions/src/genkit/chatFlow.t ## Announcing Firebase Genkit 1.0 for Node.js DevFeed: [Announcing Firebase Genkit 1.0 for Node.js]() Original publisher: [Read original article]() Author: Chris Gill; Peter Friese Published: 2025-02-12T04:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Genkit](), [Artificial Intelligence](), [Node.js](), [Tooling](), [Open Source](), [Retrieval Augmented Generation (RAG)](), [JSON]() Tags: [ai](), [ai-development](), [deepseek](), [developer-tooling](), [firebase](), [gemini](), [gemma](), [genkit](), [json](), [launch](), [llama](), [node-js](), [ollama](), [rag]() ### AI overview Firebase announces Genkit 1.0 for Node.js as production-ready after its preview at Google I/O 2024. The open-source framework supports AI-powered features, agents, automations, model integrations, structured generation, function calling, vector search, and local developer tooling. ### Source excerpt Production-ready AI for your apps ## Learn to build and run AI powered apps at Firebase Demo Day '24 DevFeed: [Learn to build and run AI powered apps at Firebase Demo Day '24]() Original publisher: [Read original article]() Author: Yasmin Gehman Published: 2024-11-19T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Artificial Intelligence](), [Demo](), [Genkit](), [hosting]() Tags: [ai](), [app-hosting](), [build](), [demo](), [demo-day](), [event](), [firebase](), [full-stack-development](), [gemini](), [genkit](), [hosting](), [remote-config](), [vertex-ai]() ### AI overview Firebase Demo Day 2024 presents eight short demos showing how to build, deploy, run, monitor, and optimize AI-powered applications with Firebase products including Genkit, Vertex AI in Firebase, Gemini in Firebase, and Firebase App Hosting. ### Source excerpt See us transform our travel app with the power of Firebase and AI ## High-Precision Responses with Genkit's Google Search Integration DevFeed: [High-Precision Responses with Genkit's Google Search Integration]() Original publisher: [Read original article]() Author: tanabee Published: 2024-10-21T18:37:56Z Content type: tutorial Language: en Sources: [Firebase Developers - Medium]() Topics: [Genkit](), [Google Search](), [Google](), [Google Cloud Platform (GCP)](), [Artificial Intelligence]() Tags: [ai](), [ai-api](), [cloud](), [code](), [gemini](), [genkit](), [github](), [google](), [google-search](), [grounding](), [how-to](), [integration]() ### AI overview This tutorial explains how to integrate Google Search with Genkit 0.5.9 so applications can use current information, including details from specific domains, when generating responses. It covers enabling Vertex AI, configuring a Google Cloud project and local environment, initializing a Genkit project, and running the Genkit Developer UI. ### Source excerpt aGenkit With the release of Genkit version 0.5.9, it is now possible to leverage Google Search directly through Genkit. By using Google Search, Genkit can access the latest information, including niche details from specific domains, leading to more accurate responses. This article explains how to set up Google Search integration for Genkit. Enabling Vertex AI To use the Google Search feature, you need a Google Cloud project with Vertex AI enabled. Follow these steps to enable Vertex AI and link your local environment with Google Cloud: 1. In the Cloud console, Enable the Vertex AI API for your project. 2. Set environment variables: export GCLOUD_PROJECT= export GCLOUD_LOCATION=us-central1 3. Authenticate with gcloud: gcloud auth application-default loginInitializing a Genkit Project Next, initialize your Genkit project. % npm init -y % npm i -D genkit-cli % npm i genkit @genkit-ai/googleai @genkit-ai/vertexai % mkdir src && touch src/index.ts Paste the following initialization code into the src/index.ts file. import { genkit, z } from 'genkit'; import { vertexAI } from '@genkit-ai/vertexai'; import { gemini15Flash } from '@genkit-ai/vertexai'; const ai = genkit({ model: gemini15Flash, plugins: [vertexAI({ location: 'us-central1' })], }); export const mainFlow = ai.defineFlow( { name: 'mainFlow', inputSchema: z.string(), outputSchema: z.string(), }, async (prompt) => { const { text } = await ai.generate(prompt); return text; } ); ai.startFlowServer({ flows: [mainFlow] });Enabling Google Search To enable Google Search, specify `googleSearchRetrieval` with `withConfig` for the `gemini15Flash` model, as shown in the following code. const ai = genkit({ model: gemini15Flash.withConfig({ googleSearchRetrieval: {}}), plugins: [vertexAI({ location: 'us-central1' })], });Run locally Launch the Genkit Developer UI with the following command: % npx genkit start -- npx tsx --watch src/index.ts Try asking about the current weather in Tokyo. Ask about Tokyo's weat ## Save the date for Firebase Demo Day '24 DevFeed: [Save the date for Firebase Demo Day '24]() Original publisher: [Read original article]() Author: Yasmin Gehman Published: 2024-10-16T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Demo](), [Google](), [Artificial Intelligence](), [Genkit](), [hosting]() Tags: [ai](), [announce](), [app-hosting](), [demo-day](), [event](), [firebase](), [full-stack-development](), [genkit](), [google](), [hosting](), [products](), [virtual]() ### AI overview Firebase announces the second Firebase Demo Day, a virtual showcase scheduled for November 19, 2024. The event will feature demos of Firebase products and features, including Firebase Genkit, Vertex AI in Firebase, Gemini in Firebase, and Firebase App Hosting. ### Source excerpt Demo Day is back for round two! Mark your calendars for November 19, 2024 ## Firebase at I/O Connect 2024 DevFeed: [Firebase at I/O Connect 2024]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2024-09-27T06:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [Genkit](), [observability](), [telemetry](), [Testing](), [Generative AI](), [Google]() Tags: [ai](), [app-distribution](), [community](), [firebase](), [generative-ai](), [genkit](), [google-i-o](), [launch](), [news](), [observability](), [performance-monitoring](), [telemetry](), [testing](), [updates]() ### AI overview The Firebase team reports on its participation in regional I/O Connect events in Berlin, Bengaluru, and Beijing. The article announces Firebase AI Monitoring in private preview, an AI Testing Agent in early access preview through Firebase App Distribution, and the I/O Crossword project. ### Source excerpt News, tutorials, and updates from the Firebase team.